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Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In 10 of the investigated regions with genome wide significant associations for obesity related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Results also suggested three novel candidates for functional effect on waist-to-hip ratio adjusted for BMI (WHRBMI-adj) (rs5781117 near gene RP11-392O17.1, rs10187501 in gene COBLL1, and rs1964599 near gene CCDC92), all within the 99% CS. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggest generalizability of genetic mechanisms underpinning obesity related traits across populations.

Obesity remains a global public health burden, with 4.7 million premature deaths globally attributed to obesity. Severe obesity (SevO: defined as body mass index (BMI)≥40) is a major risk factor for other comorbidities, including heart disease and Type 2 Diabetes, which disproportionately impact historically marginalized populations, including Hispanic/Latinos. Based on BRFSS data, 24.5% of US Hispanic/Latino adults are projected to have severe obesity by 2030. However, the etiology of the underlying metabolic dysfunction remains unknown. To address this gap, we identified metabolites associated with SevO in genotyped participants aged ≥20 with 25 < BMI ≥ 40 and metabolic data in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We investigated cross-sectional associations between Blom-transformed metabolites and baseline SevO, adjusting for age, study center, background group, smoking status, and principal components of ancestry, stratified by sex (SUGEN), and meta-analyzed (SAS 9.4). For the top 20 metabolites, we extracted publicly available HCHS/SOL metabolite GWAS (mGWAS) summary statistics to derive metaboQTLs, and summary statistics from a multi-population meta-analysis of SevO (N>70,000) and implemented forward MR analysis using the MendelianRandomization R package (v0.3.0), which provides various robust causal estimation methods for summary data. Anthropometry and data for 640 known Metabolon metabolites were available for 551 females (mean age: 43.3 years, 27% SevO) and 371 males (mean age 43.4 years, 15% SevO). We identified Bonferroni-corrected significant SevO associations (p Cytidine is a pyrimidine nucleoside consisting of D-ribose and cytosine, which is a precursor of cytidine triphosphate required in the one-carbon metabolism pathway to convert phosphatidylcholine (PC) to phosphatidylethanolamine (PE). PC biosynthesis is higher in adipose tissue macrophages in obese mice and humans. Indoleproprionate is a microbial metabolite of tryptophan produced by gut bacteria. Indoleproprionate levels have been shown to be associated with higher microbiome diversity and lower incidence of T2D. Our work points to future efforts to validate findings in other cohorts, including reverse MR to further elucidate the causal relationship between metabolites and severe obesity.

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods have focused only on individuals with one primary continental ancestry, thus poorly accommodating recently-admixed individuals. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals by explicitly modeling ancestry-specific effects and jointly estimating ancestry-shared effects. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses.

Background Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. Results We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP. Conclusion This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Also, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including N=229 African American and N=381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, N = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (N = 27,955) and Hispanic/Latino (N = 28,324) ancestry participants. Results: Our results revealed 24 suggestive signals (p < 1×10^(-4)) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (N=802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

The Diagnostic and Statistical Manual of Mental Disorders’ (5th ed.) Social (Pragmatic) Communication Disorder is meant to capture the social elements of communication dysfunction in children who do not meet autism spectrum disorder criteria. It is unclear whether Social (Pragmatic) Communication Disorder captures these elements without overlapping with Autism Spectrum Disorder or the Diagnostic and Statistical Manual of Mental Disorders’ (5th ed.) Language Disorder. Standardized behavioral assessments administered during a family genetics study were used to evaluate the social communication impairment and the restricted interests and repetitive behaviors in persons with autism spectrum disorder, language impairment, or neither. Social communication impairment and restricted interests and repetitive behavior were significantly correlated in all family members regardless of affection status. Rates of social communication impairment and restricted interests and repetitive behavior were highest in individuals with autism spectrum disorder. One-third of family members with language impairment presented with at least mild/moderate levels of social communication impairment (36.6%) and restricted interests and repetitive behavior (43.3%). A subset of unaffected members also presented with mild/moderate levels of social communication impairment (parents = 10.1%, siblings 11.6%) and restricted interests and repetitive behavior (parents = 14.0%, siblings = 22.1%). The majority of child family members with mild/moderate levels of social communication impairment had similar restricted interest and repetitive behavior levels reflecting criteria representing the Broad Autism Phenotype. These data suggest that social pragmatic communication disorder does not capture the profiles of children who have both social communication impairment and restricted interests and repetitive behavior but are in need of clinical services.

Hematological measures are important intermediate clinical phenotypes for many acute and chronic diseases. Hematological measures are highly heritable, and although genome-wide association studies (GWAS) have identified thousands of loci containing trait-associated variants, the causal genes underlying these associations are often uncertain. To better understand the underlying genetic regulatory mechanisms, we performed a transcriptome-wide association study (TWAS) using PrediXcan to systematically investigate the association between genetically-predicted gene expression and hematological measures in 54,542 individuals of European ancestry from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We found 239 significant gene-trait associations with hematological measures. Among this set of 239 associations, we replicated 71 at p < 0.05 with same direction of effect for the blood cell trait in a meta-analysis of TWAS results consisting of up to 35,900 European ancestry individuals from the Women’s Health Initiative (WHI), the Atherosclerosis Risk in Communities Study (ARIC), and BioMe Biobank. We further attempted to refine this list of candidate genes by performing conditional analyses, adjusting for individual variants previously associated with these hematological measures, and performed further fine-mapping of TWAS loci. To assist with the interpretation of TWAS findings, we designed an R Shiny application to interactively visualize TWAS results, one genomic locus at a time, by integrating our TWAS results with additional genetic data sources (GWAS, TWAS from other gene expression reference panels, conditional analyses, known GWAS variants, etc.). Our results and R Shiny application highlight frequently overlooked challenges with TWAS and illustrate the complexity of TWAS fine-mapping efforts.Author SummaryTranscriptome-wide association studies (TWAS) have shown great promise in furthering our understanding of the genetic regulatory mechanisms underlying complex trait variation. However, interpreting TWAS results can be incredibly complex, especially in large-scale analyses where hundreds of signals appear throughout the genome, with multiple genes often identified in a single chromosomal region. Our research demonstrates this complexity through real data examples from our analysis of hematological traits, and we provide a useful web application to visualize TWAS results in a broadly approachable format. Together, our results and web application illustrate the importance of interpreting TWAS studies in context and highlight the need to carefully examine results in a region-wide context to draw reasonable conclusions and formulate mechanistic hypotheses.

Fear extinction is an adaptive behavioral process critical for organism’s survival, but deficiency in extinction may lead to PTSD. While the amygdala and its neural circuits are critical for fear extinction, the molecular identity and organizational logic of cell types that lie at the core of these circuits remain unclear. Here we report that mice deficient for amygdala-enrichedgastrin-releasing peptidegene (Grp-/-) exhibit enhanced neuronal activity in the basolateral amygdala (BLA) and stronger fear conditioning, as well as deficient extinction in stress-enhanced fear learning (SEFL). rAAV2-retro-based tracing combined with visualization of the GFP knocked in theGrpgene showed that BLA receives GRPergic or conditioned stimulus projections from the indirect auditory thalamus-to-auditory cortex pathway, ventral hippocampus and ventral tegmental area. Transcription of dopamine-related genes was decreased in BLA ofGrp-/-mice following SEFL extinction recall, suggesting that the GRP may mediate fear extinction regulation by dopamine.Impact statementMice deficient for the amygdala-enrichedgastrin-releasing peptidegene are susceptible to stress-enhanced fear, a behavioral protocol with relevance to PTSD, and show a decrease in dopamine-related gene transcription.

