The Genetic Analysis Workshops (GAW) are a forum for development, testing, and comparison of statistical genetic methods and software. Each contribution to the workshop includes an application to a specified data set. Here we describe the data distributed for GAW19, which focused on analysis of human genomic and transcriptomic data.

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.

The variation in serum uric acid concentrations is under significant genetic influence. Elevated SUA concentrations have been linked to increased risk for gout, kidney stones, chronic kidney disease, and cardiovascular disease whereas reduced serum uric acid concentrations have been linked to multiple sclerosis, Parkinson's disease and Alzheimer's disease. Previously, we identified a novel locus on chromosome 3p26 affecting serum uric acid concentrations in Mexican Americans from San Antonio Family Heart Study. As a follow up, we examined genome-wide single nucleotide polymorphism data in an extended cohort of 1281 Mexican Americans from multigenerational families of the San Antonio Family Heart Study and the San Antonio Family Diabetes/Gallbladder Study. We used a linear regression-based joint linkage/association test under an additive model of allelic effect, while accounting for non-independence among family members via a kinship variance component.

To identify novel coding association signals and facilitate characterization of mechanisms influencing glycemic traits and type 2 diabetes risk, we analyzed 109,215 variants derived from exome array genotyping together with an additional 390,225 variants from exome sequence in up to 39,339 normoglycemic individuals from five ancestry groups. We identified a novel association between the coding variant (p.Pro50Thr) in AKT2 and fasting plasma insulin (FI), a gene in which rare fully penetrant mutations are causal for monogenic glycemic disorders. The low-frequency allele is associated with a 12% increase in FI levels. This variant is present at 1.1% frequency in Finns but virtually absent in individuals from other ancestries. Carriers of the FI-increasing allele had increased 2-h insulin values, decreased insulin sensitivity, and increased risk of type 2 diabetes (odds ratio 1.05). In cellular studies, the AKT2-Thr50 protein exhibited a partial loss of function. We extend the allelic spectrum for coding variants in AKT2 associated with disorders of glucose homeostasis and demonstrate bidirectional effects of variants within the pleckstrin homology domain of AKT2.

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

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.

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.

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.

The prevalence of metabolic syndrome (MS) has been rising alarmingly worldwide, including in the United States, but knowledge on specific genetic determinants of MS is very limited. Therefore, we planned to identify the genetic determinants of MS as defined by National Cholesterol Education Program/Adult Treatment Panel III (NCEP/ATPIII) criteria. We performed linkage screen for MS using data from 692 Mexican Americans, who participated in the San Antonio Family Diabetes/Gallbladder Study (SAFDGS). We found strong evidence for linkage of MS on chromosome 7q (LOD = 3.6, empirical P = 6.0 × 10(-5)), between markers D7S2212 and D7S821. In addition, six chromosomal regions exhibited potential evidence for linkage (LOD ≥1.2) with MS. Furthermore, we examined 29 single-nucleotide polymorphisms (SNPs) from the fatty acid translocase (FAT or CD36, 18 SNPs) gene and guanine nucleotide binding protein, α transducing 3 (GNAT3, 11 SNPs) gene, located within the 1-LOD support interval region for their association with MS and its related traits. Several SNPs were associated with MS and its related traits. Remarkably, rs11760281 in GNAT3 and rs1194197 near CD36 exhibited the strongest associations with MS (P = 0.0003, relative risk (RR) = 1.6 and P = 0.004, RR = 1.7, respectively) and several other related traits. These two variants explained ~18% of the MS linkage evidence on chromosome 7q21, and together conferred approximately threefold increase in MS risk (RR = 2.7). In conclusion, our linkage and subsequent association studies implicate a region on chromosome 7q21 to influence MS in Mexican Americans.

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.

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.

