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The adjustments include covariates: age, HbA1c, Charlson comorbidity, finasteride use, and statin use, and propensity scores for metformin use.

BACKGROUND Metabolic regulation plays a significant role in energy homeostasis, and adolescence is a crucial life stage for the development of cardiometabolic disease (CMD). This study aims to investigate the genetic determinants of metabolic biomarkers-adiponectin, leptin, ghrelin, and orexin-and their associations with CMD risk factors. METHODS We characterized the genetic determinants of the biomarkers among Hispanic/Latino adolescents of the Santiago Longitudinal Study (SLS) and identified the cumulative effects of genetic variants on adiponectin and leptin using biomarker polygenic risk scores (PRS). We further investigated the direct and indirect effect of the biomarker PRS on downstream body fat percent (BF%) and glycemic traits using structural equation modeling. RESULTS We identified putatively novel genetic variants associated with the metabolic biomarkers. A substantial amount of biomarker variance was explained by SLS-specific PRS, and the prediction was improved by including the putatively novel loci. Fasting blood insulin and insulin resistance were associated with PRS for adiponectin, leptin, and ghrelin, and BF% was associated with PRS for adiponectin and leptin. We found evidence of substantial mediation of these associations by the biomarker levels. CONCLUSIONS The genetic underpinnings of metabolic biomarkers can affect the early development of CMD, partly mediated by the biomarkers. IMPACT This study characterized the genetic underpinnings of four metabolic hormones and investigated their potential influence on adiposity and insulin biology among Hispanic/Latino adolescents. Fasting blood insulin and insulin resistance were associated with polygenic risk score (PRS) for adiponectin, leptin, and ghrelin, with evidence of some degree of mediation by the biomarker levels. Body fat percent (BF%) was also associated with PRS for adiponectin and leptin. This provides important insight on biological mechanisms underlying early metabolic dysfunction and reveals candidates for prevention efforts. Our findings also highlight the importance of ancestrally diverse populations to facilitate valid studies of the genetic architecture of metabolic biomarker levels.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels. This work was supported by a grant from the Swedish Research Council (2016-06830) and grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 and R01HL127564. Please refer to the supplemental information for the full acknowledgements. Sí

Identity-by-descent (IBD) segments are a useful tool for applications ranging from demographic inference to relationship classification, but most detection methods rely on phasing information and therefore require substantial computation time. As genetic datasets grow, methods for inferring IBD segments that scale well will be critical. We developed IBIS, an IBD detector that locates long regions of allele sharing between unphased individuals, and benchmarked it with Refined IBD, GERMLINE, and TRUFFLE on 3,000 simulated individuals. Phasing these with Beagle 5 takes 4.3 CPU days, followed by either Refined IBD or GERMLINE segment detection in 2.9 or 1.1 h, respectively. By comparison, IBIS finishes in 6.8 min or 7.8 min with IBD2 functionality enabled: speedups of 805-946× including phasing time. TRUFFLE takes 2.6 h, corresponding to IBIS speedups of 20.2-23.3×. IBIS is also accurate, inferring ≥7 cM IBD segments at quality comparable to Refined IBD and GERMLINE. With these segments, IBIS classifies first through third degree relatives in real Mexican American samples at rates meeting or exceeding other methods tested and identifies fourth through sixth degree pairs at rates within 0.0%-2.0% of the top method. While allele frequency-based approaches that do not detect segments can infer relationship degrees faster than IBIS, the fastest are biased in admixed samples, with KING inferring 30.8% fewer fifth degree Mexican American relatives correctly compared with IBIS. Finally, we ran IBIS on chromosome 2 of the UK Biobank dataset and estimate its runtime on the autosomes to be 3.3 days parallelized across 128 cores.

Genome-wide association studies (GWAS) have identified many disease-associated variants, yet mechanisms underlying these associations remain unclear. To understand obesity-associated variants, we generate gene regulatory annotations in adipocytes and hypothalamic neurons across cellular differentiation stages. We then test variants in 97 obesity-associated loci using a massively parallel reporter assay and identify putatively causal variants that display cell type specific or cross-tissue enhancer-modulating properties. Integrating these variants with gene regulatory information suggests genes that underlie obesity GWAS associations. We also investigate a complex genomic interval on 16p11.2 where two independent loci exhibit megabase-range, cross-locus chromatin interactions. We demonstrate that variants within these two loci regulate a shared gene set. Together, our data support a model where GWAS loci contain variants that alter enhancer activity across tissues, potentially with temporally restricted effects, to impact the expression of multiple genes. This complex model has broad implications for ongoing efforts to understand GWAS., Many genetic loci have been linked to obesity, but knowledge of their functional mechanisms is limited. Here, the authors perform reporter assays and temporal functional genomics data generation to characterize obesity genetic loci and find that loci often harbor multiple functional variants.

The Rio Grande Valley (RGV) in South Texas has one of the highest prevalence of obesity and type 2 diabetes (T2D) in the United States (US). We report for the first time the T2D prevalence in persons with HIV (PWH) in the RGV and the interrelationship between T2D, cardiometabolic risk factors, HIV-related indices, and antiretroviral therapies (ART). The PWH in this study received medical care at Valley AIDS Council (VAC) clinic sites located in Harlingen and McAllen, Texas. Henceforth, this cohort will be referred to as Valley AIDS Council Cohort (VACC). Cross-sectional analyses were conducted using retrospective data obtained from 1,827 registries. It included demographic and anthropometric variables, cardiometabolic traits, and HIV-related virological and immunological indices. For descriptive statistics, we used mean values of the quantitative variables from unbalanced visits across 20 months. Robust regression methods were used to determine the associations. For comparisons, we used cardiometabolic trait data obtained from HIV-uninfected San Antonio Mexican American Family Studies (SAMAFS; N = 2,498), and the Mexican American population in the National Health and Nutrition Examination Survey (HHANES; N = 5,989). The prevalence of T2D in VACC was 51% compared to 27% in SAMAFS and 19% in HHANES, respectively. The PWH with T2D in VACC were younger (4.7 years) and had lower BMI (BMI 2.43 units less) when compared to SAMAFS individuals. In contrast, VACC individuals had increased blood pressure and dyslipidemia. The increased T2D prevalence in VACC was independent of BMI. Within the VACC, ART was associated with viral load and CD4+ T cell counts but not with metabolic dysfunction. Notably, we found that individuals with any INSTI combination had higher T2D risk: OR 2.08 (95%CI 1.67, 2.6; p < 0.001). In summary, our results suggest that VACC individuals may develop T2D at younger ages independent of obesity. The high burden of T2D in these individuals necessitates rigorously designed longitudinal studies to draw potential causal inferences and develop better treatment regimens.

Metabolic regulation plays a significant role in energy homeostasis, and adolescence is a crucial life stage for the development of cardiometabolic disease (CMD). This study aims to investigate the genetic determinants of metabolic biomarkers-adiponectin, leptin, ghrelin, and orexin-and their associations with CMD risk factors.We characterized the genetic determinants of the biomarkers among Hispanic/Latino adolescents of the Santiago Longitudinal Study (SLS) and identified the cumulative effects of genetic variants on adiponectin and leptin using biomarker polygenic risk scores (PRS). We further investigated the direct and indirect effect of the biomarker PRS on downstream body fat percent (BF%) and glycemic traits using structural equation modeling.We identified putatively novel genetic variants associated with the metabolic biomarkers. A substantial amount of biomarker variance was explained by SLS-specific PRS, and the prediction was improved by including the putatively novel loci. Fasting blood insulin and insulin resistance were associated with PRS for adiponectin, leptin, and ghrelin, and BF% was associated with PRS for adiponectin and leptin. We found evidence of substantial mediation of these associations by the biomarker levels.The genetic underpinnings of metabolic biomarkers can affect the early development of CMD, partly mediated by the biomarkers.This study characterized the genetic underpinnings of four metabolic hormones and investigated their potential influence on adiposity and insulin biology among Hispanic/Latino adolescents. Fasting blood insulin and insulin resistance were associated with polygenic risk score (PRS) for adiponectin, leptin, and ghrelin, with evidence of some degree of mediation by the biomarker levels. Body fat percent (BF%) was also associated with PRS for adiponectin and leptin. This provides important insight on biological mechanisms underlying early metabolic dysfunction and reveals candidates for prevention efforts. Our findings also highlight the importance of ancestrally diverse populations to facilitate valid studies of the genetic architecture of metabolic biomarker levels.