Dubowitz syndrome (DubS) is considered a recognizable syndrome characterized by a distinctive facial appearance and deficits in growth and development. There have been over 200 individuals reported with Dubowitz or a "Dubowitz-like" condition, although no single gene has been implicated as responsible for its cause. We have performed exome (ES) or genome sequencing (GS) for 31 individuals clinically diagnosed with DubS. After genome-wide sequencing, rare variant filtering and computational and Mendelian genomic analyses, a presumptive molecular diagnosis was made in 13/27 (48%) families. The molecular diagnoses included biallelic variants in SKIV2L, SLC35C1, BRCA1, NSUN2; de novo variants in ARID1B, ARID1A, CREBBP, POGZ, TAF1, HDAC8, and copy-number variation at1p36.11(ARID1A), 8q22.2(VPS13B), Xp22, and Xq13(HDAC8). Variants of unknown significance in known disease genes, and also in genes of uncertain significance, were observed in 7/27 (26%) additional families. Only one gene, HDAC8, could explain the phenotype in more than one family (N = 2). All but two of the genomic diagnoses were for genes discovered, or for conditions recognized, since the introduction of next-generation sequencing. Overall, the DubS-like clinical phenotype is associated with extensive locus heterogeneity and the molecular diagnoses made are for emerging clinical conditions sharing characteristic features that overlap the DubS phenotype.

Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants. Results: Our results revealed 24 suggestive signals (p < 1 × 10−4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11–34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count 86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations., Author summary Admixed African and Hispanic/Latino populations remain understudied in genetic studies of complex diseases. These populations have more complex linkage disequilibrium (LD) structure that can impair mapping of variants. Genotype imputation represents an approach to improve genome coverage, especially for rare or ancestry-specific variation; however, these understudied populations also have smaller relevant imputation reference panels. In this study, we leveraged >100,000 phased sequences generated from the multi-ethnic NHLBI TOPMed project for imputation in ~21,600 individuals of African ancestry (AAs) and ~21,700 Hispanics/Latinos. We demonstrated substantially higher imputation quality for low frequency and rare variants in comparison to the 1000 Genomes Project and Haplotype Reference Consortium reference panels. Analysis of quantitative hematological traits led to the discovery of associations with two rare variants in the HBB gene; one of these variants was replicated in an independent sample, and the other is known to cause anemia in the homozygous state. By comparison, the same HBB variants would not have been genome-wide significant using current reference panels due to lower imputation quality. Our findings demonstrate the power of TOPMed whole genome sequencing data for imputation and subsequent association analysis in admixed African and Hispanic/Latino populations.

Background: Project Healthy Bones (PHB) is a 24-week, peer-led exercise and education program for older adults at risk of osteoporosis. Method: Residents from an assisted living and senior community program were enrolled after medical clearance. Participant demographics, geriatric fitness assessments, exercise logs, quizzes, and surveys were collected at baseline and 24 weeks. Data were analyzed using paired t tests and ANOVA of change scores for the pooled data within the R statistical environment. Results: Forty of the 53 enrolled participants completed the program. Participants improved their strength, balance, posture, and flexibility, resulting in a reduced risk of falls and fractures. In addition, their knowledge of bone health, nutrition, and fall prevention increased. Conclusion: Offering low-cost disease-specific programs such as PHB helps minimize the complications of osteoporosis and improve the overall health of participants. Implementing disease-specific public health programs in assisted living centers can increase access to programs.

Background: QT interval (QT) genome-wide association studies (GWAS) have identified upwards of 35 common variant loci, including SNPs adjacent to putative transcription elongation factor TCEA3. Transcription elongation control has broad effects on gene expression, the misregulation of which is known to influence cardiac conduction system morphogenesis as well as activation or repression of key regulatory genes. Thus, we hypothesized that a genome-wide gene-gene interaction study of TCEA3 lead SNP rs2298632 would identify novel loci that influence QT. Methods: Using 1000 Genomes imputed data (>20 million SNPs) in n=67,445 participants (69% Caucasian; 18% Hispanic/Latino; 11% African American) from 10 studies, we conducted genome-wide meta-analyses to test for the presence of: interaction effect loci by examining rs2298632xSNP interactions on QT; and joint effect loci by simultaneously examining SNP main effects and rs2298632xSNP interactions on QT. Inverse-variance weighted meta-analysis of genomically controlled ancestry- and study-specific summary effects estimated using multivariable adjusted linear models or generalized estimating equations that incorporated robust standard errors was performed using METAL. SNPs demonstrating evidence of heterogeneity (Cochran’s Q P < 0.05) and SNPs that were infrequent or rare (minor allele frequency [MAF] Results: We identified one genome-wide significant interaction effect locus ( P INT -8 ) at PVT1 (lead SNP: rs4733591; mean MAF = 32%), a long non-coding RNA gene for which previous GWAS identified suggestive associations with left ventricular systolic dysfunction. We also identified four genome-wide significant joint effect loci ( P JOINT -8 ) that mapped within or nearby NUCKS1 (lead SNP: rs823094; MAF = 0.33) , CASR (lead SNP = rs17251221; MAF = 0.13) , ACTBL2 (lead SNP = rs7737409; MAF = 0.22) , and KDM1B (lead SNP = rs34969716; MAF = 0.26) , loci with roles in calcium sensing, regulation of gene expression through histone modification, and tumor suppression. Conclusion: Extension of traditional main effects GWAS to interrogate gene-gene interactions for biologically motivated loci like TCEA3 may help inform the structure and function of genetic pathways underlying complex traits like QT.

