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Prevalence of smoking is higher in Alaska Native and American Indian (ANAI) populations living in Alaska than the general US population. Genetic factors contribute to smoking and cessation rates. The objective of this study was to compare CYP2A6 genetic variation and CYP2A6 enzyme activity toward nicotine in an ANAI population. ANAI (N = 151) people trying to quit smoking were recruited. DNA samples were genotyped for CYP2A6 variants *1X2A, *1B, *2, *4, *9, *10, *12, and *35. Multiple nicotine metabolites were measured in plasma and urine samples, including cotinine and 3′‐hydroxycotinine used to determine CYP2A6 activity (e.g., nicotine metabolite ratio [NMR]). We calculated summary statistics for all of the genotypes and metabolites and assigned CYP2A6 activity scores based on known information. We studied the association of CYP2A6 variants with the NMR and smoking histories. The overall frequency of the CYP2A6*1B gain of function allele was high in the ANAI versus non‐ANAI populations in other studies. Both *4 null and *9 decrease of function alleles had frequencies similar to previous studies of ANAI populations. In a multivariate analysis, the genotype‐inferred CYP2A6 activity score was associated with both plasma and urine NMR (p value = 8.56E‐08 and 4.08E‐13, respectively). Plasma NMR was also associated with duration of smoking (p value

Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI’s Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.

Alaska Native people are under-represented in genetic research but have unique gene variation that may critically impact their response to pharmacotherapy. Full resequencing of CYP2C9 in a cross-section of this population identified CYP2C9 Met1Leu (M1L), a novel, relatively common single nucleotide polymorphism hypothesized to confer CYP2C9 poor metabolizer phenotype by disrupting the start codon. M1L is present at a minor allele frequency of 6.3% in Yup’ik Alaska Native people and thus can contribute to the risk of an adverse drug response from narrow-therapeutic-index CYP2C9 substrates such as (S)-warfarin. This study’s objective was to characterize the catalytic efficiency of the Leu1 variant enzyme in vivo by evaluating the pharmacokinetic behavior of naproxen, a probe substrate for CYP2C9 activity, in genotyped Yup’ik participants. We first confirmed the selectivity of (S)-naproxen O-demethylation by CYP2C9 using activity-phenotyped human liver microsomes and selective cytochrome P450 inhibitors and then developed and validated a novel liquid chromatography mass spectrometry method for simultaneous quantification of (S)-naproxen, (S)-O-desmethylnaproxen, and naproxen acyl glucuronide in human urine. The average ratio of (S)-O-desmethylnaproxen to unchanged (S)-naproxen in urine was 18.0 ± 8.0 (n = 11) for the homozygous CYP2C9Met1 reference group and 10.3 ± 6.6 (n = 11) for the Leu1 variant carrier group (P = 0.011). The effect of M1L variation on CYP2C9 function and its potential to alter the pharmacokinetics of drugs metabolized by the enzyme has clinical implications and should be included in a variant screening panel when pharmacogenetic testing in the Alaska Native population is warranted. Significance Statement The novel CYP2C9 Met1Leu variant in Alaska Native people was recently identified. This study validated (S)-naproxen as a CYP2C9 probe substrate to characterize the in vivo functional activity of the CYP2C9 Leu1 variant. The results of this pharmacogenetic-pharmacokinetic study suggest that the CYP2C9 Leu1 variant exhibits loss of enzyme activity. This finding may be important to consider when administering narrow-therapeutic-index medications metabolized by CYP2C9 and also compels further investigation to characterize novel genetic variation in understudied populations.

Background Prospective studies have suggested higher factor VIII (FVIII) levels are an independent risk factor for coronary heart disease (CHD) and stroke. However, limited information, including on genetic and epigenetic contributors to FVIII variation, is available specifically among African Americans (AAs), who have higher FVIII levels than Europeans. Objectives We measured FVIII levels in ~3400 AAs from the community-based Jackson Heart Study and assessed genetic, epigenetic, and epidemiological correlates of FVIII, as well as incident cardiovascular disease (CVD) associations. Methods We assessed cross-sectional associations of FVIII with CVD risk factors as well as incident CHD, stroke, heart failure, and mortality associations. We additionally assessed associations with TOPMed whole genome sequencing data and an epigenome-wide methylation array. Results Our results confirmed associations between FVIII and risk of incident CHD events and total mortality in AAs; mortality associations were largely independent of traditional risk factors. We also demonstrate an association of FVIII with incident heart failure, independent of B-type natriuretic peptide. Two genomic regions were strongly associated with FVIII (ABO and VWF). The index variant at VWF is specific to individuals of African descent and is distinct from the previously reported European VWF association signal. Epigenome-wide association analysis showed significant FVIII associations with several CpG sites in the ABO region. However, after adjusting for ABO genetic variants, ABO CpG sites were not significant. Conclusions Larger sample sizes of AAs will be required to discover additional genetic and epigenetic contributors to FVIII phenotypic variation, which may have consequences for CVD health disparities.

BACKGROUND: Asthma is a common respiratory disorder with a highly heterogeneous nature that remains poorly understood. The objective was to use whole genome sequencing (WGS) data to identify regions of common genetic variation contributing to lung function in individuals with a diagnosis of asthma. METHODS: WGS data were generated for 1,053 individuals from trios and extended pedigrees participating in the family-based Genetic Epidemiology of Asthma in Costa Rica study. Asthma affection status was defined through a physician’s diagnosis of asthma, and most participants with asthma also had airway hyperresponsiveness (AHR) to methacholine. Family-based association tests for single variants were performed to assess the associations with lung function phenotypes. RESULTS: A genome-wide significant association was identified between baseline FEV(1)/FVC ratio and a single-nucleotide polymorphism in the top hit cysteine-rich secretory protein LCCL domain-containing 2 (CRISPLD2) (rs12051168; P = 3.6 × 10(−8) in the unadjusted model) that retained suggestive significance in the covariate-adjusted model (P = 5.6 × 10(−6)). Rs12051168 was also nominally associated with other related phenotypes: baseline FEV(1) (P = 3.3 × 10(−3)), postbronchodilator (PB) FEV(1) (7.3 × 10(−3)), and PB FEV(1)/FVC ratio (P = 2.7 × 10(−3)). The identified baseline FEV(1)/FVC ratio and rs12051168 association was meta-analyzed and replicated in three independent cohorts in which most participants with asthma also had confirmed AHR (combined weighted z-score P = .015) but not in cohorts without information about AHR. CONCLUSIONS: These findings suggest that using specific asthma characteristics, such as AHR, can help identify more genetically homogeneous asthma subgroups with genotype-phenotype associations that may not be observed in all children with asthma. CRISPLD2 also may be important for baseline lung function in individuals with asthma who also may have AHR.

