Genome-wide Association Study Strategy Research Articles

Using the Strategy of Genome-Wide Association Studies to Identify Genetic Markers of Suicidal Behavior: A Narrative Review.

Several studies involving various suicidal phenotypes based on the strategy of the search of genome-wide associations with single nucleotide polymorphisms have been performed recently. These studies need to be generalized. To systematize the findings of a number of genome-wide association studies (GWAS) for suicidal phenotypes, annotate the identified markers, analyze their functionality, and possibly substantiate the hypothesis holding that these phenotypes reflect a nonspecific set of gene variants that are relevant as relates to stress-vulnerability as a key endophenotype of suicidal behavior (SB). A search on the PubMed and related resources using the combinations "suicide AND GWAS" and "suicidal behavior AND GWAS" was performed. It yielded a total of 34 independent studies and meta-analyses. For the 10 years since such studies emerged, they have undergone significant progress. Estimates of the SNP heritability of SB in some cases are comparable with estimates of heritability based on the twin method. Many studies show a high genetic correlation with the genomic markers of the most common mental disorders (depression, bipolar disorder, schizophrenia, post-traumatic stress disorder). At the same time, a genomic architecture specific to SB is also encountered. Studies utilizing the GWAS strategy have not revealed any associations of SB with candidate genes that had been previously studied in detail (different neurotransmitters, stress response system, polyamines, etc.). Frequently reported findings from various studies belong in three main groups: 1) genes involved in cell interactions, neurogenesis, the development of brain structures, inflammation, and the immune responses; 2) genes encoding receptors for neurotrophins and various components of the intracellular signaling systems involved in synaptic plasticity, embryonic development, and carcinogenesis; and 3) genes encoding various neuro-specific proteins and regulators. In general, GWAS in the field of suicidology mainly serve the purpose of a deeper understanding of the pathophysiology of suicidal behavior. However, they also demonstrate growing capability in terms of predicting and preventing suicide, especially when calculating the polygenic risk score among certain populations (psychiatric patients) and in combination with tests of different modalities. From our point of view, there exists a set of markers revealed by the GWAS strategy that seems to point to a leading role played by stress vulnerability, an endophenotype that is formed during early development and which subsequently comes to play the role of key pathogenetic mechanism in SB.

Trait selection strategy in multi-trait GWAS: Boosting SNP discoverability

Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, comprehensively mapping the genetic determinant of complex traits through univariate testing can require prohibitive sample sizes. Multi-trait GWAS can circumvent this issue and improve statistical power by leveraging the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been solved, the strategy to select traits has been overlooked. In this study, we conducted multi-trait GWAS on approximately 20,000 combinations of 72 traits using an omnibus test as implemented in JASS (Joint Analysis of Summary Statistics). We assessed which genetic features of the sets of traits analysed were associated with an increased detection of variants compared to univariate screening. Several features of the set of traits, including the heritability, the number of traits, and the genetic correlation, drive the multi-trait test gain. Using these features jointly in predictive models captures a large fraction of the power gain of the multi-trait test (Pearson's γ between the observed and predicted gain equals 0.43, P < 1.6 x 10-60). Applying an alternative multi-trait approach (MTAG), we identified similar features of interest, but with an overall 70% lower number of new associations. Finally, selecting sets based on our data-driven models systematically outperformed the common strategy of selecting clinically similar traits. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.

Cis-eQTLs in seven duck tissues identify novel candidate genes for growth and carcass traits

