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1. Beyond the Global Brain Differences: Intraindividual Variability Differences in 1q21.1 Distal and 15q11.2 BP1-BP2 Deletion Carriers

Author: Boen, R, Kaufmann, T, van der Meer, D, Frei, O, Agartz, I, Ames, D, Andersson, M, Armstrong, NJ, Artiges, E, Atkins, JR, Bauer, J, Benedetti, F, Boomsma, DI, Brodaty, H, Brosch, K, Buckner, RL, Cairns, MJ, Calhoun, V, Caspers, S, Cichon, S, Corvin, AP, Crespo-Facorro, B, Dannlowski, U, David, FS, de Geus, EJC, de Zubicaray, GI, Desrivieres, S, Doherty, JL, Donohoe, G, Ehrlich, S, Eising, E, Espeseth, T, Fisher, SE, Forstner, AJ, Fortaner-Uya, L, Frouin, V, Fukunaga, M, Ge, T, Glahn, DC, Goltermann, J, Grabe, HJ, Green, MJ, Groenewold, NA, Grotegerd, D, Grontvedt, GR, Hahn, T, Hashimoto, R, Hehir-Kwa, JY, Henskens, FA, Holmes, AJ, Haberg, AK, Haavik, J, Jacquemont, S, Jansen, A, Jockwitz, C, Joensson, EG, Kikuchi, M, Kircher, T, Kumar, K, Le Hellard, S, Leu, C, Linden, DE, Liu, J, Loughnan, R, Mather, KA, Mcmahon, KL, Mcrae, AF, Medland, SE, Meinert, S, Moreau, CA, Morris, DW, Mowry, BJ, Muehleisen, TW, Nenadic, I, Noethen, MM, Nyberg, L, Ophoff, RA, Owen, MJ, Pantelis, C, Paolini, M, Paus, T, Pausova, Z, Persson, K, Quide, Y, Marques, TR, Sachdev, PS, Sando, SB, Schall, U, Scott, RJ, Selbaek, G, Shumskaya, E, Silva, AI, Sisodiya, SM, Stein, F, Stein, DJ, Straube, B, Streit, F, Strike, LT, Teumer, A, Teutenberg, L, Thalamuthu, A, Tooney, PA, Tordesillas-Gutierrez, D, Trollor, JN, Van't Ent, D, van den Bree, MBM, van Haren, NEM, Vazquez-Bourgon, J, Voelzke, H, Wen, W, Wittfeld, K, Ching, CRK, Westlye, LT, Thompson, PM, Bearden, CE, Selmer, KK, Alnaes, D, Andreassen, OA, Sonderby, IE, Boen, R, Kaufmann, T, van der Meer, D, Frei, O, Agartz, I, Ames, D, Andersson, M, Armstrong, NJ, Artiges, E, Atkins, JR, Bauer, J, Benedetti, F, Boomsma, DI, Brodaty, H, Brosch, K, Buckner, RL, Cairns, MJ, Calhoun, V, Caspers, S, Cichon, S, Corvin, AP, Crespo-Facorro, B, Dannlowski, U, David, FS, de Geus, EJC, de Zubicaray, GI, Desrivieres, S, Doherty, JL, Donohoe, G, Ehrlich, S, Eising, E, Espeseth, T, Fisher, SE, Forstner, AJ, Fortaner-Uya, L, Frouin, V, Fukunaga, M, Ge, T, Glahn, DC, Goltermann, J, Grabe, HJ, Green, MJ, Groenewold, NA, Grotegerd, D, Grontvedt, GR, Hahn, T, Hashimoto, R, Hehir-Kwa, JY, Henskens, FA, Holmes, AJ, Haberg, AK, Haavik, J, Jacquemont, S, Jansen, A, Jockwitz, C, Joensson, EG, Kikuchi, M, Kircher, T, Kumar, K, Le Hellard, S, Leu, C, Linden, DE, Liu, J, Loughnan, R, Mather, KA, Mcmahon, KL, Mcrae, AF, Medland, SE, Meinert, S, Moreau, CA, Morris, DW, Mowry, BJ, Muehleisen, TW, Nenadic, I, Noethen, MM, Nyberg, L, Ophoff, RA, Owen, MJ, Pantelis, C, Paolini, M, Paus, T, Pausova, Z, Persson, K, Quide, Y, Marques, TR, Sachdev, PS, Sando, SB, Schall, U, Scott, RJ, Selbaek, G, Shumskaya, E, Silva, AI, Sisodiya, SM, Stein, F, Stein, DJ, Straube, B, Streit, F, Strike, LT, Teumer, A, Teutenberg, L, Thalamuthu, A, Tooney, PA, Tordesillas-Gutierrez, D, Trollor, JN, Van't Ent, D, van den Bree, MBM, van Haren, NEM, Vazquez-Bourgon, J, Voelzke, H, Wen, W, Wittfeld, K, Ching, CRK, Westlye, LT, Thompson, PM, Bearden, CE, Selmer, KK, Alnaes, D, Andreassen, OA, and Sonderby, IE
Abstract: BACKGROUND: Carriers of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants exhibit regional and global brain differences compared with noncarriers. However, interpreting regional differences is challenging if a global difference drives the regional brain differences. Intraindividual variability measures can be used to test for regional differences beyond global differences in brain structure. METHODS: Magnetic resonance imaging data were used to obtain regional brain values for 1q21.1 distal deletion (n = 30) and duplication (n = 27) and 15q11.2 BP1-BP2 deletion (n = 170) and duplication (n = 243) carriers and matched noncarriers (n = 2350). Regional intra-deviation scores, i.e., the standardized difference between an individual's regional difference and global difference, were used to test for regional differences that diverge from the global difference. RESULTS: For the 1q21.1 distal deletion carriers, cortical surface area for regions in the medial visual cortex, posterior cingulate, and temporal pole differed less and regions in the prefrontal and superior temporal cortex differed more than the global difference in cortical surface area. For the 15q11.2 BP1-BP2 deletion carriers, cortical thickness in regions in the medial visual cortex, auditory cortex, and temporal pole differed less and the prefrontal and somatosensory cortex differed more than the global difference in cortical thickness. CONCLUSIONS: We find evidence for regional effects beyond differences in global brain measures in 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants. The results provide new insight into brain profiling of the 1q21.1 distal and 15q11.2 BP1-BP2 copy number variants, with the potential to increase understanding of the mechanisms involved in altered neurodevelopment.
Published: 2024

2. GWAS for executive function and processing speed suggests involvement of the CADM2 gene

Author: Ibrahim-Verbaas, CA, Bressler, J, Debette, S, Schuur, M, Smith, AV, Bis, JC, Davies, G, Trompet, S, Smith, JA, Wolf, C, Chibnik, LB, Liu, Y, Vitart, V, Kirin, M, Petrovic, K, Polasek, O, Zgaga, L, Fawns-Ritchie, C, Hoffmann, P, Karjalainen, J, Lahti, J, Llewellyn, DJ, Schmidt, CO, Mather, KA, Chouraki, V, Sun, Q, Resnick, SM, Rose, LM, Oldmeadow, C, Stewart, M, Smith, BH, Gudnason, V, Yang, Q, Mirza, SS, Jukema, JW, deJager, PL, Harris, TB, Liewald, DC, Amin, N, Coker, LH, Stegle, O, Lopez, OL, Schmidt, R, Teumer, A, Ford, I, Karbalai, N, Becker, JT, Jonsdottir, MK, Au, R, Fehrmann, RSN, Herms, S, Nalls, M, Zhao, W, Turner, ST, Yaffe, K, Lohman, K, van Swieten, JC, Kardia, SLR, Knopman, DS, Meeks, WM, Heiss, G, Holliday, EG, Schofield, PW, Tanaka, T, Stott, DJ, Wang, J, Ridker, P, Gow, AJ, Pattie, A, Starr, JM, Hocking, LJ, Armstrong, NJ, McLachlan, S, Shulman, JM, Pilling, LC, Eiriksdottir, G, Scott, RJ, Kochan, NA, Palotie, A, Hsieh, Y-C, Eriksson, JG, Penman, A, Gottesman, RF, Oostra, BA, Yu, L, DeStefano, AL, Beiser, A, Garcia, M, Rotter, JI, Nöthen, MM, Hofman, A, Slagboom, PE, Westendorp, RGJ, Buckley, BM, Wolf, PA, Uitterlinden, AG, Psaty, BM, Grabe, HJ, Bandinelli, S, and Chasman, DI
Subjects: Biological Psychology, Clinical and Health Psychology, Psychology, Human Genome, Neurosciences, Genetics, Clinical Research, Aging, Aetiology, 2.1 Biological and endogenous factors, Aged, Aged, 80 and over, Cell Adhesion Molecules, Cognition, Cohort Studies, Executive Function, Female, Genetic Association Studies, Genetic Variation, Genome-Wide Association Study, Genomics, Humans, Introns, Male, Middle Aged, Neuropsychological Tests, Polymorphism, Single Nucleotide, White People, gamma-Aminobutyric Acid, Generation Scotland, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract: To identify common variants contributing to normal variation in two specific domains of cognitive functioning, we conducted a genome-wide association study (GWAS) of executive functioning and information processing speed in non-demented older adults from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium. Neuropsychological testing was available for 5429-32,070 subjects of European ancestry aged 45 years or older, free of dementia and clinical stroke at the time of cognitive testing from 20 cohorts in the discovery phase. We analyzed performance on the Trail Making Test parts A and B, the Letter Digit Substitution Test (LDST), the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Color and Word Test. Replication was sought in 1311-21860 subjects from 20 independent cohorts. A significant association was observed in the discovery cohorts for the single-nucleotide polymorphism (SNP) rs17518584 (discovery P-value=3.12 × 10(-8)) and in the joint discovery and replication meta-analysis (P-value=3.28 × 10(-9) after adjustment for age, gender and education) in an intron of the gene cell adhesion molecule 2 (CADM2) for performance on the LDST/DSST. Rs17518584 is located about 170 kb upstream of the transcription start site of the major transcript for the CADM2 gene, but is within an intron of a variant transcript that includes an alternative first exon. The variant is associated with expression of CADM2 in the cingulate cortex (P-value=4 × 10(-4)). The protein encoded by CADM2 is involved in glutamate signaling (P-value=7.22 × 10(-15)), gamma-aminobutyric acid (GABA) transport (P-value=1.36 × 10(-11)) and neuron cell-cell adhesion (P-value=1.48 × 10(-13)). Our findings suggest that genetic variation in the CADM2 gene is associated with individual differences in information processing speed.
Published: 2016

3. High polygenic risk score for exceptional longevity is associated with a healthy metabolic profile

Author: Revelas, M, Thalamuthu, A, Zettergren, A, Oldmeadow, C, Najar, J, Seidu, NM, Armstrong, NJ, Riveros, C, Kwok, JB, Schofield, PR, Trollor, JN, Waern, M, Wright, MJ, Zetterberg, H, Ames, D, Belnnow, K, Brodaty, H, Scott, RJ, Skoog, I, Attia, JR, Sachdev, PS, Mather, KA, Revelas, M, Thalamuthu, A, Zettergren, A, Oldmeadow, C, Najar, J, Seidu, NM, Armstrong, NJ, Riveros, C, Kwok, JB, Schofield, PR, Trollor, JN, Waern, M, Wright, MJ, Zetterberg, H, Ames, D, Belnnow, K, Brodaty, H, Scott, RJ, Skoog, I, Attia, JR, Sachdev, PS, and Mather, KA
Abstract: Healthy metabolic measures in humans are associated with longevity. Dysregulation leads to metabolic syndrome (MetS) and negative health outcomes. Recent exceptional longevity (EL) genome wide association studies have facilitated estimation of an individual's polygenic risk score (PRS) for EL. We tested the hypothesis that individuals with high ELPRS have a low prevalence of MetS. Participants were from five cohorts of middle-aged to older adults. The primary analyses were performed in the UK Biobank (UKBB) (n = 407,800, 40-69 years). Replication analyses were undertaken using three Australian studies: Hunter Community Study (n = 2122, 55-85 years), Older Australian Twins Study (n = 539, 65-90 years) and Sydney Memory and Ageing Study (n = 925, 70-90 years), as well as the Swedish Gothenburg H70 Birth Cohort Studies (n = 2273, 70-93 years). MetS was defined using established criteria. Regressions and meta-analyses were performed with the ELPRS and MetS and its components. Generally, MetS prevalence (22-30%) was higher in the older cohorts. In the UKBB, high EL polygenic risk was associated with lower MetS prevalence (OR = 0.94, p = 1.84 × 10-42) and its components (p < 2.30 × 10-8). Meta-analyses of the replication cohorts showed nominal associations with MetS (p = 0.028) and 3 MetS components (p < 0.05). This work suggests individuals with a high polygenic risk for EL have a healthy metabolic profile promoting longevity.
Published: 2023

4. Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease.

Author: Duperron, M-G, Knol, MJ, Le Grand, Q, Evans, TE, Mishra, A, Tsuchida, A, Roshchupkin, G, Konuma, T, Trégouët, D-A, Romero, JR, Frenzel, S, Luciano, M, Hofer, E, Bourgey, M, Dueker, ND, Delgado, P, Hilal, S, Tankard, RM, Dubost, F, Shin, J, Saba, Y, Armstrong, NJ, Bordes, C, Bastin, ME, Beiser, A, Brodaty, H, Bülow, R, Carrera, C, Chen, C, Cheng, C-Y, Deary, IJ, Gampawar, PG, Himali, JJ, Jiang, J, Kawaguchi, T, Li, S, Macalli, M, Marquis, P, Morris, Z, Muñoz Maniega, S, Miyamoto, S, Okawa, M, Paradise, M, Parva, P, Rundek, T, Sargurupremraj, M, Schilling, S, Setoh, K, Soukarieh, O, Tabara, Y, Teumer, A, Thalamuthu, A, Trollor, JN, Valdés Hernández, MC, Vernooij, MW, Völker, U, Wittfeld, K, Wong, TY, Wright, MJ, Zhang, J, Zhao, W, Zhu, Y-C, Schmidt, H, Sachdev, PS, Wen, W, Yoshida, K, Joutel, A, Satizabal, CL, Sacco, RL, Bourque, G, CHARGE consortium, Lathrop, M, Paus, T, Fernandez-Cadenas, I, Yang, Q, Mazoyer, B, Boutinaud, P, Okada, Y, Grabe, HJ, Mather, KA, Schmidt, R, Joliot, M, Ikram, MA, Matsuda, F, Tzourio, C, Wardlaw, JM, Seshadri, S, Adams, HHH, Debette, S, Duperron, M-G, Knol, MJ, Le Grand, Q, Evans, TE, Mishra, A, Tsuchida, A, Roshchupkin, G, Konuma, T, Trégouët, D-A, Romero, JR, Frenzel, S, Luciano, M, Hofer, E, Bourgey, M, Dueker, ND, Delgado, P, Hilal, S, Tankard, RM, Dubost, F, Shin, J, Saba, Y, Armstrong, NJ, Bordes, C, Bastin, ME, Beiser, A, Brodaty, H, Bülow, R, Carrera, C, Chen, C, Cheng, C-Y, Deary, IJ, Gampawar, PG, Himali, JJ, Jiang, J, Kawaguchi, T, Li, S, Macalli, M, Marquis, P, Morris, Z, Muñoz Maniega, S, Miyamoto, S, Okawa, M, Paradise, M, Parva, P, Rundek, T, Sargurupremraj, M, Schilling, S, Setoh, K, Soukarieh, O, Tabara, Y, Teumer, A, Thalamuthu, A, Trollor, JN, Valdés Hernández, MC, Vernooij, MW, Völker, U, Wittfeld, K, Wong, TY, Wright, MJ, Zhang, J, Zhao, W, Zhu, Y-C, Schmidt, H, Sachdev, PS, Wen, W, Yoshida, K, Joutel, A, Satizabal, CL, Sacco, RL, Bourque, G, CHARGE consortium, Lathrop, M, Paus, T, Fernandez-Cadenas, I, Yang, Q, Mazoyer, B, Boutinaud, P, Okada, Y, Grabe, HJ, Mather, KA, Schmidt, R, Joliot, M, Ikram, MA, Matsuda, F, Tzourio, C, Wardlaw, JM, Seshadri, S, Adams, HHH, and Debette, S
Abstract: Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in up to 40,095 participants (18 population-based cohorts, 66.3 ± 8.6 yr, 96.9% European ancestry) revealed 24 genome-wide significant PVS risk loci, mainly in the white matter. These were associated with white matter PVS already in young adults (N = 1,748; 22.1 ± 2.3 yr) and were enriched in early-onset leukodystrophy genes and genes expressed in fetal brain endothelial cells, suggesting early-life mechanisms. In total, 53% of white matter PVS risk loci showed nominally significant associations (27% after multiple-testing correction) in a Japanese population-based cohort (N = 2,862; 68.3 ± 5.3 yr). Mendelian randomization supported causal associations of high blood pressure with basal ganglia and hippocampal PVS, and of basal ganglia PVS and hippocampal PVS with stroke, accounting for blood pressure. Our findings provide insight into the biology of PVS and cerebral small vessel disease, pointing to pathways involving extracellular matrix, membrane transport and developmental processes, and the potential for genetically informed prioritization of drug targets.
Published: 2023