Summary/AbstractGenome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by ~30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery., The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services. Funding for this study was provided by the Aase and Ejner Danielsens Foundation; Academy of Finland (102318; 104781, 120315, 123885, 129619, 286284, 134309, 126925, 121584, 124282, 129378, 117787, 250207, 258753, 41071, 77299, 124243, 1114194, 24300796); Accare Center for Child and Adolescent Psychiatry; Action on Hearing Loss (G51); Agence Nationale de la Recherche; Agency for Health Care Policy Research (HS06516); Age UK Research into Ageing Fund; Åke Wiberg Foundation; ALF/LUA Research Grant in Gothenburg; ALFEDIAM; ALK-Abello´ A/S (Hørsholm, Denmark); American Heart Association (13POST16500011, 10SDG269004); Ardix Medical; Arthritis Research UK; Association Diabète Risque Vasculaire; AstraZeneca; Australian Associated Brewers; Australian National Health and Medical Research Council (241944, 339462, 389927, 389875, 389891, 389892, 389938, 442915, 442981, 496739, 552485, 552498); Avera Research Institute; Bayer Diagnostics; Becton Dickinson; Biobanking and Biomolecular Resources Research Infrastructure (BBMRI –NL, 184.021.007); Biocentrum Helsinki; Boston Obesity Nutrition Research Center (DK46200); British Heart Foundation (RG/10/12/28456, SP/04/002); Canada Foundation for Innovation; Canadian Institutes of Health Research (FRN-CCT-83028); Cancer Research UK; Cardionics; Center for Medical Systems Biology; Center of Excellence in Complex Disease Genetics and SALVECenter of Excellence in Genomics (EXCEGEN); Chief Scientist Office of the Scottish Government; City of Kuopio; Cohortes Santé TGIR; Contrat de Projets État-Région; Croatian Science Foundation (8875); Danish Agency for Science, Technology and Innovation; Danish Council for Independent Research (DFF–1333-00124, DFF–1331-007308); Danish Diabetes Academy; Danish Medical Research Council; Department of Psychology and Education of the VU University Amsterdam; Diabetes Hilfs- und Forschungsfonds Deutschland; Dutch Brain Foundation; Dutch Ministry of Justice; Emil Aaltonen Foundation; Erasmus Medical Center; Erasmus University; Estonian Government (IUT20-60, IUT24-6); Estonian Ministry of Education and Research (3.2.0304.11-0312); European Commission (230374, 284167, 323195, 692145, FP7 EurHEALTHAgeing-277849, FP7 BBMRI-LPC 313010, nr 602633, HEALTH-F2-2008-201865-GEFOS, HEALTH-F4-2007-201413, FP6 LSHM-CT-2004-005272, FP5 QLG2-CT-2002-01254, FP6 LSHG-CT-2006-01947, FP7 HEALTH-F4-2007-201413, FP7 279143, FP7 201668, FP7 305739, FP6 LSHG-CT-2006-018947, HEALTH-F4-2007-201413, QLG1-CT-2001-01252); European Regional Development Fund; European Science Foundation (EuroSTRESS project FP-006, ESF, EU/QLRT-2001-01254); Faculty of Biology and Medicine of Lausanne; Federal Ministry of Education and Research (01ZZ9603, 01ZZ0103, 01ZZ0403, 03ZIK012, 03IS2061A); Federal State of Mecklenburg - West Pomerania; Fédération Française de Cardiologie; Finnish Cultural Foundation; Finnish Diabetes Association; Finnish Foundation of Cardiovascular Research; Finnish Heart Association; Food Standards Agency; Fondation de France; Fonds Santé; Genetic Association Information Network of the Foundation for the National Institutes of Health; German Diabetes Association; German Federal Ministry of Education and Research (BMBF, 01ER1206, 01ER1507); German Research Council (SFB-1052, SPP 1629 TO 718/2-1); GlaxoSmithKline; Göran Gustafssons Foundation; Göteborg Medical Society; Health and Safety Executive; Heart Foundation of Northern Sweden; Icelandic Heart Association; Icelandic Parliament; Imperial College Healthcare NHS Trust; INSERM, Réseaux en Santé Publique, Interactions entre les déterminants de la santé; Interreg IV Oberrhein Program (A28); Italian Ministry of Economy and Finance; Italian Ministry of Health (ICS110.1/RF97.71); John D and Catherine T MacArthur Foundation; Juho Vainio Foundation; King's College London; Kjell och Märta Beijers Foundation; Kuopio University Hospital; Kuopio, Tampere and Turku University Hospital Medical Funds (X51001); Leiden University Medical Center; Lilly; LMUinnovativ; Lundbeck Foundation; Lundberg Foundation; Medical Research Council of Canada; MEKOS Laboratories (Denmark); Merck Santé; Mid-Atlantic Nutrition Obesity Research Center (P30 DK72488); Ministère de l’Économie, de l’Innovation et des Exportations; Ministry for Health, Welfare and Sports of the Netherlands; Ministry of Cultural Affairs of the Federal State of Mecklenburg-West Pomerania; Ministry of Education and Culture of Finland (627;2004-2011); Ministry of Education, Culture and Science of the Netherlands; MRC Human Genetics Unit; MRC-GlaxoSmithKline Pilot Programme Grant (G0701863); Municipality of Rotterdam; Netherlands Bioinformatics Centre (2008.024); Netherlands Consortium for Healthy Aging (050-060-810); Netherlands Genomics Initiative; Netherlands Organisation for Health Research and Development (904-61-090, 985-10-002, 904-61-193, 480-04-004, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192); Netherlands Organisation for Health Research and Development (2010/31471/ZONMW); Netherlands Organisation for Scientific Research (10-000-1002, GB-MW 940-38-011, 100-001-004, 60-60600-97-118, 261-98-710, GB-MaGW 480-01-006, GB-MaGW 480-07-001, GB-MaGW 452-04-314, GB-MaGW 452-06-004, 175.010.2003.005, 175.010.2005.011, 481-08-013, 480-05-003, 911-03-012); Neuroscience Campus Amsterdam; NHS Foundation Trust; Novartis Pharmaceuticals; Novo Nordisk; Office National Interprofessionel des Vins; Paavo Nurmi Foundation; Påhlssons Foundation; Päivikki and Sakari Sohlberg Foundation; Pierre Fabre; Republic of Croatia Ministry of Science, Education and Sport (108-1080315-0302); Research Centre for Prevention and Health, the Capital Region for Denmark; Research Institute for Diseases in the Elderly (014-93-015, RIDE2); Roche; Russian Foundation for Basic Research (NWO-RFBR 047.017.043); Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06); Sanofi-Aventis; Scottish Executive Health Department (CZD/16/6); Siemens Healthcare; Social Insurance Institution of Finland (4/26/2010); Social Ministry of the Federal State of Mecklenburg-West Pomerania; Société Francophone du Diabète; State of Bavaria; Stroke Association; Swedish Diabetes Association; Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation (20140543); Swedish Research Council (2015-03657); Swedish Medical Research Council (K2007-66X-20270-01-3, 2011-2354); Swedish Society for Medical Research; Swiss National Science Foundation (33CSCO-122661, 33CS30-139468, 33CS30-148401); Tampere Tuberculosis Foundation; The Marcus Borgström Foundation; The Royal Society; The Wellcome Trust (084723/Z/08/Z, 088869/B/09/Z); Timber Merchant Vilhelm Bangs Foundation; Topcon; Torsten and Ragnar Söderberg's Foundation; UK Department of Health; UK Diabetes Association; UK Medical Research Council (MC_U106179471, G0500539, G0600705, G0601966, G0700931, G1002319, K013351, MC_UU_12019/1); UK National Institute for Health Research BioResource Clinical Research Facility and Biomedical Research Centre; UK National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre; UK National Institute for Health Research (RP-PG-0407-10371); Umeå University Career Development Award; United States – Israel Binational Science Foundation Grant (2011036); University Hospital Oulu (75617); University Medical Center Groningen; University of Tartu (SP1GVARENG); National Institutes of Health (AG13196, CA047988, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSC271201100004C, HHSN268200900041C, HHSN268201300025C, HHSN268201300026C, HHSN268201300027C, HHSN268201300028C, HHSN268201300029C, HHSN268201500001I, HL36310, HG002651, HL034594, HL054457, HL054481, HL071981, HL084729, HL119443, HL126024, N01-AG12100, N01-AG12109, N01-HC25195, N01-HC55015, N01-HC55016, N01-HC55018, N01-HC55019, N01-HC55020, N01-HC55021, N01-HC55022, N01-HD95159, N01-HD95160, N01-HD95161, N01-HD95162, N01-HD95163, N01-HD95164, N01-HD95165, N01-HD95166, N01-HD95167, N01-HD95168, N01-HD95169, N01-HG65403, N02-HL64278, R01-HD057194, R01-HL087641, R01-HL59367, R01HL-086694, R01-HL088451, R24-HD050924, U01-HG-004402, HHSN268200625226C, UL1-RR025005, UL1-RR025005, UL1-TR-001079, UL1-TR-00040, AA07535, AA10248, AA11998, AA13320, AA13321, AA13326, AA14041, AA17688, DA12854, MH081802, MH66206, R01-D004215701A, R01-DK075787, R01-DK089256, R01-DK8925601, R01-HL088451, R01-HL117078, R01-DK062370, R01-DK072193, DK091718, DK100383, DK078616, 1Z01-HG000024, HL087660, HL100245, R01DK089256, 2T32HL007055-36, U01-HL072515-06, U01-HL84756, NIA-U01AG009740, RC2-AG036495, RC4-AG039029, R03 AG046389, 263-MA-410953, 263-MD-9164, 263-MD-821336, U01-HG004802, R37CA54281, R01CA63, P01CA33619, U01-CA136792, U01-CA98758, RC2-MH089951, MH085520, R01-D0042157-01A, MH081802, 1RC2-MH089951, 1RC2-MH089995, 1RL1MH08326801, U01-HG007376, 5R01-HL08767902, 5R01MH63706:02, HG004790, N01-WH22110, U01-HG007033, UM1CA182913, 24152, 32100-2, 32105-6, 32108-9, 32111-13, 32115, 32118-32119, 32122, 42107-26, 42129-32, 44221); USDA National Institute of Food and Agriculture (2007-35205-17883); Västra Götaland Foundation; Velux Foundation; Veterans Affairs (1 IK2 BX001823); Vleugels Foundation; VU University’s Institute for Health and Care Research (EMGO+, HEALTH-F4-2007-201413) and Neuroscience Campus Amsterdam; Wellcome Trust (090532, 091551, 098051, 098381); Wissenschaftsoffensive TMO; and Yrjö Jahnsson Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Introduction: Mean corpuscular volume (MCV) of red blood cells (RBCs) is a heritable index commonly used in clinical workup of anemia. Macrocytosis, or high MCV, is associated with cardiovascular events in chronic kidney disease patients and all-cause mortality. Genome-wide association studies (GWAS) of MCV have been conducted primarily in European and Asian populations; few have included populations of African or American ancestry. The Multiethnic Genotyping Array (MEGA) was designed to improve variant discovery and fine-mapping in US minorities by better capturing population-specific genetic variation. To identify novel MCV loci and examine evidence of generalization of previously reported MCV loci to populations with African and Amerindian genetic admixture, we conducted a GWAS of African American and Hispanic/Latino Population Architecture using Genomics and Epidemiology (PAGE II) participants on the MEGA array. Methods: We employed Illumina MEGA genotype data to evaluate the association between ~1.5 million SNPs and MCV. Generalized estimating equation models were adjusted for age, sex, current smoking status, study, study center, and the top 10 study-specific principal components of genetic ancestry. Race/ethnic-stratified analyses were combined using inverse-variance weighted fixed-effects meta-analysis to evaluate the entire study sample. Significance thresholds of 1.8x10 -3 and 5x10 -8 were used to define generalization and genome-wide significance, respectively. Results: The study population of 3,734 African Americans and 15,505 Hispanic/Latinos was 62% female with a mean age of 50 (range: 18 to 94 years); 19% of participants were current smokers. Trans-ethnic meta-analysis identified 12 genome-wide significant loci for MCV, including two associations at previously unreported loci: FIGNL1/IKZF1 (chromosome 7p12.2, coded allele frequency [CAF] range=51 to 57%), and LRP6 (chromosome 12p13.2, CAF range=0.3 to 1.7%). FIGNL1 is involved in DNA double-strand-break repair, and IKZF1 is a zinc-finger protein that functions in the hemo-lymphopoetic system. LRP6 encodes an LDL receptor involved in the Wnt/beta-catenin signaling cascade. Ninety-three percent of previously reported MCV loci generalized to the combined PAGE study population, ten at genome-wide significant levels. No previously unreported MCV loci were detected in race/ethnic stratified analyses. Conclusion: Generalization of previously reported MCV loci to African Americans and Hispanic/Latinos highlights the shared genetic architecture of MCV. The potentially novel associations identified at chromosomes 7p12.2 and 12p13.2 also underscore the benefits of performing GWAS in ancestrally diverse populations using arrays that better capture global genetic variation.

