Trans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained

Similarity and diversity of genetic architecture for complex traits between East Asian and European populations

BACKGROUND

Genome-wide association studies have detected a large number of single-nucleotide polymorphisms (SNPs) associated with complex traits in diverse ancestral groups. However, the trans-ethnic similarity and diversity of genetic architecture is not well understood currently.

RESULTS

By leveraging summary statistics of 37 traits from East Asian (N_max=254,373) or European (N_max=693,529) populations, we first evaluated the trans-ethnic genetic correlation (ρ_g) and found substantial evidence of shared genetic overlap underlying these traits between the two populations, with [Formula: see text] ranging from 0.53 (se = 0.11) for adult-onset asthma to 0.98 (se = 0.17) for hemoglobin A1c. However, 88.9% of the genetic correlation estimates were significantly less than one, indicating potential heterogeneity in genetic effect across populations. We next identified common associated SNPs using the conjunction conditional false discovery rate method and observed 21.7% of trait-associated SNPs can be identified simultaneously in both populations. Among these shared associated SNPs, 20.8% showed heterogeneous influence on traits between the two ancestral populations. Moreover, we demonstrated that population-common associated SNPs often exhibited more consistent linkage disequilibrium and allele frequency pattern across ancestral groups compared to population-specific or null ones. We also revealed population-specific associated SNPs were much likely to undergo natural selection compared to population-common associated SNPs.

CONCLUSIONS

Our study provides an in-depth understanding of similarity and diversity regarding genetic architecture for complex traits across diverse populations, and can assist in trans-ethnic association analysis, genetic risk prediction, and causal variant fine mapping.

Genetic heterogeneity: Challenges, impacts, and methods through an associative lens

Genetic heterogeneity describes the occurrence of the same or similar phenotypes through different genetic mechanisms in different individuals. Robustly characterizing and accounting for genetic heterogeneity is crucial to pursuing the goals of precision medicine, for discovering novel disease biomarkers, and for identifying targets for treatments. Failure to account for genetic heterogeneity may lead to missed associations and incorrect inferences. Thus, it is critical to review the impact of genetic heterogeneity on the design and analysis of population level genetic studies, aspects that are often overlooked in the literature. In this review, we first contextualize our approach to genetic heterogeneity by proposing a high-level categorization of heterogeneity into "feature," "outcome," and "associative" heterogeneity, drawing on perspectives from epidemiology and machine learning to illustrate distinctions between them. We highlight the unique nature of genetic heterogeneity as a heterogeneous pattern of association that warrants specific methodological considerations. We then focus on the challenges that preclude effective detection and characterization of genetic heterogeneity across a variety of epidemiological contexts. Finally, we discuss systems heterogeneity as an integrated approach to using genetic and other high-dimensional multi-omic data in complex disease research.

Quantitative Genetic Analysis of Interactions in the Pepper-Phytophthora capsici Pathosystem

The development of pepper cultivars with durable resistance to the oomycete Phytophthora capsici has been challenging due to differential interactions between the species that allow certain pathogen isolates to cause disease on otherwise resistant host genotypes. Currently, little is known about the pathogen genes involved in these interactions. To investigate the genetic basis of P. capsici virulence on individual pepper genotypes, we inoculated sixteen pepper accessions, representing commercial varieties, sources of resistance, and host differentials, with 117 isolates of P. capsici, for a total of 1,864 host-pathogen combinations. Analysis of disease outcomes revealed a significant effect of inter-species genotype-by-genotype interactions, although these interactions were quantitative rather than qualitative in scale. Isolates were classified into five pathogen subpopulations, as determined by their genotypes at over 60,000 single-nucleotide polymorphisms (SNPs). While absolute virulence levels on certain pepper accessions significantly differed between subpopulations, a multivariate phenotype reflecting relative virulence levels on certain pepper genotypes compared with others showed the strongest association with pathogen subpopulation. A genome-wide association study (GWAS) identified four pathogen loci significantly associated with virulence, two of which colocalized with putative RXLR effector genes and another with a polygalacturonase gene cluster. All four loci appeared to represent broad-spectrum virulence genes, as significant SNPs demonstrated consistent effects regardless of the host genotype tested. Host genotype-specific virulence variants in P. capsici may be difficult to map via GWAS with all but excessively large sample sizes, perhaps controlled by genes of small effect or by multiple allelic variants that have arisen independently. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Identification of RNA modification-associated single nucleotide polymorphisms in genomic loci for low-density lipoprotein cholesterol concentrations

Introduction: Genome-wide association studies have identified approximately 1000 lipid-associated loci, but functional variants are less known. Materials & methods: The authors identified RNA modification-related single nucleotide polymorphisms (RNAm-SNPs) in summary data from a genome-wide association study. By applying Mendelian randomization analysis, the authors identified gene expression levels involved in the regulation of RNAm-SNPs on low-density lipoprotein cholesterol (LDL-C) levels. Results: The authors identified 391 RNAm-SNPs that were significantly associated with LDL-C levels. RNAm-SNPs in NPC1L1, LDLR, APOB, MYLIP, LDLRAP1 and ABCA6 were identified. The RNAm-SNPs were associated with gene expression. The expression levels of 112 genes were associated with LDL-C levels, and some of them (e.g., APOB, SMARCA4 and SH2B3) were associated with coronary artery disease. Conclusion: This study identified many RNAm-SNPs in LDL-C loci and elucidated the relationship among the SNPs, gene expression and LDL-C.

Exome Sequencing Data Analysis and a Case-Control Study in Mexican Population Reveals Lipid Trait Associations of New and Known Genetic Variants in Dyslipidemia-Associated Loci

Background: Plasma lipid levels are a major risk factor for cardiovascular diseases. Although international efforts have identified a group of loci associated with the risk of dyslipidemia, Latin American populations have been underrepresented in these studies.

Objective: To know the genetic variation occurring in lipid-related loci in the Mexican population and its association with dyslipidemia.

Methods: We searched for single-nucleotide variants in 177 lipid candidate genes using previously published exome sequencing data from 2838 Mexican individuals belonging to three different cohorts. With the extracted variants, we performed a case-control study. Logistic regression and quantitative trait analyses were implemented in PLINK software. We used an LD pruning using a 50-kb sliding window size, a 5-kb window step size and a r2 threshold of 0.1.

Results: Among the 34251 biallelic variants identified in our sample population, 33% showed low frequency. For case-control study, we selected 2521 variants based on a minor allele frequency ≥1% in all datasets. We found 19 variants in 9 genes significantly associated with at least one lipid trait, with the most significant associations found in the APOA1/C3/A4/A5-ZPR1-BUD13 gene cluster on chromosome 11. Notably, all 11 variants associated with hypertriglyceridemia were within this cluster; whereas variants associated with hypercholesterolemia were located at chromosome 2 and 19, and for low high density lipoprotein cholesterol were in chromosomes 9, 11, and 19. No significant associated variants were found for low density lipoprotein. We found several novel variants associated with different lipemic traits: rs3825041 in BUD13 with hypertriglyceridemia, rs7252453 in CILP2 with decreased risk to hypercholesterolemia and rs11076176 in CETP with increased risk to low high density lipoprotein cholesterol.

Conclusions: We identified novel variants in lipid-regulation candidate genes in the Mexican population, an underrepresented population in genomic studies, demonstrating the necessity of more genomic studies on multi-ethnic populations to gain a deeper understanding of the genetic structure of the lipemic traits.

Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits

Genetic associations for lipid traits have identified hundreds of variants with clear differences across European, Asian and African studies. Based on a sub-Saharan-African GWAS for lipid traits in the population cross-sectional AWI-Gen cohort (N = 10,603) we report a novel LDL-C association in the GATB region (P-value=1.56 × 10⁻⁸). Meta-analysis with four other African cohorts (N = 23,718) provides supporting evidence for the LDL-C association with the GATB/FHIP1A region and identifies a novel triglyceride association signal close to the FHIT gene (P-value =2.66 × 10⁻⁸). Our data enable fine-mapping of several well-known lipid-trait loci including LDLR, PMFBP1 and LPA. The transferability of signals detected in two large global studies (GLGC and PAGE) consistently improves with an increase in the size of the African replication cohort. Polygenic risk score analysis shows increased predictive accuracy for LDL-C levels with the narrowing of genetic distance between the discovery dataset and our cohort. Novel discovery is enhanced with the inclusion of African data.

Genetic associations and polygenic scores for lipid traits have low transferability to African individuals. Here, the authors perform a large sub-Sarahan African lipid GWAS and find that larger datasets and better global representation in discovery GWAS help to bridge this gap.

Genome-Wide Association Study Identifies a Functional SIDT2 Variant Associated With HDL-C (High-Density Lipoprotein Cholesterol) Levels and Premature Coronary Artery Disease

Objective

Low HDL-C (high-density lipoprotein cholesterol) is the most frequent dyslipidemia in Mexicans, but few studies have examined the underlying genetic basis. Our purpose was to identify genetic variants associated with HDL-C levels and cardiovascular risk in the Mexican population.

Approach and Results

A genome-wide association studies for HDL-C levels in 2335 Mexicans, identified four loci associated with genome-wide significance: CETP, ABCA1, LIPC, and SIDT2. The SIDT2 missense Val636Ile variant was associated with HDL-C levels and was replicated in 3 independent cohorts (P=5.9×10−18 in the conjoint analysis). The SIDT2/Val636Ile variant is more frequent in Native American and derived populations than in other ethnic groups. This variant was also associated with increased ApoA1 and glycerophospholipid serum levels, decreased LDL-C (low-density lipoprotein cholesterol) and ApoB levels, and a lower risk of premature CAD. Because SIDT2 was previously identified as a protein involved in sterol transport, we tested whether the SIDT2/Ile636 protein affected this function using an in vitro site-directed mutagenesis approach. The SIDT2/Ile636 protein showed increased uptake of the cholesterol analog dehydroergosterol, suggesting this variant affects function. Finally, liver transcriptome data from humans and the Hybrid Mouse Diversity Panel are consistent with the involvement of SIDT2 in lipid and lipoprotein metabolism.

Conclusions

This is the first genome-wide association study for HDL-C levels seeking associations with coronary artery disease in the Mexican population. Our findings provide new insight into the genetic architecture of HDL-C and highlight SIDT2 as a new player in cholesterol and lipoprotein metabolism in humans.

The genetic architecture of temperature adaptation is shaped by population ancestry and not by selection regime

Background

Understanding the genetic architecture of temperature adaptation is key for characterizing and predicting the effect of climate change on natural populations. One particularly promising approach is Evolve and Resequence, which combines advantages of experimental evolution such as time series, replicate populations, and controlled environmental conditions, with whole genome sequencing. Recent analysis of replicate populations from two different Drosophila simulans founder populations, which were adapting to the same novel hot environment, uncovered very different architectures—either many selection targets with large heterogeneity among replicates or fewer selection targets with a consistent response among replicates.

Results

Here, we expose the founder population from Portugal to a cold temperature regime. Although almost no selection targets are shared between the hot and cold selection regime, the adaptive architecture was similar. We identify a moderate number of targets under strong selection (19 selection targets, mean selection coefficient = 0.072) and parallel responses in the cold evolved replicates. This similarity across different environments indicates that the adaptive architecture depends more on the ancestry of the founder population than the specific selection regime.

Conclusions

These observations will have broad implications for the correct interpretation of the genomic responses to a changing climate in natural populations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-021-02425-9.

The Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS): Design and Methodology

Background. Each year, approximately 5000 New Zealanders are admitted to hospital with first-time acute coronary syndrome (ACS). The Multi-Ethnic New Zealand Study of Acute Coronary Syndromes (MENZACS) is a prospective longitudinal cohort study embedded within the All New Zealand Acute Coronary Syndrome Quality Improvement (ANZACS-QI) registry in six hospitals. The objective of MENZACS is to examine the relationship between clinical, genomic, and cardiometabolic markers in relation to presentation and outcomes post-ACS. Methods. Patients with first-time ACS are enrolled and study-specific research data is collected alongside the ANZACS-QI registry. The research blood samples are stored for future genetic/biomarker assays. Dietary information is collected with a food frequency questionnaire and information about physical activity, smoking, and stress is also collected via questionnaire. Detailed family history, ancestry, and ethnicity data are recorded on all participants. Results. During the period between 2015 and 2019, there were 2015 patients enrolled. The mean age was 61 years, with 60% of patients aged <65 years and 21% were female. Ethnicity and cardiovascular (CV) risk factor distribution was similar to ANZACS-QI: 13% Māori, 5% Pacific, 5% Indian, and 74% NZ European. In terms of CV risk factors, 56% were ex-/current smokers, 42% had hypertension, and 19% had diabetes. ACS subtype was ST elevation myocardial infarction (STEMI) in 41%, non-ST elevation myocardial infarction (NSTEM) in 54%, and unstable angina in 5%. Ninety-nine percent of MENZACS participants underwent coronary angiography and 90% had revascularization; there were high rates of prescription of secondary prevention medications upon discharge from hospital. Conclusion. MENZACS represents a cohort with optimal contemporary management and will be a significant epidemiological bioresource for the study of environmental and genetic factors contributing to ACS in New Zealand’s multi-ethnic environment. The study will utilise clinical, nutritional, lifestyle, genomic, and biomarker analyses to explore factors influencing the progression of coronary disease and develop risk prediction models for health outcomes.

Tractor uses local ancestry to enable the inclusion of admixed individuals in GWAS and to boost power

Admixed populations are routinely excluded from genomic studies due to concerns over population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with simulated and empirical two-way admixed African-European cohorts. Tractor generates accurate ancestry-specific effect-size estimates and P values, can boost genome-wide association study (GWAS) power and improves the resolution of association signals. Using a local ancestry-aware regression model, we replicate known hits for blood lipids, discover novel hits missed by standard GWAS and localize signals closer to putative causal variants.