5. Reproducibility in the absence of selective reporting: An illustration from large-scale brain asymmetry research

Author: Kong, XZ, Francks, C, Mathias, SR, Guadalupe, T, Abé, C, Agartz, I, Akudjedu, TN, Aleman, A, Alhusaini, S, Allen, NB, Ames, D, Andreassen, OA, Vasquez, AA, Armstrong, NJ, Asherson, P, Bergo, F, Bastin, ME, Batalla, A, Bauer, J, Baune, BT, Baur-Streubel, R, Biederman, J, Blaine, SK, Boedhoe, P, Bøen, E, Bose, A, Bralten, J, Brandeis, D, Brem, S, Brodaty, H, Yüksel, D, Brooks, SJ, Buitelaar, J, Bürger, C, Bülow, R, Calhoun, V, Calvo, A, Canales-Rodríguez, EJ, Cannon, DM, Caparelli, EC, Castellanos, FX, Cendes, F, Chaim-Avancini, TM, Chantiluke, K, Chen, QL, Chen, X, Cheng, Y, Christakou, A, Clark, VP, Coghill, D, Connolly, CG, Conzelmann, A, Córdova-Palomera, A, Cousijn, J, Crow, T, Cubillo, A, Dannlowski, U, de Bruttopilo, SA, de Zeeuw, P, Deary, IJ, Demeter, DV, Di Martino, A, Dickie, EW, Dietsche, B, Doan, NT, Doherty, CP, Doyle, A, Durston, S, Earl, E, Ehrlich, S, Ekman, CJ, Elvsåshagen, T, Epstein, JN, Fair, DA, Faraone, SV, Fernández, G, Flint, C, Filho, GB, Förster, K, Fouche, JP, Foxe, JJ, Frodl, T, Fuentes-Claramonte, P, Fullerton, JM, Garavan, H, do Santos Garcia, D, Gotlib, IH, Goudriaan, AE, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gurholt, T, Haavik, J, Hahn, T, Hansell, NK, Harris, MA, Hartman, CA, del Carmen Valdés Hernández, M, Kong, XZ, Francks, C, Mathias, SR, Guadalupe, T, Abé, C, Agartz, I, Akudjedu, TN, Aleman, A, Alhusaini, S, Allen, NB, Ames, D, Andreassen, OA, Vasquez, AA, Armstrong, NJ, Asherson, P, Bergo, F, Bastin, ME, Batalla, A, Bauer, J, Baune, BT, Baur-Streubel, R, Biederman, J, Blaine, SK, Boedhoe, P, Bøen, E, Bose, A, Bralten, J, Brandeis, D, Brem, S, Brodaty, H, Yüksel, D, Brooks, SJ, Buitelaar, J, Bürger, C, Bülow, R, Calhoun, V, Calvo, A, Canales-Rodríguez, EJ, Cannon, DM, Caparelli, EC, Castellanos, FX, Cendes, F, Chaim-Avancini, TM, Chantiluke, K, Chen, QL, Chen, X, Cheng, Y, Christakou, A, Clark, VP, Coghill, D, Connolly, CG, Conzelmann, A, Córdova-Palomera, A, Cousijn, J, Crow, T, Cubillo, A, Dannlowski, U, de Bruttopilo, SA, de Zeeuw, P, Deary, IJ, Demeter, DV, Di Martino, A, Dickie, EW, Dietsche, B, Doan, NT, Doherty, CP, Doyle, A, Durston, S, Earl, E, Ehrlich, S, Ekman, CJ, Elvsåshagen, T, Epstein, JN, Fair, DA, Faraone, SV, Fernández, G, Flint, C, Filho, GB, Förster, K, Fouche, JP, Foxe, JJ, Frodl, T, Fuentes-Claramonte, P, Fullerton, JM, Garavan, H, do Santos Garcia, D, Gotlib, IH, Goudriaan, AE, Grabe, HJ, Groenewold, NA, Grotegerd, D, Gruber, O, Gurholt, T, Haavik, J, Hahn, T, Hansell, NK, Harris, MA, Hartman, CA, and del Carmen Valdés Hernández, M
Published: 2022

6. Early life affects late-life health through determining DNA methylation across the lifespan: A twin study

Author: Li, S, Ye, Z, Mather, KA, Nguyen, TL, Dite, GS, Armstrong, NJ, Wong, EM, Thalamuthu, A, Giles, GG, Craig, Jeffrey, Saffery, R, Southey, MC, Tan, Q, Sachdev, PS, Hopper, JL, Li, S, Ye, Z, Mather, KA, Nguyen, TL, Dite, GS, Armstrong, NJ, Wong, EM, Thalamuthu, A, Giles, GG, Craig, Jeffrey, Saffery, R, Southey, MC, Tan, Q, Sachdev, PS, and Hopper, JL
Abstract: Background: Previous findings for the genetic and environmental contributions to DNA methylation variation were for limited age ranges only. We investigated the lifespan contributions and their implications for human health for the first time. Methods: 1,720 monozygotic twin (MZ) pairs and 1,107 dizygotic twin (DZ) pairs aged 0-92 years were included. Familial correlations (i.e., correlations between twins) for 353,681 methylation sites were estimated and modelled as a function of twin pair cohabitation history. Findings: The methylome average familial correlation was around zero at birth (MZ pair: -0.01; DZ pair: -0.04), increased with the time of twins living together during childhood at rates of 0.16 (95%CI: 0.12-0.20) for MZ pairs and 0.13 (95%CI: 0.07-0.20) for DZ pairs per decade, and decreased with the time of living apart during adulthood at rates of 0.026 (95%CI: 0.019-0.033) for MZ pairs and 0.027 (95%CI: 0.011-0.043) for DZ pairs per decade. Neither the increasing nor decreasing rate differed by zygosity (both P>0.1), consistent with cohabitation environment shared by twins, rather than genetic factors, influencing the methylation familial correlation changes. Familial correlations for 6.6% (23,386/353,681) sites changed with twin pair cohabitation history. These sites were enriched for high heritability, proximal promoters, and epigenetic/genetic associations with various early-life factors and late-life health conditions. Interpretation: Early life strongly influences DNA methylation variation across the lifespan, and the effects are stronger for heritable sites and sites biologically relevant to the regulation of gene expression. Early life could affect late-life health through influencing DNA methylation. Funding: Victorian Cancer Agency, Cancer Australia, Cure Cancer Foundation.
Published: 2022

7. Genetic variants associated with longitudinal changes in brain structure across the lifespan

Author: Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, Hulshoff Pol, HE, Brouwer, RM, Klein, M, Grasby, KL, Schnack, HG, Jahanshad, N, Teeuw, J, Thomopoulos, SI, Sprooten, E, Franz, CE, Gogtay, N, Kremen, WS, Panizzon, MS, Olde Loohuis, LM, Whelan, CD, Aghajani, M, Alloza, C, Alanaes, D, Artiges, E, Ayesa-Arriola, R, Barker, GJ, Bastin, ME, Blok, E, Boen, E, Breukelaar, IA, Bright, JK, Buimer, EEL, Bulow, R, Cannon, DM, Ciufolini, S, Crossley, NA, Damatac, CG, Dazzan, P, de Mol, CL, de Zwarte, SMC, Desrivieres, S, Diaz-Caneja, CM, Doan, NT, Dohm, K, Froehner, JH, Goltermann, J, Grigis, A, Grotegerd, D, Han, LKM, Harris, MA, Hartman, CA, Heany, SJ, Heindel, W, Heslenfeld, DJ, Hohmann, S, Ittermann, B, Jansen, PR, Janssen, J, Jia, T, Jiang, J, Jockwitz, C, Karali, T, Keeser, D, Koevoets, MGJC, Lenroot, RK, Malchow, B, Mandl, RCW, Medel, V, Meinert, S, Morgan, CA, Muehleisen, TW, Nabulsi, L, Opel, N, de la Foz, VO-G, Overs, BJ, Paillere Martinot, M-L, Redlich, R, Marques, TR, Repple, J, Roberts, G, Roshchupkin, GV, Setiaman, N, Shumskaya, E, Stein, F, Sudre, G, Takahashi, S, Thalamuthu, A, Tordesillas-Gutierrez, D, van der Lugt, A, van Haren, NEM, Wardlaw, JM, Wen, W, Westeneng, H-J, Wittfeld, K, Zhu, AH, Zugman, A, Armstrong, NJ, Bonfiglio, G, Bralten, J, Dalvie, S, Davies, G, Di Forti, M, Ding, L, Donohoe, G, Forstner, AJ, Gonzalez-Penas, J, Guimaraes, JPOFT, Homuth, G, Hottenga, J-J, Knol, MJ, Kwok, JBJ, Le Hellard, S, Mather, KA, Milaneschi, Y, Morris, DW, Noethen, MM, Papiol, S, Rietschel, M, Santoro, ML, Steen, VM, Stein, JL, Streit, F, Tankard, RM, Teumer, A, van 't Ent, D, van der Meer, D, van Eijk, KR, Vassos, E, Vazquez-Bourgon, J, Witt, SH, Adams, HHH, Agartz, I, Ames, D, Amunts, K, Andreassen, OA, Arango, C, Banaschewski, T, Baune, BT, Belangero, SI, Bokde, ALW, Boomsma, DI, Bressan, RA, Brodaty, H, Buitelaar, JK, Cahn, W, Caspers, S, Cichon, S, Crespo-Facorro, B, Cox, SR, Dannlowski, U, Elvsashagen, T, Espeseth, T, Falkai, PG, Fisher, SE, Flor, H, Fullerton, JM, Garavan, H, Gowland, PA, Grabe, HJ, Hahn, T, Heinz, A, Hillegers, M, Hoare, J, Hoekstra, PJ, Ikram, MA, Jackowski, AP, Jansen, A, Jonsson, EG, Kahn, RS, Kircher, T, Korgaonkar, MS, Krug, A, Lemaitre, H, Malt, UF, Martinot, J-L, McDonald, C, Mitchell, PB, Muetzel, RL, Murray, RM, Nees, F, Nenadic, I, Oosterlaan, J, Ophoff, RA, Pan, PM, Penninx, BWJH, Poustka, L, Sachdev, PS, Salum, GA, Schofield, PR, Schumann, G, Shaw, P, Sim, K, Smolka, MN, Stein, DJ, Trollor, JN, van den Berg, LH, Veldink, JH, Walter, H, Westlye, LT, Whelan, R, White, T, Wright, MJ, Medland, SE, Franke, B, Thompson, PM, and Hulshoff Pol, HE
Abstract: Human brain structure changes throughout the lifespan. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental and neurodegenerative diseases. In this study, we identified common genetic variants that affect rates of brain growth or atrophy in what is, to our knowledge, the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal magnetic resonance imaging data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genes GPR139, DACH1 and APOE are associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and aging.
Published: 2022

8. Genome-wide meta-analyses reveal novel loci for verbal short-term memory and learning

Author: Lahti, J, Tuominen, S, Yang, Q, Pergola, G, Ahmad, S, Amin, N, Armstrong, NJ, Beiser, A, Bey, K, Bis, JC, Boerwinkle, E, Bressler, J, Campbell, A, Campbell, H, Chen, Q, Corley, J, Cox, SR, Davies, G, De Jager, PL, Derks, EM, Faul, JD, Fitzpatrick, AL, Fohner, AE, Ford, I, Fornage, M, Gerring, Z, Grabe, HJ, Grodstein, F, Gudnason, V, Simonsick, E, Holliday, EG, Joshi, PK, Kajantie, E, Kaprio, J, Karell, P, Kleineidam, L, Knol, MJ, Kochan, NA, Kwok, JB, Leber, M, Lam, M, Lee, T, Li, S, Loukola, A, Luck, T, Marioni, RE, Mather, KA, Medland, S, Mirza, SS, Nalls, MA, Nho, K, O'Donnell, A, Oldmeadow, C, Painter, J, Pattie, A, Reppermund, S, Risacher, SL, Rose, RJ, Sadashivaiah, V, Scholz, M, Satizabal, CL, Schofield, PW, Schraut, KE, Scott, RJ, Simino, J, Smith, AV, Smith, JA, Stott, DJ, Surakka, I, Teumer, A, Thalamuthu, A, Trompet, S, Turner, ST, van der Lee, SJ, Villringer, A, Voelker, U, Wilson, RS, Wittfeld, K, Vuoksimaa, E, Xia, R, Yaffe, K, Yu, L, Zare, H, Zhao, W, Ames, D, Attia, J, Bennett, DA, Brodaty, H, Chasman, DI, Goldman, AL, Hayward, C, Ikram, MA, Jukema, JW, Kardia, SLR, Lencz, T, Loeffler, M, Mattay, VS, Palotie, A, Psaty, BM, Ramirez, A, Ridker, PM, Riedel-Heller, SG, Sachdev, PS, Saykin, AJ, Scherer, M, Schofield, PR, Sidney, S, Starr, JM, Trollor, J, Ulrich, W, Wagner, M, Weir, DR, Wilson, JF, Wright, MJ, Weinberger, DR, Debette, S, Eriksson, JG, Mosley, TH, Launer, LJ, van Duijn, CM, Deary, IJ, Seshadri, S, Raikkonen, K, Lahti, J, Tuominen, S, Yang, Q, Pergola, G, Ahmad, S, Amin, N, Armstrong, NJ, Beiser, A, Bey, K, Bis, JC, Boerwinkle, E, Bressler, J, Campbell, A, Campbell, H, Chen, Q, Corley, J, Cox, SR, Davies, G, De Jager, PL, Derks, EM, Faul, JD, Fitzpatrick, AL, Fohner, AE, Ford, I, Fornage, M, Gerring, Z, Grabe, HJ, Grodstein, F, Gudnason, V, Simonsick, E, Holliday, EG, Joshi, PK, Kajantie, E, Kaprio, J, Karell, P, Kleineidam, L, Knol, MJ, Kochan, NA, Kwok, JB, Leber, M, Lam, M, Lee, T, Li, S, Loukola, A, Luck, T, Marioni, RE, Mather, KA, Medland, S, Mirza, SS, Nalls, MA, Nho, K, O'Donnell, A, Oldmeadow, C, Painter, J, Pattie, A, Reppermund, S, Risacher, SL, Rose, RJ, Sadashivaiah, V, Scholz, M, Satizabal, CL, Schofield, PW, Schraut, KE, Scott, RJ, Simino, J, Smith, AV, Smith, JA, Stott, DJ, Surakka, I, Teumer, A, Thalamuthu, A, Trompet, S, Turner, ST, van der Lee, SJ, Villringer, A, Voelker, U, Wilson, RS, Wittfeld, K, Vuoksimaa, E, Xia, R, Yaffe, K, Yu, L, Zare, H, Zhao, W, Ames, D, Attia, J, Bennett, DA, Brodaty, H, Chasman, DI, Goldman, AL, Hayward, C, Ikram, MA, Jukema, JW, Kardia, SLR, Lencz, T, Loeffler, M, Mattay, VS, Palotie, A, Psaty, BM, Ramirez, A, Ridker, PM, Riedel-Heller, SG, Sachdev, PS, Saykin, AJ, Scherer, M, Schofield, PR, Sidney, S, Starr, JM, Trollor, J, Ulrich, W, Wagner, M, Weir, DR, Wilson, JF, Wright, MJ, Weinberger, DR, Debette, S, Eriksson, JG, Mosley, TH, Launer, LJ, van Duijn, CM, Deary, IJ, Seshadri, S, and Raikkonen, K
Abstract: Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.
Published: 2022

9. In Vitro Gut Microbiome Response to Carbohydrate Supplementation is Minimally Affected by a Sudden Change in Diet

Author: Racicot K, Jason W. Soares, McClung Hl, Laurel A Doherty, Karl Jp, Pantoja-Feliciano Ig, Matthew Perisin, Armstrong Nj, Arcidiacono S, Rebecca L. Renberg, and Branck T
Subjects: Physiology, Biology, Carbohydrate supplementation, Gut microbiome, In vitro
Abstract: Background: Interactions between diet, stress and the gut microbiome are of interest as a means to modulate health and performance. Here, in vitro fermentation was used to explore the effects of a sudden change in diet, 21 days sole sustenance on the Meal, Ready-to-Eat (MRE) U.S. military combat ration, on inter-species competition and functional potential of the human gut microbiota. Human fecal samples collected before and after MRE intervention or consuming a habitual diet (HAB) were introduced to nutrient-rich media supplemented with starch for in vitro fermentation under ascending colon conditions. 16S rRNA amplicon and Whole Genome Sequencing (WGS) were used to measure community composition and functional potential. Specific statistical analyses were implemented to detect changes in relative abundance from taxa, genes and pathways.Results: Differential changes in relative abundance of five taxa, Dorea spp., Akkermansia muciniphila, Prevotella spp., Desulfovibrio spp., and Dialister spp., and four Carbohydrate-Active Enzymes, specifically GH13_14, over the 24 h fermentation were observed as a function of the diet intervention and correlated to specific taxa of interest. Conclusions: These findings suggest that consuming MRE for 21 days minimally effects changes in gut microbiota structure in response to carbohydrate, but may induce alterations in metabolic capacity. Additionally, these findings demonstrate the potential of starch as a candidate supplemental strategy to functionally modulate specific gut commensals during stress-induced states.
Published: 2021
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10. Genome-Wide Association Study of Circulating Interleukin 6 Levels Identifies Novel Loci

