Interpretation of association signals and identification of causal variants from genome-wide association studies

Utility of genetic determinants of lipids and cardiovascular events in assessing risk

The prevention of coronary heart disease (CHD) is a major public-health goal, but disease architecture is such that a larger proportion of clinical events occur among the average majority than among the high-risk minority--the prevention paradox. Genetic findings over the past few years have resulted in the reopening of the old debate on whether an individualized or a population-based approach to prevention is preferable. Genetic testing is an attractive tool for CHD risk prediction because it is a low-cost, high-fidelity technology with multiplex capability. Moreover, by contrast with nongenetic markers, genotype is invariant and determined from conception, which eliminates biological variability and makes prediction from early life possible. Mindful of the prevention paradox, this Review examines the potential applications and challenges of using genetic information for predicting CHD, focusing on lipid risk factors and drawing on experience in the evaluation of nongenetic risk factors as screening tests for CHD. Many of the issues we discuss hold true for any late-onset common disease with modifiable risk factors and proven preventative strategies.

The use of genomic information to optimize cancer chemotherapy

The field of pharmacogenomics is focused on the characterization of genetic factors contributing to the response of patients to pharmacological interventions. Drug response and toxicity are complex traits; therefore the effects are likely influenced by multiple genes. The investigation of the genetic basis of drug response has evolved from a focus on single genes to relevant pathways to the entire genome. Preclinical (cell-based models) and clinical genome-wide association studies (GWAS) in oncology provide an unprecedented opportunity for a comprehensive and unbiased assessment of the heritable factors associated with drug response. The primary challenge with attempting to identify pharmacogenomic markers from clinical studies is that they require a homogeneous population of patients treated with the same dosage regimen and minimal confounding variables. Therefore, the development of cell-based models for pharmacogenomic marker identification has utility for the field since performing these types of studies in humans is difficult and costly. This review intends to provide a current report on the status of genomic studies in oncology, the methods for discovery, and implications for patient care. We present a perspective and summary of the challenges and opportunities in translating heritable genomic discoveries to patients.

Combined admixture mapping and association analysis identifies a novel blood pressure genetic locus on 5p13: contributions from the CARe consortium

Admixture mapping based on recently admixed populations is a powerful method to detect disease variants with substantial allele frequency differences in ancestral populations. We performed admixture mapping analysis for systolic blood pressure (SBP) and diastolic blood pressure (DBP), followed by trait-marker association analysis, in 6303 unrelated African-American participants of the Candidate Gene Association Resource (CARe) consortium. We identified five genomic regions (P< 0.001) harboring genetic variants contributing to inter-individual BP variation. In follow-up association analyses, correcting for all tests performed in this study, three loci were significantly associated with SBP and one significantly associated with DBP (P< 10(-5)). Further analyses suggested that six independent single-nucleotide polymorphisms (SNPs) contributed to the phenotypic variation observed in the admixture mapping analysis. These six SNPs were examined for replication in multiple, large, independent studies of African-Americans [Women's Health Initiative (WHI), Maywood, Genetic Epidemiology Network of Arteriopathy (GENOA) and Howard University Family Study (HUFS)] as well as one native African sample (Nigerian study), with a total replication sample size of 11 882. Meta-analysis of the replication set identified a novel variant (rs7726475) on chromosome 5 between the SUB1 and NPR3 genes, as being associated with SBP and DBP (P< 0.0015 for both); in meta-analyses combining the CARe samples with the replication data, we observed P-values of 4.45 × 10(-7) for SBP and 7.52 × 10(-7) for DBP for rs7726475 that were significant after accounting for all the tests performed. Our study highlights that admixture mapping analysis can help identify genetic variants missed by genome-wide association studies because of drastically reduced number of tests in the whole genome.

Genome-wide association studies on IgE regulation: are genetics of IgE also genetics of atopic disease?

PURPOSE OF REVIEW

Total IgE levels are considered a useful endophenotype for studying the genetics of atopic diseases. However, the role and significance of genetic factors influencing IgE regulation for atopic diseases as endpoints is unclear.

RECENT FINDINGS

Recently, genome-wide association studies (GWASs) have been applied to atopic traits with considerable success. A total of seven published GWASs on asthma, one GWAS on eczema, and one GWAS on total IgE have reported 11 new loci. Most of these loci appear to be trait-specific. A notable exception is the Th2 cytokine cluster, where genetic variation seems to be relevant across atopic phenotypes.

