Interactions In Genome-wide Association Studies Research Articles

Identifying latent genetic interactions in genome-wide association studies using multiple traits

The "missing" heritability of complex traits may be partly explained by genetic variants interacting with other genes or environments that are difficult to specify, observe, and detect. We propose a new kernel-based method called Latent Interaction Testing (LIT) to screen for genetic interactions that leverages pleiotropy from multiple related traits without requiring the interacting variable to be specified or observed. Using simulated data, we demonstrate that LIT increases power to detect latent genetic interactions compared to univariate methods. We then apply LIT to obesity-related traits in the UK Biobank and detect variants with interactive effects near known obesity-related genes (URL: https://CRAN.R-project.org/package=lit).

Recent advances in artificial intelligence, mechanistic models and speed breeding offer exciting opportunities for precise and accelerated genomics-assisted breeding.

Given the challenges of population growth and climate change, there is an urgent need to expedite the development of high-yielding stress-tolerant crop cultivars. While traditional breeding methods have been instrumental in ensuring global food security, their efficiency, precision, and labor intensiveness have become increasingly inadequate to address present and future challenges. Fortunately, recent advances in high-throughput phenomics and genomics-assisted breeding (GAB) provide a promising platform for enhancing crop cultivars with greater efficiency. However, several obstacles must be overcome to optimize the use of these techniques in crop improvement, such as the complexity of phenotypic analysis of big image data. In addition, the prevalent use of linear models in genome-wide association studies (GWAS) and genomic selection (GS) fails to capture the non-linear interactions of complex traits, limiting their applicability for GAB and impeding crop improvement. Recent advances in artificial intelligence (AI) techniques have opened doors to non-linear modeling approaches in crop breeding, enabling the capture of non-linear and epistatic interactions in GWAS and GS and thus making this variation available for GAB. While statistical and software challenges persist in AI-based models, they are expected to be resolved soon. Furthermore, recent advances in speed breeding have significantly reduced the time (3-5-fold) required for conventional breeding. Thus, integrating speed breeding with AI and GAB could improve crop cultivar development within a considerably shorter timeframe while ensuring greater accuracy and efficiency. In conclusion, this integrated approach could revolutionize crop breeding paradigms and safeguard food production in the face of population growth and climate change. This article is protected by copyright. All rights reserved.

Genomic analyses of claw disorders in Holstein cows: Genetic parameters, trait associations, and genome-wide associations considering interactions of SNP and heat stress.

The aim of the present study was an in-depth genomic analysis to understand the genomic mechanisms of the 3 claw disorders dermatitis digitalis (DD), interdigital hyperplasia (HYP), and sole ulcer (SU). In this regard, we estimated genetic parameters based on genomic relationship matrices, performed genome-wide association studies, annotated potential candidate genes, and inferred genetic associations with breeding goal traits considering the most important chromosomal segments. As a further novelty of this study, we inferred possible SNP × heat stress interactions for claw disorders. The study consisted of 17,264 first-lactation Holstein Friesian cows kept in 50 large-scale contract herds. The disease prevalence was 15.96, 2.36, and 8.20% for DD, HYP, and SU, respectively. The remaining breeding goal traits consisted of type traits of the feet and leg composite, female fertility, health traits, and 305-d production traits. The final genotype data set included 44,474 SNPs from the 17,264 genotyped cows. Heritabilities for DD, HYP, and SU were estimated in linear and threshold models considering the genomic relationship matrix (G matrix). Genetic correlations with breeding goal traits based on G were estimated in a series of bivariate linear models, which were verified via SNP effect correlations for specific chromosome segments (i.e., segments harboring potential candidate genes for DD, HYP, and SU). Genome-wide association studies were performed for all traits in a case-control design by applying a single SNP linear mixed model. Furthermore, for DD, HYP, and SU, we modeled SNP × heat stress interactions in genome-wide association studies. Single nucleotide polymorphism-based heritabilities were 0.04 and 0.08 for DD, 0.03 and 0.10 for SU, and 0.03 and 0.23 for HYP from linear and threshold models, respectively. The genetic correlations between DD, HYP, and SU with conformation traits from the feet and leg composite were positive throughout, indicating the value of indirect selection on conformation traits to improve claw health. Genetic correlations between DD, SU, and HYP with other breeding goal traits indicated impaired female fertility, impaired udder health status, and productivity decline of diseased cows. Genetic correlations among DD, SU, and HYP were moderate to large, indicating that different claw disorders have similar genetic mechanisms. Nevertheless, we identified disease-specific potential candidate genes, and genetic associations based on the surrounding SNPs partly differed from the genetic correlations. Especially for candidate genes contributing to 2 traits simultaneously, correlations based on SNP effects from the respective chromosome segment were close to 1 or to -1. In this regard, we annotated the candidate genes KRT33A and KRT33B for HYP and DD, KIF27 for HYP and calving to first insemination, and MAN1A1 for SU and the production traits. For SNP × heat stress interactions, we identified significant SNPs on BTA 2, 4, 5, 7, 8, 9, 13, 22, 25, and 28, and we annotated the potential candidate genes FSIP2, CLCN1, ADGRV1, DOP1A, THBD, and RHOBTB1. Results indicate gene-specific mechanisms of the claw disorders only in specific environments.