Candidate Gene Analysis Reveals Strong Association of CETP Variants With High Density Lipoprotein Cholesterol and PCSK9 Variants With Low Density Lipoprotein Cholesterol in Ghanaian Adults: An AWI-Gen Sub-Study

Variations in lipid levels are attributed partly to genetic factors. Genome-wide association studies (GWASs) mainly performed in European, African American and Asian cohorts have identified variants associated with LDL-C, HDL-C, total cholesterol (TC) and triglycerides (TG), but few studies have been performed in sub-Saharan Africans. This study evaluated the effect of single nucleotide variants (SNVs) in eight candidate loci (ABCA1, LCAT, LPL, PON1, CETP, PCSK9, MVK, and MMAB) on lipid levels among 1855 Ghanaian adults. All lipid levels were measured directly using an automated analyser. DNA was extracted and genotyped using the H3Africa SNV array. Linear regression models were used to test the association between SNVs and log-transformed lipid levels, adjusting for sex, age and waist circumference. In addition Bonferroni correction was performed to account for multiple testing. Several variants of CETP, LCAT, PCSK9, and PON1 (MAF > 0.05) were associated with HDL-C, LDL-C and TC levels at p < 0.05. The lead variants for association with HDL-C were rs17231520 in CETP (β = 0.139, p < 0.0001) and rs1109166 in LCAT (β = −0.044, p = 0.028). Lower LDL-C levels were associated with an intronic variant in PCSK9 (rs11806638 [β = −0.055, p = 0.027]) and increased TC was associated with a variant in PON1 (rs854558 [β = 0.040, p = 0.020]). In silico functional analyses indicated that these variants likely influence gene function through their effect on gene transcription. We replicated a strong association between CETP variants and HDL-C and between PCSK9 variant and LDL-C in West Africans, with two potentially functional variants and identified three novel variants in linkage disequilibrium in PON1 which were associated with increasing TC levels in Ghanaians.

Genome-wide landscape establishes novel association signals for metabolic traits in the Arab population

While the Arabian population has a high prevalence of metabolic disorders, it has not been included in global studies that identify genetic risk loci for metabolic traits. Determining the transferability of such largely Euro-centric established risk loci is essential to transfer the research tools/resources, and drug targets generated by global studies to a broad range of ethnic populations. Further, consideration of populations such as Arabs, that are characterized by consanguinity and a high level of inbreeding, can lead to identification of novel risk loci. We imputed published GWAS data from two Kuwaiti Arab cohorts (n = 1434 and 1298) to the 1000 Genomes Project haplotypes and performed meta-analysis for associations with 13 metabolic traits. We compared the observed association signals with those established for metabolic traits. Our study highlighted 70 variants from 9 different genes, some of which have established links to metabolic disorders. By relaxing the genome-wide significance threshold, we identified ‘novel’ risk variants from 11 genes for metabolic traits. Many novel risk variant association signals were observed at or borderline to genome-wide significance. Furthermore, 349 previously established variants from 187 genes were validated in our study. Pleiotropic effect of risk variants on multiple metabolic traits were observed. Fine-mapping illuminated rs7838666/CSMD1 rs1864163/CETP and rs112861901/[INTS10,LPL] as candidate causal variants influencing fasting plasma glucose and high-density lipoprotein levels. Computational functional analysis identified a variety of gene regulatory signals around several variants. This study enlarges the population ancestry diversity of available GWAS and elucidates new variants in an ethnic group burdened with metabolic disorders.

Colocalization of GWAS and eQTL signals at loci with multiple signals identifies additional candidate genes for body fat distribution

Integration of genome-wide association study (GWAS) signals with expression quantitative trait loci (eQTL) studies enables identification of candidate genes. However, evaluating whether nearby signals may share causal variants, termed colocalization, is affected by the presence of allelic heterogeneity, different variants at the same locus impacting the same phenotype. We previously identified eQTL in subcutaneous adipose tissue from 770 participants in the Metabolic Syndrome in Men (METSIM) study and detected 15 eQTL signals that colocalized with GWAS signals for waist-hip ratio adjusted for body mass index (WHRadjBMI) from the Genetic Investigation of Anthropometric Traits consortium. Here, we reevaluated evidence of colocalization using two approaches, conditional analysis and the Bayesian test COLOC, and show that providing COLOC with approximate conditional summary statistics at multi-signal GWAS loci can reconcile disagreements in colocalization classification between the two tests. Next, we performed conditional analysis on the METSIM subcutaneous adipose tissue data to identify conditionally distinct or secondary eQTL signals. We used the two approaches to test for colocalization with WHRadjBMI GWAS signals and evaluated the differences in colocalization classification between the two tests. Through these analyses, we identified four GWAS signals colocalized with secondary eQTL signals for FAM13A, SSR3, GRB14 and FMO1. Thus, at loci with multiple eQTL and/or GWAS signals, analyzing each signal independently enabled additional candidate genes to be identified.

Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data

Despite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze nine complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8× enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWASs due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

Genetic associations between serum low LDL-cholesterol levels and variants in LDLR, APOB, PCSK9 and LDLRAP1 in African populations

Non-communicable diseases, including cardiovascular diseases (CVDs), are increasing in African populations. High serum low density lipoprotein cholesterol (LDL-cholesterol) levels are a known risk factor for CVDs in European populations, but the link remains poorly understood among Africans. This study investigated the associations between serum LDL-cholesterol levels and selected variants in the low density lipoprotein receptor (LDLR), apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9) and low density lipoprotein receptor adaptor protein 1 (LDLRAP1) genes in some selected African populations. Nineteen SNPs were selected from publicly available African whole genome sequence data based on functional prediction and allele frequency. SNPs were genotyped in 1000 participants from the AWI-Gen, study selected from the extremes of LDL-cholesterol level distribution (500 with LDL-cholesterol>3.5 mmol/L and 500 with LDL-cholesterol<1.1 mmol/L). The minor alleles at five of the six associated SNPs were significantly associated (P<0.05) with lower LDL-cholesterol levels: LDLRAP1 rs12071264 (OR 0.56, 95% CI: 0.39-0.75, P = 2.73x10-4) and rs35910270 (OR 0.78, 95% CI: 0.64-0.94, P = 0.008); APOB rs6752026 (OR 0. 55, 95% CI: 0.41-0.72, P = 2.82x10-5); LDLR: rs72568855 (OR 0.47, 95% CI: 0.27-0.82, P = 0.008); and PCSK9 rs45613943 (OR = 0.72, 95% CI: 0.58-0.88, P = 0.001). The minor allele of the sixth variant was associated with higher LDL-cholesterol levels: APOB rs679899 (OR 1.41, 95% CI: 1.06-1.86, P = 0.016). A replication analysis in the Africa America Diabetes Mellitus (AADM) study found the PCSK9 variant to be significantly associated with low LDL-cholesterol levels (Beta = -0.10). Since Africans generally have lower LDL-cholesterol levels, these LDL-cholesterol associated variants may be involved in adaptation due to unique gene-environment interactions. In conclusion, using a limited number of potentially functional variants in four genes, we identified significant associations with lower LDL-cholesterol levels in sub-Saharan Africans.

A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.

Two functional variants at 6p21.1 were associated with lean mass

Background

Low lean body mass is the most important predictor of sarcopenia with strong genetic background. The aim of this study was to uncover genetic factors underlying lean mass development.

Materials and methods

We performed a genome-wide association study (GWAS) of fat-adjusted leg lean mass in the Framingham Heart Study (FHS, N = 6587), and replicated in the Women’s Health Initiative–African American sub-sample (WHI-AA, N = 847) and the Kansas City Osteoporosis Study (KCOS, N = 2219). We also cross-validated significant variants in the publicly available body mass index (BMI) summary results (N ~ 700,000). We then performed a series of functional investigations on the identified variants.