SUMMARY

GWASs have identified several novel asthma and eczema loci as well as novel loci for IgE levels. In this review, we evaluate the interrelation between these loci and summarize to which degree recent findings on IgE reflect genetic vulnerability for atopic disease.

Synthetic associations are unlikely to account for many common disease genome-wide association signals

Synthetic associations have been posited as a possible explanation for missing heritability in complex disease. We show several lines of evidence which suggest that, while possible, these synthetic associations are not common.

Genome-wide association study SNPs in the human genome diversity project populations: does selection affect unlinked SNPs with shared trait associations?

Genome-wide association studies (GWAS) have identified more than 2,000 trait-SNP associations, and the number continues to increase. GWAS have focused on traits with potential consequences for human fitness, including many immunological, metabolic, cardiovascular, and behavioral phenotypes. Given the polygenic nature of complex traits, selection may exert its influence on them by altering allele frequencies at many associated loci, a possibility which has yet to be explored empirically. Here we use 38 different measures of allele frequency variation and 8 iHS scores to characterize over 1,300 GWAS SNPs in 53 globally distributed human populations. We apply these same techniques to evaluate SNPs grouped by trait association. We find that groups of SNPs associated with pigmentation, blood pressure, infectious disease, and autoimmune disease traits exhibit unusual allele frequency patterns and elevated iHS scores in certain geographical locations. We also find that GWAS SNPs have generally elevated scores for measures of allele frequency variation and for iHS in Eurasia and East Asia. Overall, we believe that our results provide evidence for selection on several complex traits that has caused changes in allele frequencies and/or elevated iHS scores at a number of associated loci. Since GWAS SNPs collectively exhibit elevated allele frequency measures and iHS scores, selection on complex traits may be quite widespread. Our findings are most consistent with this selection being either positive or negative, although the relative contributions of the two are difficult to discern. Our results also suggest that trait-SNP associations identified in Eurasian samples may not be present in Africa, Oceania, and the Americas, possibly due to differences in linkage disequilibrium patterns. This observation suggests that non-Eurasian and non-East Asian sample populations should be included in future GWAS.

Planning a genome-wide association study: points to consider

It is well established that genetic diversity combined with specific environmental exposures contributes to disease susceptibility. However, it has turned out to be challenging to isolate the genes underlying the genetic component conferring susceptibility to most complex disorders. Traditional candidate gene and family-based linkage studies, which dominated gene discovery efforts for many years, were largely unsuccessful in unraveling the genetics of these traits due to the relatively limited information gained. Within the last 5 years, new advances in high-throughput methods have allowed for large volumes of single nucleotide polymorphisms (SNPs) throughout the genome to be genotyped across large and comprehensively phenotyped patient cohorts. Unlike previous approaches, these 'genome-wide association studies' (GWAS) have extensively delivered on the promise of uncovering genetic determinants of complex diseases, with hundreds of novel disease-associated variants being largely replicated by independent groups. This review provides an overview of these recent breakthroughs in the context of the pitfalls and challenges related to designing and carrying out a successful GWAS.

Investigation of a genome wide association signal for obesity: synthetic association and haplotype analyses at the melanocortin 4 receptor gene locus

Background

Independent genome-wide association studies (GWAS) showed an obesogenic effect of two single nucleotide polymorphisms (SNP; rs12970134 and rs17782313) more than 150 kb downstream of the melanocortin 4 receptor gene (MC4R). It is unclear if the SNPs directly influence MC4R function or expression, or if the SNPs are on a haplotype that predisposes to obesity or includes functionally relevant genetic variation (synthetic association). As both exist, functionally relevant mutations and polymorphisms in the MC4R coding region and a robust association downstream of the gene, MC4R is an ideal model to explore synthetic association.

Methodology/Principal Findings

We analyzed a genomic region (364.9 kb) encompassing the MC4R in GWAS data of 424 obesity trios (extremely obese child/adolescent and both parents). SNP rs12970134 showed the lowest p-value (p = 0.004; relative risk for the obesity effect allele: 1.37); conditional analyses on this SNP revealed that 7 of 78 analyzed SNPs provided independent signals (p≤0.05). These 8 SNPs were used to derive two-marker haplotypes. The three best (according to p-value) haplotype combinations were chosen for confirmation in 363 independent obesity trios. The confirmed obesity effect haplotype includes SNPs 3′ and 5′ of the MC4R. Including MC4R coding variants in a joint model had almost no impact on the effect size estimators expected under synthetic association.