Few genome-wide association studies (GWAS) account for environmental exposures, like smoking, potentially impacting the overall trait variance when investigating the genetic contribution to obesity-related traits. Here, we use GWAS data from 51,080 current smokers and 190,178 nonsmokers (87% European descent) to identify loci influencing BMI and central adiposity, measured as waist circumference and waist-to-hip ratio both adjusted for BMI. We identify 23 novel genetic loci, and 9 loci with convincing evidence of gene-smoking interaction (GxSMK) on obesity-related traits. We show consistent direction of effect for all identified loci and significance for 18 novel and for 5 interaction loci in an independent study sample. These loci highlight novel biological functions, including response to oxidative stress, addictive behaviour, and regulatory functions emphasizing the importance of accounting for environment in genetic analyses. Our results suggest that tobacco smoking may alter the genetic susceptibility to overall adiposity and body fat distribution., A full list of acknowledgments appears in the Supplementary Note 4. Co-author A.J.M.d.C. recently passed away while this work was in process. This work was performed under the auspices of the Genetic Investigation of ANthropometric Traits (GIANT) consortium. We acknowledge the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium for encouraging CHARGE studies to participate in this effort and for the contributions of CHARGE members to the analyses conducted for this research. Funding for this study was provided by the Aase and Ejner Danielsens Foundation; Academy of Finland (41071, 77299, 102318, 110413, 117787, 121584, 123885, 124243, 124282, 126925, 129378, 134309, 286284); Accare Center for Child and Adolescent Psychiatry; Action on Hearing Loss (G51); Agence Nationale de la 359 Recherche; Agency for Health Care Policy Research (HS06516); ALF/LUA research grant in Gothenburg; ALFEDIAM; ALK-Abelló A/S; Althingi; American Heart Association (13POST16500011); Amgen; Andrea and Charles Bronfman Philanthropies; Ardix Medical; Arthritis Research UK; Association Diabète Risque Vasculaire; Australian National Health and Medical Research Council (241944, 339462, 389875, 389891, 389892, 389927, 389938, 442915, 442981, 496739, 552485, 552498); Avera Institute; Bayer Diagnostics; Becton Dickinson; BHF (RG/14/5/30893); Boston Obesity Nutrition Research Center (DK46200), Bristol-Myers Squibb; British Heart Foundation (RG/10/12/28456, RG2008/08, RG2008/014, SP/04/002); Medical Research Council of Canada; Canadian Institutes for Health Research (FRCN-CCT-83028); Cancer Research UK; Cardionics; Cavadis B.V., Center for Medical Systems Biology; Center of Excellence in Genomics; CFI; CIHR; City of Kuopio; CNAMTS; Cohortes Santé TGIR; Contrat de Projets État-Région; Croatian Science Foundation (8875); Danish Agency for Science, Technology and Innovation; Danish Council for Independent Research (DFF-1333-00124, DFF-1331-00730B); County Council of Dalarna; Dalarna University; Danish Council for Strategic Research; Danish Diabetes Academy; Danish Medical Research Council; Department of Health, UK; Development Fund from the University of Tartu (SP1GVARENG); Diabetes Hilfs- und Forschungsfonds Deutschland; Diabetes UK; Diabetes Research and Wellness Foundation Fellowship; Donald W. Reynolds Foundation; Dr Robert Pfleger-Stiftung; Dutch Brain Foundation; Dutch Diabetes Research Foundation; Dutch Inter University Cardiology Institute; Dutch Kidney Foundation (E033); Dutch Ministry of Justice; the DynaHEALTH action No. 633595, Economic Structure Enhancing Fund of the Dutch Government; Else Kröner-Fresenius-Stiftung (2012_A147, P48/08//A11/08); Emil Aaltonen Foundation; Erasmus University Medical Center Rotterdam; Erasmus MC and Erasmus University Rotterdam; the Municipality of Rotterdam; Estonian Government (IUT20-60, IUT24-6); Estonian Research Roadmap through the Estonian Ministry of Education and Research (3.2.0304.11-0312); European Research Council (ERC Starting Grant and 323195:SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC); European Regional Development Fund; European Science Foundation (EU/QLRT-2001-01254); European Commission (018947, 018996, 201668, 223004, 230374, 279143, 284167, 305739, BBMRI-LPC-313010, HEALTH-2011.2.4.2-2-EU-MASCARA, HEALTH-2011-278913, HEALTH-2011-294713-EPLORE, HEALTH-F2-2008-201865-GEFOS, HEALTH-F2-2013-601456, HEALTH-F4-2007-201413, HEALTH-F4-2007-201550-HYPERGENES, HEALTH-F7-305507 HOMAGE, IMI/115006, LSHG-CT-2006-018947, LSHG-CT-2006-01947, LSHM-CT-2004-005272, LSHM-CT-2006-037697, LSHM-CT-2007-037273, QLG1-CT-2002-00896, QLG2-CT-2002-01254); Faculty of Biology and Medicine of Lausanne; Federal Ministry of Education and Research (01ZZ0103, 01ZZ0403, 01ZZ9603, 03IS2061A, 03ZIK012); Federal State of Mecklenburg-West Pomerania; Fédération Française de Cardiologie; Finnish Cultural Foundation; Finnish Diabetes Association; Finnish Foundation of Cardiovascular Research; Finnish Heart Association; Fondation Leducq; Food Standards Agency; Foundation for Strategic Research; French Ministry of Research; FRSQ; Genetic Association Information Network (GAIN) of the Foundation for the NIH; German Federal Ministry of Education and Research (BMBF, 01ER1206, 01ER1507); GlaxoSmithKline; Greek General Secretary of Research and Technology; Göteborg Medical Society; Health and Safety Executive; Healthcare NHS Trust; Healthway; Western Australia; Heart Foundation of Northern Sweden; Helmholtz Zentrum München—German Research Center for Environmental Health; Hjartavernd; Ingrid Thurings Foundation; INSERM; InterOmics (PB05 MIUR-CNR); INTERREG IV Oberrhein Program (A28); Interuniversity Cardiology Institute of the Netherlands (ICIN, 09.001); Italian Ministry of Health (ICS110.1/RF97.71); Italian Ministry of Economy and Finance (FaReBio di Qualità); Marianne and Marcus Wallenberg Foundation; the Ministry of Health, Welfare and Sports, the Netherlands; J.D.E. and Catherine T, MacArthur Foundation Research Networks on Successful Midlife Development and Socioeconomic Status and Health; Juho Vainio Foundation; Juvenile Diabetes Research Foundation International; KfH Stiftung Präventivmedizin e.V.; King's College London; Knut and Alice Wallenberg Foundation; Kuopio University Hospital; Kuopio, Tampere and Turku University Hospital Medical Funds (X51001); La Fondation de France; Leenaards Foundation; Lilly; LMUinnovativ; Lundberg Foundation; Magnus Bergvall Foundation; MDEIE; Medical Research Council UK (G0000934, G0601966, G0700931, MC_U106179471, MC_UU_12019/1); MEKOS Laboratories; Merck Santé; Ministry for Health, Welfare and Sports, The Netherlands; Ministry of Cultural Affairs of Mecklenburg-West Pomerania; Ministry of Economic Affairs, The Netherlands; Ministry of Education and Culture of Finland (627;2004-2011); Ministry of Education, Culture and Science, The Netherlands; Ministry of Science, Education and Sport in the Republic of Croatia (108-1080315-0302); MRC centre for Causal Analyses in Translational Epidemiology; MRC Human Genetics Unit; MRC-GlaxoSmithKline pilot programme (G0701863); MSD Stipend Diabetes; National Institute for Health Research; Netherlands Brain Foundation (F2013(1)-28); Netherlands CardioVascular Research Initiative (CVON2011-19); Netherlands Genomics Initiative (050-060-810); Netherlands Heart Foundation (2001 D 032, NHS2010B280); Netherlands Organization for Scientific Research (NWO) and Netherlands Organisation for Health Research and Development (ZonMW) (56-464-14192, 60-60600-97-118, 100-001-004, 261-98-710, 400-05-717, 480-04-004, 480-05-003, 481-08-013, 904-61-090, 904-61-193, 911-11-025, 985-10-002, Addiction-31160008, BBMRI–NL 184.021.007, GB-MaGW 452-04-314, GB-MaGW 452-06-004, GB-MaGW 480-01-006, GB-MaGW 480-07-001, GB-MW 940-38-011, Middelgroot-911-09-032, NBIC/BioAssist/RK 2008.024, Spinozapremie 175.010.2003.005, 175.010.2007.006); Neuroscience Campus Amsterdam; NHS Foundation Trust; National Institutes of Health (1RC2MH089951, 1Z01HG000024, 24152, 263MD9164, 263MD821336, 2R01LM010098, 32100-2, 32122, 32108, 5K99HL130580-02, AA07535, AA10248, AA11998, AA13320, AA13321, AA13326, AA14041, AA17688, AG13196, CA047988, DA12854, DK56350, DK063491, DK078150, DK091718, DK100383, DK078616, ES10126, HG004790, HHSN268200625226C, HHSN268200800007C, HHSN268201200036C, HHSN268201500001I, HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, HHSN271201100004C, HL043851, HL45670, HL080467, HL085144, HL087660, HL054457, HL119443, HL118305, HL071981, HL034594, HL126024, HL130114, KL2TR001109, MH66206, MH081802, N01AG12100, N01HC55015, N01HC55016, N01C55018, N01HC55019, N01HC55020, N01HC55021, N01HC55022, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, N01HC95159, N01HC95160, N01HC95161, N01HC95162, N01HC95163, N01HC95164, N01HC95165, N01HC95166, N01HC95167, N01HC95168, N01HC95169, N01HG65403, N01WH22110, N02HL6‐4278, N01-HC-25195, P01CA33619, R01HD057194, R01HD057194, R01AG023629, R01CA63, R01D004215701A, R01DK075787, R01DK062370, R01DK072193, R01DK075787, R01DK089256, R01HL53353, R01HL59367, R01HL086694, R01HL087641, R01HL087652, R01HL103612, R01HL105756, R01HL117078, R01HL120393, R03 AG046389, R37CA54281, RC2AG036495, RC4AG039029, RPPG040710371, RR20649, TW008288, TW05596, U01AG009740, U01CA98758, U01CA136792, U01DK062418, U01HG004402, U01HG004802, U01HG007376, U01HL080295, UL1RR025005, UL1TR000040, UL1TR000124, UL1TR001079, 2T32HL007055-36, T32GM074905, HG002651, HL084729, N01-HC-25195, UM1CA182913); NIH, National Institute on Aging (Intramural funding, NO1-AG-1-2109); Northern Netherlands Collaboration of Provinces; Novartis Pharma; Novo Nordisk; Novo Nordisk Foundation; Nutricia Research Foundation (2016-T1); ONIVINS; Parnassia Bavo group; Pierre Fabre; Province of Groningen; Päivikki and Sakari Sohlberg Foundation; Påhlssons Foundation; Paavo Nurmi Foundation; Radboud Medical Center Nijmegen; Research Centre for Prevention and Health, the Capital Region of Denmark; the Research Institute for Diseases in the Elderly; Research into Ageing; Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center; Roche; Royal Society; Russian Foundation for Basic Research (NWO-RFBR 047.017.043); Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06); Sanofi-Aventis; Scottish Government Health Directorates, Chief Scientist Office (CZD/16/6); Siemens Healthcare; Social Insurance Institution of Finland (4/26/2010); Social Ministry of the Federal State of Mecklenburg-West Pomerania; Société Francophone du 358 Diabète; State of Bavaria; Stiftelsen för Gamla Tjänarinnor; Stockholm County Council (560183, 592229); Strategic Cardiovascular and Diabetes Programmes of Karolinska Institutet and Stockholm County Council; Stroke Association; Swedish Diabetes Association; Swedish Diabetes Foundation (2013-024); Swedish Foundation for Strategic Research; Swedish Heart-Lung Foundation (20120197, 20150711); Swedish Research Council (0593, 8691, 2012-1397, 2012-1727, and 2012-2215); Swedish Society for Medical Research; Swiss Institute of Bioinformatics; Swiss National Science Foundation (3100AO-116323/1, 31003A-143914, 33CSCO-122661, 33CS30-139468, 33CS30-148401, 51RTP0_151019); Tampere Tuberculosis Foundation; Technology Foundation STW (11679); The Fonds voor Wetenschappelijk Onderzoek Vlaanderen, Ministry of the Flemish Community (G.0880.13, G.0881.13); The Great Wine Estates of the Margaret River Region of Western Australia; Timber Merchant Vilhelm Bangs Foundation; Topcon; Tore Nilsson Foundation; Torsten and Ragnar Söderberg's Foundation; United States – Israel Binational Science Foundation (Grant 2011036), Umeå University; University Hospital of Regensburg; University of Groningen; University Medical Center Groningen; University of Michigan; University of Utrecht; Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) (b2011036); Velux Foundation; VU University’s Institute for Health and Care Research; Västra Götaland Foundation; Wellcome Trust (068545, 076113, 079895, 084723, 088869, WT064890, WT086596, WT098017, WT090532, WT098051, 098381); Wissenschaftsoffensive TMO; Yrjö Jahnsson Foundation; and Åke Wiberg Foundation. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute (NHLBI); the National Institutes of Health (NIH); or the U.S. Department of Health and Human Services.