Background: Hypertriglyceridemia has emerged as a critical coronary artery disease (CAD) risk factor. Rare loss-of function (LoF) variants in apolipoprotein C-III have been reported to reduce triglycerides (TG) and are cardioprotective in American Indians and Europeans. However, there is a lack of data in other Europeans and non-Europeans. Also, whether genetically increased plasma TG due to ApoC-III is causally associated with increased CAD risk is still unclear and inconsistent. The objectives of this study were to verify the cardioprotective role of earlier reported six LoF variants of APOC3 in South Asians and other multi-ethnic cohorts and to evaluate the causal association of TG raising common variants for increasing CAD risk. Methods: We performed gene-centric and Mendelian randomization analyses and evaluated the role of genetic variation encompassing APOC3 for affecting circulating TG and the risk for developing CAD. Results: One rare LoF variant (rs138326449) with a 37% reduction in TG was associated with lowered risk for CAD in Europeans (p = 0.007), but we could not confirm this association in Asian Indians (p = 0.641). Our data could not validate the cardioprotective role of other five LoF variants analysed. A common variant rs5128 in the APOC3 was strongly associated with elevated TG levels showing a p-value 2.8 × 10− 424. Measures of plasma ApoC-III in a small subset of Sikhs revealed a 37% increase in ApoC-III concentrations among homozygous mutant carriers than the wild-type carriers of rs5128. A genetically instrumented per 1SD increment of plasma TG level of 15 mg/dL would cause a mild increase (3%) in the risk for CAD (p = 0.042). Conclusions: Our results highlight the challenges of inclusion of rare variant information in clinical risk assessment and the generalizability of implementation of ApoC-III inhibition for treating atherosclerotic disease. More studies would be needed to confirm whether genetically raised TG and ApoC-III concentrations would increase CAD risk. Ministry of Health (MOH) National Medical Research Council (NMRC) Published version AIDHS/SDS: The Sikh Diabetes Study/ Asian Indian Diabetic Heart Study was supported by NIH grants-R01DK082766; R01DK118427 (NIDDK) and NOT-HG11-009 (NHGRI) and grants from Presbyterian Health Foundation and Harald Hamm Diabetes Center of Oklahoma. Sequencing services were provided through the RS&G Service by the Northwest Genomics Center at the University of Washington, Department of Genome Sciences, under US Federal Government contract number HHSN268201100037C from the National Heart, Lung, and Blood Institute of the NIH. LOLIPOP: The LOLIPOP study is supported by the National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre Imperial College Healthcare NHS. Trust, the British Heart Foundation (S.P./04/002), the Medical Research Council (G0601966, G0700931), the Wellcome Trust (084723/Z/08/Z, 090532 & 098381) the NIHR (RP-PG-0407-10371), the NIHR Official Development Assistance (ODA, award 16/136/68), the European Union FP7 (EpiMigrant, 279143) and H2020 programs (iHealth-T2D, 643774). We acknowledge the support of the MRC-PHE Centre for Environment and Health, and the NIHR Health Protection Research Unit on Health Impact of Environmental Hazards. The work was carried out in part at the NIHR/Wellcome Trust Imperial Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the Imperial College Healthcare NHS. Trust, the NHS, the NIHR, or the Department of Health. We thank the participants and research staff who made the study possible. JC is supported by the Singapore Ministry of Health’s National Medical Research Council under its Singapore Translational Research Investigator (STaR) Award (NMRC/ STaR/0028/2017). SAMAFS: SAMAFS is supported in part by National Institutes of Health (NIH) grants P01 HL045522, R01 DK047482, DK053889, R01 HL113323, R37 MH059490, and T2D-GENES Consortium grants (U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545). We thank the participants of the San Antonio Family Heart Study and the San Antonio Family Diabetes/Gallbladder Study for their continued cooperation and participation in our research programs. MISS-OLIVER: The OLIVER and MISS are partly supported by NIH grants -R01DK082766 funded by the National Institute of Health (NIDDK), Presbyterian Health Foundation Grants, the College of Medicine Alumni Association grant, and Leinbach Seed Grant from the University of Oklahoma Health Sciences Center. The authors thank all the participants of MISS-OLIVER participants and are grateful for their contribution to this study. UKBB: YT is supported by a Funai Overseas Scholarship from the Funai Foundation for Information Technology and the Stanford University School of Medicine. MAR is partially supported by Stanford University and a National Institute of Health center for Multi-and Trans-ethnic Mapping of Mendelian and Complex Diseases grant (5 U01 HG009080) and partially supported by the National Human Genome Research Institute (NHGRI) of the National Institutes of Health (NIH) under award R01HG010140.

High concentrations of carotenoids are protective against cardiometabolic risk traits (CMTs) in adults and children. We recently showed in non-diabetic Mexican American (MA) children that serum α-carotene and β-carotene are inversely correlated with obesity measures and triglycerides and positively with HDL cholesterol and that they were under strong genetic influences. Additionally, we previously described a Pediatric Metabolic Index (PMI) that helps in the identification of children who are at risk for cardiometabolic diseases. Here, we quantified serum lycopene and β-cryptoxanthin concentrations in approximately 580 children from MA families using an ultraperformance liquid chromatography-photodiode array and determined their heritabilities and correlations with CMTs. Using response surface methodology (RSM), we determined two-way interactions of carotenoids and PMI on Matsuda insulin sensitivity index (ISI). The concentrations of lycopene and β-cryptoxanthin were highly heritable [h2 = 0.98, P = 7 × 10–18 and h2 = 0.58, P = 1 × 10–7]. We found significant (P ≤ 0.05) negative phenotypic correlations between β-cryptoxanthin and five CMTs: body mass index (− 0.22), waist circumference (− 0.25), triglycerides (− 0.18), fat mass (− 0.23), fasting glucose (− 0.09), and positive correlations with HDL cholesterol (0.29). In contrast, lycopene only showed a significant negative correlation with fasting glucose (− 0.08) and a positive correlation with HDL cholesterol (0.18). Importantly, we found that common genetic influences significantly contributed to the observed phenotypic correlations. RSM showed that increased serum concentrations of α- and β-carotenoids rather than that of β-cryptoxanthin or lycopene had maximal effects on ISI. In summary, our findings suggest that the serum carotenoids are under strong additive genetic influences and may have differential effects on susceptibility to CMTs in children.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

Hundreds of thousands of genetic variants have been reported to cause severe monogenic diseases, but the probability that a variant carrier will develop the disease (termed penetrance) is unknown for virtually all of them. Additionally, the clinical utility of common polygenetic variation remains uncertain. Using exome sequencing from 77,184 adult individuals (38,618 multi-ancestral individuals from a type 2 diabetes case-control study and 38,566 participants from the UK Biobank, for whom genotype array data were also available), we applied clinical standard-of-care gene variant curation for eight monogenic metabolic conditions. Rare variants causing monogenic diabetes and dyslipidemias displayed effect sizes significantly larger than the top 1% of the corresponding polygenic scores. Nevertheless, penetrance estimates for monogenic variant carriers averaged below 60% in both studies for all conditions except monogenic diabetes. We assessed additional epidemiologic and genetic factors contributing to risk prediction, demonstrating that inclusion of common polygenic variation significantly improved biomarker estimation for two monogenic dyslipidemias.