Conclusions/Significance

A haplotype reaching from a region 5′ of the MC4R to a region at least 150 kb from the 3′ end of the gene showed a stronger association to obesity than single SNPs. Synthetic association analyses revealed that MC4R coding variants had almost no impact on the association signal. Carriers of the haplotype should be enriched for relevant mutations outside the MC4R coding region and could thus be used for re-sequencing approaches. Our data also underscore the problems underlying the identification of relevant mutations depicted by GWAS derived SNPs.

Resequencing of nicotinic acetylcholine receptor genes and association of common and rare variants with the Fagerström test for nicotine dependence

Common single-nucleotide polymorphisms (SNPs) at nicotinic acetylcholine receptor (nAChR) subunit genes have previously been associated with measures of nicotine dependence. We investigated the contribution of common SNPs and rare single-nucleotide variants (SNVs) in nAChR genes to Fagerström test for nicotine dependence (FTND) scores in treatment-seeking smokers. Exons of 10 genes were resequenced with next-generation sequencing technology in 448 European-American participants of a smoking cessation trial, and CHRNB2 and CHRNA4 were resequenced by Sanger technology to improve sequence coverage. A total of 214 SNP/SNVs were identified, of which 19.2% were excluded from analyses because of reduced completion rate, 73.9% had minor allele frequencies <5%, and 48.1% were novel relative to dbSNP build 129. We tested associations of 173 SNP/SNVs with the FTND score using data obtained from 430 individuals (18 were excluded because of reduced completion rate) using linear regression for common, the cohort allelic sum test and the weighted sum statistic for rare, and the multivariate distance matrix regression method for both common and rare SNP/SNVs. Association testing with common SNPs with adjustment for correlated tests within each gene identified a significant association with two CHRNB2 SNPs, eg, the minor allele of rs2072660 increased the mean FTND score by 0.6 Units (P=0.01). We observed a significant evidence for association with the FTND score of common and rare SNP/SNVs at CHRNA5 and CHRNB2, and of rare SNVs at CHRNA4. Both common and/or rare SNP/SNVs from multiple nAChR subunit genes are associated with the FTND score in this sample of treatment-seeking smokers.

Statistical analysis strategies for association studies involving rare variants

The limitations of genome-wide association (GWA) studies that focus on the phenotypic influence of common genetic variants have motivated human geneticists to consider the contribution of rare variants to phenotypic expression. The increasing availability of high-throughput sequencing technologies has enabled studies of rare variants but these methods will not be sufficient for their success as appropriate analytical methods are also needed. We consider data analysis approaches to testing associations between a phenotype and collections of rare variants in a defined genomic region or set of regions. Ultimately, although a wide variety of analytical approaches exist, more work is needed to refine them and determine their properties and power in different contexts.

New loci associated with central cornea thickness include COL5A1, AKAP13 and AVGR8

Central corneal thickness (CCT) is a highly heritable trait, which has been proposed to influence disorders of the anterior segment of the eye. A genome-wide association study (GWAS) of CCT was performed in 2269 individuals from three Croatian and one Scottish population. In the discovery set (1445 individuals), two genome-wide significant associations were identified for single nucleotide polymorphisms rs12447690 (β = 0.23 SD, P = 4.4 × 10(-9)) and rs1536482 (β = 0.22 SD, P = 7.1 × 10(-8)) for which the closest candidate genes (although ≥90 kb away) were zinc finger 469 (ZNF469) on 16q24.2 and collagen 5 alpha 1 (COL5A1) on 9q34.2, respectively. Only the ZNF469 association was confirmed in our replication set (824 individuals, P = 8.0 × 10(-4)) but COL5A1 remained a suggestive association in the combined sample (β = 0.16 SD, P = 1.1 × 10(-6)). Following a larger meta-analysis including recently published CCT GWAS summary data, COL5A1 was genome-wide significant (β = 0.13 SD, P = 5.1 × 10(-8)), together with two additional novel loci. The second new locus (defined by rs1034200) was 5 kb from the AVGR8 gene, encoding a putative transcription factor with typical ZNF and KRAB domains, in chromosomal region 13q12.11 (β = 0.14 SD, P = 3.5 × 10(-9)). The third new locus (rs6496932), on 15q25.3 (β = 0.13, P = 1.4 × 10(-8)), was within a wide linkage disequilibrium block extending into the 5' end of the AKAP13 gene, encoding a scaffold protein concerned with signal transduction from the cell surface. These associations offer mechanistic insights into the regulation of CCT and offer new candidate genes for susceptibility to common disorders in which CCT has been implicated, including primary open-angle glaucoma and keratoconus.