Author: Ahluwalia, TS, Prins, BP, Abdollahi, M, Armstrong, NJ, Aslibekyan, S, Bain, L, Jefferis, B, Baumert, J, Beekman, M, Ben-Shlomo, Y, Bis, JC, Mitchell, BD, de Geus, E, Delgado, GE, Marek, D, Eriksson, J, Kajantie, E, Kanoni, S, Kemp, JP, Lu, C, Marioni, RE, McLachlan, S, Milaneschi, Y, Nolte, IM, Petrelis, AM, Porcu, E, Sabater-Lleal, M, Naderi, E, Seppälä, I, Shah, T, Singhal, G, Standl, M, Teumer, A, Thalamuthu, A, Thiering, E, Trompet, S, Ballantyne, CM, Benjamin, EJ, Casas, JP, Toben, C, Dedoussis, G, Deelen, J, Durda, P, Engmann, J, Feitosa, MF, Grallert, H, Hammarstedt, A, Harris, SE, Homuth, G, Hottenga, J-J, Jalkanen, S, Jamshidi, Y, Jawahar, MC, Jess, T, Kivimaki, M, Kleber, ME, Lahti, J, Liu, Y, Marques-Vidal, P, Mellström, D, Mooijaart, SP, Müller-Nurasyid, M, Penninx, B, Revez, JA, Rossing, P, Räikkönen, K, Sattar, N, Scharnagl, H, Sennblad, B, Silveira, A, Pourcain, BS, Timpson, NJ, Trollor, J, CHARGE Inflammation Working Group, van Dongen, J, Van Heemst, D, Visvikis-Siest, S, Vollenweider, P, Völker, U, Waldenberger, M, Willemsen, G, Zabaneh, D, Morris, RW, Arnett, DK, Baune, BT, Boomsma, DI, Chang, Y-PC, Deary, IJ, Deloukas, P, Eriksson, JG, Evans, DM, Ferreira, MA, Gaunt, T, Gudnason, V, Hamsten, A, Heinrich, J, Hingorani, A, Humphries, SE, Jukema, JW, Koeing, W, Kumari, M, Kutalik, Z, Lawlor, DA, Lehtimäki, T, März, W, Mather, K, Naitza, S, Nauck, M, Ohlsson, C, Price, JF, Raitakari, O, Rice, K, Sachdev, PS, Slagboom, E, Sørensen, TIA, Spector, T, Stacey, D, Stathopoulou, MG, Tanaka, T, Wannamethee, SG, Whincup, P, Rotter, JI, Dehghan, A, Boerwinkle, E, Psaty, BM, Snieder, H, and Alizadeh, BZ
Abstract: Interleukin-6 (IL-6) is a multifunctional cytokine with both pro- and anti-inflammatory properties with a heritability estimate of up to 61%. The circulating levels of IL-6 in blood have been associated with an increased risk of complex disease pathogenesis. We conducted a two-staged, discovery, and replication meta genome-wide association study (GWAS) of circulating serum IL-6 levels comprising up to 67 428 (ndiscovery = 52 654 and nreplication = 14 774) individuals of European ancestry. The inverse variance fixed-effects based discovery meta-analysis, followed by replication led to the identification of two independent loci, IL1F10/IL1RN rs6734238 on Chromosome (Chr) 2q14, (pcombined = 1.8 × 10-11), HLA-DRB1/DRB5 rs660895 on Chr6p21 (pcombined = 1.5 × 10-10) in the combined meta-analyses of all samples. We also replicated the IL6R rs4537545 locus on Chr1q21 (pcombined = 1.2 × 10-122). Our study identifies novel loci for circulating IL-6 levels uncovering new immunological and inflammatory pathways that may influence IL-6 pathobiology.
Published: 2021

11. Investigating olfactory gene variation and odour identification in older adults

Author: Raj, S, Thalamuthu, A, Armstrong, NJ, Wright, MJ, Kwok, JB, Trollor, JN, Ames, D, Schofield, PR, Brodaty, H, Sachdev, PS, Mather, KA, Raj, S, Thalamuthu, A, Armstrong, NJ, Wright, MJ, Kwok, JB, Trollor, JN, Ames, D, Schofield, PR, Brodaty, H, Sachdev, PS, and Mather, KA
Abstract: Ageing is associated with a decrease in odour identification. Additionally, deficits in olfaction have been linked to age-related disease and mortality. Heritability studies suggest genetic variation contributes to olfactory identification. The olfactory receptor (OR) gene family is the largest in the human genome and responsible for overall odour identification. In this study, we sought to find olfactory gene family variants associated with individual and overall odour identification and to examine the relationships between polygenic risk scores (PRS) for olfactory-related phenotypes and olfaction. Participants were Caucasian older adults from the Sydney Memory and Ageing Study and the Older Australian Twins Study with genome-wide genotyping data (n = 1395, mean age = 75.52 ± 6.45). The Brief-Smell Identification Test (BSIT) was administered in both cohorts. PRS were calculated from independent GWAS summary statistics for Alzheimer’s disease (AD), white matter hyperintensities (WMH), Parkinson’s disease (PD), hippocampal volume and smoking. Associations with olfactory receptor genes (n = 967), previously identified candidate olfaction-related SNPs (n = 36) and different PRS with BSIT scores (total and individual smells) were examined. All of the relationships were analysed using generalised linear mixed models (GLMM), adjusted for age and sex. Genes with suggestive evidence for odour identification were found for 8 of the 12 BSIT items. Thirteen out of 36 candidate SNPs previously identified from the literature were suggestively associated with several individual BSIT items but not total score. PRS for smoking, WMH and PD were negatively associated with chocolate identification. This is the first study to conduct genetic analyses with individual odorant identification, which found suggestive olfactory-related genes and genetic variants for multiple individual BSIT odours. Replication in independent and larger cohorts is needed.
Published: 2021

12. 1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans

Author: Sonderby, IE, van der Meer, D, Moreau, C, Kaufmann, T, Walters, GB, Ellegaard, M, Abdellaoui, A, Ames, D, Amunts, K, Andersson, M, Armstrong, NJ, Bernard, M, Blackburn, NB, Blangero, J, Boomsma, DI, Brodaty, H, Brouwer, RM, Buelow, R, Boen, R, Cahn, W, Calhoun, VD, Caspers, S, Ching, CRK, Cichon, S, Ciufolini, S, Crespo-Facorro, B, Curran, JE, Dale, AM, Dalvie, S, Dazzan, P, de Geus, EJC, de Zubicaray, GI, de Zwarte, SMC, Desrivieres, S, Doherty, JL, Donohoe, G, Draganski, B, Ehrlich, S, Eising, E, Espeseth, T, Fejgin, K, Fisher, SE, Fladby, T, Frei, O, Frouin, V, Fukunaga, M, Gareau, T, Ge, T, Glahn, DC, Grabe, HJ, Groenewold, NA, Gustafsson, O, Haavik, J, Haberg, AK, Hall, J, Hashimoto, R, Hehir-Kwa, JY, Hibar, DP, Hillegers, MHJ, Hoffmann, P, Holleran, L, Holmes, AJ, Homuth, G, Hottenga, J-J, Hulshoff Pol, HE, Ikeda, M, Jahanshad, N, Jockwitz, C, Johansson, S, Joensson, EG, Jorgensen, NR, Kikuchi, M, Knowles, EEM, Kumar, K, Le Hellard, S, Leu, C, Linden, DEJ, Liu, J, Lundervold, A, Lundervold, AJ, Maillard, AM, Martin, NG, Martin-Brevet, S, Mather, KA, Mathias, SR, McMahon, KL, McRae, AF, Medland, SE, Meyer-Lindenberg, A, Moberget, T, Modenato, C, Sanchez, JM, Morris, DW, Muehleisen, TW, Murray, RM, Nielsen, J, Nordvik, JE, Nyberg, L, Loohuis, LMO, Ophoff, RA, Owen, MJ, Paus, T, Pausova, Z, Peralta, JM, Pike, GB, Prieto, C, Quinlan, EB, Reinbold, CS, Marques, TR, Rucker, JJH, Sachdev, PS, Sando, SB, Schofield, PR, Schork, AJ, Schumann, G, Shin, J, Shumskaya, E, Silva, AI, Sisodiya, SM, Steen, VM, Stein, DJ, Strike, LT, Suzuki, IK, Tamnes, CK, Teumer, A, Thalamuthu, A, Tordesillas-Gutierrez, D, Uhlmann, A, Ulfarsson, MO, van 't Ent, D, van den Bree, MBM, Vanderhaeghen, P, Vassos, E, Wen, W, Wittfeld, K, Wright, MJ, Agartz, I, Djurovic, S, Westlye, LT, Stefansson, H, Stefansson, K, Jacquemont, S, Thompson, PM, Andreassen, OA, Sonderby, IE, van der Meer, D, Moreau, C, Kaufmann, T, Walters, GB, Ellegaard, M, Abdellaoui, A, Ames, D, Amunts, K, Andersson, M, Armstrong, NJ, Bernard, M, Blackburn, NB, Blangero, J, Boomsma, DI, Brodaty, H, Brouwer, RM, Buelow, R, Boen, R, Cahn, W, Calhoun, VD, Caspers, S, Ching, CRK, Cichon, S, Ciufolini, S, Crespo-Facorro, B, Curran, JE, Dale, AM, Dalvie, S, Dazzan, P, de Geus, EJC, de Zubicaray, GI, de Zwarte, SMC, Desrivieres, S, Doherty, JL, Donohoe, G, Draganski, B, Ehrlich, S, Eising, E, Espeseth, T, Fejgin, K, Fisher, SE, Fladby, T, Frei, O, Frouin, V, Fukunaga, M, Gareau, T, Ge, T, Glahn, DC, Grabe, HJ, Groenewold, NA, Gustafsson, O, Haavik, J, Haberg, AK, Hall, J, Hashimoto, R, Hehir-Kwa, JY, Hibar, DP, Hillegers, MHJ, Hoffmann, P, Holleran, L, Holmes, AJ, Homuth, G, Hottenga, J-J, Hulshoff Pol, HE, Ikeda, M, Jahanshad, N, Jockwitz, C, Johansson, S, Joensson, EG, Jorgensen, NR, Kikuchi, M, Knowles, EEM, Kumar, K, Le Hellard, S, Leu, C, Linden, DEJ, Liu, J, Lundervold, A, Lundervold, AJ, Maillard, AM, Martin, NG, Martin-Brevet, S, Mather, KA, Mathias, SR, McMahon, KL, McRae, AF, Medland, SE, Meyer-Lindenberg, A, Moberget, T, Modenato, C, Sanchez, JM, Morris, DW, Muehleisen, TW, Murray, RM, Nielsen, J, Nordvik, JE, Nyberg, L, Loohuis, LMO, Ophoff, RA, Owen, MJ, Paus, T, Pausova, Z, Peralta, JM, Pike, GB, Prieto, C, Quinlan, EB, Reinbold, CS, Marques, TR, Rucker, JJH, Sachdev, PS, Sando, SB, Schofield, PR, Schork, AJ, Schumann, G, Shin, J, Shumskaya, E, Silva, AI, Sisodiya, SM, Steen, VM, Stein, DJ, Strike, LT, Suzuki, IK, Tamnes, CK, Teumer, A, Thalamuthu, A, Tordesillas-Gutierrez, D, Uhlmann, A, Ulfarsson, MO, van 't Ent, D, van den Bree, MBM, Vanderhaeghen, P, Vassos, E, Wen, W, Wittfeld, K, Wright, MJ, Agartz, I, Djurovic, S, Westlye, LT, Stefansson, H, Stefansson, K, Jacquemont, S, Thompson, PM, and Andreassen, OA
Abstract: Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.
Published: 2021

13. Plasma secretory phospholipase A2 as an early marker for late-onset sepsis in preterm infants-a pilot study

Author: Hibbert, J, Armstrong, NJ, Granland, C, Ng, S, Simmer, K, Richmond, P, Burgner, D, Strunk, T, Currie, A, Hibbert, J, Armstrong, NJ, Granland, C, Ng, S, Simmer, K, Richmond, P, Burgner, D, Strunk, T, and Currie, A
Abstract: Preterm infants are particularly susceptible to bacterial late-onset sepsis (LOS). Diagnosis by blood culture and inflammatory markers have sub-optimal sensitivity and specificity and prolonged reporting times. There is an urgent need for more rapid, accurate adjunctive diagnostics in LOS to improve management and minimise antibiotic exposure. We measured the diagnostic performance of secretory phospholipase A2 type IIA (sPLA2-IIA) in very preterm infants (<30 weeks gestational age) with suspected LOS. Plasma sPLA2-IIA levels were elevated in infants with LOS (n = 28) compared to those without LOS (n = 21; median 30,970 vs. 2534 pg/ml, p < 0.0001). The mean area under the curve was 0.884 (95% CI: 0.771, 0.977) with a sensitivity of 0.907 (95% CI: 0.667, 1.00) and specificity of 0.804 (95% CI: 0.600, 1.00). The positive and negative predictive values were 0.833 (95% CI: 0.664, 0.927) and 0.842 (95% CI: 0.624, 0.945), respectively. This pilot study suggests that sPLA2-IIA may have clinical utility for the early diagnosis of LOS in very preterm infants, potentially informing clinical management and antibiotic stewardship.
Published: 2021

14. Epigenome-wide meta-analysis of blood DNA methylation and its association with subcortical volumes: findings from the ENIGMA Epigenetics Working Group

Author: Jia, T, Chu, C, Liu, Y, van Dongen, J, Papastergios, E, Armstrong, NJ, Bastin, ME, Carrillo-Roa, T, den Braber, A, Harris, M, Jansen, R, Liu, J, Luciano, M, Ori, APS, Santianez, RR, Ruggeri, B, Sarkisyan, D, Shin, J, Sungeun, K, Gutierrez, DT, van't Ent, D, Ames, D, Artiges, E, Bakalkin, G, Banaschewski, T, Bokde, ALW, Brodaty, H, Bromberg, U, Brouwer, R, Buchel, C, Quinlan, EB, Cahn, W, de Zubicaray, G, Ehrlich, S, Ekstrom, TJ, Flor, H, Frohner, JH, Frouin, V, Garavan, H, Gowland, P, Heinz, A, Hoare, J, Ittermann, B, Jahanshad, N, Jiang, J, Kwok, JB, Martin, NG, Martinot, J-L, Mather, KA, McMahon, KL, McRae, AF, Nees, F, Orfanos, DP, Paus, T, Poustka, L, Samann, PG, Schofield, PR, Smolka, MN, Stein, DJ, Strike, LT, Teeuw, J, Thalamuthu, A, Trollor, J, Walter, H, Wardlaw, JM, Wen, W, Whelan, R, Apostolova, LG, Binder, EB, Boomsma, D, Calhoun, V, Crespo-Facorro, B, Deary, IJ, Pol, HH, Ophoff, RA, Pausova, Z, Sachdev, PS, Saykin, A, Wright, MJ, Thompson, PM, Schumann, G, Desrivieres, S, Jia, T, Chu, C, Liu, Y, van Dongen, J, Papastergios, E, Armstrong, NJ, Bastin, ME, Carrillo-Roa, T, den Braber, A, Harris, M, Jansen, R, Liu, J, Luciano, M, Ori, APS, Santianez, RR, Ruggeri, B, Sarkisyan, D, Shin, J, Sungeun, K, Gutierrez, DT, van't Ent, D, Ames, D, Artiges, E, Bakalkin, G, Banaschewski, T, Bokde, ALW, Brodaty, H, Bromberg, U, Brouwer, R, Buchel, C, Quinlan, EB, Cahn, W, de Zubicaray, G, Ehrlich, S, Ekstrom, TJ, Flor, H, Frohner, JH, Frouin, V, Garavan, H, Gowland, P, Heinz, A, Hoare, J, Ittermann, B, Jahanshad, N, Jiang, J, Kwok, JB, Martin, NG, Martinot, J-L, Mather, KA, McMahon, KL, McRae, AF, Nees, F, Orfanos, DP, Paus, T, Poustka, L, Samann, PG, Schofield, PR, Smolka, MN, Stein, DJ, Strike, LT, Teeuw, J, Thalamuthu, A, Trollor, J, Walter, H, Wardlaw, JM, Wen, W, Whelan, R, Apostolova, LG, Binder, EB, Boomsma, D, Calhoun, V, Crespo-Facorro, B, Deary, IJ, Pol, HH, Ophoff, RA, Pausova, Z, Sachdev, PS, Saykin, A, Wright, MJ, Thompson, PM, Schumann, G, and Desrivieres, S
Abstract: DNA methylation, which is modulated by both genetic factors and environmental exposures, may offer a unique opportunity to discover novel biomarkers of disease-related brain phenotypes, even when measured in other tissues than brain, such as blood. A few studies of small sample sizes have revealed associations between blood DNA methylation and neuropsychopathology, however, large-scale epigenome-wide association studies (EWAS) are needed to investigate the utility of DNA methylation profiling as a peripheral marker for the brain. Here, in an analysis of eleven international cohorts, totalling 3337 individuals, we report epigenome-wide meta-analyses of blood DNA methylation with volumes of the hippocampus, thalamus and nucleus accumbens (NAcc)-three subcortical regions selected for their associations with disease and heritability and volumetric variability. Analyses of individual CpGs revealed genome-wide significant associations with hippocampal volume at two loci. No significant associations were found for analyses of thalamus and nucleus accumbens volumes. Cluster-based analyses revealed additional differentially methylated regions (DMRs) associated with hippocampal volume. DNA methylation at these loci affected expression of proximal genes involved in learning and memory, stem cell maintenance and differentiation, fatty acid metabolism and type-2 diabetes. These DNA methylation marks, their interaction with genetic variants and their impact on gene expression offer new insights into the relationship between epigenetic variation and brain structure and may provide the basis for biomarker discovery in neurodegeneration and neuropsychiatric conditions.
Published: 2021