Background Genetic studies have primarily been conducted in European ancestry populations, identifying dozens of loci associated with late-onset Alzheimer’s disease (AD). However, much of AD’s heritability remains unexplained; as the prevalence of AD varies across populations, the genetic architecture of the disease may also vary by population with the presence of novel variants or loci. Methods We conducted genome-wide analyses of AD in a sample of 2565 Caribbean Hispanics to better understand the genetic contribution to AD in this population. Statistical analysis included both admixture mapping and association testing. Evidence for differential gene expression within regions of interest was collected from independent transcriptomic studies comparing AD cases and controls in samples with primarily European ancestry. Results Our genome-wide association study of AD identified no loci reaching genome-wide significance. However, a genome-wide admixture mapping analysis that tests for association between a haplotype’s ancestral origin and AD status detected a genome-wide significant association with chromosome 3q13.11 (103.7–107.7Mb, P = 8.76E−07), driven by a protective effect conferred by the Native American ancestry (OR = 0.58, 95%CI = 0.47−0.73). Within this region, two variants were significantly associated with AD after accounting for the number of independent tests (rs12494162, P = 2.33E−06; rs1731642, P = 6.36E−05). The significant admixture mapping signal is composed of 15 haplotype blocks spanning 5 protein-coding genes (ALCAM, BBX, CBLB, CCDC54, CD47) and four brain-derived topologically associated domains, and includes markers significantly associated with the expression of ALCAM, BBX, CBLB, and CD47 in the brain. ALCAM and BBX were also significantly differentially expressed in the brain between AD cases and controls with European ancestry. Conclusion These results provide multiethnic evidence for a relationship between AD and multiple genes at 3q13.11 and illustrate the utility of leveraging genetic ancestry diversity via admixture mapping for new insights into AD.

The cytochrome P450 2D6 (CYP2D6) gene locus is challenging to accurately genotype due to numerous single nucleotide variants and complex structural variation. Our goal was to determine whether the CYP2D6 genotype-phenotype correlation is improved when diplotype assignments incorporate structural variation, identified by the bioinformatics tool Stargazer, with next-generation sequencing data. Using CYP2D6 activity measured with substrates dextromethorphan and metoprolol, activity score explained 40% and 34% of variability in metabolite formation rates, respectively, when diplotype calls incorporated structural variation, increasing from 36% and 31%, respectively, when diplotypes did not incorporate structural variation. We also investigated whether the revised Clinical Pharmacogenetics Implementation Consortium (CPIC) recommendations for translating genotype to phenotype improve CYP2D6 activity predictions over the current system. Although the revised recommendations do not improve the correlation between activity score and CYP2D6 activity, perhaps because of low frequency of the CYP2D6*10 allele, the correlation with metabolizer phenotype group was significantly improved for both substrates. We also measured the function of seven rare coding variants: one (A449D) exhibited decreased (44%) and another (R474Q) increased (127%) activity compared with reference CYP2D6.1 protein. Allele-specific analysis found that A449D is part of a novel CYP2D6*4 suballele, CYP2D6*4.028. The novel haplotype containing R474Q was designated CYP2D6*138 by PharmVar; another novel haplotype containing R365H was designated CYP2D6*139. Accuracy of CYP2D6 phenotype prediction is improved when the CYP2D6 gene locus is interrogated using next-generation sequencing coupled with structural variation analysis. Additionally, revised CPIC genotype to phenotype translation recommendations provides an improvement in assigning CYP2D6 activity.

Alaska Native and American Indian (AN/AI) people have unique pharmacogene variation that may affect warfarin disposition and therapeutic response. We performed targeted genotyping for cytochrome P450 (CYP)2C9, vitamin K epoxide oxidase reductase complex subunit 1 (VKORC1), CYP4F2, CYP4F11, and gamma-glutamyl carboxylase (GGCX) variants in AN/AI people receiving warfarin. The primary outcome was stable warfarin dose, defined as one dose, and associated international normalized ratio within the target range, at least 6 months after starting therapy, with two matching doses at least 2 weeks apart. Genotype–phenotype relationships were assessed by multivariate regression analysis, adjusted for self-reported heritage, age, gender, and concurrent statin use. VKORC1 genotype explained 34% of dose variability, with VKORC1 −1639G>A and 1173C>T associated with a 1.7 mg/day (P = 1.4e-05) dose reduction. Additionally, CYP2C9 N218I was suggestively significant (P = 0.077), with heterozygotes requiring 1.1 mg/day less than reference individuals. Self-reported heritage was significantly associated with dose, largely driven by differences in the diagnostic VKORC1 allele frequencies among AN/AI people.

Objective: sCD14 (soluble CD14) is a circulating pattern recognition receptor involved in inflammatory signaling. Although it is known that sCD14 levels vary by race, information on the genetic and cardiovascular disease (CVD) risk relationships of sCD14 in Black participants is limited. Approach and Results: We measured sCD14 in plasma at the baseline exam from n=3492 Black participants from the JHS (Jackson Heart Study). We evaluated associations between sCD14 and subclinical CVD measures, incident CVD events, and mortality under 3 levels of covariate adjustment. We used whole-genome sequence data from the Trans-Omics for Personalized Medicine program to identify genetic associations with sCD14. Adjusting for CVD risk factors and C-reactive protein, higher sCD14 was significantly associated with increased risk of mortality (hazard ratio, 1.25 [95% CI, 1.17–1.32]), incident coronary heart disease (hazard ratio, 1.28 [95% CI, 1.11–1.47]), and incident heart failure (hazard ratio, 1.27 [95% CI, 1.15–1.41]), but not stroke (hazard ratio, 0.96 [95% CI, 0.84–1.09]). Some of these relationships differed by age or sex: the association between sCD14 and heart failure was only observed in females; there was an association between sCD14 and stroke only at younger ages (in the lowest tertile of age, CD14 region of chromosome 5q31. We additionally identified 2 novel statistically distinct genetic associations with sCD14 represented by index variants rs770147646 and rs57599368, also in the chromosome 5q31 region. Conclusions: sCD14 independently predicts CVD-related outcomes and mortality in Black participants.

Embracing Genetic Diversity to Improve Black Health Ideally, race will be replaced with genetic ancestry as a variable in medical research and practice. But until more ancestry data are available, ...

Maternal genetic variations, including variations in mitochondrial biogenesis (MB) and oxidative phosphorylation (OP), are associated with placental abruption (PA). However, the role of maternal-fetal genetic interactions (MFGI) and parent-of-origin (imprinting) effects in PA remain unknown. We investigated MFGI in MB-OP, and imprinting effects in relation to risk of PA. Among Peruvian mother-infant pairs (503 PA cases and 1,052 controls), independent single nucleotide polymorphisms (SNPs), with linkage-disequilibrium coefficient

Introduction Recent studies suggest that both sex-specific genetic risk factors and those shared between dementia and stroke are involved in dementia pathogenesis. Methods We performed both single-variant and gene-based genome-wide association studies of >11,000 whole genome sequences from the Women's Health Initiative cohort to discover loci associated with dementia, with adjustment for age, ethnicity, stroke, and venous thromboembolism status. Evidence for prior evidence of association and differential gene expression in dementia-related tissues and samples was gathered for each locus. Results Our multiethnic studies identified significant associations between variants within APOE, MYH11, FZD3, SORCS3, and GOLGA8B and risk of dementia. Ten genes implicated by these loci, including MYH11, FZD3, SORCS3, and GOLGA8B, were differentially expressed in the context of Alzheimer's disease. Discussion Our association of MYH11, FZD3, SORCS3, and GOLGA8B with dementia is supported by independent functional studies in human subjects, model systems, and associations with shared risk factors for stroke and dementia.

