Publications by Year: 2017

Studies using self-reported data suggest a gene-physical activity interaction on obesity, yet the influence of sedentary behavior, distinct from a lack of physical activity, on genetic associations with obesity remains unclear. We analyzed interactions of accelerometer-measured moderate to vigorous physical activity (MVPA) and time spent sedentary with genetic variants on obesity among 9,645 U.S. Hispanics/Latinos. An overall genetic risk score (GRS), a central nervous system (CNS)-related GRS, and a non-CNS-related GRS were calculated based on 97 BMI-associated single nucleotide polymorphisms (SNPs). Genetic association with BMI was stronger in individuals with lower MVPA (first tertile) versus higher MVPA (third tertile) (β = 0.78 kg/m2 [SE, 0.10 kg/m2] vs. 0.39 kg/m2 [0.09 kg/m2] per SD increment of GRS; Pinteraction = 0.005), and in those with more time spent sedentary (third tertile) versus less time spent sedentary (first tertile) (β = 0.73 kg/m2 [SE, 0.10 kg/m2] vs. 0.44 kg/m2 [0.09 kg/m2]; Pinteraction = 0.006). Similar significant interaction patterns were observed for obesity risk, body fat mass, fat percentage, fat mass index, and waist circumference, but not for fat-free mass. The CNS-related GRS, but not the non-CNS-related GRS, showed significant interactions with MVPA and sedentary behavior, with effects on BMI and other adiposity traits. Our data suggest that both increasing physical activity and reducing sedentary behavior may attenuate genetic associations with obesity, although the independence of these interaction effects needs to be investigated further.

QT interval prolongation is a heritable risk factor for ventricular arrhythmias and can predispose to sudden death. Most genome-wide association studies (GWAS) of QT were performed in European ancestral populations, leaving other groups uncharacterized. Herein we present the first QT GWAS of Hispanic/Latinos using data on 15,997 participants from four studies. Study-specific summary results of the association between 1000 Genomes Project (1000G) imputed SNPs and electrocardiographically measured QT were combined using fixed-effects meta-analysis. We identified 41 genome-wide significant SNPs that mapped to 13 previously identified QT loci. Conditional analyses distinguished six secondary signals at NOS1AP (n = 2), ATP1B1 (n = 2), SCN5A (n = 1), and KCNQ1 (n = 1). Comparison of linkage disequilibrium patterns between the 13 lead SNPs and six secondary signals with previously reported index SNPs in 1000G super populations suggested that the SCN5A and KCNE1 lead SNPs were potentially novel and population-specific. Finally, of the 42 suggestively associated loci, AJAP1 was suggestively associated with QT in a prior East Asian GWAS; in contrast BVES and CAP2 murine knockouts caused cardiac conduction defects. Our results indicate that whereas the same loci influence QT across populations, population-specific variation exists, motivating future trans-ethnic and ancestrally diverse QT GWAS.

Most regression-based tests of the association between a low-count variant and a binary outcome do not protect type 1 error, especially when tests are rejected based on a very low significance threshold. Noted exception is the Firth test. However, it was recently shown that in meta-analyzing multiple studies all asymptotic, regression-based tests, including the Firth, may not control type 1 error in some settings, and the Firth test may suffer a substantial loss of power. The problem is exacerbated when the case-control proportions differ between studies. I propose the BinomiRare exact test that circumvents the calibration problems of regression-based estimators. It quantifies the strength of association between the variant and the disease outcome based on the departure of the number of diseased individuals carrying the variant from the expected distribution of disease probability, under the null hypothesis of no association between the disease outcome and the rare variant. I provide a meta-analytic strategy to combine tests across multiple cohorts, which requires that each cohort provides the disease probabilities of all carriers of the variant in question, and the number of diseased individuals among the carriers. I show that BinomiRare controls type 1 error in meta-analysis even when the case-control proportions differ between the studies, and does not lose power compared to pooled analysis. I demonstrate the test in studying the association of rare variants with asthma in the Hispanic Community Health Study/Study of Latinos.