Results

Four correlated SNPs at 6p21.1 were identified at the genome-wide significance (GWS, α = 5.0 × 10⁻⁸) level in the discovery FHS sample (rs551145, rs524533, rs571770, and rs545970, p = 3.40–9.77 × 10⁻⁹), and were successfully replicated in both the WHI-AA and the KCOS samples (one-sided p = 1.61 × 10⁻³–0.04). They were further cross-validated by the large-scale BMI summary results (p = 7.0–9.8 × 10⁻³). Cis-eQTL analyses associated these SNPs with the NFKBIE gene expression. Electrophoresis mobility shift assay (EMSA) in mouse C2C12 myoblast cells implied that rs524533 and rs571770 were bound to an unknown transcription factor in an allelic specific manner, while rs551145 and rs545970 did not. Dual-luciferase reporter assay revealed that both rs524533 and rs571770 downregulated luciferase expression by repressing promoter activity. Moreover, the regulation pattern was allelic specific, strengthening the evidence towards their differential regulatory effects.

Conclusions

Through a large-scale GWAS followed by a series of functional investigations, we identified 2 correlated functional variants at 6p21.1 associated with leg lean mass. Our findings not only enhanced our understanding of molecular basis of lean mass development but also provided useful candidate genes for further functional studies.

Phosphorylation-related SNPs influence lipid levels and rheumatoid arthritis risk by altering gene expression and plasma protein levels

Objectives

Phosphorylation-related single-nucleotide polymorphisms (phosSNPs) are missense SNPs that may influence protein phosphorylation. The aim of this study was to evaluate the effect of phosSNPs on lipid levels and RA.

Methods

We examined the association of phosSNPs with lipid levels and RA in large-scale genome-wide association studies (GWAS) and performed random sampling and fgwas analyses to determine whether the phosSNPs associated with lipid levels and RA were significantly enriched. Furthermore, we performed QTL analysis and Mendelian randomization analysis to obtain additional evidence to be associated with the identified phosSNPs and genes.

Results

We found 483 phosSNPs for lipid levels and 243 phosSNPs for RA in the GWAS loci (P &lt; 1.0 × 10−5). SNPs associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, Total cholesterol (TC) and RA were significantly enriched with phosSNPs. Almost all of the identified phosSNPs showed expression quantitative trait loci (eQTL) effects. A total of 48 protein QTLs and 9 metabolite QTLs were found. The phosSNP rs3184504 (p.Trp262Arg) at SH2B3 was significantly associated with RA, SH2B3 expression level, and plasma levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, TC, hypoxanthine and 80 proteins, including beta-2-microglobulin. SH2B3 was differentially expressed between RA cases and controls in peripheral blood mononuclear cells and synovial tissues. Mendelian randomization analysis showed that SH2B3 expression level was significantly associated with TC level and RA. Plasma beta-2-microglobulin level was causally associated with high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, TC levels and RA.

Conclusion

The findings suggested that phosSNPs may play important roles in lipid metabolism and the pathological mechanisms of RA. PhosSNPs may influence lipid levels and RA risk by altering gene expression and plasma protein levels.

The transferability of lipid loci across African, Asian and European cohorts

Most genome-wide association studies are based on samples of European descent. We assess whether the genetic determinants of blood lipids, a major cardiovascular risk factor, are shared across populations. Genetic correlations for lipids between European-ancestry and Asian cohorts are not significantly different from 1. A genetic risk score based on LDL-cholesterol-associated loci has consistent effects on serum levels in samples from the UK, Uganda and Greece (r = 0.23-0.28, p < 1.9 × 10^-14). Overall, there is evidence of reproducibility for ~75% of the major lipid loci from European discovery studies, except triglyceride loci in the Ugandan samples (10% of loci). Individual transferable loci are identified using trans-ethnic colocalization. Ten of fourteen loci not transferable to the Ugandan population have pleiotropic associations with BMI in Europeans; none of the transferable loci do. The non-transferable loci might affect lipids by modifying food intake in environments rich in certain nutrients, which suggests a potential role for gene-environment interactions.

Large-Scale Phenome-Wide Association Study of PCSK9 Variants Demonstrates Protection Against Ischemic Stroke

BACKGROUND

PCSK9 inhibition is a potent new therapy for hypercholesterolemia and cardiovascular disease. Although short-term clinical trial results have not demonstrated major adverse effects, long-term data will not be available for some time. Genetic studies in large biobanks offer a unique opportunity to predict drug effects and provide context for the evaluation of future clinical trial outcomes.

METHODS

We tested the association of the PCSK9 missense variant rs11591147 with predefined phenotypes and phenome-wide, in 337 536 individuals of British ancestry in the UK Biobank, with independent discovery and replication. Using a Bayesian statistical method, we leveraged phenotype correlations to evaluate the phenome-wide impact of PCSK9 inhibition with higher power at a finer resolution.

RESULTS

The T allele of rs11591147 showed a protective effect on hyperlipidemia (odds ratio, 0.63±0.04; P=2.32×10^-38), coronary heart disease (odds ratio, 0.73±0.09; P=1.05×10^-6), and ischemic stroke (odds ratio, 0.61±0.18; P=2.40×10^-3) and was associated with increased type 2 diabetes mellitus risk adjusted for lipid-lowering medication status (odds ratio, 1.24±0.10; P=1.98×10^-7). We did not observe associations with cataracts, heart failure, atrial fibrillation, and cognitive dysfunction. Leveraging phenotype correlations, we observed evidence of a protective association with cerebral infarction and vascular occlusion. These results explore the effects of direct PCSK9 inhibition; off-target effects cannot be predicted using this approach.

CONCLUSIONS

This result represents the first genetic evidence in a large cohort for the protective effect of PCSK9 inhibition on ischemic stroke and corroborates exploratory evidence from clinical trials. PCSK9 inhibition was not associated with variables other than those related to LDL (low-density lipoprotein) cholesterol, atherosclerosis, and type 2 diabetes mellitus, suggesting that other effects are either small or absent.

Genetically regulated gene expression underlies lipid traits in Hispanic cohorts

Plasma lipid levels are risk factors for cardiovascular disease, a leading cause of death worldwide. While many studies have been conducted in genetic variation underlying lipid levels, they mainly comprise individuals of European ancestry and thus their transferability to non-European populations is unclear. We performed genome-wide (GWAS) and imputed transcriptome-wide association studies of four lipid traits in the Hispanic Community Health Study/Study of Latinos cohort (HCHS/SoL, n = 11,103), replicated top hits in the Multi-Ethnic Study of Atherosclerosis (MESA, n = 3,855), and compared the results to the larger, predominantly European ancestry meta-analysis by the Global Lipids Genetics Consortium (GLGC, n = 196,475). In our GWAS, we found significant SNP associations in regions within or near known lipid genes, but in our admixture mapping analysis, we did not find significant associations between local ancestry and lipid phenotypes. In the imputed transcriptome-wide association study in multiple tissues and in different ethnicities, we found 59 significant gene-tissue-phenotype associations (P < 3.61×10-8) with 14 unique significant genes, many of which occurred across multiple phenotypes, tissues, and ethnicities and replicated in MESA (45/59) and in GLGC (44/59). These include well-studied lipid genes such as SORT1, CETP, and PSRC1, as well as genes that have been implicated in cardiovascular phenotypes, such as CCL22 and ICAM1. The majority (40/59) of significant associations colocalized with expression quantitative trait loci (eQTLs), indicating a possible mechanism of gene regulation in lipid level variation. To fully characterize the genetic architecture of lipid traits in diverse populations, larger studies in non-European ancestry populations are needed.