BackgroundExpression quantitative trait loci (eQTL) studies aim to understand the influence of genetic variants on gene expression. The colocalization of eQTL mapping and GWAS strategy could help identify essential candidate genes and causal DNA variants vital to complex traits in human and many farm animals. However, eQTL mapping has not been conducted in ducks. It is desirable to know whether eQTLs within GWAS signals contributed to duck economic traits.ResultsIn this study, we conducted an eQTL analysis using publicly available RNA sequencing data from 820 samples, focusing on liver, muscle, blood, adipose, ovary, spleen, and lung tissues. We identified 113,374 cis-eQTLs for 12,266 genes, a substantial fraction 39.1% of which were discovered in at least two tissues. The cis-eQTLs of blood were less conserved across tissues, while cis-eQTLs from any tissue exhibit a strong sharing pattern to liver tissue. Colocalization between cis-eQTLs and genome-wide association studies (GWAS) of 50 traits uncovered new associations between gene expression and potential loci influencing growth and carcass traits. SRSF4, GSS, and IGF2BP1 in liver, NDUFC2 in muscle, ELF3 in adipose, and RUNDC1 in blood could serve as the candidate genes for duck growth and carcass traits.ConclusionsOur findings highlight substantial differences in genetic regulation of gene expression across duck primary tissues, shedding light on potential mechanisms through which candidate genes may impact growth and carcass traits. Furthermore, this availability of eQTL data offers a valuable resource for deciphering further genetic association signals that may arise from ongoing extensive endeavors aimed at enhancing duck production traits.

A genomic association study revealing subphenotypes of childhood steroid-sensitive nephrotic syndrome in a larger genomic sequencing cohort

Dissecting the genetic components that contribute to the two main subphenotypes of steroid-sensitive nephrotic syndrome (SSNS) using genome-wide association studies (GWAS) strategy is important for understanding the disease. We conducted a multicenter cohort study (360 patients and 1835 controls) combined with a GWAS strategy to identify susceptibility variants associated with the following two subphenotypes of SSNS: steroid-sensitive nephrotic syndrome without relapse (SSNSWR, 181 patients) and steroid-dependent/frequent relapse nephrotic syndrome (SDNS/FRNS, 179 patients). The distribution of two single-nucleotide polymorphisms (SNPs) in ANKRD36 and ALPG was significant between SSNSWR and healthy controls, and that of two SNPs in GAD1 and HLA-DQA1 was significant between SDNS/FRNS and healthy controls. Interestingly, rs1047989 in HLA-DQA1 was a candidate locus for SDNS/FRNS but not for SSNSWR. No significant SNPs were observed between SSNSWR and SDNS/FRNS. Meanwhile, chromosome 2:171713702 in GAD1 was associated with a greater steroid dose (>0.75 mg/kg/d) upon relapse to first remission in patients with SDNS/FRNS (odds ratio = 3.14; 95% confidence interval, 0.97–9.87; P = 0.034). rs117014418 in APOL4 was significantly associated with a decrease in eGFR of greater than 20% compared with the baseline in SDNS/FRNS patients (P = 0.0001). Protein–protein intersection network construction suggested that HLA-DQA1 and HLA-DQB1 function together through GSDMA. Thus, SSNSWR belongs to non-HLA region-dependent nephropathy, and the HLA-DQA/DQB region is likely strongly associated with disease relapse, especially in SDNS/FRNS. The study provides a novel approach for the GWAS strategy of SSNS and contributes to our understanding of the pathological mechanisms of SSNSWR and SDNS/FRNS.

Genome-wide association study of a semicontinuous trait: illustration of the impact of the modeling strategy through the study of Neutrophil Extracellular Traps levels.

Over the last years, there has been a considerable expansion of genome-wide association studies (GWAS) for discovering biological pathways underlying pathological conditions or disease biomarkers. These GWAS are often limited to binary or quantitative traits analyzed through linear or logistic models, respectively. In some situations, the distribution of the outcome may require more complex modeling, such as when the outcome exhibits a semicontinuous distribution characterized by an excess of zero values followed by a non-negative and right-skewed distribution. We here investigate three different modeling for semicontinuous data: Tobit, Negative Binomial and Compound Poisson-Gamma. Using both simulated data and a real GWAS on Neutrophil Extracellular Traps (NETs), an emerging biomarker in immuno-thrombosis, we demonstrate that Compound Poisson-Gamma was the most robust model with respect to low allele frequencies and outliers. This model further identified the MIR155HG locus as significantly (P=1.4×10-8) associated with NETs plasma levels in a sample of 657 participants, a locus recently highlighted to be involved in NETs formation in mice. This work highlights the importance of the modeling strategy for GWAS of a semicontinuous outcome and suggests Compound Poisson-Gamma as an elegant but neglected alternative to Negative Binomial for modeling semicontinuous outcome in the context of genomic investigations.

Reciprocal adaptation of rice and Xanthomonas oryzae pv. oryzae: cross-species 2D GWAS reveals the underlying genetics.

A 1D/2D genome-wide association study strategy was adopted to investigate the genetic systems underlying the reciprocal adaptation of rice (Oryza sativa) and its bacterial pathogen, Xanthomonas oryzae pv. oryzae (Xoo) using the whole-genome sequencing and large-scale phenotyping data of 701 rice accessions and 23 diverse Xoo strains. Forty-seven Xoo virulence-related genes and 318 rice quantitative resistance genes (QR-genes) mainly located in 41 genomic regions, and genome-wide interactions between the detected virulence-related genes and QR genes were identified, including well-known resistance genes/virulence genes plus many previously uncharacterized ones. The relationship between rice and Xoo was characterized by strong differentiation among Xoo races corresponding to the subspecific differentiation of rice, by strong shifts toward increased resistance/virulence of rice/Xoo populations and by rich genetic diversity at the detected rice QR-genes and Xoo virulence genes, and by genome-wide interactions between many rice QR-genes and Xoo virulence genes in a multiple-to-multiple manner, presumably resulting either from direct protein–protein interactions or from genetic epistasis. The observed complex genetic interaction system between rice and Xoo likely exists in other crop–pathogen systems that would maintain high levels of diversity at their QR-loci/virulence-loci, resulting in dynamic coevolutionary consequences during their reciprocal adaptation.

The impact of non-additive genetic associations on age-related complex diseases

Genome-wide association studies (GWAS) are not fully comprehensive, as current strategies typically test only the additive model, exclude the X chromosome, and use only one reference panel for genotype imputation. We implement an extensive GWAS strategy, GUIDANCE, which improves genotype imputation by using multiple reference panels and includes the analysis of the X chromosome and non-additive models to test for association. We apply this methodology to 62,281 subjects across 22 age-related diseases and identify 94 genome-wide associated loci, including 26 previously unreported. Moreover, we observe that 27.7% of the 94 loci are missed if we use standard imputation strategies with a single reference panel, such as HRC, and only test the additive model. Among the new findings, we identify three novel low-frequency recessive variants with odds ratios larger than 4, which need at least a three-fold larger sample size to be detected under the additive model. This study highlights the benefits of applying innovative strategies to better uncover the genetic architecture of complex diseases.

Genome-Wide Prediction of Complex Traits in Two Outcrossing Plant Species Through Deep Learning and Bayesian Regularized Neural Network.

Genomic selection models were investigated to predict several complex traits in breeding populations of Zea mays L. and Eucalyptus globulus Labill. For this, the following methods of Machine Learning (ML) were implemented: (i) Deep Learning (DL) and (ii) Bayesian Regularized Neural Network (BRNN) both in combination with different hyperparameters. These ML methods were also compared with Genomic Best Linear Unbiased Prediction (GBLUP) and different Bayesian regression models [Bayes A, Bayes B, Bayes Cπ, Bayesian Ridge Regression, Bayesian LASSO, and Reproducing Kernel Hilbert Space (RKHS)]. DL models, using Rectified Linear Units (as the activation function), had higher predictive ability values, which varied from 0.27 (pilodyn penetration of 6 years old eucalypt trees) to 0.78 (flowering-related traits of maize). Moreover, the larger mini-batch size (100%) had a significantly higher predictive ability for wood-related traits than the smaller mini-batch size (10%). On the other hand, in the BRNN method, the architectures of one and two layers that used only the pureline function showed better results of prediction, with values ranging from 0.21 (pilodyn penetration) to 0.71 (flowering traits). A significant increase in the prediction ability was observed for DL in comparison with other methods of genomic prediction (Bayesian alphabet models, GBLUP, RKHS, and BRNN). Another important finding was the usefulness of DL models (through an iterative algorithm) as an SNP detection strategy for genome-wide association studies. The results of this study confirm the importance of DL for genome-wide analyses and crop/tree improvement strategies, which holds promise for accelerating breeding progress.