Biomarkers of chronic inflammation (such as C-reactive protein) have long been associated with cardiovascular disease and mortality; however, biomarkers involved in antiviral cytokine induction and adaptive immune system activation remain largely unexamined. We hypothesized the cytokine interferon gamma inducible protein 10 (IP-10) would be associated with clinical and subclinical cardiovascular disease and all-cause mortality in African Americans. We assessed these associations in the Jackson Heart Study (JHS) cohort and the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. There was a modest association of IP-10 with higher odds of left ventricular hypertrophy (OR = 1.20 (95% confidence interval (CI) 1.03, 1.41) per standard deviation (SD) higher natural log-transformed IP-10 in JHS). We did not observe associations with ankle brachial index, intima-media thickness, or arterial calcification. Each SD higher increment of ln-transformed IP-10 concentration was associated with incident heart failure (hazard ratio (HR) 1.26; 95% CI 1.11, 1.42, p = 4x10-4) in JHS, and with overall mortality in both JHS (HR 1.12 per SD, 95% CI 1.03, 1.21, p = 7.5x10-3) and REGARDS (HR 1.31 per SD, 95% CI 1.10, 1.55, p = 2.0 x 10-3), adjusting for cardiovascular risk factors and C-reactive protein. However, we found no association between IP-10 and stroke or coronary heart disease. These results suggest a role of IP-10 in heart failure and mortality risk independent of C-reactive protein. Further research is needed to investigate how the body's response to chronic viral infection may mediate heart failure and overall mortality risk in African Americans.

Polygenic risk scores (PRSs) are weighted sums of risk allele counts of single-nucleotide polymorphisms (SNPs) associated with a disease or trait. PRSs are typically constructed based on published results from Genome-Wide Association Studies (GWASs), and the majority of which has been performed in large populations of European ancestry (EA) individuals. Although many genotype-trait associations have generalized across populations, the optimal choice of SNPs and weights for PRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns. We compare various approaches for PRS construction, using GWAS results from both large EA studies and a smaller study in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL,mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:min/mml:mimml:mo=/mml:momml:mn12/mml:mnmml:mo,/mml:momml:mn803/mml:mn/mml:math). We consider multiple approaches for selecting SNPs and for computing SNP weights. We study the performance of the resulting PRSs in an independent study of Hispanics/Latinos from the Women's Health Initiative (WHI,mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"mml:min/mml:mimml:mo=/mml:momml:mn3/mml:mnmml:mo,/mml:momml:mn582/mml:mn/mml:math). We support our investigation with simulation studies of potential genetic architectures in a single locus. We observed that selecting variants based on EA GWASs generally performs well, except for blood pressure trait. However, the use of EA GWASs for weight estimation was suboptimal. Using non-EA GWAS results to estimate weights improved results.

The Alzheimer’s Disease Sequencing Project (ADSP) undertook whole exome sequencing in 5,740 late-onset Alzheimer disease (AD) cases and 5,096 cognitively normal controls primarily of European ancestry (EA), among whom 218 cases and 177 controls were Caribbean Hispanic (CH). An age-, sex- and APOE based risk score and family history were used to select cases most likely to harbor novel AD risk variants and controls least likely to develop AD by age 85 years. We tested ~1.5 million single nucleotide variants (SNVs) and 50,000 insertion-deletion polymorphisms (indels) for association to AD, using multiple models considering individual variants as well as gene-based tests aggregating rare, predicted functional, and loss of function variants. Sixteen single variants and 19 genes that met criteria for significant or suggestive associations after multiple-testing correction were evaluated for replication in four independent samples; three with whole exome sequencing (2,778 cases, 7,262 controls) and one with genome-wide genotyping imputed to the Haplotype Reference Consortium panel (9,343 cases, 11,527 controls). The top findings in the discovery sample were also followed-up in the ADSP whole-genome sequenced family-based dataset (197 members of 42 EA families and 501 members of 157 CH families). We identified novel and predicted functional genetic variants in genes previously associated with AD. We also detected associations in three novel genes: IGHG3 (p = 9.8 × 10−7), an immunoglobulin gene whose antibodies interact with β-amyloid, a long non-coding RNA AC099552.4 (p = 1.2 × 10−7), and a zinc-finger protein ZNF655 (gene-based p = 5.0 × 10−6). The latter two suggest an important role for transcriptional regulation in AD pathogenesis.

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle that contains apolipoprotein(a), encoded by LPA, and is a highly heritable, causal risk factor for cardiovascular diseases that varies in concentrations across ancestries. Here, we use deep-coverage whole genome sequencing in 8392 individuals of European and African ancestry to discover and interpret both single-nucleotide variants and copy number (CN) variation associated with Lp(a). We observe that genetic determinants between Europeans and Africans have several unique determinants. The common variant rs12740374 associated with Lp(a) cholesterol is an eQTL for SORT1 and independent of LDL cholesterol. Observed associations of aggregates of rare non-coding variants are largely explained by LPA structural variation, namely the LPA kringle IV 2 (KIV2)-CN. Finally, we find that LPA risk genotypes confer greater relative risk for incident atherosclerotic cardiovascular diseases compared to directly measured Lp(a), and are significantly associated with measures of subclinical atherosclerosis in African Americans., Circulating lipoprotein(a) is an important risk factor for cardiovascular disease and shows variability between different ethnic groups. Here, Zekavat et al. perform whole-genome sequencing in individuals of European and African ancestries and find ancestry-specific genetic determinants for lipoprotein(a) levels.

The major objective of this study was to investigate the association of genetic and nongenetic factors with variability in protein abundance and in vitro activity of the androgen-metabolizing enzyme UGT2B17 in human liver microsomes (n = 455). UGT2B17 abundance was quantified by liquid chromatography-tandem mass spectrometry proteomics, and enzyme activity was determined by using testosterone and dihydrotestosterone as in vitro probe substrates. Genotyping or gene resequencing and mRNA expression were also evaluated. Multivariate analysis was used to test the association of UGT2B17 copy number variation, single nucleotide polymorphisms (SNPs), age, and sex with its mRNA expression, abundance, and activity. UGT2B17 gene copy number and SNPs (rs7436962, rs9996186, rs28374627, and rs4860305) were associated with gene expression, protein levels, and androgen glucuronidation rates in a gene dose-dependent manner. UGT2B17 protein (mean ± S.D. picomoles per milligram of microsomal protein) is sparsely expressed in children younger than 9 years (0.12 ± 0.24 years) but profoundly increases from age 9 years to adults (∼10-fold) with ∼2.6-fold greater abundance in males than in females (1.2 vs. 0.47). Association of androgen glucuronidation with UGT2B15 abundance was observed only in the low UGT2B17 expressers. These data can be used to predict variability in the metabolism of UGT2B17 substrates. Drug companies should include UGT2B17 in early phenotyping assays during drug discovery to avoid late clinical failures.