Multilocus associations of inflammatory genes with the risk of type 1 diabetes

BACKGROUND

Genome-wide association studies have captured a large proportion of genetic variation related to type 1 diabetes mellitus (T1D). However, most of these studies are performed in populations of European ancestry and therefore the disease risk estimations can be inaccurate when extrapolated to other world populations.

METHODS

We conducted a case-control study in 1866 individuals from the three major populations of the Republic of Bashkortostan (Russians, Tatars, and Bashkirs) in Russian Federation, using single-locus and multilocus approach to identify genetic predictors of T1D.

RESULTS

We found that LTA rs909253 and TNF rs1800629 polymorphisms were associated with T1D in the group of Tatars. Meta-analysis of the association study results in the three ethnic groups has confirmed the association between the T1D risk and LTA rs909253 genetic variant. LTA rs909253 and TNF rs1800629 loci were also featured in combinations most significantly associated with T1D.

CONCLUSION

Our findings suggest that LTA rs909253 and TNF rs1800629 polymorphisms are associated with the risk of T1D both independently and in combination with polymorphic markers in other inflammatory genes, and the analysis of multi-allelic combinations provides valuable insight in the study of polygenic traits.

The critical needs and challenges for genetic architecture studies in Africa

Human genetic studies have long been vastly Eurocentric, raising a key question about the generalizability of these study findings to other populations. Because humans originated in Africa, these populations retain more genetic diversity, and yet individuals of African descent have been tremendously underrepresented in genetic studies. The diversity in Africa affords ample opportunities to improve fine-mapping resolution for associated loci, discover novel genetic associations with phenotypes, build more generalizable genetic risk prediction models, and better understand the genetic architecture of complex traits and diseases subject to varying environmental pressures. Thus, it is both ethically and scientifically imperative that geneticists globally surmount challenges that have limited progress in African genetic studies to date. Additionally, African investigators need to be meaningfully included, as greater inclusivity and enhanced research capacity afford enormous opportunities to accelerate genomic discoveries that translate more effectively to all populations. We review the advantages, challenges, and examples of genetic architecture studies of complex traits and diseases in Africa. For example, with greater genetic diversity comes greater ancestral heterogeneity; this higher level of understudied diversity can yield novel genetic findings, but some methods that assume homogeneous population structure and work well in European populations may work less well in the presence of greater heterogeneity in African populations. Consequently, we advocate for methodological development that will accelerate studies important for all populations, especially those currently underrepresented in genetics.

Deciphering the Emerging Complexities of Molecular Mechanisms at GWAS Loci

Genome-wide association studies (GWASs) have identified thousands of loci associated with hundreds of complex diseases and traits, and progress is being made toward elucidating the causal variants and genes underlying these associations. Functional characterization of mechanisms at GWAS loci is a multi-faceted challenge. Challenges include linkage disequilibrium and allelic heterogeneity at each locus, the noncoding nature of most loci, and the time and cost needed for experimentally evaluating the potential mechanistic contributions of genes and variants. As GWAS sample sizes increase, more loci are identified, and the complexities of individual loci emerge. Loci can consist of multiple association signals, each of which can reflect the influence of multiple variants, inseparable by association analyses. Each signal within a locus can influence the same or different target genes. Experimental studies of genes and variants can differ on the basis of cell type, cellular environment, or other context-specific variables. In this review, we describe the complexity of mechanisms at GWAS loci-including multiple signals, multiple variants, and/or multiple genes-and the implications these complexities hold for experimental study design and interpretation of GWAS mechanisms.

Genome-wide association study identifies novel recessive genetic variants for high TGs in an Arab population

Abnormal blood lipid levels are influenced by genetic and lifestyle/dietary factors. Although many genetic variants associated with blood lipid traits have been identified in Europeans, similar data in Middle Eastern populations are limited. We performed a genome-wide association study with Arab individuals (discovery cohort: 1,353; replication cohort: 1,176) from Kuwait to identify possible associations of genetic variants with high lipid levels. We used Illumina HumanOmniExpress BeadChip and candidate SNP genotyping in the discovery and replication phases, respectively. For association tests, we used genetic models that were based on additive and recessive modes of inheritance. High triglycerides (TGs) were recessively associated with six risk variants (rs1002487/RPS6KA1, rs11805972/LAD1) rs7761746/Or5v1, rs39745/CTTNBP2-LSM8, rs2934952/PGAP3, and rs9626773/RP11-191L9.4-CERK) at genome-wide significance (P  6.12E-09), and another six variants (rs10873925/ST6GALNAC5, rs4663379/SPP2-ARL4C, rs10033119/NPY1R, rs17709449/LINC00911-FLRT2, rs11654954/CDK12-NEUROD2, and rs9972882/STARD3) were associated at borderline significance (P  5.0E-08). High TG was also additively associated with rs11654954. All of the 12 identified markers are novel and are harbored in runs of homozygosity. Literature evidence supports the involvement of these gene loci in lipid-related processes. This study in an Arab population augments international efforts to identify genetic regulation of lipid traits.

Identity-by-Descent Mapping Identifies Major Locus for Serum Triglycerides in Amerindians Largely Explained by an APOC3 Founder Mutation

BACKGROUND

Identity-by-descent mapping using empirical estimates of identity-by-descent allele sharing may be useful for studies of complex traits in founder populations, where hidden relationships may augment the inherent genetic information that can be used for localization.

METHODS AND RESULTS

Through identity-by-descent mapping, using ≈400 000 single-nucleotide polymorphisms (SNPs), of serum lipid profiles, we identified a major linkage signal for triglycerides in 1007 Pima Indians (LOD=9.23; P=3.5×10^-11 on chromosome 11q). In subsequent fine-mapping and replication association studies in ≈7500 Amerindians, we determined that this signal reflects effects of a loss-of-function Ala43Thr substitution in APOC3 (rs147210663) and 3 established functional SNPs in APOA5. The association with rs147210663 was particularly strong; each copy of the Thr allele conferred 42% lower triglycerides (β=-0.92±0.059 SD unit; P=9.6×10^-55 in 4668 Pimas and 2793 Southwest Amerindians combined). The Thr allele is extremely rare in most global populations but has a frequency of 2.5% in Pimas. We further demonstrated that 3 APOA5 SNPs with established functional impact could explain the association with the most well-replicated SNP (rs964184) for triglycerides identified by genome-wide association studies. Collectively, these 4 SNPs account for 6.9% of variation in triglycerides in Pimas (and 4.1% in Southwest Amerindians), and their inclusion in the original linkage model reduced the linkage signal to virtually null.

CONCLUSIONS

APOC3/APOA5 constitutes a major locus for serum triglycerides in Amerindians, especially the Pimas, and these results provide an empirical example for the concept that population-based linkage analysis is a useful strategy to identify complex trait variants.

Meta-analysis of airway epithelium gene expression in asthma

Differential gene expression in the airway epithelium of patients with asthma versus controls has been reported in several studies. However, there is no consensus on which genes are reproducibly affected in asthma. We sought to identify a consensus list of differentially expressed genes (DEGs) using a meta-analysis approach.We identified eight studies with data that met defined inclusion criteria. These studies comprised 355 cases and 193 controls and involved sampling either bronchial or nasal epithelium. We conducted study-level analyses, followed by a meta-analysis. Likewise, we applied a meta-analysis framework to the results of study-level pathway enrichment.We identified 1273 DEGs, 431 of which had not been identified in previous studies. 450 DEGs exhibited large effect sizes and were robust to study population differences in age, sex, race/ethnicity, medication use, smoking status and exacerbations. The magnitude of differential expression of these 450 genes was highly similar in bronchial and nasal airway epithelia. Meta-analysis of pathway enrichment revealed a number of consistently dysregulated biological pathways, including putative transcriptional and post-transcriptional regulators.In total, we identified a set of genes that is consistently dysregulated in asthma, that links to known and novel biological pathways, and that will inform asthma subtype identification.