Progress and prospect of genome wide association study (GWAS) in forest trees

With the rapid development of next-generation genome sequencing technologies, the application of systems genetics based on genome-wide association study (GWAS) strategy is extensively increasing in revealing the genetic variation underlying complex traits and dissecting the genetic regulation mechanism of key genes in forest trees. Firstly, we introduced the core theories, research methods and progress of GWAS in the wood traits and adaptation of forest trees. Next, we proposed a joint use of high-throughput phenome platform, multiple genetic markers, multi-omics data and the models of additive, dominance and epistasis to resolve the problem of “missing heritability” of quantitative traits in forest trees. Finally, we provided an outlook of the GWAS strategy in the molecular breeding of forest trees.

Genome-Wide Identification of Myxobacterial Predation Genes and Demonstration of Formaldehyde Secretion as a Potentially Predation-Resistant Trait of Pseudomonas aeruginosa.

Despite widespread use in human biology, genome-wide association studies (GWAS) of bacteria are few and have, to date, focused primarily on pathogens. Myxobacteria are predatory microbes with large patchwork genomes, with individual strains secreting unique cocktails of predatory proteins and metabolites. We investigated whether a GWAS strategy could be applied to myxobacteria to identify genes associated with predation. Deduced proteomes from 29 myxobacterial genomes (including eight Myxococcus genomes sequenced for this study), were clustered into orthologous groups, and the presence/absence of orthologues assessed in superior and inferior predators of ten prey organisms. 139 ‘predation genes’ were identified as being associated significantly with predation, including some whose annotation suggested a testable predatory mechanism. Formaldehyde dismutase (fdm) was associated with superior predation of Pseudomonas aeruginosa, and predatory activity of a strain lacking fdm could be increased by the exogenous addition of a formaldehyde detoxifying enzyme, suggesting that production of formaldehyde by P. aeruginosa acts as an anti-predation behaviour. This study establishes the utility of bacterial GWAS to investigate microbial processes beyond pathogenesis, giving plausible and verifiable associations between gene presence/absence and predatory phenotype. We propose that the slow growth rate of myxobacteria, coupled with their predatory mechanism of constitutive secretion, has rendered them relatively resistant to genome streamlining. The resultant genome expansion made possible their observed accumulation of prey-specific predatory genes, without requiring them to be selected for by frequent or recent predation on diverse prey, potentially explaining both the large pan-genome and broad prey range of myxobacteria.

Haplotype Networking of GWAS Hits for Citrulline Variation Associated with the Domestication of Watermelon.

Watermelon is a good source of citrulline, a non-protein amino acid. Citrulline has several therapeutic and clinical implications as it produces nitric oxide via arginine. In plants, citrulline plays a pivotal role in nitrogen transport and osmoprotection. The purpose of this study was to identify single nucleotide polymorphism (SNP) markers associated with citrulline metabolism using a genome-wide association study (GWAS) and understand the role of citrulline in watermelon domestication. A watermelon collection consisting of 187 wild, landraces, and cultivated accessions was used to estimate citrulline content. An association analysis involved a total of 12,125 SNPs with a minor allele frequency (MAF)>0.05 in understanding the population structure and phylogeny in light of citrulline accumulation. Wild egusi types and landraces contained low to medium citrulline content, whereas cultivars had higher content, which suggests that obtaining higher content of citrulline is a domesticated trait. GWAS analysis identified candidate genes (ferrochelatase and acetolactate synthase) showing a significant association of SNPs with citrulline content. Haplotype networking indicated positive selection from wild to domesticated watermelon. To our knowledge, this is the first study showing genetic regulation of citrulline variation in plants by using a GWAS strategy. These results provide new insights into the citrulline metabolism in plants and the possibility of incorporating high citrulline as a trait in watermelon breeding programs.

Single-Locus and Multi-Locus Genome-Wide Association Studies for Intramuscular Fat in Duroc Pigs.