15. Cerebral small vessel disease genomics and its implications across the lifespan

Author: Sargurupremraj M, Suzuki H, Jian X, Sarnowski C, Evans TE, Bis JC, Eiriksdottir G, Sakaue S,Terzikhan N, Habes M, Zhao W, Armstrong NJ, Hofer E, Yanek LR, Hagenaars SP, KumarRB, van den Akker EB, McWhirter RE, Trompet S, Mishra A, Saba Y, Satizabal CL, BeaudetG, Petit L, Tsuchida A, Zago L, Schilling S, Sigurdsson S, Gottesman RF, Lewis CE, AggarwalNT, Lopez OL, Smith JA, Valdés Hernández MC
Published: 2020
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16. Genetic correlations and genome-wide associations of cortical structure in general population samples of 22,824 adults

Author: Hofer, E, Roshchupkin, GV, Adams, HHH, Knol, MJ, Lin, H, Li, S, Zare, H, Ahmad, S, Armstrong, NJ, Satizabal, CL, Bernard, M, Bis, JC, Gillespie, NA, Luciano, M, Mishra, A, Scholz, M, Teumer, A, Xia, R, Jian, X, Mosley, TH, Saba, Y, Pirpamer, L, Seiler, S, Becker, JT, Carmichael, O, Rotter, JI, Psaty, BM, Lopez, OL, Amin, N, van der Lee, SJ, Yang, Q, Himali, JJ, Maillard, P, Beiser, AS, Decarli, C, Karama, S, Lewis, L, Harris, M, Bastin, ME, Deary, IJ, Witte, AV, Beyer, F, Loeffler, M, Mather, KA, Schofield, PR, Thalamuthu, A, Kwok, JB, Wright, MJ, Ames, D, Trollor, J, Jiang, J, Brodaty, H, Wen, W, Vernooij, MW, Hofman, A, Uitterlinden, AG, Niessen, WJ, Wittfeld, K, Bülow, R, Völker, U, Pausova, Z, Pike, GB, Maingault, S, Crivello, F, Tzourio, C, Amouyel, P, Mazoyer, B, Neale, MC, Franz, CE, Lyons, MJ, Panizzon, MS, Andreassen, OA, Dale, AM, Logue, M, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Stein, JL, Thompson, PM, Medland, SE, Sachdev, PS, Kremen, WS, Wardlaw, JM, Villringer, A, van Duijn, CM, Grabe, HJ, Longstreth, WT, Fornage, M, Paus, T, Debette, S, Ikram, MA, Schmidt, H, Schmidt, R, Seshadri, S, Ching, CRK, Hofer, E, Roshchupkin, GV, Adams, HHH, Knol, MJ, Lin, H, Li, S, Zare, H, Ahmad, S, Armstrong, NJ, Satizabal, CL, Bernard, M, Bis, JC, Gillespie, NA, Luciano, M, Mishra, A, Scholz, M, Teumer, A, Xia, R, Jian, X, Mosley, TH, Saba, Y, Pirpamer, L, Seiler, S, Becker, JT, Carmichael, O, Rotter, JI, Psaty, BM, Lopez, OL, Amin, N, van der Lee, SJ, Yang, Q, Himali, JJ, Maillard, P, Beiser, AS, Decarli, C, Karama, S, Lewis, L, Harris, M, Bastin, ME, Deary, IJ, Witte, AV, Beyer, F, Loeffler, M, Mather, KA, Schofield, PR, Thalamuthu, A, Kwok, JB, Wright, MJ, Ames, D, Trollor, J, Jiang, J, Brodaty, H, Wen, W, Vernooij, MW, Hofman, A, Uitterlinden, AG, Niessen, WJ, Wittfeld, K, Bülow, R, Völker, U, Pausova, Z, Pike, GB, Maingault, S, Crivello, F, Tzourio, C, Amouyel, P, Mazoyer, B, Neale, MC, Franz, CE, Lyons, MJ, Panizzon, MS, Andreassen, OA, Dale, AM, Logue, M, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Stein, JL, Thompson, PM, Medland, SE, Sachdev, PS, Kremen, WS, Wardlaw, JM, Villringer, A, van Duijn, CM, Grabe, HJ, Longstreth, WT, Fornage, M, Paus, T, Debette, S, Ikram, MA, Schmidt, H, Schmidt, R, Seshadri, S, and Ching, CRK
Abstract: Cortical thickness, surface area and volumes vary with age and cognitive function, and in neurological and psychiatric diseases. Here we report heritability, genetic correlations and genome-wide associations of these cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprises 22,824 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. We identify genetic heterogeneity between cortical measures and brain regions, and 160 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There is enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.
Published: 2020

17. Cerebral small vessel disease genomics and its implications across the lifespan

Author: Sargurupremraj, M, Suzuki, H, Jian, X, Sarnowski, C, Evans, TE, Bis, JC, Eiriksdottir, G, Sakaue, S, Terzikhan, N, Habes, M, Zhao, W, Armstrong, NJ, Hofer, E, Yanek, LR, Hagenaars, SP, Kumar, RB, van den Akker, EB, McWhirter, RE, Trompet, S, Mishra, A, Saba, Y, Satizabal, CL, Beaudet, G, Petit, L, Tsuchida, A, Zago, L, Schilling, S, Sigurdsson, S, Gottesman, RF, Lewis, CE, Aggarwal, NT, Lopez, OL, Smith, JA, Valdés Hernández, MC, van der Grond, J, Wright, MJ, Knol, MJ, Dörr, M, Thomson, RJ, Bordes, C, Le Grand, Q, Duperron, MG, Smith, AV, Knopman, DS, Schreiner, PJ, Evans, DA, Rotter, JI, Beiser, AS, Maniega, SM, Beekman, M, Trollor, J, Stott, DJ, Vernooij, MW, Wittfeld, K, Niessen, WJ, Soumaré, A, Boerwinkle, E, Sidney, S, Turner, ST, Davies, G, Thalamuthu, A, Völker, U, van Buchem, MA, Bryan, RN, Dupuis, J, Bastin, ME, Ames, D, Teumer, A, Amouyel, P, Kwok, JB, Bülow, R, Deary, IJ, Schofield, PR, Brodaty, H, Jiang, J, Tabara, Y, Setoh, K, Miyamoto, S, Yoshida, K, Nagata, M, Kamatani, Y, Matsuda, F, Psaty, BM, Bennett, DA, De Jager, PL, Mosley, TH, Sachdev, PS, Schmidt, R, Warren, HR, Evangelou, E, Trégouët, DA, de Andrade, M, Basu, S, Berr, C, Brody, JA, Chasman, DI, Dartigues, JF, Folsom, AR, Germain, M, Sargurupremraj, M, Suzuki, H, Jian, X, Sarnowski, C, Evans, TE, Bis, JC, Eiriksdottir, G, Sakaue, S, Terzikhan, N, Habes, M, Zhao, W, Armstrong, NJ, Hofer, E, Yanek, LR, Hagenaars, SP, Kumar, RB, van den Akker, EB, McWhirter, RE, Trompet, S, Mishra, A, Saba, Y, Satizabal, CL, Beaudet, G, Petit, L, Tsuchida, A, Zago, L, Schilling, S, Sigurdsson, S, Gottesman, RF, Lewis, CE, Aggarwal, NT, Lopez, OL, Smith, JA, Valdés Hernández, MC, van der Grond, J, Wright, MJ, Knol, MJ, Dörr, M, Thomson, RJ, Bordes, C, Le Grand, Q, Duperron, MG, Smith, AV, Knopman, DS, Schreiner, PJ, Evans, DA, Rotter, JI, Beiser, AS, Maniega, SM, Beekman, M, Trollor, J, Stott, DJ, Vernooij, MW, Wittfeld, K, Niessen, WJ, Soumaré, A, Boerwinkle, E, Sidney, S, Turner, ST, Davies, G, Thalamuthu, A, Völker, U, van Buchem, MA, Bryan, RN, Dupuis, J, Bastin, ME, Ames, D, Teumer, A, Amouyel, P, Kwok, JB, Bülow, R, Deary, IJ, Schofield, PR, Brodaty, H, Jiang, J, Tabara, Y, Setoh, K, Miyamoto, S, Yoshida, K, Nagata, M, Kamatani, Y, Matsuda, F, Psaty, BM, Bennett, DA, De Jager, PL, Mosley, TH, Sachdev, PS, Schmidt, R, Warren, HR, Evangelou, E, Trégouët, DA, de Andrade, M, Basu, S, Berr, C, Brody, JA, Chasman, DI, Dartigues, JF, Folsom, AR, and Germain, M
Abstract: White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.
Published: 2020

18. Risk prediction of late-onset Alzheimer’s disease implies an oligogenic architecture

Author: Zhang, Q, Sidorenko, J, Couvy-Duchesne, B, Marioni, RE, Wright, MJ, Goate, AM, Marcora, E, Huang, KL, Porter, T, Laws, SM, Masters, CL, Bush, AI, Fowler, C, Darby, D, Pertile, K, Restrepo, C, Roberts, B, Robertson, J, Rumble, R, Ryan, T, Collins, S, Thai, C, Trounson, B, Lennon, K, Li, QX, Ugarte, FY, Volitakis, I, Vovos, M, Williams, R, Baker, J, Russell, A, Peretti, M, Milicic, L, Lim, L, Rodrigues, M, Taddei, K, Taddei, T, Hone, E, Lim, F, Fernandez, S, Rainey-Smith, S, Pedrini, S, Martins, R, Doecke, J, Bourgeat, P, Fripp, J, Gibson, S, Leroux, H, Hanson, D, Dore, V, Zhang, P, Burnham, S, Rowe, CC, Villemagne, VL, Yates, P, Pejoska, SB, Jones, G, Ames, D, Cyarto, E, Lautenschlager, N, Barnham, K, Cheng, L, Hill, A, Killeen, N, Maruff, P, Silbert, B, Brown, B, Sohrabi, H, Savage, G, Vacher, M, Sachdev, PS, Mather, KA, Armstrong, NJ, Thalamuthu, A, Brodaty, H, Yengo, L, Yang, J, Wray, NR, McRae, AF, Visscher, PM, Zhang, Q, Sidorenko, J, Couvy-Duchesne, B, Marioni, RE, Wright, MJ, Goate, AM, Marcora, E, Huang, KL, Porter, T, Laws, SM, Masters, CL, Bush, AI, Fowler, C, Darby, D, Pertile, K, Restrepo, C, Roberts, B, Robertson, J, Rumble, R, Ryan, T, Collins, S, Thai, C, Trounson, B, Lennon, K, Li, QX, Ugarte, FY, Volitakis, I, Vovos, M, Williams, R, Baker, J, Russell, A, Peretti, M, Milicic, L, Lim, L, Rodrigues, M, Taddei, K, Taddei, T, Hone, E, Lim, F, Fernandez, S, Rainey-Smith, S, Pedrini, S, Martins, R, Doecke, J, Bourgeat, P, Fripp, J, Gibson, S, Leroux, H, Hanson, D, Dore, V, Zhang, P, Burnham, S, Rowe, CC, Villemagne, VL, Yates, P, Pejoska, SB, Jones, G, Ames, D, Cyarto, E, Lautenschlager, N, Barnham, K, Cheng, L, Hill, A, Killeen, N, Maruff, P, Silbert, B, Brown, B, Sohrabi, H, Savage, G, Vacher, M, Sachdev, PS, Mather, KA, Armstrong, NJ, Thalamuthu, A, Brodaty, H, Yengo, L, Yang, J, Wray, NR, McRae, AF, and Visscher, PM
Abstract: Genetic association studies have identified 44 common genome-wide significant risk loci for late-onset Alzheimer’s disease (LOAD). However, LOAD genetic architecture and prediction are unclear. Here we estimate the optimal P-threshold (Poptimal) of a genetic risk score (GRS) for prediction of LOAD in three independent datasets comprising 676 cases and 35,675 family history proxy cases. We show that the discriminative ability of GRS in LOAD prediction is maximised when selecting a small number of SNPs. Both simulation results and direct estimation indicate that the number of causal common SNPs for LOAD may be less than 100, suggesting LOAD is more oligogenic than polygenic. The best GRS explains approximately 75% of SNP-heritability, and individuals in the top decile of GRS have ten-fold increased odds when compared to those in the bottom decile. In addition, 14 variants are identified that contribute to both LOAD risk and age at onset of LOAD.
Published: 2020

19. Genetic and environmental causes of variation in epigenetic aging across the lifespan

Author: Li, S, Nguyen, TL, Wong, EM, Dugué, PA, Dite, GS, Armstrong, NJ, Craig, JM, Mather, KA, Sachdev, PS, Saffery, R, Sung, J, Tan, Q, Thalamuthu, A, Milne, RL, Giles, GG, Southey, MC, Hopper, JL, Li, S, Nguyen, TL, Wong, EM, Dugué, PA, Dite, GS, Armstrong, NJ, Craig, JM, Mather, KA, Sachdev, PS, Saffery, R, Sung, J, Tan, Q, Thalamuthu, A, Milne, RL, Giles, GG, Southey, MC, and Hopper, JL
Abstract: Background: DNA methylation-based biological age (DNAm age) is an important biomarker for adult health. Studies in specific age ranges have found widely varying results about its genetic and environmental causes of variation. However, these studies are not able to provide a comprehensive view of the causes of variation over the lifespan. Results: In order to investigate the genetic and environmental causes of DNAm age variation across the lifespan, we pooled genome-wide DNA methylation data for 4217 people aged 0–92 years from 1871 families. DNAm age was calculated using the Horvath epigenetic clock. We estimated familial correlations in DNAm age for monozygotic (MZ) twin, dizygotic (DZ) twin, sibling, parent–offspring, and spouse pairs by cohabitation status. Genetic and environmental variance components models were fitted and compared. We found that twin pair correlations were − 0.12 to 0.18 around birth, not different from zero (all P > 0.29). For all pairs of relatives, their correlations increased with time spent living together (all P < 0.02) at different rates (MZ > DZ and siblings > parent–offspring; P < 0.001) and decreased with time spent living apart (P = 0.02) at similar rates. These correlation patterns were best explained by cohabitation-dependent shared environmental factors, the effects of which were 1.41 (95% confidence interval [CI] 1.16 to 1.66) times greater for MZ pairs than for DZ and sibling pairs, and the latter were 2.03 (95% CI 1.13 to 9.47) times greater than for parent–offspring pairs. Genetic factors explained 13% (95% CI − 10 to 35%) of variation (P = 0.27). Similar results were found for another two epigenetic clocks, suggesting that our observations are robust to how DNAm age is measured. In addition, results for the other clocks were consistent with there also being a role for prenatal environmental factors in determining their variation. Conclusions: Variation in DNAm age is mostly caused by environmental factors, including those sh
Published: 2020

20. Age-Related Changes of Peak Width Skeletonized Mean Diffusivity (PSMD) Across the Adult Lifespan: A Multi-Cohort Study

Author: Beaudet, G, Tsuchida, A, Petit, L, Tzourio, C, Caspers, S, Schreiber, J, Pausova, Z, Patel, Y, Paus, T, Schmidt, R, Pirpamer, L, Sachdev, PS, Brodaty, H, Kochan, N, Trollor, J, Wen, W, Armstrong, NJ, Deary, IJ, Bastin, ME, Wardlaw, JM, Munõz Maniega, S, Witte, AV, Villringer, A, Duering, M, Debette, S, Mazoyer, B, Beaudet, G, Tsuchida, A, Petit, L, Tzourio, C, Caspers, S, Schreiber, J, Pausova, Z, Patel, Y, Paus, T, Schmidt, R, Pirpamer, L, Sachdev, PS, Brodaty, H, Kochan, N, Trollor, J, Wen, W, Armstrong, NJ, Deary, IJ, Bastin, ME, Wardlaw, JM, Munõz Maniega, S, Witte, AV, Villringer, A, Duering, M, Debette, S, and Mazoyer, B
Abstract: Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD, and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing skeletonized DTI metrics from DTI maps. For each individual, MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD, and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40, and 50 years, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant incre
Published: 2020