Objective: Innate immune abnormalities have been frequently reported in children with autism spectrum disorders (ASD), but a role of innate immunity in ASD is not well understood. This study explored a possible role of innate immunity in ASD clinical features and co-morbidities.Methods: Purified peripheral blood monocytes (PBMo) from ASD (N=125) and non-ASD (N=36) subjects were cultured overnight with or without stimulants of innate immunity, and production of pro-inflammatory and counter-regulator cytokines were assessed. Behavioral symptoms were assessed by aberrant behavioral checklist (ABC) at the time of PBMo sampling.Results: ASD PBMo revealed highly variable IL-1β/IL-10 ratios, in contrast to a tight range of IL-1β/IL-10 ratios in non-ASD control cells. There was no association between cytokine levels or IL-1β/IL-10 ratios and ABC subscale scores when ASD data was analyzed, as a whole. However, when ASD data was separated into high, low, or normal (equivalent to controls) IL-1β/IL-1 ratio groups, IL-1β levels were positively associated with stereotypy in the high ratio group. In contrast, IL-1β and IL-10 levels were negatively associated with irritability, lethargy, and hyperactivity in the normal ratio group. The low ratio group revealed a negative association between IL-1β levels and lethargy. When longitudinal changes in cytokine production from PBMo were studied in selected ASD subjects, fluctuating ratios were found in ASD subjects with deviated (high or low) IL-1β/IL-10 ratios, but ratios remained stable in ASD subjects with normal ratios. ASD subjects with deviated ratios were found to have higher frequencies of non-IgE mediated food allergy (NFA) (p