miRNA regulates the expression of protein coding genes and plays a regulatory role in human development and disease. The human iPSCs and their differentiated progenies provide a unique opportunity to identify these miRNA-mediated regulatory mechanisms. To identify miRNA&ndash, mRNA regulatory interactions in human nervous system development, well characterized NSCs were differentiated from six validated iPSC lines and analyzed for differentially expressed (DE) miRNome and transcriptome by RNA sequencing. Following the criteria, moderated t statistics, FDR-corrected p-value &le, 0.05 and fold change&mdash, absolute (FC-abs) &ge, 2.0, 51 miRNAs and 4033 mRNAs were found to be significantly DE between iPSCs and NSCs. The miRNA target prediction analysis identified 513 interactions between 30 miRNA families (mapped to 51 DE miRNAs) and 456 DE mRNAs that were paradoxically oppositely expressed. These 513 interactions were highly enriched in nervous system development functions (154 mRNAs, FDR-adjusted p-value range: 8.06 &times, 10&minus, 15&ndash, 1.44 &times, 4). Furthermore, we have shown that the upregulated miR-10a-5p, miR-30c-5p, miR23-3p, miR130a-3p and miR-17-5p miRNA families were predicted to down-regulate several genes associated with the differentiation of neurons, neurite outgrowth and synapse formation, suggesting their role in promoting the self-renewal of undifferentiated NSCs. This study also provides a comprehensive characterization of iPSC-generated NSCs as dorsal neuroepithelium, important for their potential use in in vitro modeling of human brain development and disease.

AimAcanthosis nigricans (AN) is a strong correlate of obesity and is considered a marker of insulin resistance (IR). AN is associated with various other cardiometabolic risk factors (CMRFs). However, the direct causal relationship of IR with AN in obesity has been debated. Therefore, we aimed to examine the complex causal relationships among the troika of AN, obesity, and IR in Mexican Americans (MAs).MethodsWe used data from 670 non-diabetic MA children, aged 6-17 years (49% girls). AN (prevalence 33%) severity scores (range 0-5) were used as a quasi-quantitative trait (AN-q) for analysis. We used the program SOLAR for determining phenotypic, genetic, and environmental correlations between AN-q and CMRFs (e.g., BMI, HOMA-IR, lipids, blood pressure, hs-C-reactive protein (CRP), and Harvard physical fitness score (PFS)). The genetic and environmental correlations were subsequently used in mediation analysis (AMOS program). Model comparisons were made using goodness-of-fit indexes.ResultsHeritability of AN-q was 0.75 (pConclusionOur study suggests that obesity explains the association of IR with AN, but no causal relationship between IR and AN in Mexican American children.

Simulations of close relatives and identical by descent (IBD) segments are common in genetic studies, yet most past efforts have utilized sex averaged genetic maps and ignored crossover interference, thus omitting features known to affect the breakpoints of IBD segments. We developed Ped-sim, a method for simulating relatives that can utilize either sex-specific or sex averaged genetic maps and also either a model of crossover interference or the traditional Poisson model for inter-crossover distances. To characterize the impact of previously ignored mechanisms, we simulated data for all four combinations of these factors. We found that modeling crossover interference decreases the standard deviation of pairwise IBD proportions by 10.4% on average in full siblings through second cousins. By contrast, sex-specific maps increase this standard deviation by 4.2% on average, and also impact the number of segments relatives share. Most notably, using sex-specific maps, the number of segments half-siblings share is bimodal; and when combined with interference modeling, the probability that sixth cousins have non-zero IBD sharing ranges from 9.0 to 13.1%, depending on the sexes of the individuals through which they are related. We present new analytical results for the distributions of IBD segments under these models and show they match results from simulations. Finally, we compared IBD sharing rates between simulated and real relatives and find that the combination of sex-specific maps and interference modeling most accurately captures IBD rates in real data. Ped-sim is open source and available from https://github.com/williamslab/ped-sim., Author summary Simulations are ubiquitous throughout statistical genetics in order to generate data with known properties, enabling tests of inference methods and analyses of real world processes in settings where experimental data are challenging to collect. Simulating genetic data for relatives in a pedigree requires the synthesis of chromosomes parents transmit to their children. These chromosomes form as a mosaic of a given parent’s two chromosomes, with the location of switches between the two parental chromosomes known as crossovers. Detailed information about crossover generation based on real data from humans now exists, including the fact that men and women have overall different rates (women produce ∼1.6 times more crossovers) and that real crossovers are subject to interference—whereby crossovers are further apart from one another than expected under a model that selects their locations randomly. Our new method, Ped-sim, can simulate pedigree data using these less commonly modeled crossover features, and we used it to evaluate the impact of sex-specific rates and interference compared to real data. These comparisons show that both factors shape the amount of DNA two relatives share, and that their inclusion in models of crossover better fit data from real relatives.

As genetic datasets increase in size, the fraction of samples with one or more close relatives grows rapidly, resulting in sets of mutually related individuals. We present DRUID—deep relatedness utilizing identity by descent—a method that works by inferring the identical-by-descent (IBD) sharing profile of an ungenotyped ancestor of a set of close relatives. Using this IBD profile, DRUID infers relatedness between unobserved ancestors and more distant relatives, thereby combining information from multiple samples to remove one or more generations between the deep relationships to be identified. DRUID constructs sets of close relatives by detecting full siblings and also uses an approach to identify the aunts/uncles of two or more siblings, recovering 92.2% of real aunts/uncles with zero false positives. In real and simulated data, DRUID correctly infers up to 10.5% more relatives than PADRE when using data from two sets of distantly related siblings, and 10.7%–31.3% more relatives given two sets of siblings and their aunts/uncles. DRUID frequently infers relationships either correctly or within one degree of the truth, with PADRE classifying 43.3%–58.3% of tenth degree relatives in this way compared to 79.6%–96.7% using DRUID.

Knowledge on genetic and environmental (G × E) interaction effects on cardiometabolic risk factors (CMRFs) in children is limited. The purpose of this study was to examine the impact of G × E interaction effects on CMRFs in Mexican American (MA) children (n = 617, ages 6-17 years). The environments examined were sedentary activity (SA), assessed by recalls from "yesterday" (SAy) and "usually" (SAu) and physical fitness (PF) assessed by Harvard PF scores (HPFS). CMRF data included body mass index (BMI), waist circumference (WC), fat mass (FM), fasting insulin (FI), homeostasis model of assessment-insulin resistance (HOMA-IR), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), systolic (SBP) and diastolic (DBP) blood pressure, and number of metabolic syndrome components (MSC). We examined potential G × E interaction in the phenotypic expression of CMRFs using variance component models and likelihood-based statistical inference. Significant G × SA interactions were identified for six CMRFs: BMI, WC, FI, HOMA-IR, MSC, and HDL, and significant G × HPFS interactions were observed for four CMRFs: BMI, WC, FM, and HOMA-IR. However, after correcting for multiple hypothesis testing, only WC × SAy, FM × SAy, and FI × SAu interactions became marginally significant. After correcting for multiple testing, most of CMRFs exhibited significant G × E interactions (Reduced G × E model vs. Constrained model). These findings provide evidence that genetic factors interact with SA and PF to influence variation in CMRFs, and underscore the need for better understanding of these relationships to develop strategies and interventions to effectively reduce or prevent cardiometabolic risk in children.