Despite evidence that pharmacogenetics can improve tamoxifen pharmacotherapy, there are few studies with American Indian and Alaska Native (AIAN) people. We examined variation in cytochrome P450 (CYP) genes (CYP2D6, CYP3A4, CYP3A5, and CYP2C9) and tamoxifen biotransformation in AIAN patients with breast cancer (n = 42) from the Southcentral Foundation in Alaska and the Confederated Salish and Kootenai Tribes in Montana. We tested for associations between CYP diplotypes and plasma concentrations of tamoxifen and metabolites. Only the CYP2D6 variation was significantly associated with concentrations of endoxifen (P = 0.0008) and 4-hydroxytamoxifen (P = 0.0074), tamoxifen's principal active metabolites, as well as key metabolic ratios. The CYP2D6 was also the most significant predictor of active metabolites and metabolic ratios in a multivariate regression model, including all four genes as predictors, with minor roles for other CYP genes. In AIAN populations, CYP2D6 is the largest contributor to tamoxifen bioactivation, illustrating the importance of validating pharmacogenetic testing for therapy optimization in an understudied population.

Summary The Genomic Data Storage (GDS) format provides efficient storage and retrieval of genotypes measured by microarrays and sequencing. We developed GENESIS to perform various single- and aggregate-variant association tests using genotype data stored in GDS format. GENESIS implements highly flexible mixed models, allowing for different link functions, multiple variance components and phenotypic heteroskedasticity. GENESIS integrates cohesively with other R/Bioconductor packages to build a complete genomic analysis workflow entirely within the R environment. Availability and implementation https://bioconductor.org/packages/GENESIS; vignettes included. Supplementary information Supplementary data are available at Bioinformatics online.

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 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.

Genetic association studies often collect a wide range of complex traits, including high-dimensional and structured omics measurements. The complex... Many genetic association studies collect a wide range of complex traits. As these traits may be correlated and share a common genetic mechanism, joint analysis can be statistically more powerful and biologically more meaningful. However, most existing tests for multiple traits cannot be used for high-dimensional and possibly structured traits, such as network-structured transcriptomic pathway expressions. To overcome potential limitations, in this article we propose the dual kernel-based association test (DKAT) for testing the association between multiple traits and multiple genetic variants, both common and rare. In DKAT, two individual kernels are used to describe the phenotypic and genotypic similarity, respectively, between pairwise subjects. Using kernels allows for capturing structure while accommodating dimensionality. Then, the association between traits and genetic variants is summarized by a coefficient which measures the association between two kernel matrices. Finally, DKAT evaluates the hypothesis of nonassociation with an analytical P-value calculation without any computationally expensive resampling procedures. By collapsing information in both traits and genetic variants using kernels, the proposed DKAT is shown to have a correct type-I error rate and higher power than other existing methods in both simulation studies and application to a study of genetic regulation of pathway gene expressions.

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

In genome-wide association studies (GWAS), "generalization" is the replication of genotype-phenotype association in a population with different ancestry than the population in which it was first identified. Current practices for declaring generalizations rely on testing associations while controlling the family-wise error rate (FWER) in the discovery study, then separately controlling error measures in the follow-up study. This approach does not guarantee control over the FWER or false discovery rate (FDR) of the generalization null hypotheses. It also fails to leverage the two-stage design to increase power for detecting generalized associations. We provide a formal statistical framework for quantifying the evidence of generalization that accounts for the (in)consistency between the directions of associations in the discovery and follow-up studies. We develop the directional generalization FWER (FWERg ) and FDR (FDRg ) controlling r-values, which are used to declare associations as generalized. This framework extends to generalization testing when applied to a published list of Single Nucleotide Polymorphism-(SNP)-trait associations. Our methods control FWERg or FDRg under various SNP selection rules based on P-values in the discovery study. We find that it is often beneficial to use a more lenient P-value threshold than the genome-wide significance threshold. In a GWAS of total cholesterol in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), when testing all SNPs with P-values

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

Summary paragraphThe Trans-Omics for Precision Medicine (TOPMed) program seeks to elucidate the genetic architecture and disease biology of heart, lung, blood, and sleep disorders, with the ultimate goal of improving diagnosis, treatment, and prevention. The initial phases of the program focus on whole genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here, we describe TOPMed goals and design as well as resources and early insights from the sequence data. The resources include a variant browser, a genotype imputation panel, and sharing of genomic and phenotypic data via dbGaP. In 53,581 TOPMed samples, >400 million single-nucleotide and insertion/deletion variants were detected by alignment with the reference genome. Additional novel variants are detectable through assembly of unmapped reads and customized analysis in highly variable loci. Among the >400 million variants detected, 97% have frequency

Admixture mapping studies have become more common in recent years, due in part to technological advances and growing international efforts to increase the diversity of genetic studies. However, many open questions remain about appropriate implementation of admixture mapping studies, including how best to control for multiple testing, particularly in the presence of population structure. In this study, we develop a theoretical framework to characterize the correlation of local ancestry and admixture mapping test statistics in admixed populations with contributions from any number of ancestral populations and arbitrary population structure. Based on this framework, we develop an analytical approach for obtaining genome-wide significance thresholds for admixture mapping studies. We validate our approach via analysis of simulated traits with real genotype data for 8,064 unrelated African American and 3,425 Hispanic/Latina women from the Women’s Health Initiative SNP Health Association Resource (WHI SHARe). In an application to these WHI SHARe data, our approach yields genome-wide significant p value thresholds of 2.1 × 10(−5) and 4.5 × 10(−6) for admixture mapping studies in the African American and Hispanic/Latina cohorts, respectively. Compared to other commonly used multiple testing correction procedures, our method is fast, easy to implement (using our publicly available R package), and controls the family-wise error rate even in structured populations. Importantly, we note that the appropriate admixture mapping significance threshold depends on the number of ancestral populations, generations since admixture, and population structure of the sample; as a result, significance thresholds are not, in general, transferable across studies.