Introduction: Variability within the normal population range of hematocrit is associated with stroke and myocardial infarction. Published GWAS of hematocrit have identified multiple loci, yet few studies have included populations of Hispanic or African descent, thereby limiting opportunities to identify population-specific variants or narrow associated regions for functional analysis. We present a fine-mapping analysis of six previously identified hematocrit loci in African American and Hispanic/Latino participants of the PAGE study. Methods: Approximately 200,000 genotyped or imputed Metabochip variants were examined for association with hematocrit (proportion of whole blood comprising red blood cells) in 19,822 Hispanic/Latino and 19,973 African American participants. SNPs were excluded on a population-specific basis if effective heterozygosity was < 30. Primary and conditional analyses were performed in Plink, ProbABEL, or SuGen; fixed-effects meta-analyses were performed in Metal. Trans-ethnic and ancestry-specific meta-analyses were performed in MANTRA to generate 99% credible intervals for previously published variants that generalized to our populations. Results: We first examined whether 8,261 variants in five previously identified hematocrit loci ( HFE , ABO , HK1 , SH2B3 / ATXN2 , and TMPRSS6 ) were associated with hematocrit in our study populations. Three loci generalized (p-4 ) to Hispanic/Latino participants ( ABO, HK1, and TMPRSS6 ) and three generalized to African Americans ( HFE , ABO , and SH2B3/ATXN2 ). Among generalized loci, conditional analyses adjusting for published variants in European-ancestry or East Asian populations did not identify any independently associated SNPs in Hispanic Latinos or African Americans (p-5 ). Trans-ethnic meta-analysis for the ABO locus resulted in a 5 SNP, 13kb 99% credible interval, shorter than both the Hispanic/Latino (17kb) and African American (360kb) credible intervals. In discovery analysis, we identified one variant associated with hematocrit in Hispanic/Latinos at array-wide significance levels ( PROX1 locus, p-7 ). No novel loci were identified in African Americans. Conclusion: Our findings provide evidence that the same genomic loci influence normal variation in hematocrit values across diverse ancestral populations. Trans-ethnic fine mapping of the gene-rich ABO locus—which has been associated with ischemic stroke, thrombosis, and myocardial infarction in addition to hematocrit GWA studies—suggests that a functional variant may reside in the first intron of the ABO coding region. Additionally, identification of previously unidentified associations in Hispanic/Latinos emphasizes the importance of including diverse populations in association studies as well as the potential to identify population-specific functional variants within known or discovery loci.