The literature on risk factors for venous thromboembolism (VTE) is replete with complex combinations of genetic and environmental determinants. Here we apply statistical genetic models to data from Mexican Americans of South Texas participating in the San Antonio Family Study (SAFS) to help disentangle some of this complexity. The SAFS has data for nearly 50 large extended pedigrees (Figure A) that are extensively phenotyped, especially for traits related to the pathophysiology of cardiovascular disease. Using a linear mixed model approach, while accounting for age, sex, and their interactions (including age-squared, age-by-sex, and age-squared-by-sex) as confounders, we found significant heritability for plasma FII (46%; N=640; p=6.9E-12) and fibrinogen (28%; N=759; p=1.6E-06) coagulant activity levels and that the Prothrombin G20210A mutation was significantly associated with both traits (FII: p=0.002; fibrinogen: p=0.037; Figure B). We also examined a dichotomous obesity variable based on the Adult Treatment Panel III criterion of sex-specific waist circumferences (>102 cm in males; >88 cm females) denoted as OBWC. Under a threshold and liability model, we found a significant heritability of the liability of OBWC (71%; N=654; p=4.4E-08) and that Prothrombin G20210A was a significant predictor (p=0.031), while still adjusting for age, sex, and their interactions. As can be seen in Figure C, the G20210A mutation profoundly impacts the liability of OBWC such that obesity prevalence, where the prevalence parameter is denoted by Kp in the figure, increases by 27% from individuals homozygous for the major G-allele (G/G) with a prevalence of 34% to heterozygous (G/A) individuals with a prevalence of 61%. To the best of our knowledge, this appears to be the largest single-allele-dose effect for obesity reported in the literature. We next performed bivariate trait analysis (each time accounting for age, sex, and their interactions as confounders) to discover potentially meaningful correlations between the three traits of interest and to see if these would influence their association with G20210A. Under a bivariate model for any two traits, denoted as trait A and B say, the parameters of the following equation are estimated: r_p(A,B) = r_g(A,B)*sqrt(h2_A)*sqrt(h2_B) + r_e(A,B)* sqrt(1-h2_A)*sqrt(1-h2_B), where r_p(A,B), r_g(A,B), and r_e(A,B) respectively denote the phenotypic, genetic, and environmental correlation coefficients of A and B, and where h2_A and h2_B denote the trait heritabilities. Moreover, we can test their significance using likelihood-based inferential statistics. For the FII and OBWC bivariate analysis, none of the correlation coefficients were significant. For the fibrinogen and OBWC bivariate analysis, we found significant phenotypic (r_p=0.24; p=2.0E-05) and genetic (r_g=0.41; p=0.024) correlations. For the FII and fibrinogen bivariate analysis, we found significant phenotypic (r_p=0.26; p=3.1E-10) and environmental (r_e=0.28; p=0.003) correlations. For all bivariate analyses, the Prothrombin G20210A mutation was always a significant predictor for both traits of any given pair. In conclusion, Prothrombin G20210A is pleiotropically associated with plasma FII and fibrinogen coagulant activity levels, and OBWC, and profoundly impacts the prevalence of obesity in our sample. The associations of the prothrombin G20210A mutation are not affected by the consideration of inter-correlations between the three traits examined and thus appears to be fairly robust. Figure 1 Figure 1. Disclosures DeFronzo: AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novo Nordisk: Membership on an entity's Board of Directors or advisory committees; Boehringer-Ingelheim: Membership on an entity's Board of Directors or advisory committees, Research Funding; Intarcia: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding.

Studies have reported that the G505A nonsynonymous (ns)-SNP encoding the Ala147Thr amino acid substitution in TAFI, is associated with a reduction in risk of venous thrombosis and myocardial infarction. Interestingly, homozygosity for the A-allele (AA) of G505A is associated with increased TAFI antigen (TAFI:Ag) levels in plasma. In our study of the genetic determinants of cardiovascular disease (CVD) in Mexican Americans of South Texas, we performed an exome-wide scan in relation to plasma TAFI antigen levels in 784 individuals. While accounting for age, sex, and their interactions as confounders in linear mixed model, we found a heritability of 59% for TAFI:Ag levels (p=1.4E-19), and that ns-SNP G505A was the only variant showing exome-wide significance (p=3.5E-14; Figure A). Figure B shows the quantile-quantile distribution of the p-values from all the exome-wide tests. Clearly, the p-value distribution reveals that there is no systematic bias that may act to skew the p-values, and that the lone exception¾in agreement between observed and expected quantiles¾is due to this TAFI SNP, which strongly suggests a truly significant effect. Consistent with previous reports, the regression coefficient for G505A as a predictor of TAFI:Ag levels showed them to be increasing from the homozygous for the major G-allele (GG), to the heterozygous individuals (GA), to the individuals homozygous for the minor A-allele (AA) (Figure B). We also investigated if G505A is associated with our CHD variable. Using a statistical genetic model for the liability to disease conditional on a threshold, which is equivalent to a probit mixed model, we found that the G505A ns-SNP in TAFI, encoding TAFI Ala147Thr, is significantly associated with a reduction in the risk of CHD (p=0.002). As observed in existing literature, potential limitations of this study include the ELISA assay used for the quantification of TAFI levels. Some ELISA assays measure proTAFI, TAFIa, and TAFIi. However, recent evidence suggests that there may be cross reactivity between TAFIa and TAFIi. This can result in measuring ongoing TAFI activation peptides and elevated levels of TAFIa which ultimately goes on to make resistant fibrin. This is likely the marker that results in the increased risk of venous thromboembolism. To mitigate this confounding factor, an ELISA assay specific to measuring TAFI antigen is needed. In conclusion, we found that TAFI G505A is pleiotropically associated with TAFI:Ag levels and risk of CHD. Figure 1 Figure 1. Disclosures DeFronzo: AstraZeneca: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Novo Nordisk: Membership on an entity's Board of Directors or advisory committees; Boehringer-Ingelheim: Membership on an entity's Board of Directors or advisory committees, Research Funding; Intarcia: Membership on an entity's Board of Directors or advisory committees; Merck: Research Funding.

Type 2 diabetes (T2D) affects more than 415 million people worldwide, and its costs to the health care system continue to rise. To identify common or rare genetic variation with potential therapeutic implications for T2D, we analyzed and replicated genome-wide protein coding variation in a total of 8,227 individuals with T2D and 12,966 individuals without T2D of Latino descent. We identified a novel genetic variant in the IGF2 gene associated with ∼20% reduced risk for T2D. This variant, which has an allele frequency of 17% in the Mexican population but is rare in Europe, prevents splicing between IGF2 exons 1 and 2. We show in vitro and in human liver and adipose tissue that the variant is associated with a specific, allele-dosage–dependent reduction in the expression of IGF2 isoform 2. In individuals who do not carry the protective allele, expression of IGF2 isoform 2 in adipose is positively correlated with both incidence of T2D and increased plasma glycated hemoglobin in individuals without T2D, providing support that the protective effects are mediated by reductions in IGF2 isoform 2. Broad phenotypic examination of carriers of the protective variant revealed no association with other disease states or impaired reproductive health. These findings suggest that reducing IGF2 isoform 2 expression in relevant tissues has potential as a new therapeutic strategy for T2D, even beyond the Latin American population, with no major adverse effects on health or reproduction.

Relatedness inference is an essential component of many genetic analyses and popular in consumer genetic testing. Ramstetter et al. evaluate twelve....., Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these approaches in real data has been lacking. Here, we report an assessment of 12 state-of-the-art pairwise relatedness inference methods using a data set with 2485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (92–99%) when detecting first- and second-degree relationships, but their accuracy dwindles to 76% of relative pairs. Overall, the most accurate methods are Estimation of Recent Shared Ancestry (ERSA) and approaches that compute total IBD sharing using the output from GERMLINE and Refined IBD to infer relatedness. Combining information from the most accurate methods provides little accuracy improvement, indicating that novel approaches, such as new methods that leverage relatedness signals from multiple samples, are needed to achieve a sizeable jump in performance.