21. The genetic architecture of the human cerebral cortex

Author: Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Ching, CRK, McMahon, MAB, Shatokhina, N, Zsembik, LCP, Thomopoulos, SI, Zhu, AH, Strike, LT, Agartz, I, Alhusaini, S, Almeida, MAA, Alnæs, D, Amlien, IK, Andersson, M, Ard, T, Armstrong, NJ, Ashley-Koch, A, Atkins, JR, Bernard, M, Brouwer, RM, Buimer, EEL, Bülow, R, Bürger, C, Cannon, DM, Chakravarty, M, Chen, Q, Cheung, JW, Couvy-Duchesne, B, Dale, AM, Dalvie, S, de Araujo, TK, de Zubicaray, GI, de Zwarte, SMC, den Braber, A, Doan, NT, Dohm, K, Ehrlich, S, Engelbrecht, HR, Erk, S, Fan, CC, Fedko, IO, Foley, SF, Ford, JM, Fukunaga, M, Garrett, ME, Ge, T, Giddaluru, S, Goldman, AL, Green, MJ, Groenewold, NA, Grotegerd, D, Gurholt, TP, Gutman, BA, Hansell, NK, Harris, MA, Harrison, MB, Haswell, CC, Hauser, M, Herms, S, Heslenfeld, DJ, Ho, NF, Hoehn, D, Hoffmann, P, Holleran, L, Hoogman, M, Hottenga, JJ, Ikeda, M, Janowitz, D, Jansen, IE, Jia, T, Jockwitz, C, Kanai, R, Karama, S, Kasperaviciute, D, Kaufmann, T, Kelly, S, Kikuchi, M, Klein, M, Knapp, M, Knodt, AR, Krämer, B, Lam, M, Lancaster, TM, Lee, PH, Lett, TA, Lewis, LB, Lopes-Cendes, I, Luciano, M, Macciardi, F, Marquand, AF, Mathias, SR, Melzer, TR, Milaneschi, Y, Grasby, KL, Jahanshad, N, Painter, JN, Colodro-Conde, L, Bralten, J, Hibar, DP, Lind, PA, Pizzagalli, F, Ching, CRK, McMahon, MAB, Shatokhina, N, Zsembik, LCP, Thomopoulos, SI, Zhu, AH, Strike, LT, Agartz, I, Alhusaini, S, Almeida, MAA, Alnæs, D, Amlien, IK, Andersson, M, Ard, T, Armstrong, NJ, Ashley-Koch, A, Atkins, JR, Bernard, M, Brouwer, RM, Buimer, EEL, Bülow, R, Bürger, C, Cannon, DM, Chakravarty, M, Chen, Q, Cheung, JW, Couvy-Duchesne, B, Dale, AM, Dalvie, S, de Araujo, TK, de Zubicaray, GI, de Zwarte, SMC, den Braber, A, Doan, NT, Dohm, K, Ehrlich, S, Engelbrecht, HR, Erk, S, Fan, CC, Fedko, IO, Foley, SF, Ford, JM, Fukunaga, M, Garrett, ME, Ge, T, Giddaluru, S, Goldman, AL, Green, MJ, Groenewold, NA, Grotegerd, D, Gurholt, TP, Gutman, BA, Hansell, NK, Harris, MA, Harrison, MB, Haswell, CC, Hauser, M, Herms, S, Heslenfeld, DJ, Ho, NF, Hoehn, D, Hoffmann, P, Holleran, L, Hoogman, M, Hottenga, JJ, Ikeda, M, Janowitz, D, Jansen, IE, Jia, T, Jockwitz, C, Kanai, R, Karama, S, Kasperaviciute, D, Kaufmann, T, Kelly, S, Kikuchi, M, Klein, M, Knapp, M, Knodt, AR, Krämer, B, Lam, M, Lancaster, TM, Lee, PH, Lett, TA, Lewis, LB, Lopes-Cendes, I, Luciano, M, Macciardi, F, Marquand, AF, Mathias, SR, Melzer, TR, and Milaneschi, Y
Abstract: The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder.
Published: 2020

22. Global and Regional Development of the Human Cerebral Cortex: Molecular Architecture and Occupational Aptitudes

Author: Shin, J, Ma, S, Hofer, E, Patel, Y, Vosberg, DE, Tilley, S, Roshchupkin, G, Sousa, AMM, Jian, X, Gottesman, R, Mosley, TH, Fornage, M, Saba, Y, Pirpamer, L, Schmidt, R, Schmidt, H, Carrion-Castillo, A, Crivello, F, Mazoyer, B, Bis, JC, Li, S, Yang, Q, Luciano, M, Karama, S, Lewis, L, Bastin, ME, Harris, MA, Wardlaw, JM, Deary, IE, Scholz, M, Loeffler, M, Witte, AV, Beyer, F, Villringer, A, Armstrong, NJ, Mather, KA, Ames, D, Jiang, J, Kwok, JB, Schofield, PR, Thalamuthu, A, Trollor, JN, Wright, MJ, Brodaty, H, Wen, W, Sachdev, PS, Terzikhan, N, Evans, TE, Adams, HHHH, Ikram, MA, Frenzel, S, Van der Auwera-Palitschka, S, Wittfeld, K, Bulow, R, Grabe, HJ, Tzourio, C, Mishra, A, Maingault, S, Debette, S, Gillespie, NA, Franz, CE, Kremen, WS, Ding, L, Jahanshad, N, Sestan, N, Pausova, Z, Seshadri, S, Paus, T, Shin, J, Ma, S, Hofer, E, Patel, Y, Vosberg, DE, Tilley, S, Roshchupkin, G, Sousa, AMM, Jian, X, Gottesman, R, Mosley, TH, Fornage, M, Saba, Y, Pirpamer, L, Schmidt, R, Schmidt, H, Carrion-Castillo, A, Crivello, F, Mazoyer, B, Bis, JC, Li, S, Yang, Q, Luciano, M, Karama, S, Lewis, L, Bastin, ME, Harris, MA, Wardlaw, JM, Deary, IE, Scholz, M, Loeffler, M, Witte, AV, Beyer, F, Villringer, A, Armstrong, NJ, Mather, KA, Ames, D, Jiang, J, Kwok, JB, Schofield, PR, Thalamuthu, A, Trollor, JN, Wright, MJ, Brodaty, H, Wen, W, Sachdev, PS, Terzikhan, N, Evans, TE, Adams, HHHH, Ikram, MA, Frenzel, S, Van der Auwera-Palitschka, S, Wittfeld, K, Bulow, R, Grabe, HJ, Tzourio, C, Mishra, A, Maingault, S, Debette, S, Gillespie, NA, Franz, CE, Kremen, WS, Ding, L, Jahanshad, N, Sestan, N, Pausova, Z, Seshadri, S, and Paus, T
Abstract: We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade-albeit in a very limited manner-to behaviors as complex as the choice of one's occupation.
Published: 2020

23. Genetic and environmental determinants of variation in the plasma lipidome of older Australian twins

Author: Wong, MWK, Thalamuthu, A, Braidy, N, Mather, KA, Liu, Y, Ciobanu, L, Baune, BT, Armstrong, NJ, Kwok, J, Schofield, P, Wright, MJ, Ames, D, Pickford, R, Lee, T, Poljak, A, Sachdev, PS, Wong, MWK, Thalamuthu, A, Braidy, N, Mather, KA, Liu, Y, Ciobanu, L, Baune, BT, Armstrong, NJ, Kwok, J, Schofield, P, Wright, MJ, Ames, D, Pickford, R, Lee, T, Poljak, A, and Sachdev, PS
Abstract: The critical role of blood lipids in a broad range of health and disease states is well recognised but less explored is the interplay of genetics and environment within the broader blood lipidome. We examined heritability of the plasma lipidome among healthy older-aged twins (75 monozygotic/55 dizygotic pairs) enrolled in the Older Australian Twins Study (OATS) and explored corresponding gene expression and DNA methylation associations. 27/209 lipids (13.3%) detected by liquid chromatography-coupled mass spectrometry (LC-MS) were significantly heritable under the classical ACE twin model (h2 = 0.28-0.59), which included ceramides (Cer) and triglycerides (TG). Relative to non-significantly heritable TGs, heritable TGs had a greater number of associations with gene transcripts, not directly associated with lipid metabolism, but with immune function, signalling and transcriptional regulation. Genome-wide average DNA methylation (GWAM) levels accounted for variability in some non-heritable lipids. We reveal a complex interplay of genetic and environmental influences on the ageing plasma lipidome.
Published: 2020

24. Author Correction: Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function (Nature Communications, (2018), 9, 1, (2098), 10.1038/s41467-018-04362-x)

Author: Davies, G, Lam, M, Harris, SE, Trampush, JW, Luciano, M, Hill, WD, Hagenaars, SP, Ritchie, SJ, Marioni, RE, Fawns-Ritchie, C, Liewald, DCM, Okely, JA, Ahola-Olli, AV, Barnes, CLK, Bertram, L, Bis, JC, Burdick, KE, Christoforou, A, DeRosse, P, Djurovic, S, Espeseth, T, Giakoumaki, S, Giddaluru, S, Gustavson, DE, Hayward, C, Hofer, E, Ikram, MA, Karlsson, R, Knowles, E, Lahti, J, Leber, M, Li, S, Mather, KA, Melle, I, Morris, D, Oldmeadow, C, Palviainen, T, Payton, A, Pazoki, R, Petrovic, K, Reynolds, CA, Sargurupremraj, M, Scholz, M, Smith, JA, Smith, AV, Terzikhan, N, Thalamuthu, A, Trompet, S, van der Lee, SJ, Ware, EB, Windham, BG, Wright, MJ, Yang, J, Yu, J, Ames, D, Amin, N, Amouyel, P, Andreassen, OA, Armstrong, NJ, Assareh, AA, Attia, JR, Attix, D, Avramopoulos, D, Bennett, DA, Böhmer, AC, Boyle, PA, Brodaty, H, Campbell, H, Cannon, TD, Cirulli, ET, Congdon, E, Conley, ED, Corley, J, Cox, SR, Dale, AM, Dehghan, A, Dick, D, Dickinson, D, Eriksson, JG, Evangelou, E, Faul, JD, Ford, I, Freimer, NA, Gao, H, Giegling, I, Gillespie, NA, Gordon, SD, Gottesman, RF, Griswold, ME, Gudnason, V, Harris, TB, Hartmann, AM, Hatzimanolis, A, Heiss, G, Holliday, EG, Joshi, PK, Kähönen, M, Kardia, SLR, Karlsson, I, Kleineidam, L, Davies, G, Lam, M, Harris, SE, Trampush, JW, Luciano, M, Hill, WD, Hagenaars, SP, Ritchie, SJ, Marioni, RE, Fawns-Ritchie, C, Liewald, DCM, Okely, JA, Ahola-Olli, AV, Barnes, CLK, Bertram, L, Bis, JC, Burdick, KE, Christoforou, A, DeRosse, P, Djurovic, S, Espeseth, T, Giakoumaki, S, Giddaluru, S, Gustavson, DE, Hayward, C, Hofer, E, Ikram, MA, Karlsson, R, Knowles, E, Lahti, J, Leber, M, Li, S, Mather, KA, Melle, I, Morris, D, Oldmeadow, C, Palviainen, T, Payton, A, Pazoki, R, Petrovic, K, Reynolds, CA, Sargurupremraj, M, Scholz, M, Smith, JA, Smith, AV, Terzikhan, N, Thalamuthu, A, Trompet, S, van der Lee, SJ, Ware, EB, Windham, BG, Wright, MJ, Yang, J, Yu, J, Ames, D, Amin, N, Amouyel, P, Andreassen, OA, Armstrong, NJ, Assareh, AA, Attia, JR, Attix, D, Avramopoulos, D, Bennett, DA, Böhmer, AC, Boyle, PA, Brodaty, H, Campbell, H, Cannon, TD, Cirulli, ET, Congdon, E, Conley, ED, Corley, J, Cox, SR, Dale, AM, Dehghan, A, Dick, D, Dickinson, D, Eriksson, JG, Evangelou, E, Faul, JD, Ford, I, Freimer, NA, Gao, H, Giegling, I, Gillespie, NA, Gordon, SD, Gottesman, RF, Griswold, ME, Gudnason, V, Harris, TB, Hartmann, AM, Hatzimanolis, A, Heiss, G, Holliday, EG, Joshi, PK, Kähönen, M, Kardia, SLR, Karlsson, I, and Kleineidam, L
Abstract: Christina M. Lill, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this article. This has now been corrected in both the PDF and HTML versions of the article.
Published: 2019

25. Exceptional longevity and polygenic risk for cardiovascular health

Author: Revelas, M, Thalamuthu, A, Oldmeadow, C, Evans, TJ, Armstrong, NJ, Riveros, C, Kwok, JB, Schofield, PR, Brodaty, H, Scott, RJ, Attia, JR, Sachdev, PS, Mather, KA, Revelas, M, Thalamuthu, A, Oldmeadow, C, Evans, TJ, Armstrong, NJ, Riveros, C, Kwok, JB, Schofield, PR, Brodaty, H, Scott, RJ, Attia, JR, Sachdev, PS, and Mather, KA
Abstract: Studies investigating exceptionally long-lived (ELL) individuals, including genetic studies, have linked cardiovascular-related pathways, particularly lipid and cholesterol homeostasis, with longevity. This study explored the genetic profiles of ELL individuals (cases: n = 294, 95–106 years; controls: n = 1105, 55–65 years) by assessing their polygenic risk scores (PRS) based on a genome wide association study (GWAS) threshold of p < 5 × 10−5. PRS were constructed using GWAS summary data from two exceptional longevity (EL) analyses and eight cardiovascular-related risk factors (lipids) and disease (myocardial infarction, coronary artery disease, stroke) analyses. A higher genetic risk for exceptional longevity (EL) was significantly associated with longevity in our sample (odds ratio (OR) = 1.19–1.20, p = 0.00804 and 0.00758, respectively). Two cardiovascular health PRS were nominally significant with longevity (HDL cholesterol, triglycerides), with higher PRS associated with EL, but these relationships did not survive correction for multiple testing. In conclusion, ELL individuals did not have significantly lower polygenic risk for the majority of the investigated cardiovascular health traits. Future work in larger cohorts is required to further explore the role of cardiovascular-related genetic variants in EL.
Published: 2019

26. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Author: Chauhan, G, Adams, HHH, Satizabal, CL, Bis, JC, Teumer, A, Sargurupremraj, M, Hofer, E, Trompet, S, Hilal, S, Smith, AV, Jian, X, Malik, R, Traylor, M, Pulit, SL, Amouyel, P, Mazoyer, B, Zhu, YC, Kaffashian, S, Schilling, S, Beecham, GW, Montine, TJ, Schellenberg, GD, Kjartansson, O, Guðnason, V, Knopman, DS, Griswold, ME, Windham, BG, Gottesman, RF, Mosley, TH, Schmidt, R, Saba, Y, Schmidt, H, Takeuchi, F, Yamaguchi, S, Nabika, T, Kato, N, Rajan, KB, Aggarwal, NT, De Jager, PL, Evans, DA, Psaty, BM, Rotter, JI, Rice, K, Lopez, OL, Liao, J, Chen, C, Cheng, CY, Wong, TY, Ikram, MK, van der Lee, SJ, Amin, N, Chouraki, V, Destefano, AL, Aparicio, HJ, Romero, JR, Maillard, P, Decarli, C, Wardlaw, JM, Del C. Valdés Hernández, M, Luciano, M, Liewald, D, Deary, IJ, Starr, JM, Bastin, ME, Maniega, SM, Slagboom, PE, Beekman, M, Deelen, J, Uh, HW, Lemmens, R, Brodaty, H, Wright, MJ, Ames, D, Boncoraglio, GB, Hopewell, JC, Beecham, AH, Blanton, SH, Wright, CB, Sacco, RL, Wen, W, Thalamuthu, A, Armstrong, NJ, Chong, E, Schofield, PR, Kwok, JB, van der Grond, J, Stott, DJ, Ford, I, Jukema, JW, Vernooij, MW, Hofman, A, Uitterlinden, AG, van der Lugt, A, Wittfeld, K, Grabe, HJ, Hosten, N, von Sarnowski, B, Völker, U, Levi, C, Jimenez-Conde, J, Chauhan, G, Adams, HHH, Satizabal, CL, Bis, JC, Teumer, A, Sargurupremraj, M, Hofer, E, Trompet, S, Hilal, S, Smith, AV, Jian, X, Malik, R, Traylor, M, Pulit, SL, Amouyel, P, Mazoyer, B, Zhu, YC, Kaffashian, S, Schilling, S, Beecham, GW, Montine, TJ, Schellenberg, GD, Kjartansson, O, Guðnason, V, Knopman, DS, Griswold, ME, Windham, BG, Gottesman, RF, Mosley, TH, Schmidt, R, Saba, Y, Schmidt, H, Takeuchi, F, Yamaguchi, S, Nabika, T, Kato, N, Rajan, KB, Aggarwal, NT, De Jager, PL, Evans, DA, Psaty, BM, Rotter, JI, Rice, K, Lopez, OL, Liao, J, Chen, C, Cheng, CY, Wong, TY, Ikram, MK, van der Lee, SJ, Amin, N, Chouraki, V, Destefano, AL, Aparicio, HJ, Romero, JR, Maillard, P, Decarli, C, Wardlaw, JM, Del C. Valdés Hernández, M, Luciano, M, Liewald, D, Deary, IJ, Starr, JM, Bastin, ME, Maniega, SM, Slagboom, PE, Beekman, M, Deelen, J, Uh, HW, Lemmens, R, Brodaty, H, Wright, MJ, Ames, D, Boncoraglio, GB, Hopewell, JC, Beecham, AH, Blanton, SH, Wright, CB, Sacco, RL, Wen, W, Thalamuthu, A, Armstrong, NJ, Chong, E, Schofield, PR, Kwok, JB, van der Grond, J, Stott, DJ, Ford, I, Jukema, JW, Vernooij, MW, Hofman, A, Uitterlinden, AG, van der Lugt, A, Wittfeld, K, Grabe, HJ, Hosten, N, von Sarnowski, B, Völker, U, Levi, C, and Jimenez-Conde, J
Abstract: Objective To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n=20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. Results The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p[BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p[BI]= 4.4 × 10-10; p [SSBI] = 1.2 × 10 -4), diabetes (p[BI] = 1.7 × 10 -8; p [SSBI] = 2.8 × 10 -3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10 -24), and MRI-defined white matter hyperintensity burden (p [BI]=1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. Conclusion In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significan
Published: 2019