A compressed variance component mixed model for detecting QTNs and QTN-by-environment and QTN-by-QTN interactions in genome-wide association studies

Although genome-wide association studies are widely used to mine genes for quantitative traits, the effects to be estimated are confounded, and the methodologies for detecting interactions are imperfect. To address these issues, the mixed model proposed here first estimates the genotypic effects for AA, Aa, and aa, and the genotypic polygenic background replaces additive and dominance polygenic backgrounds. Then, the estimated genotypic effects are partitioned into additive and dominance effects using a one-way analysis of variance model. This strategy was further expanded to cover QTN-by-environment interactions (QEIs) and QTN-by-QTN interactions (QQIs) using the same mixed-model framework. Thus, a three-variance-component mixed model was integrated with our multi-locus random-SNP-effect mixed linear model (mrMLM) method to establish a new methodological framework, 3VmrMLM, that detects all types of loci and estimates their effects. In Monte Carlo studies, 3VmrMLM correctly detected all types of loci and almost unbiasedly estimated their effects, with high powers and accuracies and a low false positive rate. In re-analyses of 10 traits in 1439 rice hybrids, detection of 269 known genes, 45 known gene-by-environment interactions, and 20 known gene-by-gene interactions strongly validated 3VmrMLM. Further analyses of known genes showed more small (67.49%), minor-allele-frequency (35.52%), and pleiotropic (30.54%) genes, with higher repeatability across datasets (54.36%) and more dominance loci. In addition, a heteroscedasticity mixed model in multiple environments and dimension reduction methods in quite a number of environments were developed to detect QEIs, and variable selection under a polygenic background was proposed for QQI detection. This study provides a new approach for revealing the genetic architecture of quantitative traits.

Identifying High-Risk Phenotypes Using Three-Way Feature Interactions in Multiple Myeloma: An Analysis of Publically Available Mmrf Commpass Study