Increased urine albumin excretion is highly prevalent in Hispanics/Latinos. Previous studies have found an association between urine albumin excretion and Amerindian ancestry in Hispanic/Latino populations. Admixture between racial/ethnic groups creates long-range linkage disequilibrium between variants with different allelic frequencies in the founding populations and it can be used to localize genes. Hispanic/Latino genomes are an admixture of European, African, and Amerindian ancestries. We leveraged this admixture to identify associations between urine albumin excretion (urine albumin-to-creatinine ratio [UACR]) and genomic regions harboring variants with highly differentiated allele frequencies among the ancestral populations. Admixture mapping analysis of 12,212 Hispanic Community Health Study/Study of Latinos participants, using a linear mixed model, identified three novel genome-wide significant signals on chromosomes 2, 11, and 16. The admixture mapping signal identified on chromosome 2, spanning q11.2-14.1 and not previously reported for UACR, is driven by a difference between Amerindian ancestry and the other two ancestries (P<5.7 × 10-5). Within this locus, two common variants located at the proapoptotic BCL2L11 gene associated with UACR: rs116907128 (allele frequency =0.14; P=1.5 × 10-7) and rs586283 (C allele frequency =0.35; P=4.2 × 10-7). In a secondary analysis, rs116907128 accounted for most of the admixture mapping signal observed in the region. The rs116907128 variant is common among full-heritage Pima Indians (A allele frequency =0.54) but is monomorphic in the 1000 Genomes European and African populations. In a replication analysis using a sample of full-heritage Pima Indians, rs116907128 significantly associated with UACR (P=0.01; n=1568). Our findings provide evidence for the presence of Amerindian-specific variants influencing the variation of urine albumin excretion in Hispanics/Latinos.

This corrects the article DOI: 10.1038/ncomms15805.

Hypertension prevalence varies between ethnic groups, possibly due to differences in genetic, environmental, and cultural determinants. Hispanic/Latino Americans are a diverse and understudied population. We performed a genome-wide association study (GWAS) of blood pressure (BP) traits in 12,278 participants from the Hispanics Community Health Study/Study of Latinos (HCHS/SOL). In the discovery phase we identified eight previously unreported BP loci. In the replication stage, we tested these loci in the 1982 Pelotas Birth Cohort Study of admixed Southern Brazilians, the COGENT-BP study of African descent, women of European descent from the Women Health Initiative (WHI), and a sample of European descent from the UK Biobank. No loci met the Bonferroni-adjusted level of statistical significance (0.0024). Two loci had marginal evidence of replication: rs78701042 (NGF) with diastolic BP (P = 0.008 in the 1982 Pelotas Birth Cohort Study), and rs7315692 (SLC5A8) with systolic BP (P = 0.007 in European ancestry replication). We investigated whether previously reported loci associated with BP in studies of European, African, and Asian ancestry generalize to Hispanics/Latinos. Overall, 26% of the known associations in studies of individuals of European and Chinese ancestries generalized, while only a single association previously discovered in a people of African descent generalized.

Trans-ethnic meta-analysis of genome-wide association studies (GWAS) across diverse populations can increase power to detect complex trait loci when the underlying causal variants are shared between ancestry groups. However, heterogeneity in allelic effects between GWAS at these loci can occur that is correlated with ancestry. Here, a novel approach is presented to detect SNP association and quantify the extent of heterogeneity in allelic effects that is correlated with ancestry. We employ trans-ethnic meta-regression to model allelic effects as a function of axes of genetic variation, derived from a matrix of mean pairwise allele frequency differences between GWAS, and implemented in the MR-MEGA software. Through detailed simulations, we demonstrate increased power to detect association for MR-MEGA over fixed- and random-effects meta-analysis across a range of scenarios of heterogeneity in allelic effects between ethnic groups. We also demonstrate improved fine-mapping resolution, in loci containing a single causal variant, compared to these meta-analysis approaches and PAINTOR, and equivalent performance to MANTRA at reduced computational cost. Application of MR-MEGA to trans-ethnic GWAS of kidney function in 71,461 individuals indicates stronger signals of association than fixed-effects meta-analysis when heterogeneity in allelic effects is correlated with ancestry. Application of MR-MEGA to fine-mapping four type 2 diabetes susceptibility loci in 22,086 cases and 42,539 controls highlights: (i) strong evidence for heterogeneity in allelic effects that is correlated with ancestry only at the index SNP for the association signal at the CDKAL1 locus; and (ii) 99% credible sets with six or fewer variants for five distinct association signals.

