The power of genetic diversity in genome-wide association studies of lipids.

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use1. Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels2, heart disease remains the leading cause of death worldwide3. Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns24. Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine25, we anticipate that increased diversity of participants will lead to more accurate and equitable26 application of polygenic scores in clinical practice.

A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids.

A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.

Phenome-wide association study of the major histocompatibility complex region in the Korean population identifies novel association signals.

Human leukocyte antigen (HLA) gene variants in the major histocompatibility complex (MHC) region are associated with numerous complex human diseases and quantitative traits. Previous phenome-wide association studies (PheWAS) for this region demonstrated that HLA association patterns to the phenome have both population-specific and population-shared components. We performed MHC PheWAS in the Korean population by analyzing associations between phenotypes and genetic variants in the MHC region using the Korea Biobank Array project data samples from the Korean Genome and Epidemiology Study cohorts. Using this single-population dataset, we curated and analyzed 82 phenotypes for 125 673 Korean individuals after imputing HLA using CookHLA, a recently developed imputation framework. More than one-third of these phenotypes showed significant associations, confirming 56 known associations and discovering 13 novel association signals that were not reported previously. In addition, we analyzed heritability explained by the variants in the MHC region and genetic correlations among phenotypes based on the MHC variants.

Biological insights into systemic lupus erythematosus through an immune cell-specific transcriptome-wide association study.

OBJECTIVE: Genome-wide association studies (GWAS) have identified >100 risk loci for systemic lupus erythematosus (SLE), but the disease genes at most loci remain unclear, hampering translation of these genetic discoveries. We aimed to prioritise genes underlying the 110 SLE loci that were identified in the latest East Asian GWAS meta-analysis. METHODS: We built gene expression predictive models in blood B cells, CD4+ and CD8+ T cells, monocytes, natural killer cells and peripheral blood cells of 105 Japanese individuals. We performed a transcriptome-wide association study (TWAS) using data from the latest genome-wide association meta-analysis of 208 370 East Asians and searched for candidate genes using TWAS and three data-driven computational approaches. RESULTS: TWAS identified 171 genes for SLE (p<1.0×10-5); 114 (66.7%) showed significance only in a single cell type; 127 (74.3%) were in SLE GWAS loci. TWAS identified a strong association between CD83 and SLE (p<7.7×10-8). Meta-analysis of genetic associations in the existing 208 370 East Asian and additional 1498 cases and 3330 controls found a novel single-variant association at rs72836542 (OR=1.11, p=4.5×10-9) around CD83. For the 110 SLE loci, we identified 276 gene candidates, including 104 genes at recently-identified SLE novel loci. We demonstrated in vitro that putative causal variant rs61759532 exhibited an allele-specific regulatory effect on ACAP1, and that presence of the SLE risk allele decreased ACAP1 expression. CONCLUSIONS: Cell-level TWAS in six types of immune cells complemented SLE gene discovery and guided the identification of novel genetic associations. The gene findings shed biological insights into SLE genetic associations.

Meta-analysis of 208370 East Asians identifies 113 susceptibility loci for systemic lupus erythematosus.

OBJECTIVE: Systemic lupus erythematosus (SLE), an autoimmune disorder, has been associated with nearly 100 susceptibility loci. Nevertheless, these loci only partially explain SLE heritability and their putative causal variants are rarely prioritised, which make challenging to elucidate disease biology. To detect new SLE loci and causal variants, we performed the largest genome-wide meta-analysis for SLE in East Asian populations. METHODS: We newly genotyped 10 029 SLE cases and 180 167 controls and subsequently meta-analysed them jointly with 3348 SLE cases and 14 826 controls from published studies in East Asians. We further applied a Bayesian statistical approach to localise the putative causal variants for SLE associations. RESULTS: We identified 113 genetic regions including 46 novel loci at genome-wide significance (p<5×10-8). Conditional analysis detected 233 association signals within these loci, which suggest widespread allelic heterogeneity. We detected genome-wide associations at six new missense variants. Bayesian statistical fine-mapping analysis prioritised the putative causal variants to a small set of variants (95% credible set size ≤10) for 28 association signals. We identified 110 putative causal variants with posterior probabilities ≥0.1 for 57 SLE loci, among which we prioritised 10 most likely putative causal variants (posterior probability ≥0.8). Linkage disequilibrium score regression detected genetic correlations for SLE with albumin/globulin ratio (rg=-0.242) and non-albumin protein (rg=0.238). CONCLUSION: This study reiterates the power of large-scale genome-wide meta-analysis for novel genetic discovery. These findings shed light on genetic and biological understandings of SLE.