Polygenic influences on dyslipidemias

PURPOSE OF REVIEW

Rare large-effect genetic variants underlie monogenic dyslipidemias, whereas common small-effect genetic variants - single nucleotide polymorphisms (SNPs) - have modest influences on lipid traits. Over the past decade, these small-effect SNPs have been shown to cumulatively exert consistent effects on lipid phenotypes under a polygenic framework, which is the focus of this review.

RECENT FINDINGS

Several groups have reported polygenic risk scores assembled from lipid-associated SNPs, and have applied them to their respective phenotypes. For lipid traits in the normal population distribution, polygenic effects quantified by a score that integrates several common polymorphisms account for about 20-30% of genetic variation. Among individuals at the extremes of the distribution, that is, those with clinical dyslipidemia, the polygenic component includes both rare variants with large effects and common polymorphisms: depending on the trait, 20-50% of susceptibility can be accounted for by this assortment of genetic variants.

SUMMARY

Accounting for polygenic effects increases the numbers of dyslipidemic individuals who can be explained genetically, but a substantial proportion of susceptibility remains unexplained. Whether documenting the polygenic basis of dyslipidemia will affect outcomes in clinical trials or prospective observational studies remains to be determined.

A large electronic-health-record-based genome-wide study of serum lipids

A genome-wide association study of 94,674 multi-ethnic Kaiser Permanente members utilizing 478,866 longitudinal untreated serum lipid electronic-health-record-derived measurements (EHRs) empowered multiple novel findings: 121 new SNP associations (46 primary, 15 conditional, 60 in meta-analysis with Global Lipids Genetic Consortium); increase of 33-42% in variance explained with multiple measurements; sex differences in genetic impact (greater in females for LDL, HDL, TC, the opposite for TG); differences in variance explained amongst non-Hispanic whites, Latinos, African Americans, and East Asians; genetic dominance and epistasis, with strong evidence for both at ABOxFUT2 for LDL; and eQTL tissue-enrichment implicating the liver, adipose, and pancreas. Utilizing EHR pharmacy data, both LDL and TG genetic risk scores (477 SNPs) were strongly predictive of age-at-initiation of lipid-lowering treatment. These findings highlight the value of longitudinal EHRs for identifying novel genetic features of cholesterol and lipoprotein metabolism with implications for lipid treatment and risk of coronary heart disease.

Exome chip meta-analysis identifies novel loci and East Asian-specific coding variants that contribute to lipid levels and coronary artery disease

Most genome-wide association studies have been conducted in European individuals, even though most genetic variation in humans is seen only in non-European samples. To search for novel loci associated with blood lipid levels and clarify the mechanism of action at previously identified lipid loci, we examined protein-coding genetic variants in 47,532 East Asian individuals using an exome array. We identified 255 variants at 41 loci reaching chip-wide significance, including 3 novel loci and 14 East Asian-specific coding variant associations. After meta-analysis with > 300,000 European samples, we identified an additional 9 novel loci. The same 16 genes were identified by the protein-altering variants in both East Asians and Europeans, likely pointing to the functional genes. Our data demonstrate that most of the low-frequency or rare coding variants associated with lipids are population-specific, and that examining genomic data across diverse ancestries may facilitate the identification of functional genes at associated loci.

Common, low-frequency, and rare genetic variants associated with lipoprotein subclasses and triglyceride measures in Finnish men from the METSIM study

Lipid and lipoprotein subclasses are associated with metabolic and cardiovascular diseases, yet the genetic contributions to variability in subclass traits are not fully understood. We conducted single-variant and gene-based association tests between 15.1M variants from genome-wide and exome array and imputed genotypes and 72 lipid and lipoprotein traits in 8,372 Finns. After accounting for 885 variants at 157 previously identified lipid loci, we identified five novel signals near established loci at HIF3A, ADAMTS3, PLTP, LCAT, and LIPG. Four of the signals were identified with a low-frequency (0.005<minor allele frequency [MAF]<0.05) or rare (MAF<0.005) variant, including Arg123His in LCAT. Gene-based associations (P<10-10) support a role for coding variants in LIPC and LIPG with lipoprotein subclass traits. 30 established lipid-associated loci had a stronger association for a subclass trait than any conventional trait. These novel association signals provide further insight into the molecular basis of dyslipidemia and the etiology of metabolic disorders.

Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels

Large-scale meta-analyses of genome-wide association studies (GWAS) have identified >175 loci associated with fasting cholesterol levels, including total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglycerides (TG). With differences in linkage disequilibrium (LD) structure and allele frequencies between ancestry groups, studies in additional large samples may detect new associations. We conducted staged GWAS meta-analyses in up to 69,414 East Asian individuals from 24 studies with participants from Japan, the Philippines, Korea, China, Singapore, and Taiwan. These meta-analyses identified (P < 5 × 10-8) three novel loci associated with HDL-C near CD163-APOBEC1 (P = 7.4 × 10-9), NCOA2 (P = 1.6 × 10-8), and NID2-PTGDR (P = 4.2 × 10-8), and one novel locus associated with TG near WDR11-FGFR2 (P = 2.7 × 10-10). Conditional analyses identified a second signal near CD163-APOBEC1. We then combined results from the East Asian meta-analysis with association results from up to 187,365 European individuals from the Global Lipids Genetics Consortium in a trans-ancestry meta-analysis. This analysis identified (log10Bayes Factor ≥6.1) eight additional novel lipid loci. Among the twelve total loci identified, the index variants at eight loci have demonstrated at least nominal significance with other metabolic traits in prior studies, and two loci exhibited coincident eQTLs (P < 1 × 10-5) in subcutaneous adipose tissue for BPTF and PDGFC. Taken together, these analyses identified multiple novel lipid loci, providing new potential therapeutic targets.

Genetic architecture of lipid traits in the Hispanic community health study/study of Latinos

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.

Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network

Type 2 Diabetes (T2D) constitutes a global health burden. Efforts to uncover predisposing genetic variation have been considerable, yet detailed knowledge of the underlying pathogenesis remains poor. Here, we constructed a T2D phenotypic-linkage network (T2D-PLN), by integrating diverse gene functional information that highlight genes, which when disrupted in mice, elicit similar T2D-relevant phenotypes. Sensitising the network to T2D-relevant phenotypes enabled significant functional convergence to be detected between genes implicated in monogenic or syndromic diabetes and genes lying within genomic regions associated with T2D common risk. We extended these analyses to a recent multiethnic T2D case-control exome of 12,940 individuals that found no evidence of T2D risk association for rare frequency variants outside of previously known T2D risk loci. Examining associations involving protein-truncating variants (PTV), most at low population frequencies, the T2D-PLN was able to identify a convergent set of biological pathways that were perturbed within four of five independent T2D case/control ethnic sets of 2000 to 5000 exomes each. These same pathways were found to be over-represented among both known monogenic or syndromic diabetes genes and genes within T2D-associated common risk loci. Our study demonstrates convergent biology amongst variants representing different classes of T2D genetic risk. Although convergence was observed at the pathway level, few of the contributing genes were found in common between different cohorts or variant classes, most notably between the exome variant sets which suggests that future rare variant studies may be better focusing their power onto a single population of recent common ancestry.

Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases

Recently, many new approaches, study designs, and statistical and analytical methods have emerged for studying gene-environment interactions (G×Es) in large-scale studies of human populations. There are opportunities in this field, particularly with respect to the incorporation of -omics and next-generation sequencing data and continual improvement in measures of environmental exposures implicated in complex disease outcomes. In a workshop called "Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases," held October 17-18, 2014, by the National Institute of Environmental Health Sciences and the National Cancer Institute in conjunction with the annual American Society of Human Genetics meeting, participants explored new approaches and tools that have been developed in recent years for G×E discovery. This paper highlights current and critical issues and themes in G×E research that need additional consideration, including the improved data analytical methods, environmental exposure assessment, and incorporation of functional data and annotations.

Fine-mapping of lipid regions in global populations discovers ethnic-specific signals and refines previously identified lipid loci

Genome-wide association studies have identified over 150 loci associated with lipid traits, however, no large-scale studies exist for Hispanics and other minority populations. Additionally, the genetic architecture of lipid-influencing loci remains largely unknown. We performed one of the most racially/ethnically diverse fine-mapping genetic studies of HDL-C, LDL-C, and triglycerides to-date using SNPs on the MetaboChip array on 54,119 individuals: 21,304 African Americans, 19,829 Hispanic Americans, 12,456 Asians, and 530 American Indians. The majority of signals found in these groups generalize to European Americans. While we uncovered signals unique to racial/ethnic populations, we also observed systematically consistent lipid associations across these groups. In African Americans, we identified three novel signals associated with HDL-C (LPL, APOA5, LCAT) and two associated with LDL-C (ABCG8, DHODH). In addition, using this population, we refined the location for 16 out of the 58 known MetaboChip lipid loci. These results can guide tailored screening efforts, reveal population-specific responses to lipid-lowering medications, and aid in the development of new targeted drug therapies.

Genetic effects influencing risk for major depressive disorder in China and Europe

Major depressive disorder (MDD) is a common, complex psychiatric disorder and a leading cause of disability worldwide. Despite twin studies indicating its modest heritability (~30-40%), extensive heterogeneity and a complex genetic architecture have complicated efforts to detect associated genetic risk variants. We combined single-nucleotide polymorphism (SNP) summary statistics from the CONVERGE and PGC studies of MDD, representing 10 502 Chinese (5282 cases and 5220 controls) and 18 663 European (9447 cases and 9215 controls) subjects. We determined the fraction of SNPs displaying consistent directions of effect, assessed the significance of polygenic risk scores and estimated the genetic correlation of MDD across ancestries. Subsequent trans-ancestry meta-analyses combined SNP-level evidence of association. Sign tests and polygenic score profiling weakly support an overlap of SNP effects between East Asian and European populations. We estimated the trans-ancestry genetic correlation of lifetime MDD as 0.33; female-only and recurrent MDD yielded estimates of 0.40 and 0.41, respectively. Common variants downstream of GPHN achieved genome-wide significance by Bayesian trans-ancestry meta-analysis (rs9323497; log10 Bayes Factor=8.08) but failed to replicate in an independent European sample (P=0.911). Gene-set enrichment analyses indicate enrichment of genes involved in neuronal development and axonal trafficking. We successfully demonstrate a partially shared polygenic basis of MDD in East Asian and European populations. Taken together, these findings support a complex etiology for MDD and possible population differences in predisposing genetic factors, with important implications for future genetic studies.

Genetic associations with lipoprotein subfraction measures differ by ethnicity in the multi-ethnic study of atherosclerosis (MESA)

A recent genome-wide association study associated 62 single nucleotide polymorphisms (SNPs) from 43 genomic loci, with fasting lipoprotein subfractions in European–Americans (EAs) at genome-wide levels of significance across three independent samples. Whether these associations are consistent across ethnicities with a non-European ancestry is unknown. We analyzed 15 lipoprotein subfraction measures, on 1677 African–Americans (AAs), 1450 Hispanic–Americans (HAs), and 775 Chinese–Americans (CHN) participating in the multi-ethnic study of atherosclerosis (MESA). Genome-wide data were obtained using the Affymetrix 6.0 and Illumina HumanOmni chips. Linear regression models between genetic variables and lipoprotein subfractions were adjusted for age, gender, body mass index, smoking, study center, and genetic ancestry (based on principal components), and additionally adjusted for Mexican/Non-Mexican status in HAs. A false discovery rate correction was applied separately within the results for each ethnicity to correct for multiple testing. Power calculations revealed that we did not have the power for SNP-based measures of association, so we analyzed phenotype-specific genetic risk scores (GRSs), constructed as in the original genome-wide analysis. We successfully replicated all 15 GRS–lipoprotein associations in 2527 EAs. Among the 15 significant GRS–lipoprotein associations in EAs, 11 were significant in AAs, 13 in HAs, and 1 in CHNs. Further analyses revealed that ethnicity differences could not be explained by differences in linkage disequilibrium, lipid lowering drugs, diabetes, or gender. Our study emphasizes the importance of ethnicity (here indexing genetic ancestry) in genetic risk for CVD and highlights the need to identify ethnicity-specific genetic variants associated with CVD risk.

The Metabolic Syndrome in Men study: a resource for studies of metabolic and cardiovascular diseases

The Metabolic Syndrome in Men (METSIM) study is a population-based study including 10,197 Finnish men examined in 2005-2010. The aim of the study is to investigate nongenetic and genetic factors associated with the risk of T2D and CVD, and with cardiovascular risk factors. The protocol includes a detailed phenotyping of the participants, an oral glucose tolerance test, fasting laboratory measurements including proton NMR measurements, mass spectometry metabolomics, adipose tissue biopsies from 1,400 participants, and a stool sample. In our ongoing follow-up study, we have, to date, reexamined 6,496 participants. Extensive genotyping and exome sequencing have been performed for essentially all METSIM participants, and >2,000 METSIM participants have been whole-genome sequenced. We have identified several nongenetic markers associated with the development of diabetes and cardiovascular events, and participated in several genetic association studies to identify gene variants associated with diabetes, hyperglycemia, and cardiovascular risk factors. The generation of a phenotype and genotype resource in the METSIM study allows us to proceed toward a "systems genetics" approach, which includes statistical methods to quantitate and integrate intermediate phenotypes, such as transcript, protein, or metabolite levels, to provide a global view of the molecular architecture of complex traits.