27. Study of 300,486 individuals identifies 148 independent genetic loci influencing general cognitive function

Author: Davies, G, Lam, M, Harris, SE, Trampush, JW, Luciano, M, Hill, WD, Hagenaars, SP, Ritchie, SJ, Marioni, RE, Fawns-Ritchie, C, Liewald, DCM, Okely, JA, Ahola-Olli, AV, Barnes, CLK, Bertram, L, Bis, JC, Burdick, KE, Christoforou, A, Derosse, P, Djurovic, S, Espeseth, T, Giakoumaki, S, Giddaluru, S, Gustavson, DE, Hayward, C, Hofer, E, Ikram, MA, Karlsson, R, Knowles, E, Lahti, J, Leber, M, Li, S, Mather, KA, Melle, I, Morris, D, Oldmeadow, C, Palviainen, T, Payton, A, Pazoki, R, Petrovic, K, Reynolds, CA, Sargurupremraj, M, Scholz, M, Smith, JA, Smith, AV, Terzikhan, N, Thalamuthu, A, Trompet, S, Van Der Lee, SJ, Ware, EB, Windham, BG, Wright, MJ, Yang, J, Yu, J, Ames, D, Amin, N, Amouyel, P, Andreassen, OA, Armstrong, NJ, Assareh, AA, Attia, JR, Attix, D, Avramopoulos, D, Bennett, DA, Böhmer, AC, Boyle, PA, and Brodaty, H
Abstract: © 2018 The Author(s). General cognitive function is a prominent and relatively stable human trait that is associated with many important life outcomes. We combine cognitive and genetic data from the CHARGE and COGENT consortia, and UK Biobank (total N = 300,486; age 16-102) and find 148 genome-wide significant independent loci (P < 5 × 10-8) associated with general cognitive function. Within the novel genetic loci are variants associated with neurodegenerative and neurodevelopmental disorders, physical and psychiatric illnesses, and brain structure. Gene-based analyses find 709 genes associated with general cognitive function. Expression levels across the cortex are associated with general cognitive function. Using polygenic scores, up to 4.3% of variance in general cognitive function is predicted in independent samples. We detect significant genetic overlap between general cognitive function, reaction time, and many health variables including eyesight, hypertension, and longevity. In conclusion we identify novel genetic loci and pathways contributing to the heritability of general cognitive function.
Published: 2018
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28. Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume

Author: Vojinovic, D, Adams, HH, Jian, X, Yang, Q, Smith, AV, Bis, JC, Teumer, A, Scholz, M, Armstrong, NJ, Hofer, E, Saba, Y, Luciano, M, Bernard, M, Trompet, S, Yang, J, Gillespie, NA, van der Lee, SJ, Neumann, A, Ahmad, S, Andreassen, OA, Ames, D, Amin, N, Arfanakis, K, Bastin, ME, Becker, DM, Beiser, AS, Beyer, F, Brodaty, H, Bryan, RN, Bülow, R, Dale, AM, De Jager, PL, Deary, IJ, DeCarli, C, Fleischman, DA, Gottesman, RF, van der Grond, J, Gudnason, V, Harris, TB, Homuth, G, Knopman, DS, Kwok, JB, Lewis, CE, Li, S, Loeffler, M, Lopez, OL, Maillard, P, El Marroun, H, Mather, KA, Mosley, TH, Muetzel, RL, Nauck, M, Nyquist, PA, Panizzon, MS, Pausova, Z, Psaty, BM, Rice, K, Rotter, JI, Royle, N, Satizabal, CL, Schmidt, R, Schofield, PR, Schreiner, PJ, Sidney, S, Stott, DJ, Thalamuthu, A, Uitterlinden, AG, and Valdés Hernández, MC
Abstract: © 2018, The Author(s). The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (?genetic = -0.59, p-value = 3.14 × 10-6), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology.
Published: 2018
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29. Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA consortium

Author: Kong, XZ, Mathias, SR, Guadalupe, T, Abé, C, Agartz, I, Akudjedu, TN, Aleman, A, Alhusaini, S, Allen, NB, Ames, D, Andreassen, OA, Vasquez, AA, Armstrong, NJ, Bergo, F, Bastin, ME, Batalla, A, Bauer, J, Baune, BT, Baur-Streubel, R, Biederman, J, Blaine, SK, Boedhoe, P, Bøen, E, Bose, A, Bralten, J, Brandeis, D, Brem, S, Brodaty, H, Yüksel, D, Brooks, SJ, Buitelaar, J, Bürger, C, Bülow, R, Calhoun, V, Calvo, A, Canales-Rodríguez, EJ, Canive, JM, Cannon, DM, Caparelli, EC, Castellanos, FX, Cavalleri, GL, Cendes, F, Chaim-Avancini, TM, Chantiluke, K, Chen, QL, Chen, X, Cheng, Y, Christakou, A, Clark, VP, Coghill, D, Connolly, CG, Conzelmann, A, Córdova-Palomera, A, Cousijn, J, Crow, T, Cubillo, A, Dale, A, Dannlowski, U, De Bruttopilo, SA, De Zeeuw, P, Deary, IJ, Delanty, N, Demeter, DV, Di Martino, A, and Dickie, EW
Abstract: © 2018 National Academy of Sciences. All rights reserved. Hemispheric asymmetry is a cardinal feature of human brain organization. Altered brain asymmetry has also been linked to some cognitive and neuropsychiatric disorders. Here, the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium presents the largest-ever analysis of cerebral cortical asymmetry and its variability across individuals. Cortical thickness and surface area were assessed in MRI scans of 17,141 healthy individuals from 99 datasets worldwide. Results revealed widespread asymmetries at both hemispheric and regional levels, with a generally thicker cortex but smaller surface area in the left hemisphere relative to the right. Regionally, asymmetries of cortical thickness and/or surface area were found in the inferior frontal gyrus, transverse temporal gyrus, parahippocampal gyrus, and entorhinal cortex. These regions are involved in lateralized functions, including language and visuospatial processing. In addition to population-level asymmetries, variability in brain asymmetry was related to sex, age, and intracranial volume. Interestingly, we did not find significant associations between asymmetries and handedness. Finally, with two independent pedigree datasets (n = 1,443 and 1,113, respectively), we found several asymmetries showing significant, replicable heritability. The structural asymmetries identified and their variabilities and heritability provide a reference resource for future studies on the genetic basis of brain asymmetry and altered laterality in cognitive, neurological, and psychiatric disorders.
Published: 2018
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30. The association of regional white matter lesions with cognition in a community-based cohort of older individuals

Author: Jiang, J, Paradise, M, Liu, T, Armstrong, NJ, Zhu, W, Kochan, N, Brodaty, H, Sachdev, PS, Wen, W, Jiang, J, Paradise, M, Liu, T, Armstrong, NJ, Zhu, W, Kochan, N, Brodaty, H, Sachdev, PS, and Wen, W
Published: 2018

31. Genome-wide average DNA methylation is determined in utero

Author: Li, S, Wong, EM, Dugue, PA, McRae, AF, Kim, E, Joo, JE, Nguyen, TL, Stone, J, Dite, GS, Armstrong, NJ, Mather, KA, Thalamuthu, A, Wright, MJ, Ames, D, Milne, RJ, Craig, JM, Saffery, R, Montgomery, GW, Song, YM, Sung, J, Spector, TD, Sachdev, PS, Giles, GG, Southey, MC, Hopper, JL, Li, S, Wong, EM, Dugue, PA, McRae, AF, Kim, E, Joo, JE, Nguyen, TL, Stone, J, Dite, GS, Armstrong, NJ, Mather, KA, Thalamuthu, A, Wright, MJ, Ames, D, Milne, RJ, Craig, JM, Saffery, R, Montgomery, GW, Song, YM, Sung, J, Spector, TD, Sachdev, PS, Giles, GG, Southey, MC, and Hopper, JL
Published: 2018

32. A meta-analysis of genome-wide association studies of growth differentiation factor-15 concentration in blood

Author: Jiang, J, Thalamuthu, A, Ho, JE, Mahajan, A, Ek, WE, Brown, DA, Breit, SN, Wang, TJ, Gyllensten, U, Chen, MH, Enroth, S, Januzzi, JL, Lind, L, Armstrong, NJ, Kwok, JB, Schofield, PR, Wen, W, Trollor, JN, Johansson, Å, Morris, AP, Vasan, RS, Sachdev, PS, Mather, KA, Jiang, J, Thalamuthu, A, Ho, JE, Mahajan, A, Ek, WE, Brown, DA, Breit, SN, Wang, TJ, Gyllensten, U, Chen, MH, Enroth, S, Januzzi, JL, Lind, L, Armstrong, NJ, Kwok, JB, Schofield, PR, Wen, W, Trollor, JN, Johansson, Å, Morris, AP, Vasan, RS, Sachdev, PS, and Mather, KA
Abstract: Blood levels of growth differentiation factor-15 (GDF-15), also known as macrophage inhibitory cytokine-1 (MIC-1), have been associated with various pathological processes and diseases, including cardiovascular disease and cancer. Prior studies suggest genetic factors play a role in regulating blood MIC-1/GDF-15 concentration. In the current study, we conducted the largest genome-wide association study (GWAS) to date using a sample of ~5,400 community-based Caucasian participants, to determine the genetic variants associated with MIC-1/GDF-15 blood concentration. Conditional and joint (COJO), gene-based association, and gene-set enrichment analyses were also carried out to identify novel loci, genes, and pathways. Consistent with prior results, a locus on chromosome 19, which includes nine single nucleotide polymorphisms (SNPs) (top SNP, rs888663, p = 1.690 × 10-35), was significantly associated with blood MIC-1/GDF-15 concentration, and explained 21.47% of its variance. COJO analysis showed evidence for two independent signals within this locus. Gene-based analysis confirmed the chromosome 19 locus association and in addition, a putative locus on chromosome 1. Gene-set enrichment analyses showed that the"COPI-mediated anterograde transport" gene-set was associated with MIC-1/GDF15 blood concentration with marginal significance after FDR correction (p = 0.067). In conclusion, a locus on chromosome 19 was associated with MIC-1/GDF-15 blood concentration with genome-wide significance, with evidence for a new locus (chromosome 1). Future studies using independent cohorts are needed to confirm the observed associations especially for the chromosomes 1 locus, and to further investigate and identify the causal SNPs that contribute to MIC-1/GDF-15 levels.
Published: 2018

33. Genome-wide association study of 23,500 individuals identifies 7 loci associated with brain ventricular volume

Author: Vojinovic, Dina, Adams, Hieab, Jian, XQ, Yang, Q, Smith, AV, Bis, JC, Teumer, A, Scholz, M, Armstrong, NJ, Hofer, E, Saba, Y, Luciano, M, Bernard, M, Trompet, S, Yang, JY, Gillespie, NA, van der Lee, Sven, Neumann, Alexander, Ahmad, Shahzad, Andreassen, OA, Ames, D, Amin, Najaf, Arfanakis, K, Bastin, ME, Becker, DM, Beiser, AS, Beyer, F, Brodaty, H, Bryan, RN, Bulow, R, Dale, AM, De Jager, PL, Deary, IJ, DeCarli, C, Fleischman, DA, Gottesman, RF, van der Grond, J, Gudnason, V, Harris, TB, Homuth, G, Knopman, DS, Kwok, JB, Lewis, CE, Li, S, Loeffler, M, Lopez, OL, Maillard, P, El Marroun, Hanan, Mather, KA, Mosley, TH, Muetzel, Ryan, Nauck, M, Nyquist, PA, Panizzon, MS, Pausova, Z, Psaty, BM, Rice, K, Rotter, JI, Royle, N, Satizabal, CL, Schmidt, R, Schofield, PR, Schreiner, PJ, Sidney, S, Stott, DJ, Thalamuthu, A, Uitterlinden, André, Hernandez, MCV, Vernooij, Meike, Wen, W, White, Tonya, Witte, AV, Wittfeld, K, Wright, MJ, Yanek, LR, Tiemeier, Henning, Kremen, WS, Bennett, DA, Jukema, JW, Paus, T, Wardlaw, JM, Schmidt, H, Sachdev, PS, Villringer, A, Grabe, HJ, Longstreth, WT, Duijn, Cornelia, Launer, LJ, Seshadri, S, Ikram, Arfan, Fornage, M, Vojinovic, Dina, Adams, Hieab, Jian, XQ, Yang, Q, Smith, AV, Bis, JC, Teumer, A, Scholz, M, Armstrong, NJ, Hofer, E, Saba, Y, Luciano, M, Bernard, M, Trompet, S, Yang, JY, Gillespie, NA, van der Lee, Sven, Neumann, Alexander, Ahmad, Shahzad, Andreassen, OA, Ames, D, Amin, Najaf, Arfanakis, K, Bastin, ME, Becker, DM, Beiser, AS, Beyer, F, Brodaty, H, Bryan, RN, Bulow, R, Dale, AM, De Jager, PL, Deary, IJ, DeCarli, C, Fleischman, DA, Gottesman, RF, van der Grond, J, Gudnason, V, Harris, TB, Homuth, G, Knopman, DS, Kwok, JB, Lewis, CE, Li, S, Loeffler, M, Lopez, OL, Maillard, P, El Marroun, Hanan, Mather, KA, Mosley, TH, Muetzel, Ryan, Nauck, M, Nyquist, PA, Panizzon, MS, Pausova, Z, Psaty, BM, Rice, K, Rotter, JI, Royle, N, Satizabal, CL, Schmidt, R, Schofield, PR, Schreiner, PJ, Sidney, S, Stott, DJ, Thalamuthu, A, Uitterlinden, André, Hernandez, MCV, Vernooij, Meike, Wen, W, White, Tonya, Witte, AV, Wittfeld, K, Wright, MJ, Yanek, LR, Tiemeier, Henning, Kremen, WS, Bennett, DA, Jukema, JW, Paus, T, Wardlaw, JM, Schmidt, H, Sachdev, PS, Villringer, A, Grabe, HJ, Longstreth, WT, Duijn, Cornelia, Launer, LJ, Seshadri, S, Ikram, Arfan, and Fornage, M
Abstract: The volume of the lateral ventricles (LV) increases with age and their abnormal enlargement is a key feature of several neurological and psychiatric diseases. Although lateral ventricular volume is heritable, a comprehensive investigation of its genetic determinants is lacking. In this meta-analysis of genome-wide association studies of 23,533 healthy middle-aged to elderly individuals from 26 population-based cohorts, we identify 7 genetic loci associated with LV volume. These loci map to chromosomes 3q28, 7p22.3, 10p12.31, 11q23.1, 12q23.3, 16q24.2, and 22q13.1 and implicate pathways related to tau pathology, S1P signaling, and cytoskeleton organization. We also report a significant genetic overlap between the thalamus and LV volumes (?genetic?=?-0.59, p-value?=?3.14?×?10-6), suggesting that these brain structures may share a common biology. These genetic associations of LV volume provide insights into brain morphology
Published: 2018

34. An epigenome-wide association study meta-analysis of educational attainment

Author: Karlsson Linnér, R, Marioni, RE, Rietveld, CA, Simpkin, AJ, Davies, NM, Watanabe, K, Armstrong, NJ, Auro, K, Baumbach, C, Bonder, MJ, Buchwald, J, Fiorito, G, Ismail, K, Iurato, S, Joensuu, A, Karell, P, Kasela, S, Lahti, J, McRae, AF, Mandaviya, PR, Seppälä, I, Wang, Y, Baglietto, L, Binder, EB, Harris, SE, Hodge, AM, Horvath, S, Hurme, M, Johannesson, M, Latvala, A, Mather, KA, Medland, SE, Metspalu, A, Milani, L, Milne, RL, Pattie, A, Pedersen, NL, Peters, A, Polidoro, S, Räikkönen, K, Severi, G, Starr, JM, Stolk, L, Waldenberger, M, BIOS Consortium, Eriksson, JG, Esko, T, Franke, L, Gieger, C, Giles, GG, Hägg, S, Jousilahti, P, Kaprio, J, Kähönen, M, Lehtimäki, T, Martin, NG, Van Meurs, JBC, Ollikainen, M, Perola, M, Posthuma, D, Raitakari, OT, Sachdev, PS, Taskesen, E, Uitterlinden, AG, Vineis, P, Wijmenga, C, Wright, MJ, Relton, C, Davey Smith, G, Deary, IJ, Koellinger, PD, and Benjamin, DJ
Subjects: 17 Psychology And Cognitive Sciences, Psychiatry, BIOS Consortium, 11 Medical And Health Sciences, 06 Biological Sciences
Abstract: The epigenome is associated with biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here we provide evidence on the associations between epigenetic modifications-in our case, CpG methylation-and educational attainment (EA), a biologically distal environmental factor that is arguably among the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10 767 individuals. We find nine CpG probes significantly associated with EA. However, robustness analyses show that all nine probes have previously been found to be associated with smoking. Only two associations remain when we perform a sensitivity analysis in the subset of never-smokers, and these two probes are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, the effect sizes of the associations with EA are far smaller than the known associations with the biologically proximal environmental factors alcohol consumption, body mass index, smoking and maternal smoking during pregnancy. Follow-up analyses that combine the effects of many probes also point to small methylation associations with EA that are highly correlated with the combined effects of smoking. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.Molecular Psychiatry advance online publication, 31 October 2017; doi:10.1038/mp.2017.210.
Published: 2017

35. Novel genetic loci associated with hippocampal volume

Author: Hibar, DP, Adams, HHH, Jahanshad, N, Chauhan, G, Stein, JL, Hofer, E, Renteria, ME, Bis, JC, Arias-Vasquez, A, Ikram, MK, Desrivières, S, Vernooij, MW, Abramovic, L, Alhusaini, S, Amin, N, Andersson, M, Arfanakis, K, Aribisala, BS, Armstrong, NJ, Athanasiu, L, Axelsson, T, Beecham, AH, Beiser, A, Bernard, M, Blanton, SH, Bohlken, MM, Boks, MP, Bralten, J, Brickman, AM, Carmichael, O, Chakravarty, MM, Chen, Q, Ching, CRK, Chouraki, V, Cuellar-Partida, G, Crivello, F, Den Braber, A, Doan, NT, Ehrlich, S, Giddaluru, S, Goldman, AL, Gottesman, RF, Grimm, O, Griswold, ME, Guadalupe, T, Gutman, BA, Hass, J, Haukvik, UK, Hoehn, D, Holmes, AJ, Hoogman, M, Janowitz, D, Jia, T, Jørgensen, KN, Karbalai, N, Kasperaviciute, D, Kim, S, Klein, M, Kraemer, B, and Lee, PH
Abstract: © The Author(s) 2017. The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg =-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.
Published: 2017
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36. Aging, exceptional longevity and comparisons of the Hannum and Horvath epigenetic clocks