BackgroundThe electrocardiographically quantified QRS duration measures ventricular depolarization and conduction. QRS prolongation has been associated with poor heart failure prognosis and cardiovascular mortality, including sudden death. While previous genome-wide association studies (GWAS) have identified 32 QRS SNPs across 26 loci among European, African, and Asian-descent populations, the genetics of QRS among Hispanics/Latinos has not been previously explored.MethodsWe performed a GWAS of QRS duration among Hispanic/Latino ancestry populations (n = 15,124) from four studies using 1000 Genomes imputed genotype data (adjusted for age, sex, global ancestry, clinical and study-specific covariates). Study-specific results were combined using fixed-effects, inverse variance-weighted meta-analysis.ResultsWe identified six loci associated with QRS (PConclusionsOur QRS duration GWAS, the first in Hispanic/Latino populations, identified two new loci, underscoring the utility of extending large scale genomic studies to currently under-examined populations.

BackgroundGenetic factors that influence kidney traits have been understudied for low frequency and ancestry-specific variants.MethodsWe combined whole genome sequencing (WGS) data from 23,732 participants from 10 NHLBI Trans-Omics for Precision Medicine (TOPMed) Program multi-ethnic studies to identify novel loci for estimated glomerular filtration rate (eGFR). Participants included European, African, East Asian, and Hispanic ancestries. We applied linear mixed models using a genetic relationship matrix estimated from the WGS data and adjusted for age, sex, study, and ethnicity.FindingsWhen testing single variants, we identified three novel loci driven by low frequency variants more commonly observed in non-European ancestry (PRKAA2, rs180996919, minor allele frequency [MAF] 0.04%, P=6.1×10-11; METTL8, rs116951054, MAF 0.09%, P=4.5×10-9; and MATK, rs539182790, MAF 0.05%, P=3.4×10-9). We also replicated two known loci for common variants (rs2461702, MAF=0.49, P=1.2×10-9, nearest gene GATM, and rs71147340, MAF=0.34, P=3.3×10-9, CDK12). Testing aggregated variants within a gene identified the MAF gene. A statistical approach based on local ancestry helped to identify replication samples for ancestry-specific variants.InterpretationThis study highlights challenges in studying variants influencing kidney traits that are low frequency in populations and more common in non-European ancestry.

Background In the past few years, imputation approaches have been mainly used in population-based designs of genome-wide association studies, although both family- and population-based imputation methods have been proposed. With the recent surge of family-based designs, family-based imputation has become more important. Imputation methods for both designs are based on identity-by-descent (IBD) information. Apart from imputation, the use of IBD information is also common for several types of genetic analysis, including pedigree-based linkage analysis. Methods We compared the performance of several family- and population-based imputation methods in large pedigrees provided by Genetic Analysis Workshop 19 (GAW19). We also evaluated the performance of a new IBD mapping approach that we propose, which combines IBD information from known pedigrees with information from unrelated individuals. Results Different combinations of the imputation methods have varied imputation accuracies. Moreover, we showed gains from the use of both known pedigrees and unrelated individuals with our IBD mapping approach over the use of known pedigrees only. Conclusions Our results represent accuracies of different combinations of imputation methods that may be useful for data sets similar to the GAW19 pedigree data. Our IBD mapping approach, which uses both known pedigree and unrelated individuals, performed better than classical linkage analysis.

Late-onset Alzheimer’s disease (LOAD) can present heterogeneously, with several subtypes recognized, including dysexecutive AD. One way to identify people with dysexecutive AD is to consider the difference between memory and executive functioning, which we refer to as the executive prominent/memory prominent spectrum. We aimed to determine if this spectrum was heritable. We used neuropsychological and genetic data from people with mild LOAD (Clinical Dementia Rating 0.5 or 1.0) from the National Alzheimer’s Coordinating Center and the Alzheimer’s Disease Neuroimaging Initiative. We cocalibrated the neuropsychological data to obtain executive functioning and memory scores and used their difference as a continuous phenotype to calculate its heritability overall and by chromosome. Narrow-sense heritability of the difference between memory and executive functioning scores was 0.68 (standard error 0.12). Single nucleotide polymorphisms on chromosomes 1, 2, 4, 11, 12, and 18 explained the largest fraction of phenotypic variance, with signals from each chromosome accounting for 5%–7%. The chromosomal pattern of heritability differed substantially from that of LOAD itself.

BACKGROUND Low blood vitamin D concentration is a concern for people living in circumpolar regions, where sunlight is insufficient for vitamin D synthesis in winter months and the consumption of traditional dietary sources of vitamin D is decreasing. OBJECTIVE The objective was to characterize the effects of diet, genetic variation, and season on serum 25-hydroxycholecalciferol [25(OH)D3] concentrations in Yup'ik Alaska Native people living in rural southwest Alaska. METHODS This study was a cross-sectional design that assessed the associations of traditional diet (via a biomarker, the RBC δ(15)N value), age, gender, body mass index (BMI), community location, and genotype of select single nucleotide polymorphisms (SNPs) in cytochrome P450 family 2, subfamily R, peptide 1 (CYP2R1), 7-dehydrocholesterol reductase (DHCR7), and vitamin D binding protein (GC) with serum 25(OH)D3 concentrations in 743 Yup'ik male and female participants, aged 14-93 y, recruited between September 2009 and December 2013. RESULTS Yup'ik participants, on average, had adequate concentrations of serum 25(OH)D3 (31.1 ± 1.0 ng/mL). Variations in diet, BMI, age, gender, season of sample collection, and inland or coastal community geography were all significantly associated with serum 25(OH)D3 concentration. In models not adjusting for other covariates, age, diet, and seasonal effects explained 33.7%, 20.7%, and 9.8%, respectively, of variability in serum 25(OH)D3 concentrations. Of the 8 SNPs interrogated in CYP2R1 and DHCR7, only rs11023374 in CYP2R1 was significantly associated with serum 25(OH)D3, explaining 1.5% of variability. The GC haplotype explained an additional 2.8% of variability. Together, age, diet, gender, season of sample collection, BMI, geography of the community, and genotype at rs11023374 explained 52.5% of the variability in serum 25(OH)D3 concentrations. CONCLUSIONS Lower consumption of the traditional diet was associated with lower serum concentrations of 25(OH)D3. Younger adults and youth in this community may be at increased risk of adverse outcomes associated with vitamin D insufficiency compared with older members of the community, especially during seasons of low sunlight exposure, because of lower consumption of dietary sources of vitamin D.

US Hispanic/Latino individuals are diverse in genetic ancestry, culture, and environmental exposures. Here, we characterized and controlled for this diversity in genome-wide association studies (GWASs) for the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). We simultaneously estimated population-structure principal components (PCs) robust to familial relatedness and pairwise kinship coefficients (KCs) robust to population structure, admixture, and Hardy-Weinberg departures. The PCs revealed substantial genetic differentiation within and among six self-identified background groups (Cuban, Dominican, Puerto Rican, Mexican, and Central and South American). To control for variation among groups, we developed a multi-dimensional clustering method to define a “genetic-analysis group” variable that retains many properties of self-identified background while achieving substantially greater genetic homogeneity within groups and including participants with non-specific self-identification. In GWASs of 22 biomedical traits, we used a linear mixed model (LMM) including pairwise empirical KCs to account for familial relatedness, PCs for ancestry, and genetic-analysis groups for additional group-associated effects. Including the genetic-analysis group as a covariate accounted for significant trait variation in 8 of 22 traits, even after we fit 20 PCs. Additionally, genetic-analysis groups had significant heterogeneity of residual variance for 20 of 22 traits, and modeling this heteroscedasticity within the LMM reduced genomic inflation for 19 traits. Furthermore, fitting an LMM that utilized a genetic-analysis group rather than a self-identified background group achieved higher power to detect previously reported associations. We expect that the methods applied here will be useful in other studies with multiple ethnic groups, admixture, and relatedness.