PR interval (PR) prolongation is a predictor of atrial fibrillation, a common cardiac arrhythmia, and is an established risk factor for pacemaker implantation, heart failure, stroke, and all-cause mortality. Previous genome-wide association studies in European (EU) and African (AA) descent populations have identified multiple loci associated with PR. However, it is unclear whether these loci are relevant in other racial groups, including Hispanic/Latinos (HL). Extending studies to include admixed populations such as HL also provides the potential to narrow regions associated with PR and identify population specific variants. Therefore, we evaluated 1,135 SNPs from four previously identified PR loci ( SCN5A, SCN10A, CAV1-CAV2, and TBX5-TBX3 ), fine-mapped on the Illumina Metabochip for association with PR. Associations were examined using linear regression assuming an additive genetic model and adjusting for global ancestry and clinical covariates (age, sex, RR interval, body mass index, height, systolic blood pressure) in 11,800 HL participants from participating studies of the Population Architecture using Genomics and Epidemiology (PAGE) consortia. Three of the four loci ( SCN5A, SCN10A, and CAV1-CAV2 ) generalized to HL populations ( P 2 >0.2 with EU-identified index SNPs). At each of these loci, we identified SNPs with stronger evidence of association with PR than the EU-identified index SNP when examined in the HL population ( SCN5A : rs7374540; SCN10A : rs6801957; CAV-CAV2 : rs717957). Conditional analyses also identified two additional independent SNPs at each of the three generalized loci ( SCN5A : rs3796387, rs9861242; SCN10A : rs10428132, rs7433723; CAV1-CAV2 : rs3801995, rs3807994), representing potential secondary signals. Furthermore, linkage disequilibrium (LD) patterns in Hispanics enabled the narrowing of the regions flanking the EU-identified index SNPs. For example, at the CAV1-CAV2 locus, HL LD patterns indicated that seven SNPs were in LD with rs3807989, the EU-identified index SNP, compared to 17 SNPs that were in LD with rs3807989 when applying EU LD patterns, reducing the size of the region by 71 kilobases. Finally, we examined evidence for population specific signals outside of the specific regions identified in EU populations which were associated with PR in Hispanics by focusing on SNPs uncorrelated with the EU index signals. At all four loci, multiple SNPs had evidence of association independent of the signals examined based on EU results. In conclusion, the same genetic loci that influence PR in European descent populations are also important in Hispanic populations, although we detected evidence for population specific SNPs. These results emphasize the importance of examining genetic associations in diverse populations in order to narrow the genomic interval for future functional interrogation.

Several loci have been discovered and replicated for urine albumin-creatinine ratio (ACR), a measure of kidney function, in European populations; however, other ethnic populations such as African-Americans have been less studied for this trait. We examined the association of genetic variants on the Metabochip with natural log transformed ACR (ln(ACR)) in 4,629 African-Americans from the Population Architecture using Genomics and Epidemiology (PAGE) Study which includes participants from the Atherosclerosis Risk in Communities (ARIC), Cardiovascular Health Study (CHS), Coronary Artery Risk Development in Young Adults (CARDIA), and Epidemiologic Architecture for Genes Linked to the Environment (EAGLE-BioVU) studies. We then replicated our findings in 1,533 African-American participants from the Multi-Ethnic Study of Atherosclerosis (MESA). Linear regression models were used to examine associations, assuming additive effects of the SNPs. Within each study, models were adjusted for age, field center (where applicable), and principal components of ancestry, and stratified by sex. Fixed effects inverse variance weighted meta-analysis was used to combine results from contributing studies. Array-wide statistical significance was concluded for SNP associations of p-value less than 2.8 x 10-7. Mean ln(ACR) ranged from 1.4 to 2.9 mg/g, average age ranged from 44 to 77 years, and percent female participants ranged from 59-65% in the contributing studies. Four SNPs were significantly associated array-wide with ACR in the discovery sample: rs17399841 in GRHL2 (β=-0.67, MAF=0.01, p=2.7x10-9), rs153914 near SPATA9/RHOBTB3 (β=-0.59, MAF=0.01, p=1.0x10-7), rs17439845 in LEF1 (β=-0.49, MAF=0.02, p=1.3x10-7), and rs11836243 near ZNF641/ANP32D (β=0.23, MAF=0.12, p=1.8x10-7). Of these four loci, only the association of rs17439845 with ACR replicated in MESA (β=-0.36, p=0.038), with a combined discovery-replication beta of -0.46 and p value of 1.8x10-8. Combined discovery-replication for rs17399841, rs153914, and rs11836243 no longer achieved array-wide significance (p-values 9.84x10-7, 1.98x10-5, and 1.32x10-4, respectively). LEF1 is a mediator in the Wnt signaling pathway, and has been previously implicated in systemic lupus erythematosus and osteoporosis, as well as previously associated with waist circumference, echocardiographic traits, bone mineral density, and blood count traits. Continuing studies in diverse populations are warranted to identify a set of loci important in contributing to the variation in ACR.

Introduction: Waist-to-hip ratio (WHR) is associated with cardiometabolic disease and its risk factors independent of body-mass-index (BMI). Previous GWAS on WHR adjusting for BMI (WHR’) have identified several SNPs but mainly among European descent populations (EA SNPs). While these SNPs point to general loci of interest, they are not likely the causal variants. Expanding analyses of these loci to other race and ethnic populations can dually assess whether these loci apply to more diverse populations and utilize differences in genetic linkage disequilibrium (LD) patterns to narrow the region containing the causal variant. Objective: To better capture genetic variants of WHR’ in self-identified African Americans (AA) within 14 loci containing previously reported WHR’ EA SNPs. Methods: Up to 14,322 AA participants (aged 20-93 years, 61% female) were included from seven studies. Individuals were genotyped using the Illumina iSelect Metabochip array consisting of nearly 200,000 SNPs enriched to fine map GWAS signals of cardiometabolic traits, including the 14 WHR’ loci. We modeled WHR’ adjusting for age, sex, study site, and the first three ancestry principle components for each study and conducted an inverse variance weighted meta-analysis. Variants reaching a Bonferroni adjusted threshold of P Results: Within the 14 regions, seven loci (in or near TBX15-WARS2, DNM3-PIGC, GRB14, NISCH-STAB1, ADAMTS9, VEGFA, and RSPO3 ) contained a significant AA SNP. In the GRB14 locus, the AA SNP (rs10195252, P =1.93x10 -06 ) was the same as the EA SNP. Among four of the seven loci, the AA SNPs (rs12096179 near TBX15-WARS2, rs2371767 in ADAMTS9 , rs1358990 near VEGFA, and rs9385487 in RSPO3 ) were not independent of the EA SNPs (LD r 2 ≥0.3), but were more significant in AA, possibly narrowing the region containing the functional variant. Two AA SNPs (rs11715796 in NISCH-STAB1 and rs4916251 near DNM3-PIGC ) suggesting independence from the EA SNP (r 2 NISCH-STAB1 had a stronger association after conditioning on the EA SNP, signifying independence. The AA SNP near DNM3-PIGC was less significant after conditioning, indicating it points to the same signal as the EA SNP. We have identified participants from additional cohorts for inclusion in future analyses and will conduct a sex-stratified analysis to further explore WHR’ loci among AA. Conclusions: Examination of 14 previously identified loci for WHR’ in AA individuals revealed one novel signal in NISCH-STAB1 . Five additional non-independent AA SNPs suggested a narrower region containing the WHR’ altering variant based on the LD structure differences between EA and AA. These findings highlight the value in expanding genetic association studies to other race and ethnic populations.

