GENETICS OF OBESITY IN STARR COUNTY, TEXAS MEXICAN AMERICANS

Currently, over two-thirds of Americans are classified as over-weight or obese. Obesity increases risk for many other diseases including type 2 diabetes, heart disease, stroke, and cancer, making obesity the largest public health problem in America and most other Westernized nations. Hispanics have a higher rate of both obesity and type 2 diabetes, making them a particularly interesting population in which to study obesity. For the last 33 years, the Starr County Health Studies has collected an array of phenotypes and biological samples from residents of Starr County, along Texas-Mexico border. This study includes 825 subjects who were not known to have diabetes at ascertainment. These subjects have now been seen a second time, on average 8.5 years later. At both visits we measured several aspects of obesity including BMI, bioimpedance to estimate percent body fat, and waist, hip, and arm circumferences. By using multivariate approaches to leverage the array of obesity measures, we have better captured both the amount of adipose tissue and the location of fat deposits. To assess association of obesity related traits with genetic variation from both genome-wide array data imputed to 1000 Genomes Phase 1 integrated dataset and exome sequencing, both gene-based and single variant tests were conducted. Through these single variant tests, we identified an association with waist to hip ratio and low frequency variants, in two adjacent GABA receptor subunit genes, GABRB2 and GABRA6, including a nonsynonymous variant in GABRA6. Additional associations include an association with a composite measure of adiposity that encompasses degree of adiposity and location of excess fat above or below the waist and TREK1, a gene responsible for trafficking the GABAA receptor to the cell membrane. Gene based tests of rare variants yielded associations between central versus peripheral adiposity and ACSL1, a gene involved in triglyceride biosynthesis. Further replication is required to confirm these associations. While the importance of neuronal signaling pathways in body fat distribution has long been known, many aspects of these pathways are poorly understood. Better understanding of these pathways may identify potential pharmaceutical targets

Quality Control Analysis of Add Health GWAS Data

At Wave IV, Add Health collected Oragene saliva samples from consenting participants (96% of n=15,701), and requested a second consent to archive their samples for future genomic studies. Approximately 80% consented to archive and were thus eligible for genome-wide genotyping. Genotyping was completed over three years funded by R01 HD073342 (PI Harris) and R01 HD060726 (PIs Harris, Boardman, and McQueen). Add Health utilized two Illumina platforms for genotyping: the Illumina Human Omni1-Quad BeadChip for the majority of samples and the Illumina Human Omni-2.5 Quad BeadChip for the remainder. The two platforms utilized tag SNP technology to identify and include over 1.1 million and 2.5 million genetic markers respectively from Omni1 and Omni2.5 derived from the International HapMap Project and the most informative markers from the 1000 Genomes Project (1KGP). The genetic markers include known disease-associated SNPs from multiple sources, ancestry-informative markers, sex chromosomes, and ABO blood typing markers. The platforms also included probes for the detection of copy number variation (CNV) covering all common CNV regions and more than 5,000 rare CNV regions. After quality control procedures (described below), genotype data were available for 9,974 individuals: n=7,917 from the Illumina HumanOmni1-Quad chip and for 2,057 individuals from the Illumina HumanOmni2.5-Quad chip (Figure 1). After filtering, the Add Health genotype GWAS data contained n=609,130 single-nucleotide polymorphisms (SNPs) common to both chips to enable joint imputation to the entire Add Health population (see below)

Ancestral diversity improves discovery and fine-mapping of genetic loci for anthropometric traits-The Hispanic/Latino Anthropometry Consortium

Hispanic/Latinos have been underrepresented in genome-wide association studies (GWAS) for anthropometric traits despite their notable anthropometric variability, ancestry proportions, and high burden of growth stunting and overweight/obesity. To address this knowledge gap, we analyzed densely imputed genetic data in a sample of Hispanic/Latino adults to identify and fine-map genetic variants associated with body mass index (BMI), height, and BMI-adjusted waist-to-hip ratio (WHRadjBMI). We conducted a GWAS of 18 studies/consortia as part of the Hispanic/Latino Anthropometry (HISLA) Consortium (stage 1, n = 59,771) and generalized our findings in 9 additional studies (stage 2, n = 10,538). We conducted a trans-ancestral GWAS with summary statistics from HISLA stage 1 and existing consortia of European and African ancestries. In our HISLA stage 1 + 2 analyses, we discovered one BMI locus, as well as two BMI signals and another height signal each within established anthropometric loci. In our trans-ancestral meta-analysis, we discovered three BMI loci, one height locus, and one WHRadjBMI locus. We also identified 3 secondary signals for BMI, 28 for height, and 2 for WHRadjBMI in established loci. We show that 336 known BMI, 1,177 known height, and 143 known WHRadjBMI (combined) SNPs demonstrated suggestive transferability (nominal significance and effect estimate directional consistency) in Hispanic/Latino adults. Of these, 36 BMI, 124 height, and 11 WHRadjBMI SNPs were significant after trait-specific Bonferroni correction. Trans-ancestral meta-analysis of the three ancestries showed a small-to-moderate impact of uncorrected population stratification on the resulting effect size estimates. Our findings demonstrate that future studies may also benefit from leveraging diverse ancestries and differences in linkage disequilibrium patterns to discover novel loci and additional signals with less residual population stratification

Natural Selection of Immune and Metabolic Genes Associated with Health in Two Lowland Bolivian Populations

A growing body of work has addressed human adaptations to diverse environments using genomic data, but few studies have connected putatively selected alleles to phenotypes, much less among underrepresented populations such as Amerindians. Studies of natural selection and genotype–phenotype relationships in underrepresented populations hold potential to uncover previously undescribed loci underlying evolutionarily and biomedically relevant traits. Here, we worked with the Tsimane and the Moseten, two Amerindian populations inhabiting the Bolivian lowlands. We focused most intensively on the Tsimane, because long-term anthropological work with this group has shown that they have a high burden of both macro and microparasites, as well as minimal cardiometabolic disease or dementia. We therefore generated genome-wide genotype data for Tsimane individuals to study natural selection, and paired this with blood mRNA-seq as well as cardiometabolic and immune biomarker data generated from a larger sample that included both populations. In the Tsimane, we identified 21 regions that are candidates for selective sweeps, as well as 5 immune traits that show evidence for polygenic selection (e.g., C-reactive protein levels and the response to coronaviruses). Genes overlapping candidate regions were strongly enriched for known involvement in immune-related traits, such as abundance of lymphocytes and eosinophils. Importantly, we were also able to draw on extensive phenotype information for the Tsimane and Moseten and link five regions (containing PSD4, MUC21 and MUC22, TOX2, ANXA6, and ABCA1) with biomarkers of immune and metabolic function. Together, our work highlights the utility of pairing evolutionary analyses with anthropological and biomedical data to gain insight into the genetic basis of health-related traits

Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls.

This corrects the article DOI: 10.1038/sdata.2017.179

Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution.

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants

Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10-7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent 'false leads' with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition

Identification and Functional Characterization of <i>G6PC2</i> Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the <i>G6PC2-ABCB11</i> Locus

Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights