A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations

Genome-wide association studies (GWAS) have become the preferred experimental design in exploring the genetic etiology of complex human traits and diseases. Standard SNP-based meta-analytic approaches have been utilized to integrate the results from multiple experiments. This fundamentally assumes that the patterns of linkage disequilibrium (LD) between the underlying causal variants and the directly genotyped SNPs are similar across the populations for the same SNPs to emerge with surrogate evidence of disease association. We introduce a novel strategy for assessing regional evidence of phenotypic association that explicitly incorporates the extent of LD in the region. This provides a natural framework for combining evidence from multi-ethnic studies of both dichotomous and quantitative traits that (i) accommodates different patterns of LD, (ii) integrates different genotyping platforms and (iii) allows for the presence of allelic heterogeneity between the populations. Our method can also be generalized to perform gene-based or pathway-based analyses. Applying this method on real GWAS data in type 2 diabetes (T2D) boosted the association evidence in regions well-established for T2D etiology in three diverse South-East Asian populations, as well as identified two novel gene regions and a biologically convincing pathway that are subsequently validated with data from the Wellcome Trust Case Control Consortium.     

Transethnic differences in GWAS signals: A simulation study

Genome‐wide association studies (GWASs) have allowed researchers to identify thousands of single nucleotide polymorphisms (SNPs) and other variants associated with particular complex traits. Previous studies have reported differences in the strength and even the direction of GWAS signals across different populations. These differences could be due to a combination of (1) lack of power, (2) allele frequency differences, (3) linkage disequilibrium (LD) differences, and (4) true differences in causal variant effect sizes. To determine whether properties (1)–(3) on their own might be sufficient to explain the patterns previously noted in strong GWAS signals, we simulated case–control data of European, Asian and African ancestry, applying realistic allele frequencies and LD from 1000 Genomes data but enforcing equal causal effect sizes across populations. Much of the observed differences in strong GWAS signals could indeed be accounted for by allele frequency and LD differences, enhanced by the Euro‐centric SNP bias and lower SNP coverage found in older GWAS panels. While we cannot rule out a role for true transethnic effect size differences, our results suggest that strong causal effects may be largely shared among human populations, motivating the use of transethnic data for fine‐mapping.     

Increasing the resolution and precision of psychiatric genome‐wide association studies by re‐imputing summary statistics using a large,diverse reference panel

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large‐scale genome‐wide association studies (GWAS). Methods for direct imputation of GWAS summary‐statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage‐disequilibrium (LD) and that most of the methods using a small reference panel for example, ~2,500‐subject coming from the 1000 Genome‐Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: (a) uses a much larger and more diverse reference panel compared to traditional reference panels, and (b) can estimate weights of ethnic‐mixture based solely on Z‐scores, when allele frequencies are not available. We applied DISTMIX2 to GWAS summary‐statistics from the psychiatric genetic consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post‐traumatic stress disorder GWAS found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most psychiatric GWAS‐studies.     

Genome-wide association studies: Where we are heading?

We have witnessed tremendous success in genome-wide association studies (GWAS) in recent years. Since the identification of variants in the complement factor H gene on the risk of age-related macular degeneration, GWAS have become ubiquitous in genetic studies and have led to the identification of genetic variants that are associated with a variety of complex human diseases and traits. These discoveries have changed our understanding of the biological architecture of common, complex diseases and have also provided new hypotheses to test. New tools, such as next-generation sequencing, will be an important part of the future of genetics research; however, GWAS studies will continue to play an important role in disease gene discovery. Many traits have yet to be explored by GWAS, especially in minority populations, and large collaborative studies are currently being conducted to maximize the return from existing GWAS data. In addition, GWAS technology continues to improve, increasing genomic coverage for major global populations and decreasing the cost of experiments. Although much of the variance attributable to genetic factors for many important traits is still unexplained, GWAS technology has been instrumental in mapping over a thousand genes to hundreds of traits. More discoveries are made each month and the scale, quality and quantity of current work has a steady trend upward. We briefly review the current key trends in GWAS, which can be summarized with three goals: increase power, increase collaborations and increase populations.     

C-reactive protein (CRP) promoter polymorphisms influence circulating CRP levels in a genome-wide association study of African Americans

C-reactive protein (CRP) is an acute phase reactant protein produced primarily by the liver. Circulating CRP levels are influenced by genetic and non-genetic factors, including infection and obesity. Genome-wide association studies (GWAS) provide an unbiased approach towards identifying loci influencing CRP levels. None of the six GWAS for CRP levels has been conducted in an African ancestry population. The present study aims to: (i) identify genetic variants that influence serum CRP in African Americans (AA) using a genome-wide association approach and replicate these findings in West Africans (WA), (ii) assess transferability of major signals for CRP reported in European ancestry populations (EA) to AA and (iii) use the weak linkage disequilibrium (LD) structure characteristic of African ancestry populations to fine-map the previously reported CRP locus. The discovery cohort comprised 837 unrelated AA, with the replication of significant single-nucleotide polymorphisms (SNPs) assessed in 486 WA. The association analysis was conducted with 2 366 856 genotyped and imputed SNPs under an additive genetic model with adjustment for appropriate covariates. Genome-wide and replication significances were set at P < 5 × 10(-8) and P < 0.05, respectively. Ten SNPs in?(CRP pseudogene-1) CRPP1 and CRP genes were associated with serum CRP (P = 2.4 × 10(-09) to 4.3 × 10(-11)). All but one of the top-scoring SNPs associated with CRP in AA were successfully replicated in WA. CRP signals previously identified in EA samples were transferable to AAs, and we were able to fine-map this signal, reducing the region of interest from the 25 kb of LD around the locus in the HapMap CEU sample to only 8 kb in our AA sample.     

Underestimation of heritability using a mixed model with a polygenic covariance structure in a genome-wide association study for complex traits

Recently, the use of a mixed model methodology in genome-wide association studies (GWAS) has been considered effective for controlling population stratification and explaining the polygenic effects of complex traits. However, estimating polygenic variance components and heritability was biased when the mixed model was used. This bias results from a diluted genetic relationship covariance structure, particularly with a limited number of underlying causal variants. We simulated disease and quantitative phenotypes with a variety of heritabilities (0.1, 0.2, 0.3, 0.4, and 0.5), prevalence rates (0.1, 0.2, 0.3, and 0.5), and causal variant numbers (10, 30, 50, and 100). Heritabilities from the simulated data using restricted maximum likelihood were underestimated in many populations (P<0.05). The underestimation increased with a large heritability, a small prevalence, and a small number of causal variants. The underestimation was larger in analyzing disease traits compared with quantitative traits. This study suggests an underestimated heritability in GWAS upon using the mixed model methodology with an excessively larger number of variants versus causal variants.     

FTO polymorphisms in oceanic populations

It has been suggested that Neel’s “thrifty genotype” model may account for high body weights in some Oceanic populations, which presumably arose in modern times. In European populations, common variants (rs1421085-C, rs17817449-G, and rs9939609-A) in the fat mass and obesity (FTO associated) were recently found to be associated with body mass index (BMI) or obesity. In this study, we investigated the population frequencies of these variants in six Oceanic populations (Melanesians, Micronesians, and Polynesians) and tested for an association with BMI. Unlike European populations, the Oceanic populations displayed no significant association between the FTO polymorphisms and BMI. These variants were in strong linkage disequilibrium. The population frequencies ranged between 4.2 and 30.3% in the six Oceanic populations, and were similar to those in southeast and east Asian populations. Our study of the FTO polymorphisms has generated no evidence to support the thrifty genotype hypothesis for Oceanic populations.     

Population differences in DNA sequence variation and linkage disequilibrium at the PON1 gene

Polymorphisms of the promoter region (?108C/T) and the coding region (192Q/R) of the paraoxonase 1 gene (PON1) showed differences in association with cardiovascular disease risk in various populations. To characterize the genetic variation underlying these important polymorphisms, we examined DNA sequence variation both in a 1.3‐kb promoter region 16.5 kb from codon 192, and in a 1.7‐kb region centered on the 192Q/R polymorphic site of the coding region of PON1, in 30 Africans, 30 Europeans and 64 Japanese. We found 10 polymorphic sites and 11 haplotypes in the 1.3‐kb promoter region and 10 biallelic polymorphic sites and 10 haplotypes in the 1.7‐kb region. From the PON1 sequences of chimpanzees and an orangutan, the ancestral type of codon 192 was found to be R. The number of pairs of polymorphic sites between the promoter and 1.7‐kb regions that were in significant linkage disequilibrium was much higher in a Japanese population than in African and European populations. In addition, the pairs of polymorphic sites in linkage disequilibrium differed among the three populations. These results suggest that some of the population differences in association with risk for coronary heart disease can be explained by population differences in haplotype frequency of PON1 haplotypes.     