Genealogical inference from genetic data is essential for a variety of applications in human genetics. In genome-wide and sequencing association studies, for example, accurate inference on both recent genetic relatedness, such as family structure, and more distant genetic relatedness, such as population structure, is necessary for protection against spurious associations. Distinguishing familial relatedness from population structure with genotype data, however, is difficult because both manifest as genetic similarity through the sharing of alleles. Existing approaches for inference on recent genetic relatedness have limitations in the presence of population structure, where they either (1) make strong and simplifying assumptions about population structure, which are often untenable, or (2) require correct specification of and appropriate reference population panels for the ancestries in the sample, which might be unknown or not well defined. Here, we propose PC-Relate, a model-free approach for estimating commonly used measures of recent genetic relatedness, such as kinship coefficients and IBD sharing probabilities, in the presence of unspecified structure. PC-Relate uses principal components calculated from genome-screen data to partition genetic correlations among sampled individuals due to the sharing of recent ancestors and more distant common ancestry into two separate components, without requiring specification of the ancestral populations or reference population panels. In simulation studies with population structure, including admixture, we demonstrate that PC-Relate provides accurate estimates of genetic relatedness and improved relationship classification over widely used approaches. We further demonstrate the utility of PC-Relate in applications to three ancestrally diverse samples that vary in both size and genealogical complexity.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multi-ethnic utility of gene expression prediction., Author summary Advances in RNA sequencing technology have reduced the cost of measuring gene expression at a genome-wide level. However, sequencing enough human RNA samples for adequately-powered disease association studies remains prohibitively costly. To this end, modern transcriptome-wide association analysis tools leverage existing paired genotype-expression datasets by creating models to predict gene expression using genotypes. These predictive models enable researchers to perform cost-effective association tests with gene expression in independently genotyped samples. However, most of these models use European reference data, and the extent to which gene expression prediction models work across populations is not fully resolved. We observe that these models predict gene expression worse than expected in a dataset of African-Americans when derived from European-descent individuals. Using simulations, we show that gene expression predictive model performance depends on both the proportion of genetic variants shared between population-specific prediction models as well as the genetic relatedness between populations. Our findings suggest a need to carefully select reference populations for prediction and point to a pressing need for more genetically diverse genotype-expression datasets.

Using genetic data to predict gene expression has garnered significant attention in recent years. PrediXcan has become one of the most widely used gene-based methods for testing associations between predicted gene expression values and a phenotype, which has facilitated novel insights into the relationship between complex traits and the component of gene expression that can be attributed to genetic variation. The gene expression prediction models for PrediXcan were developed using supervised machine learning methods and training data from the Depression Genes and Networks (DGN) study and the Genotype-Tissue Expression (GTEx) project, where the majority of subjects are of European descent. Many genetic studies, however, include samples from multi-ethnic populations, and in this paper we evaluate the accuracy of PrediXcan for predicting gene expression in diverse populations. Using transcriptomic data from the GEUVADIS (Genetic European Variation in Disease) RNA sequencing project and whole genome sequencing data from the 1000 Genomes project, we evaluate and compare the predictive performance of PrediXcan in an African population (Yoruban) and four European ancestry populations for thousands of genes. We evaluate a range of models from the PrediXcan weight databases and use Pearson's correlation coefficient to assess gene expression prediction accuracy with PrediXcan. From our evaluation, we find that the predictive performance of PrediXcan varies substantially among populations from different continents (F-test p-value < 2.2 × 10−16), where prediction accuracy is lower in the Yoruban population from West Africa compared to the European-ancestry populations. Moreover, not only do we find differences in predictive performance between populations from different continents, we also find highly significant differences in prediction accuracy among the four European ancestry populations considered (F-test p-value < 2.2 × 10−16). Finally, while there is variability in prediction accuracy across different PrediXcan weight databases, we also find consistency in the qualitative performance of PrediXcan for the five populations considered, with the African ancestry population having the lowest accuracy across databases.

Introduction Although the relationship between APOE and Alzheimer's disease (AD) is well established in populations of European descent, the effects of APOE and ancestry on AD risk in diverse populations is not well understood. Methods Logistic mixed model regression and survival analyses were performed in a sample of 3067 Caribbean Hispanics and 3028 individuals of European descent to assess the effects of APOE genotype, local ancestry, and genome-wide ancestry on AD risk and age at onset. Results Among the Caribbean Hispanics, individuals with African-derived ancestry at APOE had 39% lower odds of AD than individuals with European-derived APOE, after adjusting for APOE genotype, age, and genome-wide ancestry. While APOE E2 and E4 effects on AD risk and age at onset were significant in the Caribbean Hispanics, they were substantially attenuated compared with those in European ancestry individuals. Discussion These results suggest that additional genetic variation in the APOE region influences AD risk beyond APOE E2/E3/E4.

Linear mixed models (LMMs) have become the standard approach for genetic association testing in the presence of sample structure. However, the performance of LMMs has primarily been evaluated in relatively homogeneous populations of European ancestry, despite many of the recent genetic association studies including samples from worldwide populations with diverse ancestries. In this paper, we demonstrate that existing LMM methods can have systematic miscalibration of association test statistics genome-wide in samples with heterogenous ancestry, resulting in both increased type-I error rates and a loss of power. Furthermore, we show that this miscalibration arises due to varying allele frequency differences across the genome among populations. To overcome this problem, we developed LMM-OPS, an LMM approach which orthogonally partitions diverse genetic structure into two components: distant population structure and recent genetic relatedness. In simulation studies with real and simulated genotype data, we demonstrate that LMM-OPS is appropriately calibrated in the presence of ancestry heterogeneity and outperforms existing LMM approaches, including EMMAX, GCTA, and GEMMA. We conduct a GWAS of white blood cell (WBC) count in an admixed sample of 3,551 Hispanic/Latino American women from the Women’s Health Initiative SNP Health Association Resource where LMM-OPS detects genome-wide significant associations with corresponding p-values that are one or more orders of magnitude smaller than those from competing LMM methods. We also identify a genome-wide significant association with regulatory variant rs2814778 in the DARC gene on chromosome 1, which generalizes to Hispanic/Latino Americans a previous association with reduced WBC count identified in African Americans.