Exome chip-driven association study of lipidemia in >14,000 Koreans and evaluation of genetic effect on identified variants between different ethnic groups.

Lipid levels in blood are widely used to diagnose and monitor chronic diseases. It is essential to identify the genetic traits involved in lipid metabolism for understanding chronic diseases. However, the influence of genetic traits varies depending on race, sex, age, and ethnicity. Therefore, research focusing on populations of individual countries is required, and the results can be used as a basis for comparison of results of other studies at the cross-racial and cross-country levels. In the present study, we selected lipid-related variants and evaluated their effects on lipid-related diseases in more than 14,000 subjects of three cohorts using the Illumina Human Exome Beadchip. A genome-wide association study was conducted using EPACTs after adjusting for age, sex, and recruitment area. A genome-wide significance cutoff was defined as p < 5E-08 in all the three cohorts. Sixteen variants represented the lipid traits and were classified as vulnerable to borderline hypertriglyceridemia, hyper-LDL-cholesterolemia, or hypo-HDL-cholesterolemia. Moreover, we compared the genetic effects of the 16 variants between ethnic groups and identified the missense variants in apolipoprotein A-V, cholesterol ester transfer protein, and apolipoprotein E as Asian-specific. Our study provides candidate genes as markers for chronic diseases through the evaluation of genetic effects.

Genome-wide association study in a Korean population identifies six novel susceptibility loci for rheumatoid arthritis.

OBJECTIVE: Genome-wide association studies (GWAS) in rheumatoid arthritis (RA) have discovered over 100 RA loci, explaining patient-relevant RA pathogenesis but showing a large fraction of missing heritability. As a continuous effort, we conducted GWAS in a large Korean RA case-control population. METHODS: We newly generated genome-wide variant data in two independent Korean cohorts comprising 4068 RA cases and 36 487 controls, followed by a whole-genome imputation and a meta-analysis of the disease association results in the two cohorts. By integrating publicly available omics data with the GWAS results, a series of bioinformatic analyses were conducted to prioritise the RA-risk genes in RA loci and to dissect biological mechanisms underlying disease associations. RESULTS: We identified six new RA-risk loci (SLAMF6, CXCL13, SWAP70, NFKBIA, ZFP36L1 and LINC00158) with pmeta<5×10-8 and consistent disease effect sizes in the two cohorts. A total of 122 genes were prioritised from the 6 novel and 13 replicated RA loci based on physical distance, regulatory variants and chromatin interaction. Bioinformatics analyses highlighted potentially RA-relevant tissues (including immune tissues, lung and small intestine) with tissue-specific expression of RA-associated genes and suggested the immune-related gene sets (such as CD40 pathway, IL-21-mediated pathway and citrullination) and the risk-allele sharing with other diseases. CONCLUSION: This study identified six new RA-associated loci that contributed to better understanding of the genetic aetiology and biology in RA.

On the association analysis of CNV data: a fast and robust family-based association method.

BACKGROUND: Copy number variation (CNV) is known to play an important role in the genetics of complex diseases and several methods have been proposed to detect association of CNV with phenotypes of interest. Statistical methods for CNV association analysis can be categorized into two different strategies. First, the copy number is estimated by maximum likelihood and association of the expected copy number with the phenotype is tested. Second, the observed probe intensity measurements can be directly used to detect association of CNV with the phenotypes of interest. RESULTS: For each strategy we provide a statistic that can be applied to extended families. The computational efficiency of the proposed methods enables genome-wide association analysis and we show with simulation studies that the proposed methods outperform other existing approaches. In particular, we found that the first strategy is always more efficient than the second strategy no matter whether copy numbers for each individual are well identified or not. With the proposed methods, we performed genome-wide CNV association analyses of hematological trait, hematocrit, on 521 Korean family samples. CONCLUSIONS: We found that statistical analysis with the expected copy number is more powerful than the statistic with the probe intensity measurements regardless of the accuracy of the estimation of copy numbers.

Evaluation of pleiotropic effects among common genetic loci identified for cardio-metabolic traits in a Korean population.

BACKGROUND: The genetic contribution to complex diseases or traits, including cardio-metabolic traits, has been elucidated recently by large-scale genome-wide association studies. These genome-wide association studies have indicated that most pleiotropic loci contain genes associated with lipids. Clinically, lipid related abnormalities are strongly associated with other diseases such as type 2 diabetes, coronary artery disease and hypertension. The aim of this study was to evaluate the shared genetic background of lipids and other cardio-metabolic traits. METHODS: We conducted meta-analyses of the association between 157 published lipid-associated loci and 10 cardio-metabolic traits in 14,028 Korean individuals genotyped using the Exome chip (Illumina HumanExome BeadChip). We also examined whether the pleiotropic effects of such loci constituted independent (i.e., biological) pleiotropy or mediated pleiotropy in these metabolic pathways. RESULTS: Eighteen lipid-associated loci were significantly associated with one of six cardio-metabolic traits after correction for multiple testing (P < 3.70 × 10(-4)). Region 12q24.12 had pleiotropic effects on fasting plasma glucose, blood pressure and obesity-related traits (body mass index and waist-hip ratio) independent of its effects on the lipid profile. Lipid risk scores, calculated according to whether or not subjects carried the risk allele for lipid traits, were significantly associated with fasting plasma glucose, blood pressure and obesity-related traits. CONCLUSIONS: The 12q24.12 region showed ethnic-specific genetic pleiotropy among cardio-metabolic traits in this study. Our findings may help to account for molecular mechanisms based on shared genetic background underlying not only dyslipidemia, but also cardiovascular disease and type 2 diabetes.

Combinatorial approach to estimate copy number genotype using whole-exome sequencing data.

Copy number variations (CNVs) are known risk factors in complex diseases. Array-based approaches have been widely used to detect CNVs, but limitations of array-based CNV detection methods, such as noisy signal and low resolution, have hindered detection of small CNVs. Recently, the development of next-generation sequencing techniques has increased rapidly owing to declines in cost. Particularly, whole-exome sequencing has proved useful for finding causal genes and variants in complex diseases. Because gene copy number may affect expression, CNV genotyping can be very valuable in disease association studies. However, almost all current CNV detection tools consider only two types of CNV genotypes. In this study, we propose a CNV genotype estimation approach using a combination of existing methods. Our approach was comprehensively compared with the customized Agilent array-comparative genomic hybridization. We found that our genotyping approach proved to be accurate, and reproducible, suggesting that it can complement existing CNV genotyping methods.

A genome-wide association study of copy-number variation identifies putative loci associated with osteoarthritis in Koreans.

BACKGROUND: OA is a complex disease caused by environmental and genetic risk factors. The purpose of this study is to identify candidate copy number variations (CNVs) associated with OA. METHODS: We performed a genome-wide association study of CNV to identify potential loci that confer susceptibility to or protection from OA. CNV genotyping was conducted using NimbleGen HD2 3 × 720K comparative hybridization array and included samples from 371 OA patients and 467 healthy controls. The putative CNV regions identified were confirmed with a TaqMan assay. RESULTS: We identified six genomic regions associated with OA encompassing CNV loci. None of six loci had previously been reported in genome-wide association studies with OA, although a genetic analysis suggested that they have functional effects. The protein product of a candidate risk gene for obesity, TNKS, targets Wnt inhibition, and this gene was significantly associated with hand and knee OA. Copy number deletion on TNKS was associated with a 1.37-fold decreased risk for OA. In addition, CA10, which shows a strong association with osteoporosis, was also significant in our study. Copy number deletion on this gene was associated with a 1.69-fold decreased risk for OA. CONCLUSION: We identified several CNV loci that may contribute to OA susceptibility in Koreans. Further functional investigations of these genes are warranted to fully characterize their putative association.

Genome-wide copy number variation study reveals KCNIP1 as a modulator of insulin secretion.

Copy number variations (CNVs) have emerged as another important genetic marker in addition to SNP for understanding etiology of complex diseases. In light of this, we performed a genome-wide CNV study to identify type 2 diabetes (T2D)-associated CNV using an array comparative genomic hybridization from 3180 subjects for T2D cases (n=863) and controls (n=2,317). Thus, five CNV regions having a p-value threshold ≤0.05 were identified and evaluated by validation with quantitative PCR and comparison with previously reported CNV regions in the Database of Genomic Variants. Furthermore, we performed a functional experiment to assess the biological significance of a gene encompassing a CNV region. The inhibition of KCNIP1 led to increased insulin secretion in a glucose-dependent manner, but had no effect on insulin gene transcription as well as cell apoptosis. Taken together, these data indicate that KCNIP1 from CNV study might function as a T2D-susceptibility gene whose dysregulation alters insulin production.