Phasing is the process of inferring haplotypes from genotype data. Efficient algorithms and associated software for accurate phasing in pedigrees are needed, especially for populations lacking reference panels of sequenced individuals. We present a novel method for phasing genotypes from whole-genome sequence data in pedigrees, called PULSAR (Phasing Using Lineage Specific Alleles/Rare variants). The method is based on the property that alleles specific to a single founding chromosome within a pedigree are highly informative for identifying haplotypes that are shared identical by descent. Simulation studies are used to assess the performance of PULSAR with various pedigree sizes and structures, and the effect of genotyping errors and the presence of nonsequenced individuals is investigated. In pedigrees with complete sequencing and realistic genotyping error rates, PULSAR correctly phases >99.9% of heterozygous genotypes, excluding sites at which all individuals are heterozygous, and does so with a switch error rate frequently below 10(−4). PULSAR is highly accurate, capable of genotype error correction and imputation, and computationally competitive with alternative phasing software applicable to pedigrees. Our method has the significant advantage of not requiring reference panels that are essential for other population-based phasing algorithms. A software implementation of PULSAR is freely available.

We have recently reported that serum concentrations of α-carotene (AC) and β-carotene (BC) are under strong genetic influences and were correlated with obesity and other cardio-metabolic traits in Mexican American (MA) children. Our previously developed Pediatric Metabolic Index (PMI) correlates with insulin resistance (IR) after adjusting for age and sex using lipid and adiposity data from Mexican children. A PMI score > 2.0 predicts metabolic abnormalities including IR (i.e., HOMA-IR). Yet data on other MA children with PMI > 2 with normal IR and PMI < 2 with high IR, suggest a role for additional factors in determining insulin sensitivity (IS). In this study, we aimed to assess interactions between carotenoids and the PMI in predicting IS as measured by Matsuda index using the surface-response analysis (SRA). Four different carotenoids, namely, AC, BC, lycopene (Lyc), and β-cryptoxanthin (CX) from ∼590 children enrolled in the San Antonio Family Assessment of Metabolic Risk Indicators in Youth (SAFARI) study were measured by mass spectrometry. Glucose and insulin levels were measured after oral glucose tolerance test and IS index (ISI) was calculated using Matsuda method. SRA was used to determine two-way interactions of serum carotenoids and PMI on ISI. The computer programs SOLAR and STATISTICA 7.0 were used for statistical analysis. The SRA showed an increase in ISI in children with PMI scores between 2 to 4. This effect was modified in the presence of lower serum AC and BC levels; and, interactions between these two carotenoids and PMI had maximal effect on ISI. Conversely, MA children with moderate PMI scores can have substantial improvement in their IS with higher serum AC and BC levels. In contrast, there was a minimal surface response to Lyc and CX serum levels. Notably, despite an optimal PMI, children with lower concentrations of AC and BC showed less IS. In summary, we found that interactions between carotenoids with PMI may predict IS in MA children. Disclosure S. Mummidi: None. J. Hernandez-Ruiz: None. V.S. Farook: None. L. Reddivari: None. A. Diaz-Badillo: None. S.P. Fowler: None. R.G. Resendez: None. F. Akhtar: None. D. Lehman: None. C. Jenkinson: None. R. Arya: None. J.L. Lynch: Board Member; Self; American Academy of Pediatrics. Consultant; Self; Novo Nordisk Inc. Research Support; Self; Daiichi Sankyo Company, Limited, National Institutes of Health, Novo Nordisk Inc., Pediatric Diabetes Consortium. R.A. DeFronzo: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Elcelyx Therapeutics, Inc., Intarcia Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Novo Nordisk Inc. Research Support; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc. Speaker's Bureau; Self; AstraZeneca, Novo Nordisk Inc. D.E. Hale: None. J. Blangero: None. J.C. Lopez-Alvarenga: None. R. Duggirala: None. J.K. Vanamala: None. Funding National Institutes of Health (R01HD049051, R01AI119131, HD049051-5S1, HD041111, DK053889, DK042273, DK047482, P01HL45522, MH59490, M01-RR-01346), U.S. Department of Veterans Affairs; Max and Minnie Tomerlin Voelcker Fund; National Institute of Food and Agriculture (2009-55200-05197)

Acanthosis nigricans (AN) is highly prevalent in Mexican Americans (MAs). It is a strong correlate of obesity, and is considered a marker of insulin resistance (IR). AN is associated with various other cardiometabolic risk factors (CMF). However, the direct causal relationship of IR with AN in obesity has been recently questioned. Therefore, we aimed to assess its genetic and environmental correlations with CMFs and examine the complex causal relationships among the troika of AN, obesity, and IR in MA, using a mediation analysis. As part of our SAFARI Study, we obtained data from 670 nondiabetic MA children, aged 6-17 years (49% girls), BMI percentile median 87 (IQR 60, 97). AN (prevalence 33%) severity scores (range 0-5) were used as a quasi-quantitative trait for analysis. We used the program SOLAR for partitioning phenotypic correlations between AN and CMFs (BMI, HOMA-IR, lipids, blood pressure, hs-C-Reactive Protein (hs-CRP), and Harvard physical fitness score (PFS)). The matrices of genetic and environmental correlations were subsequently used in mediation (direct, indirect and partial) analysis using AMOS program. We computed standardized beta values for effect measurement and goodness-of-fit indices (AIC and BCC) for model comparisons. Heritability of AN was 0.75 (p HOMA-IR -> AN yielded lower goodness-of-fit than a partial mediation model where BMI explained the relationship with both HOMA-IR and AN simultaneously. Using complex models, BMI was associated with AN and IR mediating most of the CMFs; but no relationship between IR and AN. In conclusion, our models suggest that obesity explains the presence of AN and IR, but no causal relationship between IR and AN in Mexican American children. Disclosure J.C. Lopez-Alvarenga: None. R. Arya: None. G. Chittoor: None. S.F. Paul: None. S.R. Puppala: None. V.S. Farook: None. S.P. Fowler: None. R.G. Resendez: None. A. Diaz-Badillo: None. D. Lehman: None. S. Mummidi: None. C. Jenkinson: None. J.L. Lynch: Board Member; Self; American Academy of Pediatrics. Consultant; Self; Novo Nordisk Inc. Research Support; Self; Daiichi Sankyo Company, Limited, National Institutes of Health, Novo Nordisk Inc., Pediatric Diabetes Consortium. R.A. DeFronzo: Advisory Panel; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Elcelyx Therapeutics, Inc., Intarcia Therapeutics, Inc., Janssen Pharmaceuticals, Inc., Novo Nordisk Inc. Research Support; Self; AstraZeneca, Boehringer Ingelheim Pharmaceuticals, Inc., Janssen Pharmaceuticals, Inc., Merck & Co., Inc. Speaker's Bureau; Self; AstraZeneca, Novo Nordisk Inc. J. Blangero: None. D.E. Hale: None. R. Duggirala: None. Funding National Institutes of Health (R01HD049051, HD041111, DK053889, DK042273, K01DK064867, P01HL045522, DK047482, MH059490, M01-RR-01346)