Heritability is the proportion of phenotypic variance in a population that is attributable to individual genotypes. Heritability is considered an important measure in both evolutionary biology and in medicine, and is routinely estimated and reported in genetic epidemiology studies. In population-based genome-wide association studies (GWAS), mixed models are used to estimate variance components, from which a heritability estimate is obtained. The estimated heritability is the proportion of the model's total variance that is due to the genetic relatedness matrix (kinship measured from genotypes). Current practice is to use bootstrapping, which is slow, or normal asymptotic approximation to estimate the precision of the heritability estimate; however, this approximation fails to hold near the boundaries of the parameter space or when the sample size is small. In this paper we propose to estimate variance components via a Haseman-Elston regression, find the asymptotic distribution of the variance components and proportions of variance, and use them to construct confidence intervals (CIs). Our method is further developed to obtain unbiased variance components estimators and construct CIs by meta-analyzing information from multiple studies. We demonstrate our approach on data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL).

BACKGROUND: Despite ethnic disparities in lipid profiles, there are few genome-wide association studies investigating genetic variation of lipids in non-European ancestry populations. In this study, we present findings from genetic association analyses for total cholesterol, low density lipoprotein cholesterol (LDL), high density lipoprotein cholesterol (HDL), and triglycerides in a large Hispanic/Latino cohort in the U.S., the Hispanic Community Health Study / Study of Latinos (HCHS/SOL).

METHODS: We estimated a heritability of approximately 20% for each lipid trait, similar to previous estimates in Europeans. To search for novel lipid loci, we performed conditional association analysis in which the statistical model was adjusted for previously reported SNPs associated with any of the four lipid traits. SNPs that remained genome-wide significant (P < 5 × 10-8) after conditioning on known loci were evaluated for replication.

RESULTS: We identified eight potentially novel lipid signals with minor allele frequencies <1%, none of which replicated. We tested previously reported SNP-trait associations for generalization to Hispanics/Latinos via a statistical framework. The generalization analysis revealed that approximately 50% of previously established lipid variants generalize to HCHS/SOL based on directional FDR r-value < 0.05. Some failures to generalize were due to lack of power.

CONCLUSIONS: These results demonstrate that many loci associated with lipid levels are shared across populations.

BACKGROUND: Although time-domain measures of heart rate variability (HRV) are used to estimate cardiac autonomic tone and disease risk in multiethnic populations, the genetic epidemiology of HRV in Hispanics/Latinos has not been characterized.

OBJECTIVE: The purpose of this study was to conduct a genome-wide association study of heart rate (HR) and its variability in the Hispanic Community Health Study/Study of Latinos, Multi-Ethnic Study of Atherosclerosis, and Women's Health Initiative Hispanic SNP-Health Association Resource project (n = 13,767).

METHODS: We estimated HR (bpm), standard deviation of normal-to-normal interbeat intervals (SDNN, ms), and root mean squared difference in successive, normal-to-normal interbeat intervals (RMSSD, ms) from resting, standard 12-lead ECGs. We estimated associations between each phenotype and 17 million genotyped or imputed single nucleotide polymorphisms (SNPs), accounting for relatedness and adjusting for age, sex, study site, and ancestry. Cohort-specific estimates were combined using fixed-effects, inverse-variance meta-analysis. We investigated replication for select SNPs exceeding genome-wide (P <5 × 10-8) or suggestive (P <10-6) significance thresholds.

RESULTS: Two genome-wide significant SNPs replicated in a European ancestry cohort, 1 one for RMSSD (rs4963772; chromosome 12) and another for SDNN (rs12982903; chromosome 19). A suggestive SNP for HR (rs236352; chromosome 6) replicated in an African-American cohort. Functional annotation of replicated SNPs in cardiac and neuronal tissues identified potentially causal variants and mechanisms.

CONCLUSION: This first genome-wide association study of HRV and HR in Hispanics/Latinos underscores the potential for even modestly sized samples of non-European ancestry to inform the genetic epidemiology of complex traits.