KGVDB: a population-based genomic map of CNVs tagged by SNPs in Koreans.

SUMMARY: Despite a growing interest in a correlation between copy number variations (CNVs) and flanking single nucleotide polymorphisms, few databases provide such information. In particular, most information on CNV available so far was obtained in Caucasian and Yoruba populations, and little is known about CNV in Asian populations. This article presents a database that provides CNV regions tagged by single nucleotide polymorphisms in about 4700 Koreans, which were detected under strict quality control, manually curated and experimentally validated. AVAILABILITY: KGVDB is freely available for non-commercial use at http://biomi.cdc.go.kr/KGVDB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification of a genetic variant at 2q12.1 associated with blood pressure in East Asians by genome-wide scan including gene-environment interactions.

BACKGROUND: Genome-wide association studies have identified many genetic loci associated with blood pressure (BP). Genetic effects on BP can be altered by environmental exposures via multiple biological pathways. Especially, obesity is one of important environmental risk factors that can have considerable effect on BP and it may interact with genetic factors. Given that, we aimed to test whether genetic factors and obesity may jointly influence BP. METHODS: We performed meta-analyses of genome-wide association data for systolic blood pressure (SBP) and diastolic blood pressure (DBP) that included analyses of interaction between single nucleotide polymorphisms (SNPs) and the obesity-related anthropometric measures, body mass index (BMI), height, weight, and waist/hip ratio (WHR) in East-Asians (n = 12,030). RESULTS: We identified that rs13390641 on 2q12.1 demonstrated significant association with SBP when the interaction between SNPs and BMI was considered (P < 5 × 10 -8). The gene located nearest to rs13390641, TMEM182, encodes transmembrane protein 182. In stratified analyses, the effect of rs13390641 on BP was much stronger in obese individuals (BMI ≥ 30) than non-obese individuals and the effect of BMI on BP was strongest in individuals with the homozygous A allele of rs13390641. CONCLUSIONS: Our analyses that included interactions between SNPs and environmental factors identified a genetic variant associated with BP that was overlooked in standard analyses in which only genetic factors were included. This result also revealed a potential mechanism that integrates genetic factors and obesity related traits in the development of high BP.

Gene-based copy number variation study reveals a microdeletion at 12q24 that influences height in the Korean population.

Height is a classic polygenic trait with high heritability (h(2)=0.8). Recent genome-wide association studies have revealed many independent loci associated with human height. In addition, although many studies have reported an association between copy number variation (CNV) and complex diseases, few have explored the relationship between CNV and height. Recent studies reported that single nucleotide polymorphisms (SNPs) are highly correlated with common CNVs, suggesting that it is warranted to survey CNVs to identify additional genetic factors affecting heritable traits such as height. This study tested the hypothesis that there would be CNV regions (CNVRs) associated with height nearby genes from the GWASs known to affect height. We identified regions containing >1% copy number deletion frequency from 3667 population-based cohort samples using the Illumina HumanOmni1-Quad BeadChip. Among the identified CNVRs, we selected 15 candidate regions that were located within 1Mb of 283 previously reported genes. To assess the effect of these CNVRs on height, statistical analyses were conducted with samples from a case group of 370 taller (upper 10%) individuals and a control group of 1828 individuals (lower 50%). We found that a newly identified 17.7 kb deletion at chromosomal position 12q24.33, approximately 171.6 kb downstream of GPR133, significantly correlated with height; this finding was validated using quantitative PCR. These results suggest that CNVs are potentially important in determining height and may contribute to height variation in human populations.

Large-scale cross-ancestry genome-wide meta-analysis of serum urate.