Author: Armstrong, NJ, Mather, KA, Thalamuthu, A, Wright, MJ, Trollor, JN, Ames, D, Brodaty, H, Schofield, PR, Sachdev, PS, Kwok, JB, Armstrong, NJ, Mather, KA, Thalamuthu, A, Wright, MJ, Trollor, JN, Ames, D, Brodaty, H, Schofield, PR, Sachdev, PS, and Kwok, JB
Abstract: AIM: To examine the relationships between two epigenetic clocks, aging and exceptional longevity. MATERIALS & METHODS: Participants were from three adult cohorts with blood DNA methylation data (Illumina 450 K, n = 275, 34-103 years). Epigenetic age (DNAmage) and age acceleration measures were calculated using the Hannum and Horvath epigenetic clocks. RESULTS: Across all cohorts, DNAmage was correlated with chronological age. In the long-lived cohort (Sydney Centenarian Study; 95+, n = 23), DNAmage was lower than chronological age for both clocks. Mean Sydney Centenarian Study Hannum age acceleration was negative, while the converse was observed for the Horvath model. CONCLUSION: Long-lived individuals have a young epigenetic age compared with their chronological age.
Published: 2017

37. Acetylated histone variant H2A.Z is involved in the activation of neo-enhancers in prostate cancer

Author: Valdés-Mora, F, Gould, CM, Colino-Sanguino, Y, Qu, W, Song, JZ, Taylor, KM, Buske, FA, Statham, AL, Nair, SS, Armstrong, NJ, Kench, JG, Lee, KML, Horvath, LG, Qiu, M, Ilinykh, A, Yeo-Teh, NS, Gallego-Ortega, D, Stirzaker, C, Clark, SJ, Valdés-Mora, F, Gould, CM, Colino-Sanguino, Y, Qu, W, Song, JZ, Taylor, KM, Buske, FA, Statham, AL, Nair, SS, Armstrong, NJ, Kench, JG, Lee, KML, Horvath, LG, Qiu, M, Ilinykh, A, Yeo-Teh, NS, Gallego-Ortega, D, Stirzaker, C, and Clark, SJ
Abstract: Acetylation of the histone variant H2A.Z (H2A.Zac) occurs at active promoters and is associated with oncogene activation in prostate cancer, but its role in enhancer function is still poorly understood. Here we show that H2A.Zac containing nucleosomes are commonly redistributed to neo-enhancers in cancer resulting in a concomitant gain of chromatin accessibility and ectopic gene expression. Notably incorporation of acetylated H2A.Z nucleosomes is a pre-requisite for activation of Androgen receptor (AR) associated enhancers. H2A.Zac nucleosome occupancy is rapidly remodeled to flank the AR sites to initiate the formation of nucleosome-free regions and the production of AR-enhancer RNAs upon androgen treatment. Remarkably higher levels of global H2A.Zac correlate with poorer prognosis. Altogether these data demonstrate the novel contribution of H2A.Zac in activation of newly formed enhancers in prostate cancer.
Published: 2017

38. Human subcortical brain asymmetries in 15,847 people worldwide reveal effects of age and sex

Author: Guadalupe, T, Mathias, SR, vanErp, TGM, Whelan, CD, Zwiers, MP, Abe, Y, Abramovic, L, Agartz, I, Andreassen, OA, Arias-Vásquez, A, Aribisala, BS, Armstrong, NJ, Arolt, V, Artiges, E, Ayesa-Arriola, R, Baboyan, VG, Banaschewski, T, Barker, G, Bastin, ME, Baune, BT, Blangero, J, Bokde, ALW, Boedhoe, PSW, Bose, A, Brem, S, Brodaty, H, Bromberg, U, Brooks, S, Büchel, C, Buitelaar, J, Calhoun, VD, Cannon, DM, Cattrell, A, Cheng, Y, Conrod, PJ, Conzelmann, A, Corvin, A, Crespo-Facorro, B, Crivello, F, Dannlowski, U, de Zubicaray, GI, de Zwarte, SMC, Deary, IJ, Desrivières, S, Doan, NT, Donohoe, G, Dørum, ES, Ehrlich, S, Espeseth, T, Fernández, G, Flor, H, Fouche, J-P, Frouin, V, Fukunaga, M, Gallinat, J, Garavan, H, Gill, M, Suarez, AG, Gowland, P, Grabe, HJ, Grotegerd, D, Gruber, O, Hagenaars, S, Hashimoto, R, Hauser, TU, Heinz, A, Hibar, DP, Hoekstra, PJ, Hoogman, M, Howells, FM, Hu, H, Hulshoff Pol, HE, Huyser, C, Ittermann, B, Jahanshad, N, Jönsson, EG, Jurk, S, Kahn, RS, Kelly, S, Kraemer, B, Kugel, H, Kwon, JS, Lemaitre, H, Lesch, K-P, Lochner, C, Luciano, M, Marquand, AF, Martin, NG, Martínez-Zalacaín, I, Martinot, J-L, Mataix-Cols, D, Mather, K, McDonald, C, McMahon, KL, Medland, SE, Menchón, JM, Morris, DW, Mothersill, O, Maniega, SM, Mwangi, B, Nakamae, T, Nakao, T, Narayanaswaamy, JC, Nees, F, Nordvik, JE, Onnink, AMH, Opel, N, Ophoff, R, Paillère Martinot, M-L, Papadopoulos Orfanos, D, Pauli, P, Paus, T, Poustka, L, Reddy, JY, Renteria, ME, Roiz-Santiáñez, R, Roos, A, Royle, NA, Sachdev, P, Sánchez-Juan, P, Schmaal, L, Schumann, G, Shumskaya, E, Smolka, MN, Soares, JC, Soriano-Mas, C, Stein, DJ, Strike, LT, Toro, R, Turner, JA, Tzourio-Mazoyer, N, Uhlmann, A, Hernández, MV, van den Heuvel, OA, van der Meer, D, van Haren, NEM, Veltman, DJ, Venkatasubramanian, G, Vetter, NC, Vuletic, D, Walitza, S, Walter, H, Walton, E, Wang, Z, Wardlaw, J, Wen, W, Westlye, LT, Whelan, R, Wittfeld, K, Wolfers, T, Wright, MJ, Xu, J, Xu, X, Yun, J-Y, Zhao, J, Franke, B, Thompson, PM, Glahn, DC, Mazoyer, B, Fisher, SE, Francks, C, Guadalupe, T, Mathias, SR, vanErp, TGM, Whelan, CD, Zwiers, MP, Abe, Y, Abramovic, L, Agartz, I, Andreassen, OA, Arias-Vásquez, A, Aribisala, BS, Armstrong, NJ, Arolt, V, Artiges, E, Ayesa-Arriola, R, Baboyan, VG, Banaschewski, T, Barker, G, Bastin, ME, Baune, BT, Blangero, J, Bokde, ALW, Boedhoe, PSW, Bose, A, Brem, S, Brodaty, H, Bromberg, U, Brooks, S, Büchel, C, Buitelaar, J, Calhoun, VD, Cannon, DM, Cattrell, A, Cheng, Y, Conrod, PJ, Conzelmann, A, Corvin, A, Crespo-Facorro, B, Crivello, F, Dannlowski, U, de Zubicaray, GI, de Zwarte, SMC, Deary, IJ, Desrivières, S, Doan, NT, Donohoe, G, Dørum, ES, Ehrlich, S, Espeseth, T, Fernández, G, Flor, H, Fouche, J-P, Frouin, V, Fukunaga, M, Gallinat, J, Garavan, H, Gill, M, Suarez, AG, Gowland, P, Grabe, HJ, Grotegerd, D, Gruber, O, Hagenaars, S, Hashimoto, R, Hauser, TU, Heinz, A, Hibar, DP, Hoekstra, PJ, Hoogman, M, Howells, FM, Hu, H, Hulshoff Pol, HE, Huyser, C, Ittermann, B, Jahanshad, N, Jönsson, EG, Jurk, S, Kahn, RS, Kelly, S, Kraemer, B, Kugel, H, Kwon, JS, Lemaitre, H, Lesch, K-P, Lochner, C, Luciano, M, Marquand, AF, Martin, NG, Martínez-Zalacaín, I, Martinot, J-L, Mataix-Cols, D, Mather, K, McDonald, C, McMahon, KL, Medland, SE, Menchón, JM, Morris, DW, Mothersill, O, Maniega, SM, Mwangi, B, Nakamae, T, Nakao, T, Narayanaswaamy, JC, Nees, F, Nordvik, JE, Onnink, AMH, Opel, N, Ophoff, R, Paillère Martinot, M-L, Papadopoulos Orfanos, D, Pauli, P, Paus, T, Poustka, L, Reddy, JY, Renteria, ME, Roiz-Santiáñez, R, Roos, A, Royle, NA, Sachdev, P, Sánchez-Juan, P, Schmaal, L, Schumann, G, Shumskaya, E, Smolka, MN, Soares, JC, Soriano-Mas, C, Stein, DJ, Strike, LT, Toro, R, Turner, JA, Tzourio-Mazoyer, N, Uhlmann, A, Hernández, MV, van den Heuvel, OA, van der Meer, D, van Haren, NEM, Veltman, DJ, Venkatasubramanian, G, Vetter, NC, Vuletic, D, Walitza, S, Walter, H, Walton, E, Wang, Z, Wardlaw, J, Wen, W, Westlye, LT, Whelan, R, Wittfeld, K, Wolfers, T, Wright, MJ, Xu, J, Xu, X, Yun, J-Y, Zhao, J, Franke, B, Thompson, PM, Glahn, DC, Mazoyer, B, Fisher, SE, and Francks, C
Abstract: The two hemispheres of the human brain differ functionally and structurally. Despite over a century of research, the extent to which brain asymmetry is influenced by sex, handedness, age, and genetic factors is still controversial. Here we present the largest ever analysis of subcortical brain asymmetries, in a harmonized multi-site study using meta-analysis methods. Volumetric asymmetry of seven subcortical structures was assessed in 15,847 MRI scans from 52 datasets worldwide. There were sex differences in the asymmetry of the globus pallidus and putamen. Heritability estimates, derived from 1170 subjects belonging to 71 extended pedigrees, revealed that additive genetic factors influenced the asymmetry of these two structures and that of the hippocampus and thalamus. Handedness had no detectable effect on subcortical asymmetries, even in this unprecedented sample size, but the asymmetry of the putamen varied with age. Genetic drivers of asymmetry in the hippocampus, thalamus and basal ganglia may affect variability in human cognition, including susceptibility to psychiatric disorders.
Published: 2017

39. An epigenome-wide association study meta-analysis of educational attainment

Author: Linner, RK, Marioni, RE, Rietveld, Niels, Simpkin, AJ, Davies, NM, Watanabe, K, Armstrong, NJ, Auro, K, Baumbach, C, Bonder, MJ, Buchwald, J, Fiorito, G, Ismail, K, Iurato, S, Joensuu, A, Karell, P, Kasela, S, Lahti, J, Mcrae, AF, Mandaviya, Pooja, Seppala, I, Wang, Y, Baglietto, L, Binder, EB, Harris, SE, Hodge, AM, Horvath, S, Hurme, M, Johannesson, M, Latvala, A, Mather, KA, Medland, SE, Metspalu, A, Milani, L, Milne, RL, Pattie, A, Pedersen, NL, Peters, A, Polidoro, S, Raikkonen, K, Severi, G, Starr, JM, Stolk, Lisette, Waldenberger, M, Eriksson, JG, Esko, T, Franke, L, Gieger, C, Giles, GG, Hagg, S, Jousilahti, P, Kaprio, J, Kahonen, M, Lehtimaki, T, Martin, NG, van Meurs, Joyce, Ollikainen, M, Perola, M, Posthuma, D, Raitakari, OT, Sachdev, PS, Taskesen, E, Uitterlinden, André, Vineis, P, Wijmenga, C, Wright, MJ, Relton, C, Smith, GD, Deary, IJ, Koellinger, PD, Benjamin, DJ, Linner, RK, Marioni, RE, Rietveld, Niels, Simpkin, AJ, Davies, NM, Watanabe, K, Armstrong, NJ, Auro, K, Baumbach, C, Bonder, MJ, Buchwald, J, Fiorito, G, Ismail, K, Iurato, S, Joensuu, A, Karell, P, Kasela, S, Lahti, J, Mcrae, AF, Mandaviya, Pooja, Seppala, I, Wang, Y, Baglietto, L, Binder, EB, Harris, SE, Hodge, AM, Horvath, S, Hurme, M, Johannesson, M, Latvala, A, Mather, KA, Medland, SE, Metspalu, A, Milani, L, Milne, RL, Pattie, A, Pedersen, NL, Peters, A, Polidoro, S, Raikkonen, K, Severi, G, Starr, JM, Stolk, Lisette, Waldenberger, M, Eriksson, JG, Esko, T, Franke, L, Gieger, C, Giles, GG, Hagg, S, Jousilahti, P, Kaprio, J, Kahonen, M, Lehtimaki, T, Martin, NG, van Meurs, Joyce, Ollikainen, M, Perola, M, Posthuma, D, Raitakari, OT, Sachdev, PS, Taskesen, E, Uitterlinden, André, Vineis, P, Wijmenga, C, Wright, MJ, Relton, C, Smith, GD, Deary, IJ, Koellinger, PD, and Benjamin, DJ
Abstract: The epigenome is associated with biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here we provide evidence on the associations between epigenetic modifications—in our case, CpG methylation—and educational attainment (EA), a biologically distal environmental factor that is arguably among the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10?767 individuals. We find nine CpG probes significantly associated with EA. However, robustness analyses show that all nine probes have previously been found to be associated with smoking. Only two associations remain when we perform a sensitivity analysis in the subset of never-smokers, and these two probes are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, the effect sizes of the associations with EA are far smaller than the known associations with the biologically proximal environmental factors alcohol consumption, body mass index, smoking and maternal smoking during pregnancy. Follow-up analyses that combine the effects of many probes also point to small methylation associations with EA that are highly correlated with the combined effects of smoking. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.
Published: 2017

40. Novel genetic loci underlying human intracranial volume identified through genome-wide association

Author: Adams, HHH, Hibar, DP, Chouraki, V, Stein, JL, Nyquist, PA, Rentería, ME, Trompet, S, Arias-Vasquez, A, Seshadri, S, Desrivières, S, Beecham, AH, Jahanshad, N, Wittfeld, K, Van Der Lee, SJ, Abramovic, L, Alhusaini, S, Amin, N, Andersson, M, Arfanakis, K, Aribisala, BS, Armstrong, NJ, Athanasiu, L, Axelsson, T, Beiser, A, Bernard, M, Bis, JC, Blanken, LME, Blanton, SH, Bohlken, MM, Boks, MP, Bralten, J, Brickman, AM, Carmichael, O, Chakravarty, MM, Chauhan, G, Chen, Q, Ching, CRK, Cuellar-Partida, G, Braber, AD, Doan, NT, Ehrlich, S, Filippi, I, Ge, T, Giddaluru, S, Goldman, AL, Gottesman, RF, Greven, CU, Grimm, O, Griswold, ME, Guadalupe, T, Hass, J, Haukvik, UK, Hilal, S, Hofer, E, Hoehn, D, Holmes, AJ, Hoogman, M, Janowitz, D, and Jia, T
Abstract: © 2016 Nature America, Inc., part of Springer Nature. All rights reserved. Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (genetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (N combined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.
Published: 2016
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41. Common genetic variants influence human subcortical brain structures

Author: Hibar, DP, Stein, JL, Renteria, ME, Arias-Vasquez, A, Desrivières, S, Jahanshad, N, Toro, R, Wittfeld, K, Abramovic, L, Andersson, M, Aribisala, BS, Armstrong, NJ, Bernard, M, Bohlken, MM, Boks, MP, Bralten, J, Brown, AA, Mallar Chakravarty, M, Chen, Q, Ching, CRK, Cuellar-Partida, G, Den Braber, A, Giddaluru, S, Goldman, AL, Grimm, O, Guadalupe, T, Hass, J, Woldehawariat, G, Holmes, AJ, Hoogman, M, Janowitz, D, Jia, T, Kim, S, Klein, M, Kraemer, B, Lee, PH, Olde Loohuis, LM, Luciano, M, MacAre, C, Mather, KA, Mattheisen, M, Milaneschi, Y, Nho, K, Papmeyer, M, Ramasamy, A, Risacher, SL, Roiz-Santiañez, R, Rose, EJ, Salami, A, Sämann, PG, Schmaal, L, Schork, AJ, Shin, J, Strike, LT, Teumer, A, Van Donkelaar, MMJ, Van Eijk, KR, Walters, RK, Westlye, LT, and Whelan, CD
Abstract: ©2015 Macmillan Publishers Limited. All rights reserved. The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08×10 -33; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.
Published: 2015
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42. The effect of increased genetic risk for Alzheimer's disease on hippocampal and amygdala volume