BackgroundThe electrocardiographically quantified QRS duration measures ventricular depolarization and conduction. QRS prolongation has been associated with poor heart failure prognosis and cardiovascular mortality, including sudden death. While previous genome-wide association studies (GWAS) have identified 32 QRS SNPs across 26 loci among European, African, and Asian-descent populations, the genetics of QRS among Hispanics/Latinos has not been previously explored.MethodsWe performed a GWAS of QRS duration among Hispanic/Latino ancestry populations (n=15,124) from four studies using 1000 Genomes imputed genotype data (adjusted for age, sex, global ancestry, clinical and study-specific covariates). Study-specific results were combined using fixed-effects, inverse variance-weighted meta-analysis.ResultsWe identified six loci associated with QRS (P−8), including two novel loci:MYOCD, a nuclear protein expressed in the heart, andSYT1, an integral membrane protein. The top association in theMYOCDlocus, intronic SNP rs16946539, was found in Hispanics/Latinos with a minor allele frequency (MAF) of 0.04, but is monomorphic in European and African descent populations. The most significant QRS duration association was for intronic SNP rs3922344 (P= 8.56×10−26) inSCN5A/SCN10A. Three additional previously identified loci,CDKN1A,VTI1A, andHAND1, also exceeded the GWAS significance threshold among Hispanics/Latinos. A total of 27 of 32 previously identified QRS duration SNPs were shown to generalize in Hispanics/Latinos.ConclusionsOur QRS duration GWAS, the first in Hispanic/Latino populations, identified two new loci, underscoring the utility of extending large scale genomic studies to currently under-examined populations.

Obstructive sleep apnea (OSA) is a common disorder associated with increased risk of cardiovascular disease and mortality. Its prevalence and severity vary across ancestral background. Although OSA traits are heritable, few genetic associations have been identified. To identify genetic regions associated with OSA and improve statistical power, we applied admixture mapping on three primary OSA traits [the apnea hypopnea index (AHI), overnight average oxyhemoglobin saturation (SaO2) and percentage time SaO2&nbsp

Multipoint linkage analysis is an important approach for localizing disease-associated loci in pedigrees. Linkage analysis, however, is sensitive to misspecification of marker allele frequencies. Pedigrees from recently admixed populations are particularly susceptible to this problem because of the challenge of accurately accounting for population structure. Therefore, increasing emphasis on use of multiethnic samples in genetic studies requires reevaluation of best practices, given data currently available. Typical strategies have been to compute allele frequencies from the sample, or to use marker allele frequencies determined by admixture proportions averaged over the entire sample. However, admixture proportions vary among pedigrees and throughout the genome in a family-specific manner. Here, we evaluate several approaches to model admixture in linkage analysis, providing different levels of detail about ancestral origin. To perform our evaluations, for specification of marker allele frequencies, we used data on 67 Caribbean Hispanic admixed families from the Alzheimer's Disease Sequencing Project. Our results show that choice of admixture model has an effect on the linkage analysis results. Variant-specific admixture proportions, computed for individual families, provide the most detailed regional admixture estimates, and, as such, are the most appropriate allele frequencies for linkage analysis. This likely decreases the number of false-positive results, and is straightforward to implement.

BACKGROUND: Abruptio placentae is a complex multifactorial disease that is associated with maternal and neonatal death and morbidity. Abruptio placentae’s high recurrence rate, high prevalence of heritable thrombophilia among women with abruptio placentae, and aggregation of cases in families of women with the disease support the possibility of a genetic predisposition. Previous genome-wide and candidate gene association studies have identified single nucleotide polymorphisms in mitochondrial biogenesis and oxidative phosphorylation genes that potentially are associated with abruptio placentae risk. Perturbations in mitochondrial biogenesis and oxidative phosphorylation, which results in mitochondrial dysfunction, can lead to the impairment of differentiation and invasion of the trophoblast and to several obstetrics complications that include abruptio placentae. OBJECTIVE: The purpose of this study was to determine whether the results of a candidate genetic association study that indicated a link between DNA variants (implicated in mitochondrial biogenesis and oxidative phosphorylation) and abruptio placentae could be replicated. STUDY DESIGN AND METHODS: The study was conducted among participants (507 abruptio placentae cases and 1090 control subjects) of the Placental Abruption Genetic Epidemiology study. Weighted genetic risk scores were calculated with the use of abruptio placentae risk-increasing alleles of 11 single nucleotide polymorphisms in 9 mitochondrial biogenesis and oxidative phosphorylation genes (CAMK2B, NR1H3, PPARG, PRKCA, THRB, COX5A, NDUFA10, NDUFA12, and NDUFC2), which previously was reported in the Peruvian Abruptio Placentae Epidemiology study, a study with similar design and study population to the Placental Abruption Genetic Epidemiology study. Logistic regression models were fit to examine associations of weighted genetic risk scores (quartile 1, 75th percentile) with risk of abruptio placentae, adjusted for population admixture (the first 4 principal components), maternal age, infant sex, and preeclampsia. The weighted genetic risk score was also modeled as a continuous predictor. To assess potential effect modification, analyses were repeated among strata that were defined by preeclampsia status, maternal age (≥35 vs 18–34 years), and infant sex. RESULTS: Abruptio placentae cases were more likely to have preeclampsia, shorter gestational age, and lower infant birthweight. Participants in quartile 2 (score, 12.6–13.8), quartile 3 (score, 13.9–15.0) and quartile 4 (score, ≥15.1) had a genetic risk score of 1.45-fold (95% confidence interval, 1.04–2.02; P=.03), a 1.42-fold (95% confidence interval, 1.02–1.98; P=.04), and a 1.75-fold (95% confidence interval, 1.27–2.42; P=7.0E-04) higher odds of abruptio placentae, respectively, compared with those in quartile 1 (score

Sleep disordered breathing (SDB)-related overnight hypoxemia is associated with cardiometabolic disease and other comorbidities. Understanding the genetic bases for variations in nocturnal hypoxemia may help understand mechanisms influencing oxygenation and SDB-related mortality. We conducted genome-wide association tests across 10 cohorts and 4 populations to identify genetic variants associated with three correlated measures of overnight oxyhemoglobin saturation: average and minimum oxyhemoglobin saturation during sleep and the percent of sleep with oxyhemoglobin saturation under 90%. The discovery sample consisted of 8,326 individuals. Variants with p < 1 × 10(−6) were analyzed in a replication group of 14,410 individuals. We identified 3 significantly associated regions, including 2 regions in multi-ethnic analyses (2q12, 10q22). SNPs in the 2q12 region associated with minimum SpO(2) (rs78136548 p = 2.70 × 10(−10)). SNPs at 10q22 were associated with all three traits including average SpO(2) (rs72805692 p = 4.58 × 10(−8)). SNPs in both regions were associated in over 20,000 individuals and are supported by prior associations or functional evidence. Four additional significant regions were detected in secondary sex-stratified and combined discovery and replication analyses, including a region overlapping Reelin, a known marker of respiratory complex neurons.These are the first genome-wide significant findings reported for oxyhemoglobin saturation during sleep, a phenotype of high clinical interest. Our replicated associations with HK1 and IL18R1 suggest that variants in inflammatory pathways, such as the biologically-plausible NLRP3 inflammasome, may contribute to nocturnal hypoxemia.