Intramuscular fat (IMF) is an important quantitative trait of meat, which affects the associated sensory properties and nutritional value of pork. To gain a better understanding of the genetic determinants of IMF, we used a composite strategy, including single-locus and multi-locus association analyses to perform genome-wide association studies (GWAS) for IMF in 1,490 Duroc boars. We estimated the genomic heritability of IMF to be 0.23 ± 0.04. A total of 30 single nucleotide polymorphisms (SNPs) were found to be significantly associated with IMF. The single-locus mixed linear model (MLM) and multiple-locus methods multi-locus random-SNP-effect mixed linear model (mrMLM), fast multi-locus random-SNP-effect efficient mixed model association (FASTmrEMMA), and integrative sure independence screening expectation maximization Bayesian least absolute shrinkage and selection operator model (ISIS EM-BLASSO) analyses identified 5, 9, 8, and 21 significant SNPs, respectively. Interestingly, a novel quantitative trait locus (QTL) on SSC 7 was found to affect IMF. In addition, 10 candidate genes (BDKRB2, GTF2IRD1, UTRN, TMEM138, DPYD, CASQ2, ZNF518B, S1PR1, GPC6, and GLI1) were found to be associated with IMF based on their potential functional roles in IMF. GO analysis showed that most of the genes were involved in muscle and organ development. A significantly enriched KEGG pathway, the sphingolipid signaling pathway, was reported to be associated with fat deposition and obesity. Identification of novel variants and functional genes will advance our understanding of the genetic mechanisms of IMF and provide specific opportunities for marker-assisted or genomic selection in pigs. In general, such a composite single-locus and multi-locus strategy for GWAS may be useful for understanding the genetic architecture of economic traits in livestock.

Powerful gene set analysis in GWAS with the Generalized Berk-Jones statistic.

A common complementary strategy in Genome-Wide Association Studies (GWAS) is to perform Gene Set Analysis (GSA), which tests for the association between one phenotype of interest and an entire set of Single Nucleotide Polymorphisms (SNPs) residing in selected genes. While there exist many tools for performing GSA, popular methods often include a number of ad-hoc steps that are difficult to justify statistically, provide complicated interpretations based on permutation inference, and demonstrate poor operating characteristics. Additionally, the lack of gold standard gene set lists can produce misleading results and create difficulties in comparing analyses even across the same phenotype. We introduce the Generalized Berk-Jones (GBJ) statistic for GSA, a permutation-free parametric framework that offers asymptotic power guarantees in certain set-based testing settings. To adjust for confounding introduced by different gene set lists, we further develop a GBJ step-down inference technique that can discriminate between gene sets driven to significance by single genes and those demonstrating group-level effects. We compare GBJ to popular alternatives through simulation and re-analysis of summary statistics from a large breast cancer GWAS, and we show how GBJ can increase power by incorporating information from multiple signals in the same gene. In addition, we illustrate how breast cancer pathway analysis can be confounded by the frequency of FGFR2 in pathway lists. Our approach is further validated on two other datasets of summary statistics generated from GWAS of height and schizophrenia.

Strategies for Pathway Analysis Using GWAS and WGS Data.

Single-allele study designs, commonly used in genome-wide association studies (GWAS) as well as the more recently developed whole genome sequencing (WGS) studies, are a standard approach for investigating the relationship of common variation within the human genome to a given phenotype of interest. However, single-allele association results published for many GWAS studies represent only the tip of the iceberg for the information that can be extracted from these datasets. The primary analysis strategy for GWAS entails association analysis in which only the single nucleotide polymorphisms (SNPs) with the strongest p-values are declared statistically significant due to issues arising from multiple testing and type I errors. Factors such as locus heterogeneity, epistasis, and multiple genes conferring small effects contribute to the complexity of the genetic models underlying phenotype expression. Thus, many biologically meaningful associations having lower effect sizes at individual genes are overlooked, making it difficult to separate true associations from a sea of false-positive associations. Organizing these individual SNPs into biologically meaningful groups to look at the overall effects of minor perturbations to genes and pathways is desirable. This pathway-based approach provides researchers with insight into the functional foundations of the phenotype being studied and allows testing of various genetic scenarios. © 2018 by John Wiley & Sons, Inc.