Trans-ethnic follow-up of breast cancer GWAS hits using the preferential linkage disequilibrium approach

Leveraging population-distinct linkage equilibrium (LD) patterns, trans-ethnic follow-up of variants discovered from genome-wide association studies (GWAS) has proved to be useful in facilitating the identification of bona fide causal variants. We previously developed the preferential LD approach, a novel method that successfully identified causal variants driving the GWAS signals within European-descent populations even when the causal variants were only weakly linked with the GWAS-discovered variants. To evaluate the performance of our approach in a trans-ethnic setting, we applied it to follow up breast cancer GWAS hits identified mostly from populations of European ancestry in African Americans (AA). We evaluated 74 breast cancer GWAS variants in 8,315 AA women from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Only 27% of them were associated with breast cancer risk at significance level α=0.05, suggesting race-specificity of the identified breast cancer risk loci. We followed up on those replicated GWAS hits in the AMBER consortium utilizing the preferential LD approach, to search for causal variants or better breast cancer markers from the 1000 Genomes variant catalog. Our approach identified stronger breast cancer markers for 80% of the GWAS hits with at least nominal breast cancer association, and in 81% of these cases, the marker identified was among the top 10 of all 1000 Genomes variants in the corresponding locus. The results support trans-ethnic application of the preferential LD approach in search for candidate causal variants, and may have implications for future genetic research of breast cancer in AA women.

Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations.

Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities

Background

Although genome-wide association studies (GWASs) have identified thousands of disease susceptibility regions, the underlying causal mechanism in these regions is not fully known. It is likely that the GWAS signal originates from one or many as yet unidentified causal variants.

Methods

Using next-generation sequencing, we characterized 12 breast cancer susceptibility regions identified by GWASs in 2288 breast cancer cases and 2323 controls across four populations of African American, European, Japanese, and Hispanic ancestry.

Results

After genotype calling and quality control, we identified 137,530 single-nucleotide variants (SNVs); of those, 87.2 % had a minor allele frequency (MAF) <0.005. For SNVs with MAF >0.005, we calculated the smallest number of SNVs needed to obtain a posterior probability set (PPS) such that there is 90 % probability that the causal SNV is included. We found that the PPS for two regions, 2q35 and 11q13, contained less than 5 % of the original SNVs, dramatically decreasing the number of potentially causal SNVs. However, we did not find strong evidence supporting a causal role for any individual SNV. In addition, there were no significant gene-based rare SNV associations after correcting for multiple testing.

Conclusions

This study illustrates some of the challenges faced in fine-mapping studies in the post-GWAS era, most importantly the large sample sizes needed to identify rare-variant associations or to distinguish the effects of strongly correlated common SNVs.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0772-7) contains supplementary material, which is available to authorized users.

Fine-mapping, novel loci identification, and SNP association transferability in a genome-wide association study of QRS duration in African Americans

The electrocardiographic QRS duration, a measure of ventricular depolarization and conduction, is associated with cardiovascular mortality. While single nucleotide polymorphisms (SNPs) associated with QRS duration have been identified at 22 loci in populations of European descent, the genetic architecture of QRS duration in non-European populations is largely unknown. We therefore performed a genome-wide association study (GWAS) meta-analysis of QRS duration in 13,031 African Americans from ten cohorts and a transethnic GWAS meta-analysis with additional results from populations of European descent. In the African American GWAS, a single genome-wide significant SNP association was identified (rs3922844, P = 4 × 10-14) in intron 16 of SCN5A, a voltage-gated cardiac sodium channel gene. The QRS-prolonging rs3922844 C allele was also associated with decreased SCN5A RNA expression in human atrial tissue (P = 1.1 × 10-4). High density genotyping revealed that the SCN5A association region in African Americans was confined to intron 16. Transethnic GWAS meta-analysis identified novel SNP associations on chromosome 18 in MYL12A (rs1662342, P = 4.9 × 10-8) and chromosome 1 near CD1E and SPTA1 (rs7547997, P = 7.9 × 10-9). The 22 QRS loci previously identified in populations of European descent were enriched for significant SNP associations with QRS duration in African Americans (P = 9.9 × 10-7), and index SNP associations in or near SCN5A, SCN10A, CDKN1A, NFIA, HAND1, TBX5 and SETBP1 replicated in African Americans. In summary, rs3922844 was associated with QRS duration and SCN5A expression, two novel QRS loci were identified using transethnic meta-analysis, and a significant proportion of QRS-SNP associations discovered in populations of European descent were transferable to African Americans when adequate power was achieved.

Improved methods for multi-trait fine mapping of pleiotropic risk loci

MOTIVATION

Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologically causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation.

RESULTS

In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution compared to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data.

AVAILABILITY AND IMPLEMENTATION

The fastPAINTOR framework is implemented in the PAINTOR v3.0 package which is publicly available to the research community http://bogdan.bioinformatics.ucla.edu/software/paintor CONTACT: gkichaev@ucla.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Fine-scale mapping of 8q24 locus identifies multiple independent risk variants for breast cancer

Previous genome-wide association studies among women of European ancestry identified two independent breast cancer susceptibility loci represented by single nucleotide polymorphisms (SNPs) rs13281615 and rs11780156 at 8q24. A fine-mapping study across 2.06 Mb (chr8:127,561,724-129,624,067, hg19) in 55,540 breast cancer cases and 51,168 controls within the Breast Cancer Association Consortium was conducted. Three additional independent association signals in women of European ancestry, represented by rs35961416 (OR = 0.95, 95% CI = 0.93-0.97, conditional p = 5.8 × 10(-6) ), rs7815245 (OR = 0.94, 95% CI = 0.91-0.96, conditional p = 1.1 × 10(-6) ) and rs2033101 (OR = 1.05, 95% CI = 1.02-1.07, conditional p = 1.1 × 10(-4) ) were found. Integrative analysis using functional genomic data from the Roadmap Epigenomics, the Encyclopedia of DNA Elements project, the Cancer Genome Atlas and other public resources implied that SNPs rs7815245 in Signal 3, and rs1121948 in Signal 5 (in linkage disequilibrium with rs11780156, r(2)  = 0.77), were putatively functional variants for two of the five independent association signals. The results highlighted multiple 8q24 variants associated with breast cancer susceptibility in women of European ancestry.

Coding-sequence variants are associated with blood lipid levels in 14,473 Chinese

Previously identified common variants explain only a small fraction of the trait heritability and at most loci the identities of the underlying causal genes and their functional variants still remain unknown. To identify the low-frequency and rare coding variants that influence lipid levels, we conducted a meta-analysis of exome-wide association studies in 14,473 Chinese subjects, followed by a joint analysis with 1000 genomes imputed data from 6,534 samples. We replicated 24 previously reported lipid loci with exome-wide significance (P < 3.3 × 10 ^{-  7}), including fourteen coding variants at ten confirmed lipid loci (P range from 1.44 × 10 ^{-  7} to 1.64 × 10 ^{-  45}). Of these, six coding variants showed population-specific associations and were independent of previously identified associations in European populations, including four low-frequency (PCSK9 p.Arg93Cys, HMGCR p.Tyr311Ser, APOA5 p.Gly185Cys and CETP p.Asp399Gly) and two common (APOB p.Arg532Trp and APOA4 p.Ser147Asn) variants. Furthermore, we detected three new lead non-coding variants at LPA, LIPC and LDLR in Chinese. The independent variants at PCSK9, HMGCR, LPA, APOA5 and LDLR were also associated with increased risk of coronary artery disease in the expected direction. In gene-based tests, the burden of rare or low frequency variants in PCSK9, HMGCR and CEPT exhibited strong associations with blood lipid levels (P < 2.8 × 10 ^{-  6}). Our findings identify additional population-specific possible causal variants. Our data demonstrate that the inter-ethnic differences in allele frequencies of coding variants may lead to different association signals across ethnic groups, highlighting the importance of including diverse populations to uncover genetic variation associated with lipid levels.