Author: Lupton, MK, Strike, L, Hansell, NK, Wen, W, Mather, KA, Armstrong, NJ, Thalamuthu, A, McMahon, KL, de Zubicaray, GI, Assareh, AA, Simmons, A, Proitsi, P, Powell, JF, Montgomery, GW, Hibar, DP, Westman, E, Tsolaki, M, Kloszewska, I, Soininen, H, Mecocci, P, Velas, B, Lovestone, S, Brodaty, H, Ames, D, Trollor, JN, Martin, NG, Thompson, PM, Sachdev, PS, Wright, MJ, Lupton, MK, Strike, L, Hansell, NK, Wen, W, Mather, KA, Armstrong, NJ, Thalamuthu, A, McMahon, KL, de Zubicaray, GI, Assareh, AA, Simmons, A, Proitsi, P, Powell, JF, Montgomery, GW, Hibar, DP, Westman, E, Tsolaki, M, Kloszewska, I, Soininen, H, Mecocci, P, Velas, B, Lovestone, S, Brodaty, H, Ames, D, Trollor, JN, Martin, NG, Thompson, PM, Sachdev, PS, and Wright, MJ
Abstract: Reduction in hippocampal and amygdala volume measured via structural magnetic resonance imaging is an early marker of Alzheimer's disease (AD). Whether genetic risk factors for AD exert an effect on these subcortical structures independent of clinical status has not been fully investigated. We examine whether increased genetic risk for AD influences hippocampal and amygdala volumes in case-control and population cohorts at different ages, in 1674 older (aged >53 years; 17% AD, 39% mild cognitive impairment [MCI]) and 467 young (16-30 years) adults. An AD polygenic risk score combining common risk variants excluding apolipoprotein E (APOE), and a single nucleotide polymorphism in TREM2, were both associated with reduced hippocampal volume in healthy older adults and those with MCI. APOE ε4 was associated with hippocampal and amygdala volume in those with AD and MCI but was not associated in healthy older adults. No associations were found in young adults. Genetic risk for AD affects the hippocampus before the clinical symptoms of AD, reflecting a neurodegenerative effect before clinical manifestations in older adults.
Published: 2016

43. Genome-wide significant results identified for plasma apolipoprotein H levels in middle-aged and older adults

Author: Mather, KA, Thalamuthu, A, Oldmeadow, C, Song, F, Armstrong, NJ, Poljak, A, Holliday, EG, McEvoy, M, Kwok, JB, Assareh, AA, Reppermund, S, Kochan, NA, Lee, T, Ames, D, Wright, MJ, Trollor, JN, Schofield, PW, Brodaty, H, Scott, RJ, Schofield, PR, Attia, JR, Sachdev, PS, Mather, KA, Thalamuthu, A, Oldmeadow, C, Song, F, Armstrong, NJ, Poljak, A, Holliday, EG, McEvoy, M, Kwok, JB, Assareh, AA, Reppermund, S, Kochan, NA, Lee, T, Ames, D, Wright, MJ, Trollor, JN, Schofield, PW, Brodaty, H, Scott, RJ, Schofield, PR, Attia, JR, and Sachdev, PS
Abstract: Apolipoprotein H (ApoH) is a multi-functional plasma glycoprotein that has been associated with negative health outcomes. ApoH levels have high heritability. We undertook a genome-wide association study of ApoH levels using the largest sample to date and replicated the results in an independent cohort (total N = 1,255). In the discovery phase, a meta-analysis of two cohorts, the Sydney Memory and Ageing Study (Sydney MAS) and the Older Australian Twins Study (OATS) (n = 942) revealed genome-wide significant results in or near the APOH gene on chromosome 17 (top SNP, rs7211380, p = 1 × 10-11). The results were replicated in an independent cohort, the Hunter Community Study (p < 0.002) (n = 313). Conditional and joint analysis (COJO) confirmed the association of the chromosomal 17 region with ApoH levels. The set of independent SNPs identified by COJO explained 23% of the variance. The relationships between the top SNPs and cardiovascular/lipid/cognition measures and diabetes were assessed in Sydney MAS, with suggestive results observed for diabetes and cognitive performance. However, replication of these results in the smaller OATS cohort was not found. This work provides impetus for future research to better understand the contribution of genetics to ApoH levels and its possible impacts on health.
Published: 2016

44. Genetic influences on schizophrenia and subcortical brain volumes: Large-scale proof of concept

Author: Franke, B, van Hulzen, KJE, Arias-Vasquez, A, Bralten, J, Hoogman, M, Klein, M, van Donkelaar, MMJ, Hakobjan, MMH, Heister, AJGAM, Makkinje, RRR, Naber, MAM, van der Marel, SSL, Mostert, JC, Brunner, HG, van Bokhoven, H, Zwiers, MP, Buitelaar, JK, Fernández, G, Fisher, SE, Francks, C, Stein, JL, Hibar, DP, Thompson, PM, Ripke, S, Anttila, V, Neale, BM, Farh, KH, Bulik-Sullivan, B, Huang, H, Fromer, M, Goldstein, JI, Daly, MJ, Walters, RK, Smoller, JW, Lee, P, Belliveau, RA, Bergen, SE, Bevilacqua, E, Chambert, KD, Genovese, G, O'Dushlaine, C, Scolnick, EM, McCarroll, SA, Moran, JL, Palotie, A, Petryshen, TL, Erk, S, Heinz, A, Mohnke, S, Romanczuk-Seiferth, N, Walter, H, DeLisi, LE, McCarley, RW, Mesholam-Gately, RI, Seidman, LJ, Nichols, TE, Neale, MC, McIntosh, AM, Papmeyer, M, Sprooten, E, Lawrie, SM, Sussmann, JE, McMahon, FJ, Yao, Y, Meyer-Lindenberg, A, Schwarz, E, Grimm, O, Mattheisen, M, Agerbo, E, Demontis, D, Hansen, T, Mors, O, Olsen, L, Rasmussen, HB, Børglum, AD, Mortensen, PB, Werge, T, Andreassen, OA, Brown, AA, Athanasiu, L, Hartberg, CB, Haukvik, U, Melle, I, Gruber, O, Kraemer, B, Keil, M, Sachdev, PS, Mather, KA, Thalamuthu, A, Armstrong, NJ, Assareh, AA, Brodaty, H, Reppermund, S, Wen, W, Roiz-Santiañez, R, Perez-Iglesias, R, Saykin, AJ, Kim, S, Nho, K, Risacher, SL, Franke, B, van Hulzen, KJE, Arias-Vasquez, A, Bralten, J, Hoogman, M, Klein, M, van Donkelaar, MMJ, Hakobjan, MMH, Heister, AJGAM, Makkinje, RRR, Naber, MAM, van der Marel, SSL, Mostert, JC, Brunner, HG, van Bokhoven, H, Zwiers, MP, Buitelaar, JK, Fernández, G, Fisher, SE, Francks, C, Stein, JL, Hibar, DP, Thompson, PM, Ripke, S, Anttila, V, Neale, BM, Farh, KH, Bulik-Sullivan, B, Huang, H, Fromer, M, Goldstein, JI, Daly, MJ, Walters, RK, Smoller, JW, Lee, P, Belliveau, RA, Bergen, SE, Bevilacqua, E, Chambert, KD, Genovese, G, O'Dushlaine, C, Scolnick, EM, McCarroll, SA, Moran, JL, Palotie, A, Petryshen, TL, Erk, S, Heinz, A, Mohnke, S, Romanczuk-Seiferth, N, Walter, H, DeLisi, LE, McCarley, RW, Mesholam-Gately, RI, Seidman, LJ, Nichols, TE, Neale, MC, McIntosh, AM, Papmeyer, M, Sprooten, E, Lawrie, SM, Sussmann, JE, McMahon, FJ, Yao, Y, Meyer-Lindenberg, A, Schwarz, E, Grimm, O, Mattheisen, M, Agerbo, E, Demontis, D, Hansen, T, Mors, O, Olsen, L, Rasmussen, HB, Børglum, AD, Mortensen, PB, Werge, T, Andreassen, OA, Brown, AA, Athanasiu, L, Hartberg, CB, Haukvik, U, Melle, I, Gruber, O, Kraemer, B, Keil, M, Sachdev, PS, Mather, KA, Thalamuthu, A, Armstrong, NJ, Assareh, AA, Brodaty, H, Reppermund, S, Wen, W, Roiz-Santiañez, R, Perez-Iglesias, R, Saykin, AJ, Kim, S, Nho, K, and Risacher, SL
Abstract: Schizophrenia is a devastating psychiatric illness with high heritability. Brain structure and function differ, on average, between people with schizophrenia and healthy individuals. As common genetic associations are emerging for both schizophrenia and brain imaging phenotypes, we can now use genome-wide data to investigate genetic overlap. Here we integrated results from common variant studies of schizophrenia (33,636 cases, 43,008 controls) and volumes of several (mainly subcortical) brain structures (11,840 subjects). We did not find evidence of genetic overlap between schizophrenia risk and subcortical volume measures either at the level of common variant genetic architecture or for single genetic markers. These results provide a proof of concept (albeit based on a limited set of structural brain measures) and define a roadmap for future studies investigating the genetic covariance between structural or functional brain phenotypes and risk for psychiatric disorders.
Published: 2016

45. Tick tock: DNA methylation, the epigenetic clock and exceptional longevity

Author: Mather, KA, Armstrong, NJ, Thalamuthu, A, Kwok, JBJ, Mather, KA, Armstrong, NJ, Thalamuthu, A, and Kwok, JBJ
Published: 2016

46. ImmunoCAP® ISAC and Microtest for multiplex allergen testing in people with difficult to manage allergic disease: a systematic reveiw and cost analysis

Author: Westwood, M, Ramaekers, B, Lang, S, Armstrong, NJ, Noake, C, de Kock, S, Joore, M, Severens, Hans, Kleijnen, J, Westwood, M, Ramaekers, B, Lang, S, Armstrong, NJ, Noake, C, de Kock, S, Joore, M, Severens, Hans, and Kleijnen, J
Published: 2016

47. The ENIGMA Consortium: Large-scale collaborative analyses of neuroimaging and genetic data

Author: Thompson, PM, Stein, JL, Medland, SE, Hibar, DP, Vasquez, AA, Renteria, ME, Toro, R, Jahanshad, N, Schumann, G, Franke, B, Wright, MJ, Martin, NG, Agartz, I, Alda, M, Alhusaini, S, Almasy, L, Almeida, J, Alpert, K, Andreasen, NC, Andreassen, OA, Apostolova, LG, Appel, K, Armstrong, NJ, Aribisala, B, Bastin, ME, Bauer, M, Bearden, CE, Bergmann, Ø, Binder, EB, Blangero, J, Bockholt, HJ, Bøen, E, Bois, C, Boomsma, DI, Booth, T, Bowman, IJ, Bralten, J, Brouwer, RM, Brunner, HG, Brohawn, DG, Buckner, RL, Buitelaar, J, Bulayeva, K, Bustillo, JR, Calhoun, VD, Cannon, DM, Cantor, RM, Carless, MA, Caseras, X, Cavalleri, GL, Chakravarty, MM, Chang, KD, Ching, CRK, Christoforou, A, Cichon, S, Clark, VP, Conrod, P, Coppola, G, Crespo-Facorro, B, Curran, JE, Czisch, M, Deary, IJ, de Geus, EJC, den Braber, A, Delvecchio, G, Depondt, C, de Haan, L, de Zubicaray, GI, Dima, D, Dimitrova, R, and Djurovic, S
Subjects: endocrine system
Abstract: The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way. © 2014 The Author(s).
Published: 2014
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48. DNA methylation in the apolipoprotein-A1 gene is associated with episodic memory performance in healthy older individuals

Author: Lazarus, J, Mather, KA, Armstrong, NJ, Song, F, Poljak, A, Thalamuthu, A, Lee, T, Kochan, NA, Brodaty, H, Wright, MJ, Ames, D, Sachdev, PS, Kwok, JBJ, Lazarus, J, Mather, KA, Armstrong, NJ, Song, F, Poljak, A, Thalamuthu, A, Lee, T, Kochan, NA, Brodaty, H, Wright, MJ, Ames, D, Sachdev, PS, and Kwok, JBJ
Abstract: © 2015-IOS Press and the authors. All rights reserved.Background: DNA methylation variation has been implicated in memory, cognitive performance, and dementia. Plasma apolipoprotein-A1 (ApoA1) levels may act as a biomarker of age-associated cognitive performance and decline. Objectives: To estimate the heritability of plasma ApoA1 protein levels; to examine DNA methylation variation within the APOA1 gene; and to investigate whether APOA1 methylation is associated with plasma ApoA1 levels and episodic memory performance. Method: Heritability of ApoA1 protein levels in Older Australian Twins Study (OATS) was assessed using structural equation modelling. APOA1 methylation levels were assayed in two cohorts of cognitively normal older individuals. The methylation status of 12 CpGs in 24 twin pairs from OATS was assayed using the Illumina 450K methylation array. Candidate CpGs were assayed in 454 individuals from Sydney Memory and Ageing Study (Sydney MAS) using pyrosequencing. Regression analyses assessed associations between APOA1 methylation levels, ApoA1 plasma levels, and memory performance. Results: No significant heritability was observed for ApoA1 protein levels. APOA1 candidate-gene analyses revealed CpG sites associated with memory performance in the twin study (p < 0.050). Replication of an association between methylation of a specific CpG (cg03010018) in APOA1 and memory performance was observed in Sydney MAS (β =-0.145, p = 0.010). Methylation of this CpG site was also significantly correlated with ApoA1 protein levels (β = 0.161, p = 0.019). However, no relationship between a composite memory domain score and methylation was observed (p = 0.389). Conclusion: Findings demonstrated that epigenetic control of APOA1 expression and DNA methylation levels are associated with episodic memory performance in older adults.
Published: 2015

49. Genetics of hand grip strength in mid to late life

Author: Chan, JPL, Thalamuthu, A, Oldmeadow, C, Armstrong, NJ, Holliday, EG, McEvoy, M, Kwok, JB, Assareh, AA, Peel, R, Hancock, SJ, Reppermund, S, Menant, J, Trollor, JN, Brodaty, H, Schofield, PR, Attia, JR, Sachdev, PS, Scott, RJ, Mather, KA, Chan, JPL, Thalamuthu, A, Oldmeadow, C, Armstrong, NJ, Holliday, EG, McEvoy, M, Kwok, JB, Assareh, AA, Peel, R, Hancock, SJ, Reppermund, S, Menant, J, Trollor, JN, Brodaty, H, Schofield, PR, Attia, JR, Sachdev, PS, Scott, RJ, and Mather, KA
Abstract: Hand grip strength (GS) is a predictor of mortality in older adults and is moderately to highly heritable, but no genetic variants have been consistently identified. We aimed to identify single nucleotide polymorphisms (SNPs) associated with GS in middle-aged to older adults using a genome-wide association study (GWAS). GS was measured using handheld dynamometry in community-dwelling men and women aged 55-85 from the Hunter Community Study (HCS, N = 2088) and the Sydney Memory and Ageing Study (Sydney MAS, N = 541). Genotyping was undertaken using Affymetrix microarrays with imputation to HapMap2. Analyses were performed using linear regression. No genome-wide significant results were observed in HCS nor were any of the top signals replicated in Sydney MAS. Gene-based analyses in HCS identified two significant genes (ZNF295, C2CD2), but these results were not replicated in Sydney MAS. One out of eight SNPs previously associated with GS, rs550942, located near the CNTF gene, was significantly associated with GS (p = 0.005) in the HCS cohort only. Study differences may explain the lack of consistent results between the studies, including the smaller sample size of the Sydney MAS cohort. Our modest sample size also had limited power to identify variants of small effect. Our results suggest that similar to various other complex traits, many genetic variants of small effect size may influence GS. Future GWAS using larger samples and consistent measures may prove more fruitful at identifying genetic contributors for GS in middle-aged to older adults.
Published: 2015

50. Investigating the influence of KIBRA and CLSTN2 genetic polymorphisms on cross-sectional and longitudinal measures of memory performance and hippocampal volume in older individuals

Author: Boraxbekk, CJ, Ames, D, Kochan, NA, Lee, T, Thalamuthu, A, Wen, W, Armstrong, NJ, Kwok, JBJ, Schofield, PR, Reppermund, S, Wright, MJ, Trollor, JN, Brodaty, H, Sachdev, P, Mather, KA, Boraxbekk, CJ, Ames, D, Kochan, NA, Lee, T, Thalamuthu, A, Wen, W, Armstrong, NJ, Kwok, JBJ, Schofield, PR, Reppermund, S, Wright, MJ, Trollor, JN, Brodaty, H, Sachdev, P, and Mather, KA
Abstract: The variability of episodic memory decline and hippocampal atrophy observed with increasing age may partly be explained by genetic factors. KIBRA (kidney and brain expressed protein) and CLSTN2 (calsyntenin 2) are two candidate genes previously linked to episodic memory performance and volume of the hippocampus, a key memory structure. However, whether polymorphisms in these two genes also influence age-related longitudinal memory decline and hippocampal atrophy is still unknown. Using data from two independent cohorts, the Sydney Memory and Ageing Study and the Older Australian Twins Study, we investigated whether the KIBRA and CLSTN2 genetic polymorphisms (rs17070145 and rs6439886) are associated with episodic memory performance and hippocampal volume in older adults (65-90 years at baseline). We were able to examine these polymorphisms in relation to memory and hippocampal volume using cross-sectional data and, more importantly, also using longitudinal data (2 years between testing occasions). Overall we did not find support for an association of KIBRA either alone or in combination with CLSTN2 with memory performance or hippocampal volume, nor did variation in these genes influence longitudinal memory decline or hippocampal atrophy in two cohorts of older adults.
Published: 2015

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