Identification of genetic risk factors of aggressive periodontitis using genomewide association studies in association with those of chronic periodontitis.

To identify the genetic risk factors for aggressive periodontitis (AgP), it is important to understand the progression and pathogenesis of AgP. The purpose of this review was to summarize the genetic risk factors for AgP identified through a case-control genomewide association study (GWAS) and replication study. The initial studies to identify novel AgP risk factors were potentially biased because they relied on previous studies. To overcome this kind of issue, an unbiased GWAS strategy was introduced to identify genetic risk factors for various diseases. Currently, three genes glycosyltransferase 6 domain containing 1 (GLT6D1), defensin α1 and α3 (DEFA1A3), and sialic acid-binding Ig-like lectin 5 (SIGLEC5) that reach the threshold for genomewide significance have been identified as genetic risk factors for AgP through a case-control GWAS.

Multimarker and rare variants genomewide association studies for bone weight in Simmental cattle.

Bone weight, defined as the total weight of the bones in all the forequarter and hindquarter joints, can reflect somebody conformation traits and skeletal diseases. To gain a better understanding of the genetic determinants of bone weight, we used a composite strategy including multimarker and rare-marker association to perform genomewide association studies (GWAS) for that character in Simmental cattle. Our strategy consisted of three models: (i) A traditional linear mixed model (LMM) was applied (Q+K-LMM); (ii) single nucleotide polymorphisms (SNPs) with p-values less than .05 from the LMM were selected to undergo the least absolute shrinkage and selector operator (Lasso) in the second stage (LMM-Lasso); (iii) genes containing two or more rare SNPs were examined by performing the sequence kernel association test (gene-based SKAT). A total of 1,225 cattle were genotyped with an Illumina BovineHD BeadChip containing 770,000 SNPs. After the quality-control procedures, 1,217 individuals with 608,696 common SNPs and 105,787 rare SNPs (with 0.001 < minor allele frequency [MAF] <0.05) remained in the sample for analysis. A traditional LMM successfully mapped three genes associated with bone weight, while LMM-Lasso identified nine genes, which included all genes found by traditional LMM. Only a single gene, EPHB3, surpassed the significance threshold after Bonferroni correction in gene-based SKAT. In conclusion, based on functional annotation and results from previous endeavours, we believe that LCORL, RIMS2, LAP3, PRKAR2B, CHSY1, MAP2K6 and EPHB3 are candidate genes for bone weight. In general, such a comprehensive strategy for GWAS may be useful for researchers seeking to probe the full genetic architecture underlying economic traits in livestock.

Genetics of addictive behavior: the example of nicotine dependence

The majority of addictive disorders have a significant heritability-roughly around 50%. Surprisingly, the most convincing association (a nicotinic acetylcholine receptor CHRNA5-A3-B4 gene cluster in nicotine dependence), with a unique attributable risk of 14%, was detected through a genome-wide association study (GWAS) on lung cancer, although lung cancer has a low heritability. We propose some explanations of this finding, potentially helping to understand how a GWAS strategy can be successful. Many endophenotypes were also assessed as potentially modulating the effect of nicotine, indirectly facilitating the development of nicotine dependence. Challenging the involved phenotype led to the demonstration that other potentially overlapping disorders, such as schizophrenia and Parkinson disease, could also be involved, and further modulated by parent monitoring or the existence of a smoking partner. Such a complex mechanism of action is compatible with a gene-environment interaction, most clearly explained by epigenetic factors, especially as such factors were shown to be, at least partly, genetically driven.

Explicating heterogeneity of complex traits has strong potential for improving GWAS efficiency.