INTRODUCTION: Accumulating epidemiological evidence points to strong genetic susceptibility to placental abruption (PA). However, characterization of genes associated with PA remains incomplete. We conducted a genome-wide association study (GWAS) of PA and a meta-analysis of GWAS. METHODS: Participants of the Placental Abruption Genetic Epidemiology (PAGE) study, a population based case-control study of PA conducted in Lima, Peru, were genotyped using the Illumina HumanCore-24 BeadChip platform. Genotypes were imputed using the 1000 genomes reference panel, and >4.9 million SNPs that passed quality control were analyzed. We performed a GWAS in PAGE participants (507 PA cases and 1,090 controls) and a GWAS meta-analysis in 2,512 participants (959 PA cases and 1,553 controls) that included PAGE and the previously reported Peruvian Abruptio Placentae Epidemiology (PAPE) study. We fitted population stratification-adjusted logistic regression models and fixed-effects meta-analyses using inverse-variance weighting. RESULTS: Independent loci (linkage-disequilibrium

Obstructive sleep apnea (OSA) is a common heritable disorder displaying marked sexual dimorphism in disease prevalence and progression. Previous genetic association studies have identified a few genetic loci associated with OSA and related quantitative traits, but they have only focused on single ethnic groups, and a large proportion of the heritability remains unexplained. The apnea-hypopnea index (AHI) is a commonly used quantitative measure characterizing OSA severity. Because OSA differs by sex, and the pathophysiology of obstructive events differ in rapid eye movement (REM) and non-REM (NREM) sleep, we hypothesized that additional genetic association signals would be identified by analyzing the NREM/REM-specific AHI and by conducting sex-specific analyses in multiethnic samples. We performed genome-wide association tests for up to 19,733 participants of African, Asian, European, and Hispanic/Latino American ancestry in 7 studies. We identified rs12936587 on chromosome 17 as a possible quantitative trait locus for NREM AHI in men (N = 6,737; P = 1.7 × 10 -8 ) but not in women (P = 0.77). The association with NREM AHI was replicated in a physiological research study (N = 67; P = 0.047). This locus overlapping the RAI1 gene and encompassing genes PEMT1, SREBF1, and RASD1 was previously reported to be associated with coronary artery disease, lipid metabolism, and implicated in Potocki-Lupski syndrome and Smith-Magenis syndrome, which are characterized by abnormal sleep phenotypes. We also identified gene-by-sex interactions in suggestive association regions, suggesting that genetic variants for AHI appear to vary by sex, consistent with the clinical observations of strong sexual dimorphism.

Genetic risk scores (GRSs) are weighted sums of risk allele counts of single nucleotide polymorphisms (SNPs) associated with a disease or trait. Construction of GRSs is typically based on published results from Genome-Wide Association Studies (GWASs), the majority of which have been performed in large populations of European ancestry (EA) individuals. While many genotype-trait associations have been shown to generalize from EA populations to other populations, such as Hispanics/Latinos, the optimal choice of SNPs and weights for GRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns. This is further complicated by the fact that different Hispanic/Latino populations may have different admixture patterns, so that LD and allele frequency patterns may not be the same among non-EA populations. Here, we compare various approaches for GRS construction, using GWAS results from both large EA studies and a smaller study in Hispanics/Latinos, the Hispanic Community Health Study/Study of Latinos (HCHS/SOL, n = 12, 803). We consider multiple ways to select SNPs from association regions and to calculate the SNP weights. We study the performance of the resulting GRSs in an independent study of Hispanics/Latinos from the Woman Health Initiative (WHI, n = 3, 582). We support our investigation with simulation studies of potential genetic architectures in a single locus. We observed that selecting variants based on EA GWASs generally performs well, as long as SNP weights are calculated using Hispanics/Latinos GWASs, or using the meta-analysis of EA and Hispanics/Latinos GWASs. The optimal approach depends on the genetic architecture of the trait.

Background/Aims: The Alzheimer’s Disease Sequencing Project (ADSP) aims to identify novel genes influencing Alzheimer’s disease (AD). Variants within genes known to cause dementias other than AD have previously been associated with AD risk. We describe evidence of co-segregation and associations between variants in dementia genes and clinically diagnosed AD within the ADSP. Methods: We summarize the properties of known pathogenic variants within dementia genes, describe the co-segregation of variants annotated as “pathogenic” in ClinVar and new candidates observed in ADSP families, and test for associations between rare variants in dementia genes in the ADSP case-control study. The participants were clinically evaluated for AD, and they represent European, Caribbean Hispanic, and isolate Dutch populations. Results/Conclusions: Pathogenic variants in dementia genes were predominantly rare and conserved coding changes. Pathogenic variants within ARSA, CSF1R, and GRN were observed, and candidate variants in GRN and CHMP2B were nominated in ADSP families. An independent case-control study provided evidence of an association between variants in TREM2, APOE, ARSA, CSF1R, PSEN1, and MAPT and risk of AD. Variants in genes which cause dementing disorders may influence the clinical diagnosis of AD in a small proportion of cases within the ADSP.

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

cope We investigated the relationship between dietary vitamin K and plasma PIVKA-II concentration, a biomarker of hepatic vitamin K status, in a Yup'ik study population in southwestern Alaska. Methods and results A total of 659 male and female, self-reported Yup'ik people, ≥ 14 years of age, were enrolled. Blood was collected for genotyping and plasma PIVKA-II biomarker analysis. A Yup'ik-specific dietary food frequency questionnaire was used to assess vitamin K intake. Among the participants, 22% reported not consuming foods rich in vitamin K during the past year and 36% had a PIVKA-II concentration ≥ 2 ng/mL, indicating vitamin K insufficiency. The odds of an elevated PIVKA-II concentration were 33% lower in individuals reporting any versus no consumption of vitamin K rich foods. The association was significant after adjusting for CYP4F2*3 genotype. Tundra greens were high in vitamin K1 content, but an exploratory analysis suggested that subsistence meat sources had a greater effect on vitamin K status. Conclusions A substantial proportion of the Yup'ik population exhibits vitamin K insufficiency, which was associated with low consumption of vitamin K rich foods and which might affect an individual's response to anticoagulant drugs such as warfarin that target the vitamin K cycle. This article is protected by copyright. All rights reserved

Nature Communications 8: Article number: 15805 (2017); Published: 14 June 2017; Updated: 2 August 2017 In Supplementary Fig. 10 of this Article, images for panels a and b were inadvertently omitted. The correct version of Supplementary Fig. 10 is provided as Supplementary Information associated withthis Erratum.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1–3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4–10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions13—the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.