Simulations of close relatives and identical by descent (IBD) segments are common in genetic studies, yet most past efforts have utilized sex averaged genetic maps and ignored crossover interference, thus omitting features known to affect the breakpoints of IBD segments. We developed Ped-sim, a method for simulating relatives that can utilize either sex-specific or sex averaged genetic maps and also either a model of crossover interference or the traditional Poisson model for inter-crossover distances. To characterize the impact of previously ignored mechanisms, we simulated data for all four combinations of these factors. We found that modeling crossover interference decreases the standard deviation of the IBD proportion by 10.4% on average in full siblings through second cousins. By contrast, sex-specific maps increase this standard deviation by 4.2% on average, and also impact the number of segments relatives share. Most notably, using sex-specific maps, the number of segments half-siblings share is bimodal; and when combined with interference modeling, the probability that sixth cousins have non-zero IBD ranges from 9.0 to 13.1%, depending on the sexes of the individuals through which they are related. We present new analytical results for the distributions of IBD segments under these models and show they match results from simulations. Finally, we compared IBD sharing rates between simulated and real relatives and find that the combination of sex-specific maps and interference modeling most accurately captures IBD rates in real data. Ped-sim is open source and available fromhttps://github.com/williamslab/ped-sim.Author summarySimulations are ubiquitous throughout statistical genetics in order to generate data with known properties, enabling tests of inference methods and analyses of real world processes in settings where experimental data are challenging to collect. Simulating genetic data for relatives in a pedigree requires the synthesis of chromosomes parents transmit to their children. These chromosomes form as a mosaic of a given parent’s two chromosomes, with the location of switches between the two parental chromosomes known as crossovers. Detailed information about crossover generation based on real data from humans now exists, including the fact that men and women have overall different rates (women produce ~1.6 times more crossovers) and that real crossovers are subject tointerference—whereby crossovers are further apart from one another than expected under a model that selects their locations randomly. Our new method, Ped-sim, can simulate pedigree data using these less commonly modeled crossover features, and we used it to evaluate the importance of sex-specific rates and interference in real data. These comparisons show that both factors shape the amount of DNA two relatives share identically, and that their inclusion in models of crossover better fit data from real relatives.

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

We previously identified a locus linked to total cholesterol (TC) concentration in Pima Indians on chromosome 19p. To characterize this locus, we genotyped >2000 SNPs in 1838 Pimas and assessed association with log(TC). We observed evidence for association with log(TC) with rs2278426 (3.5% decrease/copy of the T allele; P=5.045 x 10−6) in the ANGPTL8 (angiopoietin-like 8) gene. We replicated this association in 2413 participants of the San Antonio Mexican American Family Study (SAMAFS: 2.0% decrease per copy of the T allele; P=0.005842). In a meta-analysis of the combined data, we found the strongest estimated effect with rs2278426 (P=2.563 x 10−7). The variant T allele at rs2278426 predicts an Arg59Trp substitution and has previously been associated with LDL-C and HDL-C. In Pimas and SAMAFS participants, the T allele of rs2278426 was associated with reduced HDL-C levels (P=0.000741 and 0.00002, respectively), and the combined estimated effect for the two cohorts was −3.8% (P=8.526 x 10−8). ANGPTL8 transcript and protein levels increased in response to both glucose and insulin. The variant allele was associated with increased levels of cleaved ANGPTL3. We conclude that individuals with the variant allele may have lower TC and HDL-C levels due to increased activation of ANGPTL3 by ANGPTL8.

Protein-coding genetic variants that strongly affect disease risk can provide important clues into disease pathogenesis. Here we report an exome sequence analysis of 20,791 type 2 diabetes (T2D) cases and 24,440 controls from five ancestries. We identify rare (minor allele frequency30 SLC30A8 alleles, and (b) within 12 gene sets, including those corresponding to T2D drug targets (p=6.1×10−3) and candidate genes from knockout mice (p=5.2×10−3). Within our study, the strongest T2D rare variant gene-level signals explain at most 25% of the heritability of the strongest common single-variant signals, and the rare variant gene-level effect sizes we observe in established T2D drug targets will require 110K-180K sequenced cases to exceed exome-wide significance. To help prioritize genes using associations from current smaller sample sizes, we present a Bayesian framework to recalibrate association p-values as posterior probabilities of association, estimating that reaching ppwww.type2diabetesgenetics.org.

As genetic datasets increase in size, the fraction of samples with one or more close relatives grows rapidly, resulting in sets of mutually related individuals. We present DRUID—Deep Relatedness Utilizing Identity by Descent—a method that works by inferring the identical by descent (IBD) sharing profile of an ungenotyped ancestor of a set of close relatives. Using this IBD profile, DRUID infers relatedness between unobserved ancestors and more distant relatives, thereby combining information from multiple samples to remove one or more generations between the deep relationships to be identified. DRUID constructs sets of close relatives by detecting full siblings and also uses a novel approach to identify the aunts/uncles of two or more siblings, recovering 92.2% of real aunts/uncles with zero false positives. In real and simulated data, DRUID correctly infers up to 10.5% more relatives than PADRE when using data from two sets of distantly related siblings, and 10.7–31.3% more relatives given two sets of siblings and their aunts/uncles. DRUID frequently infers relationships either correctly or within one degree of the truth, with PADRE classifying 43.3–58.3% of tenth degree relatives in this way compared to 79.6–96.7% using DRUID.

Only few systematic studies on the contribution of copy number variation to gene expression variation have been published to date. Here we identify effects of copy number variable regions (CNVRs) on nearby gene expression by investigating 909 CNVRs and expression levels of 12059 nearby genes in white blood cells from Mexican-American participants of the San Antonio Family Heart Study. We empirically evaluate our ability to detect the contribution of CNVs to proximal gene expression (presumably in cis) at various window sizes (up to a 10 Mb distance) between the gene and CNV. We found a ~1-Mb window size to be optimal for capturing cis effects of CNVs. Up to 10% of the CNVs in this study were found to be significantly associated with the expression of at least one gene within their vicinity. As expected, we find that CNVs that directly overlap gene sequences have the largest effects on gene expression (compared with non-overlapping CNVRs located nearby), with positive correlation (except for a few exceptions) between estimated genomic dosage and expression level. We find that genes whose expression level is significantly influenced by nearby CNVRs are enriched for immunity and autoimmunity related genes. These findings add to the currently limited catalog of CNVRs that are recognized as expression quantitative trait loci, and have implications for future study designs as well as for prioritizing candidate causal variants in genomic regions associated with disease.

Aims Although newer approaches have identified several metabolites associated with obesity, there is paucity of such information in pediatric populations, especially among Mexican Americans (MAs) who are at high risk of obesity. Therefore, we performed a global serum metabolite screening in MA children to identify biomarkers of childhood obesity.

Phasing, the process of predicting haplotypes from genotype data, is an important undertaking in genetics and an ongoing area of research. Phasing methods, and associated software, designed specifically for pedigrees are urgently needed. Here we present a new method for phasing genotypes from whole genome sequencing data in pedigrees: PULSAR (Phasing Using Lineage Specific Alleles / Rare variants). The method is built upon the idea that alleles that are specific to a single founding chromosome within a pedigree, which we refer to as lineage-specific alleles, are highly informative for identifying haplotypes that are identical-by-decent between individuals within a pedigree. Through extensive simulation we assess the performance of PULSAR in a variety of pedigree sizes and structures, and we explore the effects of genotyping errors and presence of non-sequenced individuals on its performance. If the genotyping error rate is sufficiently low PULSAR can phase > 99.9% of heterozygous genotypes with a switch error rate below 1 x 10-4 in pedigrees where all individuals are sequenced. We demonstrate that the method is highly accurate and consistently outperforms the long-range phasing approach used for comparison in our benchmarking. The method also holds promise for fixing genotype errors or imputing missing genotypes. The software implementation of this method is freely available.