The introduction of next generation sequencing has exponentially increased the number of potential disease targets from a limited panel of probes to markers across the entire genome. Previous studies of NGS data in multiple myeloma have identified key features associated with outcome in additive models, typically showing involvement of TP53, gain of 1q, MYC translocations, and others.We modified the multi-factor dimensionality reduction method (MDR) method, a computationally efficient method for detecting non-linear patterns of gene-gene interactions in genome-wide association studies (GWAS) first proposed by Richie et al, to identify three-way interactions significantly associated with outcome in MM. Specifically, we altered the ranking strategy of the survival adapted Cox-MDR method of Lee et al from balanced accuracy (average of sensitivity and specificity) to the absolute sum of deviance residuals. Due to the high level of censoring (94 events in 559 cases), balanced accuracy ranking favored interactions that identified few high risk cases and failed to validate well on external data.The publically available MMRF CoMMpass trial database (release version IA10) was utilized as primary training set for our MDR method. We included in our comparisons 164 RNA-SEQ genes with significant association with overall survival, the 20 most abundant non-synonymous, non-IG variants, and 24 estimated whole chromosome arm copy number gains or loss calls from the long insert WGS data of 559 baseline cases with outcome and clinical data (median follow-up 1.8 years). We searched for all three-way interactions across 210 total features resulting in the ranking of over 1.5 million unique three-way interactions, adjusted for age, ISS, creatinine level, and treatment by modified MDR. The top three-way RNA-expression interactions from our discovery phase were validated on 540 cases with GEP microarray and clinical data from UAMS Total Therapy 2 and 3 with median follow-up of 4.66 years (publically available data from GSE2658, GSE31161, and GSE24080).Of the top 100 three-way gene expression interactions discovered that we could validate on Affymetrix GEP data, 84 were significant in the UAMS TT2 and TT3 data set (log rank p-value < 0.05). HMGB3, a proposed inhibitor of myeloid differentiation, and DDX51, known to be differentially methylated in ALL, were the most commonly involved genes in the top significant interactions across both data sets. Pathway analysis of the 150 most frequently observed genes across the top 5,000 interactions in CoMMpass revealed an enrichment of DNA replication and cell cycle associated genes: RFC5, MCM2, MCM3, MCM4, MCM6, CCNA2, CDK2, etc.Within the top interactions that included whole arm copy number variants, gain of 1q and loss of 13q were the most frequently observed. We note that the top three-way interactions with gain of 1q paired exclusively with ALYREF, a transcriptional promoter, and one additional gene. Of the top interactions that included a SNV, mutations of DIS3 and DNAH5 were the most frequently observed. Many of the top interactions including DIS3 mutations paired with HMGB3 or TRIB3, a negative regulator of NF-kB and sensitizer to apoptosis, and one additional gene.MDR models are capable of identifying complex interactions of features that are not strictly additive. We observed a variety of interaction models, and present below an example where presence of 2 or more features resulted in overall negative outcome while presence of 1 or fewer did not. This situation was observed for the interaction of high expression of HMGB3, DDX51, and PSMB2, a bortezomib sensitizer in MM cell lines, where high expression in 2 or more of these genes imparted a negative outcome in CoMMpass and TT2/TT3 data sets. In the TT2/TT3 data set, cases identified as high risk from this interaction were enriched for the PR subtype and GEP70 HR.This modified MDR method is an effective tool at mining large genomic data sets for all k -way interactions associated with outcome among binary or multi-level features, adjusted for clinical covariates. Our modifications to MDR method allow us to identify more relevant interactions and increase reliability in validation data. This method could potentially further knowledge of the complex relationships between key features in genomic landscape of MM by discovering novel interactions that identify high-risk phenotypes. [Display omitted] DisclosuresBergsagel:Phosplatin Therapeutics: Research Funding.

GEP-EpiSeeker: a gene expression programming-based method for epistatic interaction detection in genome-wide association studies

BackgroundIdentification of epistatic interactions provides a systematic way for exploring associations among different single nucleotide polymorphism (SNP) and complex diseases. Although considerable progress has been made in epistasis detection, efficiently and accurately identifying epistatic interactions remains a challenge due to the intensive growth of measuring SNP combinations.ResultsIn this work, we formulate the detection of epistatic interactions by a combinational optimization problem, and propose a novel evolutionary-based framework, called GEP-EpiSeeker, to detect epistatic interactions using Gene Expression Programming. In GEP-EpiSeeker, we propose several tailor-made chromosome rules to describe SNP combinations, and incorporate Bayesian network-based fitness evaluation into the evolution of tailor-made chromosomes to find suspected SNP combinations, and adopt the Chi-square test to identify optimal solutions from suspected SNP combinations. Moreover, to improve the convergence and accuracy of the algorithm, we design two genetic operators with multiple and adjacent mutations and an adaptive genetic manipulation method with fuzzy control to efficiently manipulate the evolution of tailor-made chromosomes. We compared GEP-EpiSeeker with state-of-the-art methods including BEAM, BOOST, AntEpiSeeker, MACOED, and EACO in terms of power, recall, precision and F1-score on the GWAS datasets of 12 DME disease models and 10 DNME disease models. Our experimental results show that GEP-EpiSeeker outperforms comparative methods.ConclusionsHere we presented a novel method named GEP-EpiSeeker, based on the Gene Expression Programming algorithm, to identify epistatic interactions in Genome-wide Association Studies. The results indicate that GEP-EpiSeeker could be a promising alternative to the existing methods in epistasis detection and will provide a new way for accurately identifying epistasis.