Hyperuricemia is an essential causal risk factor for gout and is associated with cardiometabolic diseases. Given the limited contribution of East Asian ancestry to genome-wide association studies of serum urate, the genetic architecture of serum urate requires exploration. A large-scale cross-ancestry genome-wide association meta-analysis of 1,029,323 individuals and ancestry-specific meta-analysis identifies a total of 351 loci, including 17 previously unreported loci. The genetic architecture of serum urate control is similar between European and East Asian populations. A transcriptome-wide association study, enrichment analysis, and colocalization analysis in relevant tissues identify candidate serum urate-associated genes, including CTBP1, SKIV2L, and WWP2. A phenome-wide association study using polygenic risk scores identifies serum urate-correlated diseases including heart failure and hypertension. Mendelian randomization and mediation analyses show that serum urate-associated genes might have a causal relationship with serum urate-correlated diseases via mediation effects. This study elucidates our understanding of the genetic architecture of serum urate control.

Analyzing the Korean reference genome with meta-imputation increased the imputation accuracy and spectrum of rare variants in the Korean population.

Genotype imputation is essential for enhancing the power of association-mapping and discovering rare and indels that are missed by most genotyping arrays. Imputation analysis can be more accurate with a population-specific reference panel or a multi-ethnic reference panel with numerous samples. The National Institute of Health, Republic of Korea, initiated the Korean Reference Genome (KRG) project to identify variants in whole-genome sequences of ∼20,000 Korean participants. In the pilot phase, we analyzed the data from 1,490 participants. The genetic characteristics and imputation performance of the KRG were compared with those of the 1,000 Genomes Project Phase 3, GenomeAsia 100K Project, ChinaMAP, NARD, and TOPMed reference panels. For comparison analysis, genotype panels were artificially generated using whole-genome sequencing data from combinations of four different ancestries (Korean, Japanese, Chinese, and European) and two population-specific optimized microarrays (Korea Biobank Array and UK Biobank Array). The KRG reference panel performed best for the Korean population (R 2 = 0.78-0.84, percentage of well-imputed is 91.9% for allele frequency >5%), although the other reference panels comprised a larger number of samples with genetically different background. By comparing multiple reference panels and multi-ethnic genotype panels, optimal imputation was obtained using reference panels from genetically related populations and a population-optimized microarray. Indeed, the reference panels of KRG and TOPMed showed the best performance when applied to the genotype panels of KBA (R 2 = 0.84) and UKB (R 2 = 0.87), respectively. Using a meta-imputation approach to merge imputation results from different reference panels increased the imputation accuracy for rare variants (∼7%) and provided additional well-imputed variants (∼20%) with comparable imputation accuracy to that of the KRG. Our results demonstrate the importance of using a population-specific reference panel and meta-imputation to assess a substantial number of accurately imputed rare variants.

Shared genetic architectures of subjective well-being in East Asian and European ancestry populations.

Subjective well-being (SWB) has been explored in European ancestral populations; however, whether the SWB genetic architecture is shared across populations remains unclear. We conducted a cross-population genome-wide association study for SWB using samples from Korean (n = 110,919) and European (n = 563,176) ancestries. Five ancestry-specific loci and twelve cross-ancestry significant genomic loci were identified. One novel locus (rs12298541 near HMGA2) associated with SWB was also identified through the European meta-analysis. Significant cross-ancestry genetic correlation for SWB between samples was observed. Polygenic risk analysis in an independent Korean cohort (n = 22,455) demonstrated transferability between populations. Significant correlations between SWB and major depressive disorder, and significant enrichment of central nervous system-related polymorphisms heritability in both ancestry populations were found. Hence, large-scale cross-ancestry genome-wide association studies can advance our understanding of SWB genetic architecture and mental health.

The contribution of common and rare genetic variants to variation in metabolic traits in 288,137 East Asians.

Metabolic traits are heritable phenotypes widely-used in assessing the risk of various diseases. We conduct a genome-wide association analysis (GWAS) of nine metabolic traits (including glycemic, lipid, liver enzyme levels) in 125,872 Korean subjects genotyped with the Korea Biobank Array. Following meta-analysis with GWAS from Biobank Japan identify 144 novel signals (MAF ≥ 1%), of which 57.0% are replicated in UK Biobank. Additionally, we discover 66 rare (MAF < 1%) variants, 94.4% of them co-incident to common loci, adding to allelic series. Although rare variants have limited contribution to overall trait variance, these lead, in carriers, substantial loss of predictive accuracy from polygenic predictions of disease risk from common variant alone. We capture groups with up to 16-fold variation in type 2 diabetes (T2D) prevalence by integration of genetic risk scores of fasting plasma glucose and T2D and the I349F rare protective variant. This study highlights the need to consider the joint contribution of both common and rare variants on inherited risk of metabolic traits and related diseases.