Background: Dietary intake of phytonutrients present in fruits and vegetables, such as carotenoids, is associated with a lower risk of obesity and related traits, but the impact of genetic variation on these associations is poorly understood, especially in children.Objective: We estimated common genetic influences on serum carotenoid concentrations and obesity-related traits in Mexican American (MA) children.Design: Obesity-related data were obtained from 670 nondiabetic MA children, aged 6-17 y. Serum α- and β-carotenoid concentrations were measured in ∼570 (α-carotene in 565 and β-carotene in 572) of these children with the use of an ultraperformance liquid chromatography-photodiode array. We determined heritabilities for both carotenoids and examined their genetic relation with 10 obesity-related traits [body mass index (BMI), waist circumference (WC), high-density lipoprotein (HDL) cholesterol, triglycerides, fat mass (FM), systolic and diastolic blood pressure, fasting insulin and glucose, and homeostasis model assessment of insulin resistance] by using family data and a variance components approach. For these analyses, carotenoid values were inverse normalized, and all traits were adjusted for significant covariate effects of age and sex.Results: Carotenoid concentrations were highly heritable and significant [α-carotene: heritability (h2) = 0.81, P = 6.7 × 10-11; β-carotene: h2 = 0.90, P = 3.5 × 10-15]. After adjusting for multiple comparisons, we found significant (P ≤ 0.05) negative phenotypic correlations between carotenoid concentrations and the following traits: BMI, WC, FM, and triglycerides (range: α-carotene = -0.19 to -0.12; β-carotene = -0.24 to -0.13) and positive correlations with HDL cholesterol (α-carotene = 0.17; β-carotene = 0.24). However, when the phenotypic correlations were partitioned into genetic and environmental correlations, we found marginally significant (P = 0.051) genetic correlations only between β-carotene and BMI (-0.27), WC (-0.30), and HDL cholesterol (0.31) after accounting for multiple comparisons. None of the environmental correlations were significant.Conclusions: The findings from this study suggest that the serum carotenoid concentrations were under strong additive genetic influences based on variance components analyses, and that the common genetic factors may influence β-carotene and obesity and lipid traits in MA children.

Type 2 diabetes (T2D) affects more than 415 million people worldwide, and its costs to the health care system continue to rise. To identify common or rare genetic variation with potential therapeutic implications for T2D, we analyzed and replicated genome-wide protein coding variation in a total of 8,227 individuals with T2D and 12,966 individuals without T2D of Latino descent. We identified a novel genetic variant in the IGF2 gene associated with ∼20% reduced risk for T2D. This variant, which has an allele frequency of 17% in the Mexican population but is rare in Europe, prevents splicing between IGF2 exons 1 and 2. We show in vitro and in human liver and adipose tissue that the variant is associated with a specific, allele-dosage–dependent reduction in the expression of IGF2 isoform 2. In individuals who do not carry the protective allele, expression of IGF2 isoform 2 in adipose is positively correlated with both incidence of T2D and increased plasma glycated hemoglobin in individuals without T2D, providing support that the protective effects are mediated by reductions in IGF2 isoform 2. Broad phenotypic examination of carriers of the protective variant revealed no association with other disease states or impaired reproductive health. These findings suggest that reducing IGF2 isoform 2 expression in relevant tissues has potential as a new therapeutic strategy for T2D, even beyond the Latin American population, with no major adverse effects on health or reproduction.

Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these methods in real data has been lacking. Here, we report an assessment of 11 state-ofthe-art relatedness inference methods using a dataset with 2,485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (∼93%−99%) when reporting first and second degree relationships, but their accuracy dwindles to less than 60% for fifth degree relationships. However, the inferred relationships were correct to within one relatedness degree at a rate of 83%−99% across all methods and considered relationship degrees. Furthermore, most methods infer unrelated individuals correctly at a rate of ∼99%, suggesting a low rate of false positives. Overall, the most accurate methods were ERSA 2.0 and approaches that classify relationships using the IBD segments inferred by Refined IBD and IBDseq. Combining results from the most accurate methods provides little accuracy improvement, indicating that novel approaches for relatedness inference may be needed to achieve a sizeable jump in performance.

Recent data suggest that common genetic risks for metabolic disorders such as obesity may be human-specific and exert effects via the central nervous system. To overcome the limitation of human tissue access for study, we have generated induced human pluripotent stem cell (hiPSC)-derived neuronal cultures that recapture many features of hypothalamic neurones within the arcuate nucleus. In the present study, we have comprehensively characterised this model across development, benchmarked these neurones to in vivo events, and demonstrate a link between obesity risk variants and hypothalamic development. The dynamic transcriptome across neuronal maturation was examined using microarray and RNA sequencing methods at nine time points. K-means clustering of the longitudinal data was conducted to identify co-regulation and microRNA control of biological processes. The transcriptomes were compared with those of 103 samples from 13 brain regions reported in the Genotype-Tissue Expression database (GTEx) using principal components analysis. Genes with proximity to body mass index (BMI)-associated genetic variants were mapped to the developmentally expressed genesets, and enrichment significance was assessed with Fisher's exact test. The human neuronal cultures have a transcriptional and physiological profile of neuropeptide Y/agouti-related peptide arcuate nucleus neurones. The neuronal transcriptomes were highly correlated with adult hypothalamus compared to any other brain region from the GTEx. Also, approximately 25% of the transcripts showed substantial changes in expression across neuronal development and potential co-regulation of biological processes that mirror neuronal development in vivo. These developmentally expressed genes were significantly enriched for genes in proximity to BMI-associated variants. We confirmed the utility of this in vitro human model for studying the development of key hypothalamic neurones involved in energy balance and show that genes at loci associated with body weight regulation may share a pattern of developmental regulation. These data support the need to investigate early development to elucidate the human-specific central nervous system pathophysiology underlying obesity susceptibility.

Pediatric metabolic syndrome (MS) and its cardiometabolic components (MSCs) have become increasingly prevalent, yet little is known about the genetics underlying MS risk in children. We examined the prevalence and genetics of MS-related traits among 670 non-diabetic Mexican American (MA) children and adolescents, aged 6–17 years (49 % female), who were participants in the San Antonio Family Assessment of Metabolic Risk Indicators in Youth (SAFARI) study. These children are offspring or biological relatives of adult participants from three well-established Mexican American family studies in San Antonio, Texas, at increased risk of type 2 diabetes. MS was defined as ≥ 3 abnormalities among 6 MSC measures: waist circumference, systolic and/or diastolic blood pressure, fasting insulin, triglycerides, HDL-cholesterol, and fasting and/or 2-h OGTT glucose. Genetic analyses of MS, number of MSCs (MSC-N), MS factors, and bivariate MS traits were performed. Overweight/obesity (53 %), pre-diabetes (13 %), acanthosis nigricans (33 %), and MS (19 %) were strikingly prevalent, as were MS components, including abdominal adiposity (32 %) and low HDL-cholesterol (32 %). Factor analysis of MS traits yielded three constructs: adipo-insulin-lipid, blood pressure, and glucose factors, and their factor scores were highly heritable. MS itself exhibited 68 % heritability. MSC-N showed strong positive genetic correlations with obesity, insulin resistance, inflammation, and acanthosis nigricans, and negative genetic correlation with physical fitness. MS trait pairs exhibited strong genetic and/or environmental correlations. These findings highlight the complex genetic architecture of MS/MSCs in MA children, and underscore the need for early screening and intervention to prevent chronic sequelae in this vulnerable pediatric population.

Preterm birth (PTB) is a complex trait, but little is known regarding its major genetic determinants. The objective of this study is to localize genes that influence susceptibility to PTB in Mexican Americans (MAs), a minority population in the USA, using predominantly microfilmed birth certificate-based data obtained from the San Antonio Family Birth Weight Study. Only 1302 singleton births from 288 families with infor- mation on PTB and significant covariates were considered for genetic analysis. PTB is defined as a childbirth that occurs at ,37 completed weeks of gestation, and the prevalence of PTB in this sample was 6.4%. An � 10 cM genetic map was used to conduct a genome-wide linkage analysis using the program SOLAR. The heritability of PTB was high (h 2 + SE: 0.75+ 0.20) and significant (P ¼ 4.5 × 10 25 ), after adjusting for the sig- nificant effects of birthweight and birth order. We found significant evidence for linkage of PTB (LOD ¼ 3.6; nominal P ¼ 2.3 × 10 25 ; empirical P ¼ 1.0 × 10 25 ) on chromosome 18q between markers D18S1364 and D18S541. Several other chromosomal regions (2q, 9p, 16q and 20q) were also potentially linked with PTB. A strong positional candidate gene in the 18q linked region is SERPINB2 or PAI-2, a member of the plas- minogen activator system that is associated with various reproductive processes. In conclusion, to our knowledge, perhaps for the first time in MAs or US populations, we have localized a major susceptibility locus for PTB on chromosome 18q21.33-q23.