Clinical response to immunomodulatory therapies in multiple sclerosis (MS) is variable among patients. Currently, there are no validated biomarkers of clinical response to any of the approved treatments for MS. The objective of this study was to determine if HLA-class II alleles predict the clinical response to glatiramer acetate (GA).This was a prospective study of 64 MS patients with relapsing-remitting disease. Patients were HLA-typed and classified as GA-responders or non-responders after 2 years of treatment based on a clinical criterion. Statistical models were used to determine whether HLA-DR and DQ alleles and haplotypes predict the clinical response to GA.Tests of association of response singled out four alleles and two haplotypes with nominal p0.01. The presence of alleles DR15 or DQ6 or the absence of DR17 and DQ2 alleles was associated with favorable clinical response. The presence of the DR15-DQ6 haplotype and the absence of the DR17-DQ2 haplotype were also associated with favorable treatment response. A best fitting two-haplotype model resulted in the identification of three prognostic categories (good, neutral, and poor). A DR15-DQ6 positive but DR17-DQ2 negative combination was strongly predictive of a favorable clinical response (71%). Conversely, a DR15-DQ6 negative but DR17-DQ2 positive combination was strongly predictive of poor clinical response to GA (17%).HLA-DR and DQ typing may prove to be useful biomarkers of predicting response to GA in MS and may help select patients appropriate for this treatment.

Using a phenome-wide association study (PheWAS) approach, we comprehensively tested genetic variants for association with phenotypes available for 70,061 study participants in the Population Architecture using Genomics and Epidemiology (PAGE) network. Our aim was to better characterize the genetic architecture of complex traits and identify novel pleiotropic relationships. This PheWAS drew on five population-based studies representing four major racial/ethnic groups (European Americans (EA), African Americans (AA), Hispanics/Mexican-Americans, and Asian/Pacific Islanders) in PAGE, each site with measurements for multiple traits, associated laboratory measures, and intermediate biomarkers. A total of 83 single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) were genotyped across two or more PAGE study sites. Comprehensive tests of association, stratified by race/ethnicity, were performed, encompassing 4,706 phenotypes mapped to 105 phenotype-classes, and association results were compared across study sites. A total of 111 PheWAS results had significant associations for two or more PAGE study sites with consistent direction of effect with a significance threshold of p, Author Summary In phenome-wide association studies (PheWAS) all potential genetic variants in a dataset are systematically tested for association with all available phenotypes and traits that have been measured in study participants. By investigating the relationship between genetic variation and a diversity of phenotypes, there is the potential for uncovering novel relationships between single nucleotide polymorphisms (SNPs), phenotypes, and networks of interrelated phenotypes. PheWAS also can expose pleiotropy, provide novel mechanistic insights, and foster hypothesis generation. This approach is complementary to genome-wide association studies (GWAS) that test the association between hundreds of thousands, to over a million, single nucleotide polymorphisms and a single phenotype or limited phenotypic domain. The Population Architecture using Genomics and Epidemiology (PAGE) network has measures for a wide array of phenotypes and traits, including prevalent and incident status for clinical conditions and risk factors, as well as clinical parameters and intermediate biomarkers. We performed tests of association between a series of genome-wide association study (GWAS)–identified SNPs and a comprehensive range of phenotypes from the PAGE network in a high-throughput manner. We replicated a number of previously reported associations, validating the PheWAS approach. We also identified novel genotype–phenotype associations possibly representing pleiotropic effects.

Background: Genome-wide association studies have identified hundreds of susceptibility loci for various cancer sites including prostate cancer. Several of the risk loci have demonstrated pleiotropic effects with multiple cancers, suggesting shared biological pathways in carcinogenesis. In the present study, we systematically investigated the effects of other cancer-associated variants on prostate cancer risk. Methods: We examined 196 genetic risk variants identified for 18 cancer and cancer-related traits in a total of 28,135 prostate cancer cases and 37,218 controls. This large meta-analysis of multiethnic samples was assembled from three consortia: Population Architecture using Genetics and Epidemiology (PAGE), Prostate Cancer Association Group to Investigate Cancer Associated Alterations in the Genome (PRACTICAL) and Breast and Prostate Cancer Cohort Consortium (BPC3). Study- and ethnic-specific logistic regressions results were combined through fixed-effect meta-analysis to evaluate the association between each variant and prostate cancer risk, applying a Bonferroni-corrected significance threshold of 2.6×10-4. We also assessed the cumulative effect of other cancer risk variants on prostate cancer susceptibility via risk score analysis. Results: Three variants, RTEL rs6010620, TERT rs2853676 and HLA rs6457327, demonstrated statistically significant associations with prostate cancer risk after correcting for multiple comparisons. Originally reported as a follicular lymphoma risk variant, rs6457327 was associated with prostate cancer at the genome-wide significant level (meta-analysis OR=0.93, 95% CI: 0.91-0.96; p=4.5×10-8). The cumulative genetic risk score for risk variants of other cancers demonstrated a significant effect on prostate cancer risk (p=0.021). Conclusions: This systematic pleiotropy analysis identified HLA as a novel susceptibility locus for prostate cancer in a large multiethnic population. The genetic variant with pleiotropic effects on both follicular lymphoma and prostate cancer provides evidence for shared etiologic mechanisms between prostate cancer and other cancer sites. Citation Format: Ying Han, Shelly-Ann Love, William S. Bush, Daniele Campa, Anne M. Butler, Logan Dumitrescu, Konstantinos Tsilidis, Iona Cheng, Lynne R. Wilkens, Jay H. Fowke, Jose Luis Ambite, Steve Buyske, S. Lani Park, The PRACTICAL Consortium, The BPC3 Consortium, Christopher A. Haiman, Loic Le Marchand, Lucia A. Hindorff, Federico Canzian, Fredrick R. Schumacher. Pleiotropy analysis identifies a novel prostate cancer variant at 6p21.33: The PAGE, PRACTICAL, and BPC3 Consortia. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5064. doi:10.1158/1538-7445.AM2014-5064