A new method for exploring gene\u2013gene and gene\u2013environment interactions in GWAS with tree ensemble methods and SHAP values

BackgroundThe identification of gene–gene and gene–environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Parametric regression models are suitable to look for prespecified interactions. Nonparametric models such as tree ensemble models, with the ability to detect any unspecified interaction, have previously been difficult to interpret. However, with the development of methods for model explainability, it is now possible to interpret tree ensemble models efficiently and with a strong theoretical basis.ResultsWe propose a tree ensemble- and SHAP-based method for identifying as well as interpreting potential gene–gene and gene–environment interactions on large-scale biobank data. A set of independent cross-validation runs are used to implicitly investigate the whole genome. We apply and evaluate the method using data from the UK Biobank with obesity as the phenotype. The results are in line with previous research on obesity as we identify top SNPs previously associated with obesity. We further demonstrate how to interpret and visualize interaction candidates.ConclusionsThe new method identifies interaction candidates otherwise not detected with parametric regression models. However, further research is needed to evaluate the uncertainties of these candidates. The method can be applied to large-scale biobanks with high-dimensional data.

BAM: A block-based Bayesian method for detecting genome-wide associations with multiple diseases

Many human diseases involve multiple genes in complex interactions. Large Genome-Wide Association Studies (GWASs) have been considered to hold promise for unraveling such interactions. However, statistic tests for high-order epistatic interactions (2 Single Nucleotide Polymorphisms (SNPs)) raise enormous computational and analytical challenges. It is well known that the block-wise structure exists in the human genome due to Linkage Disequilibrium (LD) between adjacent SNPs. In this paper, we propose a novel Bayesian method, named BAM, for simultaneously partitioning SNPs into LD-blocks and detecting genome-wide multi-locus epistatic interactions that are associated with multiple diseases. Experimental results on the simulated datasets demonstrate that BAM is powerful and efficient. We also applied BAM on two GWAS datasets from WTCCC, i.e., Rheumatoid Arthritis and Type 1 Diabetes, and accurately recovered the LD-block structure. Therefore, we believe that BAM is suitable and efficient for the full-scale analysis of multi-disease-related interactions in GWASs.

SHEIB-AGM: A Novel Stochastic Approach for Detecting High-Order Epistatic Interactions Using Bioinformation With Automatic Gene Matrix in Genome-Wide Association Studies

Detecting epistatic interactions in GWAS (genome-wide association studies) data is of great significance in studying common and complex diseases; however, the ability to detect high-order epistatic interactions in GWAS data is still insufficient. Existing methods are usually used to identify two-order interactions, and they cannot detect a large number of interactions. In this article, we propose a novel stochastic approach named SHEIB-AGM (stochastic approach for detecting high-order epistatic interactions using bioinformation with automatic gene matrix). SHEIB-AGM utilizes bioinformation to construct a gene matrix. In each iteration, it randomly generate a high-order SNP combination based on the gene matrix. SHEIB-AGM utilizes k2 (the Bayesian network scoring criterion) and G-test to detect epistasis in the generated combination and automatically update the gene matrix. We have compared SHEIB-AGM with six other methods, i.e., DECMDR, SNPHarvester, MACOED, AntEpiSeeker, HS-MMGKG and SEE, on simulated data including 108 epistatic models and 17,600 files. The results demonstrate that SHEIB-AGM greatly outperforms the above methods in terms of F-measure and power. We utilized SHEIB-AGM (with and without bioinformation) to analyze a real GWAS dataset from the Wellcome Trust Case Control Consortium. The results indicate that SHEIB-AGM with bioinformation can detect 33.94~3069.40-times more epistatic interactions. We have found numerous genes and gene pairs that may play an important role in seven complex diseases. Some of them have been found in the CTD database (the Comparative Toxicogenomics Database).

Combining associative classification with multifactor dimensionality reduction for predicting higher-order SNP interactions in case-control studies

The identification and characterisation of genotype-phenotype mapping is a central focus of current genome wide association interaction studies (GWAIS). Revealing these relationships for exposing the hidden structures of diseases has received considerable attention by a number of researchers. However, the current statistical and computational approaches ignore many complex genetic contexts. A multifactor dimensionality reduction based on associative classification was previously proposed for detecting multi-locus single nucleotide polymorphism (SNP) interactions in GWAIS. The approach is further studied in detail by adjusting threshold levels, and adding noise to the datasets. The simulated studies demonstrated significant improvements in accuracy by adjusting threshold values over the previous approaches. The results also indicate that the approach is robust in the presence of noise. Further, the application of this approach to real world data has demonstrated higher-order interactions among five SNPs for the manifestation of breast cancer, and three SNPs for the manifestation of hypertension.