Investigation of ACE, ACE2 and AGTR1 genes for association with nephropathy in Type 1 diabetes mellitus

BACKGROUND

Polymorphisms in ACE and AGTR1 genes have been assessed in multiple studies for association with diabetic nephropathy; however, results are conflicting. The ACE2 gene has not been studied extensively for association with diabetic nephropathy.

METHODS

We investigated variants in ACE, ACE2 and AGTR1 for association with nephropathy in a case-control group (1467 participants with Type1 diabetes, case subjects n=718; control subjects n=749) of white descent with grandparents born in the British Isles. All recruited individuals were carefully phenotyped and genotyping was performed using Sequenom, Taqman and gel electrophoresis methods. The χ(2) -test for contingency tables was used to compare genotype and allele frequencies in case and control groups.

RESULTS

No departure from Hardy-Weinberg equilibrium was observed in cases or controls. Two variants within the ACE gene (rs4293, P(allelic) =0.02, P(genotypic) =0.008; rs4309, P(allelic) =0.02, P(genotypic) =0.01) were significantly associated with nephropathy at the 5% level. No variant remained statistically significant following adjustment for multiple comparisons. No single nucleotide polymorphisms in the ACE2 or AGTR1 genes were significantly associated with nephropathy when analysed either by genotype or allele frequencies.

CONCLUSIONS

Our independent case-control study provides no evidence that common variants in ACE, ACE2 and AGTR1 play a major role in genetic susceptibility to diabetic nephropathy in a white population with Type1 diabetes.

Synthetic associations in the context of genome-wide association scan signals

Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with complex traits, but these only explain a small proportion of the total heritability. It has been recently proposed that rare variants can create 'synthetic association' signals in GWAS, by occurring more often in association with one of the alleles of a common tag single nucleotide polymorphism. While the ultimate evaluation of this hypothesis will require the completion of large-scale sequencing studies, it is informative to place it in the broader context of what is known about the genetic architecture of complex disease. In this review, we draw from empirical and theoretical data to summarize evidence showing that synthetic associations do not underlie many reported GWAS associations.

Consistent association of type 2 diabetes risk variants found in europeans in diverse racial and ethnic groups

It has been recently hypothesized that many of the signals detected in genome-wide association studies (GWAS) to T2D and other diseases, despite being observed to common variants, might in fact result from causal mutations that are rare. One prediction of this hypothesis is that the allelic associations should be population-specific, as the causal mutations arose after the migrations that established different populations around the world. We selected 19 common variants found to be reproducibly associated to T2D risk in European populations and studied them in a large multiethnic case-control study (6,142 cases and 7,403 controls) among men and women from 5 racial/ethnic groups (European Americans, African Americans, Latinos, Japanese Americans, and Native Hawaiians). In analysis pooled across ethnic groups, the allelic associations were in the same direction as the original report for all 19 variants, and 14 of the 19 were significantly associated with risk. In summing the number of risk alleles for each individual, the per-allele associations were highly statistically significant (P<10⁻⁴) and similar in all populations (odds ratios 1.09–1.12) except in Japanese Americans the estimated effect per allele was larger than in the other populations (1.20; P_het = 3.8×10⁻⁴). We did not observe ethnic differences in the distribution of risk that would explain the increased prevalence of type 2 diabetes in these groups as compared to European Americans. The consistency of allelic associations in diverse racial/ethnic groups is not predicted under the hypothesis of Goldstein regarding “synthetic associations” of rare mutations in T2D.

Single rare causal alleles and/or collections of multiple rare alleles have been suggested to create “synthetic associations” with common variants in genome-wide association studies (GWAS). This model predicts that associations with common variants will not be consistent across populations. In this study, we examined 19 T2D variants for association with T2D risk in 6,142 cases and 7,403 controls from five racial/ethnic populations in the Multiethnic Cohort (European Americans, African Americans, Latinos, Japanese Americans, and Native Hawaiians). In racial/ethnic pooled analysis, all 19 variants were associated with T2D risk in the same direction as previous reports in Europeans, and the sum total of risk variants was significantly associated with T2D risk in each racial/ethnic group. The consistent associations across populations do not support the Goldstein hypothesis that rare causal alleles underlie GWAS signals. We also did not find evidence that these markers underlie racial/ethnic disparities in T2D prevalence. Large-scale GWAS and sequencing studies in these populations are necessary in order to both improve the current set of markers at these risk loci and identify new risk variants for T2D that may be difficult, or impossible, to detect in European populations.