Objective: Type 2 diabetes (T2DM) is a complex metabolic disease and is more prevalent in certain ethnic groups such as the Mexican Americans. The goal of our study was to perform a genome-wide linkage (GWL) analysis to localize T2DM susceptibility loci in Mexican Americans. Methods: We used the phenotypic and genotypic data from 1,122 Mexican-American individuals (307 families) who participated in the Veterans Administration Genetic Epidemiology Study (VAGES). GWL analysis was performed using the variance components approach. Data from 2 additional Mexican-American family studies, the San Antonio Family Heart Study (SAFHS) and the San Antonio Family Diabetes/Gallbladder Study (SAFDGS), were combined with the VAGES data to test for improved linkage evidence. Results: After adjusting for covariate effects, T2DM was found to be under significant genetic influences (h2 = 0.62, p = 2.7 × 10-6). The strongest evidence for linkage of T2DM occurred between markers D9S1871 and D9S2169 on chromosome 9p24.2-p24.1 (LOD = 1.8). Given that we previously reported suggestive evidence for linkage of T2DM at this region also in SAFDGS, we found the significant and increased linkage evidence (LOD = 4.3, empirical p = 1.0 × 10-5, genome-wide p = 1.6 × 10-3) for T2DM at the same chromosomal region, when we performed a GWL analysis of the VAGES data combined with the SAFHS and SAFDGS data. Conclusion: Significant T2DM linkage evidence was found on chromosome 9p24 in Mexican Americans. Importantly, the chromosomal region of interest in this study overlaps with several recent genome-wide association studies involving T2DM-related traits. Given its overlap with such findings and our own initial T2DM association findings in the 9p24 chromosomal region, high throughput sequencing of the linked chromosomal region could identify the potential causal T2DM genes.

Progranulin (GRN) loss-of-function mutations leading to progranulin protein (PGRN) haploinsufficiency are prevalent genetic causes of frontotemporal dementia. Reports also indicated PGRN-mediated neuroprotection in models of Alzheimer's and Parkinson's disease; thus, increasing PGRN levels is a promising therapeutic for multiple disorders. To uncover novel PGRN regulators, we linked whole-genome sequence data from 920 individuals with plasma PGRN levels and identified the prosaposin (PSAP) locus as a new locus significantly associated with plasma PGRN levels. Here we show that both PSAP reduction and overexpression lead to significantly elevated extracellular PGRN levels. Intriguingly, PSAP knockdown increases PGRN monomers, whereas PSAP overexpression increases PGRN oligomers, partly through a protein–protein interaction. PSAP-induced changes in PGRN levels and oligomerization replicate in human-derived fibroblasts obtained from a GRN mutation carrier, further supporting PSAP as a potential PGRN-related therapeutic target. Future studies should focus on addressing the relevance and cellular mechanism by which PGRN oligomeric species provide neuroprotection., Increasing progranulin (PGRN) levels is a promising approach for treating frontotemporal dementia and other neurodegenerative diseases. Here Nicholson et al. show that the prosaposin (PSAP) locus is associated with plasma PGRN levels and demonstrate that PSAP can alter PGRN levels and its oligomerization.

Metabolic abnormalities such as obesity, insulin resistance and hyperinsulinemia, impaired glucose tolerance, dyslipidemia, and hypertension often cluster to constitute the Metabolic Syndrome [MS]. The MS is a predictor of both type 2 diabetes (T2DM) and Coronary Heart Disease (CHD), and its prevalence has been rising alarmingly worldwide. In the United States (US), more than 47 million people are affected with MS. Specifically, its prevalence is high in minority populations such as Mexican Americans, who had the highest age-adjusted prevalence of MS [∼32%] when compared to the non-Hispanic whites, African Americans and other ethnic groups (1). Added to this burden, over the past 20 years, the prevalence of MS among children and adolescents has also been increasing at epidemic rates in several populations including Mexican Americans and African Americans (2-4). The MS and its components are complex disease conditions that are influenced by genetic and environmental factors and their interactions MS can be defined as a single, composite phenotype for genetic analysis using criteria such as the National Cholesterol Education Program [NCEP]/Adult Treatment Panel III [ATPIII] (NCEP/ATPIII) recommendations, the most widely used definition in the US (5,6). In addition to the NCEP/ATPIII criteria, several organizations have proposed different definitions for MS. While the various definitions use similar core components of MS, primarily including the measurements of obesity, glucose, lipids, and blood pressure, they differ in the number of traits and the cut-off points required for the diagnosis. Despite certain caveats, several studies have used the NCEP/ATPIII definition for its simplicity, in the context of clinical practice and epidemiological investigations (7); most importantly, as noted by Grundy et al. (6), it avoids emphasis on a single cause. Several genome-wide linkage and association studies have been performed to understand the genetic basis of the components of MS by using a number of analytical tools such as multivariate genetic analysis and factor analysis (8-12). Such studies have identified several genetic regions linked to MS related traits, but showing limited concordance in their results (11). In contrast to the above approaches, a single, composite MS phenotype using the NCEP/ATPIII or other criteria can be used to identify genetic regions with potential common genetic influences on MS; however, there have been a few such studies (13, 14). Therefore, the aim of our study was to identify the susceptibility loci for MS using the NCEP/ATPIII definition in a Mexican American population who are at high risk for the development of MS. Here, we report the results of our genome-wide linkage screen for MS performed by using the multipoint variance components linkage analysis and the phenotypic and genotypic (∼10 cM map) data collected from the Mexican American individuals who participated in our San Antonio Family Diabetes/Gallbladder Study (SAFDGS). In addition, we report the results of our preliminary MS association study performed by examining the genetic variants in two of the positional candidate genes, which are physically close to each other, located within the 1-LOD support interval region of our MS linkage signal on chromosome 7q.

Racial/ethnic disparity in prostate cancer is under studied in men with diabetes who are at a higher risk of aggressive prostate cancer. This study assessed the race/ethnic disparity in prostate cancer incidence for men with type II diabetes (T2D) and whether the impact of metformin on prostate cancer incidence varied by race/ethnicity. We conducted a retrospective study in 76,733 male veterans with T2D during 2003 to 2012. Cox proportional hazards model adjusting for covariates and propensity scores of metformin use and race/ethnic group membership was utilized to compute the HR of prostate cancer incidence associated with race/ethnicity and compare HR associated with metformin use between race/ethnic groups. Mean follow-up was 6.4 ± 2.8 years; 7% were Hispanics; 17% were African Americans (AA); mean age was 67.8 ± 9.8 years; 5.2% developed prostate cancer; and 38.9% used metformin. Among these diabetic men without metformin use, prostate cancer incidence was higher in Hispanics and AA than in non-Hispanic White (NHW). Use of metformin alone or metformin + statins was associated with a greater prostate cancer incidence reduction in Hispanics compared with NHW, but not between AA and NHW. Use of metformin + finasteride was associated with a greater prostate cancer incidence reduction in Hispanics and AA compared with NHW. Our results suggested that metformin treatment could be a potential strategy to reduce prostate cancer incidence in the minority populations who are at high risk for fatal prostate cancer. It will be important to further examine the pleiotropic effects of metformin in multi-race/ethnic prospective studies to better inform clinical management and potentially reduce racial/ethnic disparity in prostate cancer incidence among diabetic men. Cancer Prev Res; 9(10); 779–87. ©2016 AACR.