Type 2 diabetes (T2D) is a complex metabolic disease that is more prevalent in ethnic groups such as Mexican Americans, and is strongly associated with the risk factors obesity and insulin resistance. The goal of this study was to perform whole genome gene expression profiling in adipose tissue to detect common patterns of gene regulation associated with obesity and insulin resistance. We used phenotypic and genotypic data from 308 Mexican American participants from the Veterans Administration Genetic Epidemiology Study (VAGES). Basal fasting RNA was extracted from adipose tissue biopsies from a subset of 75 unrelated individuals, and gene expression data generated on the Illumina BeadArray platform. The number of gene probes with significant expression above baseline was approximately 31,000. We performed multiple regression analysis of all probes with 15 metabolic traits. Adipose tissue had 3,012 genes significantly associated with the traits of interest (false discovery rate, FDR ≤ 0.05). The significance of gene expression changes was used to select 52 genes with significant (FDR ≤ 10(-4)) gene expression changes across multiple traits. Gene sets/Pathways analysis identified one gene, alcohol dehydrogenase 1B (ADH1B) that was significantly enriched (P < 10(-60)) as a prime candidate for involvement in multiple relevant metabolic pathways. Illumina BeadChip derived ADH1B expression data was consistent with quantitative real time PCR data. We observed significant inverse correlations with waist circumference (2.8 x 10(-9)), BMI (5.4 x 10(-6)), and fasting plasma insulin (P < 0.001). These findings are consistent with a central role for ADH1B in obesity and insulin resistance and provide evidence for a novel genetic regulatory mechanism for human metabolic diseases related to these traits.

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=5.045×10(-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=2.563×10(-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=8.526×10(-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.

Whole genome sequencing (WGS) remains prohibitively expensive, which has encouraged the development of methods to impute WGS data into nonsequenced individuals using a framework of single nucleotide polymorphisms genotyped for genome-wide association studies (GWAS). Although successful methods have been developed for cohorts of unrelated individuals, current imputation methods in related individuals are limited by pedigree size, by the distance of relationships, or by computation time. In this article, we describe a method for imputation in arbitrarily shaped multigenerational pedigrees that can impute genotypes across distantly related individuals based on identity by descent. We evaluate this approach using GWAS data and apply this approach to WGS data distributed for Genetic Analysis Workshop 18.

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-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 ≤ 0.05 and fold change-absolute (FC-abs) ≥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 × 10-15-1.44 × 10-4). 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.

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.

CC-chemokine ligand 2 (CCL2) is involved in the pathogenesis of several diseases associated with monocyte/macrophage recruitment, such as HIV-associated neurocognitive disorder (HAND), tuberculosis, and atherosclerosis. The rs1024611 (alleles:A>G; G is the risk allele) polymorphism in the CCL2 cis-regulatory region is associated with increased CCL2 expression in vitro and ex vivo, leukocyte mobilization in vivo, and deleterious disease outcomes. However, the molecular basis for the rs1024611-associated differential CCL2 expression remains poorly characterized. It is conceivable that genetic variant(s) in linkage disequilibrium (LD) with rs1024611 could mediate such effects. Previously, we used rs13900 (alleles:_C>T) in the CCL2 3' untranslated region (3' UTR) that is in perfect LD with rs1024611 to demonstrate allelic expression imbalance (AEI) of CCL2 in heterozygous individuals. Here we tested the hypothesis that the rs13900 could modulate CCL2 expression by altering mRNA turnover and/or translatability. The rs13900 T allele conferred greater stability to the CCL2 transcript when compared to the rs13900 C allele. The rs13900 T allele also had increased binding to Human Antigen R (HuR), an RNA-binding protein, in vitro and ex vivo. The rs13900 alleles imparted differential activity to reporter vectors and influenced the translatability of the reporter transcript. We further demonstrated a role for HuR in mediating allele-specific effects on CCL2 expression in overexpression and silencing studies. The presence of the rs1024611G-rs13900T conferred a distinct transcriptomic signature related to inflammation and immunity. Our studies suggest that the differential interactions of HuR with rs13900 could modulate CCL2 expression and explain the interindividual differences in CCL2-mediated disease susceptibility.

Metformin and statins have shown promise for cancer prevention. This study assessed whether the effect of metformin on prostate cancer (PCa) incidence varied by statin use among type 2 diabetic patients.

Protein-coding genetic variants that strongly affect disease risk can yield relevant clues to disease pathogenesis. Here we report exome-sequencing analyses of 20,791 individuals with type 2 diabetes (T2D) and 24,440 non-diabetic control participants from 5 ancestries. We identify gene-level associations of rare variants (with minor allele frequencies of less than 0.5%) in 4 genes at exome-wide significance, including a series of more than 30 SLC30A8 alleles that conveys protection against T2D, and in 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 gene-level signals for rare variants explain at most 25% of the heritability of the strongest common single-variant signals, and the gene-level effect sizes of the rare variants that we observed in established T2D drug targets will require 75,000-185,000 sequenced cases to achieve exome-wide significance. We propose a method to interpret these modest rare-variant associations and to incorporate these associations into future target or gene prioritization efforts.

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.