Combining associative classification with multifactor dimensionality reduction for predicting higher-order SNP interactions in case-control studies

The identification and characterisation of genotype-phenotype mapping is a central focus of current genome wide association interaction studies (GWAIS). Revealing these relationships for exposing the hidden structures of diseases has received considerable attention by a number of researchers. However, the current statistical and computational approaches ignore many complex genetic contexts. A multifactor dimensionality reduction based on associative classification was previously proposed for detecting multi-locus single nucleotide polymorphism (SNP) interactions in GWAIS. The approach is further studied in detail by adjusting threshold levels, and adding noise to the datasets. The simulated studies demonstrated significant improvements in accuracy by adjusting threshold values over the previous approaches. The results also indicate that the approach is robust in the presence of noise. Further, the application of this approach to real world data has demonstrated higher-order interactions among five SNPs for the manifestation of breast cancer, and three SNPs for the manifestation of hypertension.

SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity

SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity

Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies

BackgroundIn Genome-Wide Association Studies (GWAS), the concept of linkage disequilibrium is important as it allows identifying genetic markers that tag the actual causal variants. In Genome-Wide Association Interaction Studies (GWAIS), similar principles hold for pairs of causal variants. However, Linkage Disequilibrium (LD) may also interfere with the detection of genuine epistasis signals in that there may be complete confounding between Gametic Phase Disequilibrium (GPD) and interaction. GPD may involve unlinked genetic markers, even residing on different chromosomes. Often GPD is eliminated in GWAIS, via feature selection schemes or so-called pruning algorithms, to obtain unconfounded epistasis results. However, little is known about the optimal degree of GPD/LD-pruning that gives a balance between false positive control and sufficient power of epistasis detection statistics. Here, we focus on Model-Based Multifactor Dimensionality Reduction as one large-scale epistasis detection tool. Its performance has been thoroughly investigated in terms of false positive control and power, under a variety of scenarios involving different trait types and study designs, as well as error-free and noisy data, but never with respect to multicollinear SNPs.ResultsUsing real-life human LD patterns from a homogeneous subpopulation of British ancestry, we investigated the impact of LD-pruning on the statistical sensitivity of MB-MDR. We considered three different non-fully penetrant epistasis models with varying effect sizes. There is a clear advantage in pre-analysis pruning using sliding windows at r2 of 0.75 or lower, but using a threshold of 0.20 has a detrimental effect on the power to detect a functional interactive SNP pair (power < 25%). Signal sensitivity, directly using LD-block information to determine whether an epistasis signal is present or not, benefits from LD-pruning as well (average power across scenarios: 87%), but is largely hampered by functional loci residing at the boundaries of an LD-block.ConclusionsOur results confirm that LD patterns and the position of causal variants in LD blocks do have an impact on epistasis detection, and that pruning strategies and LD-blocks definitions combined need careful attention, if we wish to maximize the power of large-scale epistasis screenings.

Enriched power of disease-concordant twin-case-only design in detecting interactions in genome-wide association studies.

Genetic interaction is a crucial issue in the understanding of functional pathways underlying complex diseases. However, detecting such interaction effects is challenging in terms of both methodology and statistical power. We address this issue by introducing a disease-concordant twin-case-only design, which applies to both monozygotic and dizygotic twins. To investigate the power, we conducted a computer simulation study by setting a series of parameter schemes with different minor allele frequencies and relative risks. Results from the simulation study reveals that the disease-concordant twin-case-only design largely reduces sample size required for sufficient power compared to the ordinary case-only design for detecting gene-gene interaction using unrelated individuals. Sample sizes for dizygotic and monozygotic twins were roughly 1/2 and 1/4 of sample sizes in the ordinary case-only design. Since dizygotic twins are genetically similar as siblings, the enriched power for dizygotic twins also applies to affected siblings, which could help to largely extend the application of the powerful twin-case-only design. In summary, our simulation reveals high value of disease-concordant twins and siblings in efficiently detecting gene-by-gene interactions.

Identifying loci affecting trait variability and detecting interactions in genome-wide association studies.