Common strategy of genome-wide association studies (GWAS) relying on large samples faces difficulties, which raise concerns that GWAS have exhausted their potential, particularly for complex traits. Here, we examine the efficiency of the traditional sample-size-centered strategy in GWAS of these traits, and its potential for improvement. The paper focuses on the results of the four largest GWAS meta-analyses of body mass index (BMI) and lipids. We show that just increasing sample size may not make p-values of genetic effects in large (N > 100,000) samples smaller but can make them larger. The efficiency of these GWAS, defined as ratio of the log-transformed p-value to the sample size, in larger samples was larger than in smaller samples for a small fraction of loci. These results emphasize the important role of heterogeneity in genetic associations with complex traits such as BMI and lipids. They highlight the substantial potential for improving GWAS by explicating this role (affecting 11–79% of loci in the selected GWAS), especially the effects of biodemographic processes, which are heavily underexplored in current GWAS and which are important sources of heterogeneity in the various study populations. Further progress in this direction is crucial for efficient use of genetic discoveries in health care.

Uncoupling associations of risk alleles with endophenotypes and phenotypes: insights from the ApoB locus and heart-related traits.

SummaryTraditionally, genomewide association studies (GWAS) have emphasized the benefits of large samples in the analyses of age‐related traits rather than their specific properties. We adopted a realistic concept of genetic susceptibility to inherently heterogeneous, age‐related traits driven by the elusive role of evolution in their properties. We analyzed in detail the associations of rs693 and rs562338 polymorphisms representing the Apolipoprotein B locus with endophenotypes (total cholesterol [TC] and high‐density lipoprotein cholesterol) and phenotypes (myocardial infarction [MI] and survival) in four large‐scale studies, which include 20 748 individuals with 2357 MI events. We showed that a strong, robust predisposition of rs693 and rs562338 to TC (β = 0.72, P = 7.7 × 10−30 for rs693 and β = −1.08, P = 9.8 × 10−42 for rs562338) is not translated into a predisposition to MI and survival. The rs693_A allele influences risks of MI and mortality after MI additively with lipids. This allele shows antagonistic effects—protecting against MI risks (β = −0.18, P = 1.1 × 10−5) or increasing MI risks (β = 0.15, P = 2.8 × 10−3) and mortality after MI, in different populations. Paradoxically, increased TC concentrations can be protective against MI for the rs693_A allele carriers. Our results uncouple the influences of the same alleles on endophenotypes and phenotypes despite potential causal relationships among the latter. Our strategy reveals virtually genomewide significance for the associations of rs693 with MI (P = 5.5 × 10−8) that is contrasted with a weak estimate following the traditional, sample‐size‐centered GWAS strategy (P = 0.16) in the same sample. These results caution against the use of the traditional GWAS strategy for gaining profound insights into genetic predisposition to healthspan and lifespan.

Pathway Analysis Incorporating Protein-Protein Interaction Networks Identified Candidate Pathways for the Seven Common Diseases.

Pathway analysis has become popular as a secondary analysis strategy for genome-wide association studies (GWAS). Most of the current pathway analysis methods aggregate signals from the main effects of single nucleotide polymorphisms (SNPs) in genes within a pathway without considering the effects of gene-gene interactions. However, gene-gene interactions can also have critical effects on complex diseases. Protein-protein interaction (PPI) networks have been used to define gene pairs for the gene-gene interaction tests. Incorporating the PPI information to define gene pairs for interaction tests within pathways can increase the power for pathway-based association tests. We propose a pathway association test, which aggregates the interaction signals in PPI networks within a pathway, for GWAS with case-control samples. Gene size is properly considered in the test so that genes do not contribute more to the test statistic simply due to their size. Simulation studies were performed to verify that the method is a valid test and can have more power than other pathway association tests in the presence of gene-gene interactions within a pathway under different scenarios. We applied the test to the Wellcome Trust Case Control Consortium GWAS datasets for seven common diseases. The most significant pathway is the chaperones modulate interferon signaling pathway for Crohn’s disease (p-value = 0.0003). The pathway modulates interferon gamma, which induces the JAK/STAT pathway that is involved in Crohn’s disease. Several other pathways that have functional implications for the seven diseases were also identified. The proposed test based on gene-gene interaction signals in PPI networks can be used as a complementary tool to the current existing pathway analysis methods focusing on main effects of genes. An efficient software implementing the method is freely available at http://puppi.sourceforge.net.