Comparison of dimension reduction-based logistic regression models for case-control genome-wide association study: principal components analysis vs. partial least squares

With recent advances in biotechnology, genome-wide association study (GWAS) has been widely used to identify genetic variants that underlie human complex diseases and traits. In case-control GWAS, typical statistical strategy is traditional logistical regression (LR) based on single-locus analysis. However, such a single-locus analysis leads to the well-known multiplicity problem, with a risk of inflating type I error and reducing power. Dimension reduction-based techniques, such as principal component-based logistic regression (PC-LR), partial least squares-based logistic regression (PLS-LR), have recently gained much attention in the analysis of high dimensional genomic data. However, the performance of these methods is still not clear, especially in GWAS. We conducted simulations and real data application to compare the type I error and power of PC-LR, PLS-LR and LR applicable to GWAS within a defined single nucleotide polymorphism (SNP) set region. We found that PC-LR and PLS can reasonably control type I error under null hypothesis. On contrast, LR, which is corrected by Bonferroni method, was more conserved in all simulation settings. In particular, we found that PC-LR and PLS-LR had comparable power and they both outperformed LR, especially when the causal SNP was in high linkage disequilibrium with genotyped ones and with a small effective size in simulation. Based on SNP set analysis, we applied all three methods to analyze non-small cell lung cancer GWAS data.     

Dissecting the genetic make-up of North-East Sardinia using a large set of haploid and autosomal markers

Sardinia has been used for genetic studies because of its historical isolation, genetic homogeneity and increased prevalence of certain rare diseases. Controversy remains concerning the genetic substructure and the extent of genetic homogeneity, which has implications for the design of genome-wide association studies (GWAS). We revisited this issue by examining the genetic make-up of a sample from North-East Sardinia using a dense set of autosomal, Y chromosome and mitochondrial markers to assess the potential of the sample for GWAS and fine mapping studies. We genotyped individuals for 500K single-nucleotide polymorphisms, Y chromosome markers and sequenced the mitochondrial hypervariable (HVI-HVII) regions. We identified major haplogroups and compared these with other populations. We estimated linkage disequilibrium (LD) and haplotype diversity across autosomal markers, and compared these with other populations. Our results show that within Sardinia there is no major population substructure and thus it can be considered a genetically homogenous population. We did not find substantial differences in the extent of LD in Sardinians compared with other populations. However, we showed that at least 9% of genomic regions in Sardinians differed in LD structure, which is helpful for identifying functional variants using fine mapping. We concluded that Sardinia is a powerful setting for genetic studies including GWAS and other mapping approaches.     

Meta-analysis of genetic association studies on bipolar disorder

Numerous candidate gene association studies of bipolar disorder (BP) have been carried out, but the results have been inconsistent. Individual studies are typically underpowered to detect associations with genes of small effect sizes. We conducted a meta-analysis of published candidate gene studies to evaluate the cumulative evidence. We systematically searched for all published candidate gene association studies of BP. We then carried out a random-effects meta-analysis on all polymorphisms that were reported on by three or more case-control studies. The results from meta-analyses of these genes were compared with the findings from a recent mega-analysis of eleven genome-wide association studies (GWAS) in BP performed by the Psychiatric GWAS Consortium (PGC). A total of 487 articles were included in our review. Among these, 33 polymorphisms in 18 genes were reported on by three or more case-control studies and included in the random-effects meta-analysis. Polymorphisms in BDNF, DRD4, DAOA, and TPH1, were found to be nominally significant with a P-value?相似文献   

Asthma is associated with single-nucleotide polymorphisms in ADAM33

BACKGROUND : The ADAM33 gene has recently been associated with asthma and bronchial hyper-reactivity. It codes for a disintegrin and metalloproteinase that triggers intra- and extracellular signalling by protein shedding. OBJECTIVE : We examined whether polymorphisms in ADAM33 are associated with asthma and related traits in two German populations. METHODS : We genotyped 15 intragenic single-nucleotide polymorphisms (SNPs) by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry of allele-specific primer extension products. The transmission disequilibrium test was used for association analysis in the German asthma family study. Additionally, we tested for association of these SNPs in a case-control sample from the European Community Respiratory Health Study using Armitage's trend test. RESULTS : In both studies, we found SNPs that were significantly associated with asthma and related traits. In the family study, significant associations were observed for the SNPs F+1, ST+4 and ST+5 (with the lowest P-value for F+1, P=0.005). Remarkably, this association is seen even in the absence of linkage with two microsatellite markers from a previous genome scan either 3.1 million bases (Mb) up- or 5.6 Mb downstream. In the case-control study, SNP ST+7 (P=0.008) was significantly associated with asthma. Some of these SNPs overlapped with those found to be associated with elevated total IgE levels and bronchial hyper-responsiveness. CONCLUSION : This study replicates the recently published association between asthma and ADAM33 gene variants. However, most of the associated SNPs were at non-identical positions in the German, UK and US samples. As linkage disequilibrium is high among the tested SNPs, and there is no known functional polymorphism, either not-tested variants in ADAM33, unknown regulatory elements or a gene in close proximity is responsible for this association.     

小家鼠群体基因组连锁不平衡研究

模式动物小鼠是复杂性状相关疾病遗传研究的重要资源.连锁不平衡(linkage disequilib-rium,LD)是群体基因组遗传的重要信息.如果群体LD程度高,相关连锁区段大,有助于用少量遗传位标来对目标基因进行初步定位;反之,如果群体LD程度低,相关连锁区段小,则利用高密度的遗传位标可以对目标基因进行精细定位.本文介绍了实验小鼠群体和野生小鼠群体相关的LD、与LD相关的部分遗传参数以及利用LD进行相关基因定位研究.并提示实验小鼠和野生小鼠各具优势,都是复杂性状基因定位的重要遗传资源.     

Joint analysis of tightly linked SNPs in screening step of genome-wide association studies leads to increased power

Recent developments in genome-wide association studies (GWAS) have lead to the localization of disease genes for many complex diseases. The scrutiny of the respective publications reveals, first, that statistical analysis is restricted typically to single-marker analysis in the first step, and that, second, the presence of multiple, independently associated SNPs within the same linkage disequilibrium (LD) region is a common phenomenon. Motivated by this observation, we show through a power simulation study that a simultaneous analysis of tightly linked SNPs in the initial GWAS analysis step would lead to increased power, when compared with that in single-marker analysis. This is true for all the three approaches we considered (implementations in BEAGLE, FAMHAP and UNPHASED). The best performance was obtained using a two-marker haplotype analysis. In conclusion, we would expect additional gene findings for re-analyzing successful GWAS with a multi-marker approach.     

Effectiveness of Shrinkage and Variable Selection Methods for the Prediction of Complex Human Traits using Data from Distantly Related Individuals

Genome‐wide association studies (GWAS) have detected large numbers of variants associated with complex human traits and diseases. However, the proportion of variance explained by GWAS‐significant single nucleotide polymorphisms has been usually small. This brought interest in the use of whole‐genome regression (WGR) methods. However, there has been limited research on the factors that affect prediction accuracy (PA) of WGRs when applied to human data of distantly related individuals. Here, we examine, using real human genotypes and simulated phenotypes, how trait complexity, marker‐quantitative trait loci (QTL) linkage disequilibrium (LD), and the model used affect the performance of WGRs. Our results indicated that the estimated rate of missing heritability is dependent on the extent of marker‐QTL LD. However, this parameter was not greatly affected by trait complexity. Regarding PA our results indicated that: (a) under perfect marker‐QTL LD WGR can achieve moderately high prediction accuracy, and with simple genetic architectures variable selection methods outperform shrinkage procedures and (b) under imperfect marker‐QTL LD, variable selection methods can achieved reasonably good PA with simple or moderately complex genetic architectures; however, the PA of these methods deteriorated as trait complexity increases and with highly complex traits variable selection and shrinkage methods both performed poorly. This was confirmed with an analysis of human height.     

A single-nucleotide polymorphism in ANK1 is associated with susceptibility to type 2 diabetes in Japanese populations

To identify a novel susceptibility locus for type 2 diabetes, we performed an imputation-based, genome-wide association study (GWAS) in a Japanese population using newly obtained imputed-genotype data for 2 229 890 single-nucleotide polymorphisms (SNPs) estimated from previously reported, directly genotyped GWAS data in the same samples (stage 1: 4470 type 2 diabetes versus 3071 controls). We directly genotyped 43 new SNPs with P-values of <10(-4) in a part of stage-1 samples (2692 type 2 diabetes versus 3071 controls), and the associations of validated SNPs were evaluated in another 11 139 Japanese individuals (stage 2: 7605 type 2 diabetes versus 3534 controls). Combined meta-analysis using directly genotyped data for stages 1 and 2 revealed that rs515071 in ANK1 and rs7656416 near MGC21675 were associated with type 2 diabetes in the Japanese population at the genome-wide significant level (P < 5 × 10(-8)). The association of rs515071 was also observed in European GWAS data (combined P for all populations = 6.14 × 10(-10)). Rs7656416 was in linkage disequilibrium to rs6815464, which had recently been identified as a top signal in a meta-analysis of East Asian GWAS for type 2 diabetes (r(2) = 0.76 in stage 2). The association of rs7656416 with type 2 diabetes disappeared after conditioning on rs6815464. These results indicate that the ANK1 locus is a new, common susceptibility locus for type 2 diabetes across different ethnic groups. The signal of association was weaker in the directly genotyped data, so the improvement in signal indicates the importance of imputation in this particular case.     

Refining genome-wide linkage intervals using a meta-analysis of genome-wide association studies identifies loci influencing personality dimensions

Personality traits are complex phenotypes related to psychosomatic health. Individually, various gene finding methods have not achieved much success in finding genetic variants associated with personality traits. We performed a meta-analysis of four genome-wide linkage scans (N=6149 subjects) of five basic personality traits assessed with the NEO Five-Factor Inventory. We compared the significant regions from the meta-analysis of linkage scans with the results of a meta-analysis of genome-wide association studies (GWAS) (N∼17 000). We found significant evidence of linkage of neuroticism to chromosome 3p14 (rs1490265, LOD=4.67) and to chromosome 19q13 (rs628604, LOD=3.55); of extraversion to 14q32 (ATGG002, LOD=3.3); and of agreeableness to 3p25 (rs709160, LOD=3.67) and to two adjacent regions on chromosome 15, including 15q13 (rs970408, LOD=4.07) and 15q14 (rs1055356, LOD=3.52) in the individual scans. In the meta-analysis, we found strong evidence of linkage of extraversion to 4q34, 9q34, 10q24 and 11q22, openness to 2p25, 3q26, 9p21, 11q24, 15q26 and 19q13 and agreeableness to 4q34 and 19p13. Significant evidence of association in the GWAS was detected between openness and rs677035 at 11q24 (P-value=2.6 × 10⁻⁰⁶, KCNJ1). The findings of our linkage meta-analysis and those of the GWAS suggest that 11q24 is a susceptible locus for openness, with KCNJ1 as the possible candidate gene.     

Replication of genetic variants from genome-wide association studies with metabolic traits in an island population of the Adriatic coast of Croatia

Twenty-two single-nucleotide polymorphisms (SNPs) in 10 gene regions previously identified in obesity and type 2 diabetes (T2D) genome-wide association studies (GWAS) were evaluated for association with metabolic traits in a sample from an island population of European descent. We performed a population-based study using 18 anthropometric and biochemical traits considered as continuous variables in a sample of 843 unrelated subjects (360 men and 483 women) aged 18-80 years old from the island of Hvar on the eastern Adriatic coast of Croatia. All eight GWAS SNPs in FTO were significantly associated with weight, body mass index, waist circumference and hip circumference; 20 of the 32 nominal P-values remained significant after permutation testing for multiple corrections. The strongest associations were found between the two TCF7L2 GWAS SNPs with fasting plasma glucose and HbA1c levels, all four P-values remained significant after permutation tests. Nominally significant associations were found between several SNPs and other metabolic traits; however, the significance did not hold after permutation tests. Although the sample size was modest, our study strongly replicated the association of FTO variants with obesity-related measures and TCF7L2 variants with T2D-related traits. The estimated effect sizes of these variants were larger or comparable to published studies. This is likely attributable to the homogenous genetic background of the relatively isolated study population.