Identification of genetic variants with effects on trait variability can provide insights into the biological mechanisms that control variation and can identify potential interactions. We propose a two-degree-of-freedom test for jointly testing mean and variance effects to identify such variants. We implement the test in a linear mixed model, for which we provide an efficient algorithm and software. To focus on biologically interesting settings, we develop a test for dispersion effects, that is, variance effects not driven solely by mean effects when the trait distribution is non-normal. We apply our approach to body mass index in the subsample of the UK Biobank population with British ancestry (n ~408,000) and show that our approach can increase the power to detect associated loci. We identify and replicate novel associations with significant variance effects that cannot be explained by the non-normality of body mass index, and we provide suggestive evidence for a connection between leptin levels and body mass index variability.

The search for gene-gene interactions in genome-wide association studies: challenges in abundance of methods, practical considerations, and biological interpretation.

One of the primary goals in this era of precision medicine is to understand the biology of human diseases and their treatment, such that each individual patient receives the best possible treatment for their disease based on their genetic and environmental exposures. One way to work towards achieving this goal is to identify the environmental exposures and genetic variants that are relevant to each disease in question, as well as the complex interplay between genes and environment. Genome-wide association studies (GWAS) have allowed for a greater understanding of the genetic component of many complex traits. However, these genetic effects are largely small and thus, our ability to use these GWAS finding for precision medicine is limited. As more and more GWAS have been performed, rather than focusing only on common single nucleotide polymorphisms (SNPs) and additive genetic models, many researchers have begun to explore alternative heritable components of complex traits including rare variants, structural variants, epigenetics, and genetic interactions. While genetic interactions are a plausible reality that could explain some of the heritabliy that has not yet been identified, especially when one considers the identification of genetic interactions in model organisms as well as our understanding of biological complexity, still there are significant challenges and considerations in identifying these genetic interactions. Broadly, these can be summarized in three categories: abundance of methods, practical considerations, and biological interpretation. In this review, we will discuss these important elements in the search for genetic interactions along with some potential solutions. While genetic interactions are theoretically understood to be important for complex human disease, the body of evidence is still building to support this component of the underlying genetic architecture of complex human traits. Our hope is that more sophisticated modeling approaches and more robust computational techniques will enable the community to identify these important genetic interactions and improve our ability to implement precision medicine in the future.

Multiobjective differential evolution-based multifactor dimensionality reduction for detecting gene\u2013gene interactions

Epistasis within disease-related genes (gene–gene interactions) was determined through contingency table measures based on multifactor dimensionality reduction (MDR) using single-nucleotide polymorphisms (SNPs). Most MDR-based methods use the single contingency table measure to detect gene–gene interactions; however, some gene–gene interactions may require identification through multiple contingency table measures. In this study, a multiobjective differential evolution method (called MODEMDR) was proposed to merge the various contingency table measures based on MDR to detect significant gene–gene interactions. Two contingency table measures, namely the correct classification rate and normalized mutual information, were selected to design the fitness functions in MODEMDR. The characteristics of multiobjective optimization enable MODEMDR to use multiple measures to efficiently and synchronously detect significant gene–gene interactions within a reasonable time frame. Epistatic models with and without marginal effects under various parameter settings (heritability and minor allele frequencies) were used to assess existing methods by comparing the detection success rates of gene–gene interactions. The results of the simulation datasets show that MODEMDR is superior to existing methods. Moreover, a large dataset obtained from the Wellcome Trust Case Control Consortium was used to assess MODEMDR. MODEMDR exhibited efficiency in identifying significant gene–gene interactions in genome-wide association studies.

Environment-Dependent Genotype-Phenotype Associations in Avian Breeding Time.

Understanding how genes shape phenotypes is essential to assess the evolutionary potential of a trait. Identifying the genes underlying quantitative behavioral or life-history traits has, however, proven to be a major challenge. The majority of these traits are phenotypically plastic and different parts of the genome can be involved in shaping the trait under different environmental conditions. These variable genotype-phenotype associations could be one explanation for the limited success of genome-wide association studies in such traits. We here use avian seasonal timing of breeding, a trait that is highly plastic in response to spring temperature, to explore effects of such genotype-by-environment interactions in genome-wide association studies. We genotyped 2045 great tit females for 384081 single nucleotide polymorphisms (SNPs) and recorded their egg-laying dates in the wild. When testing for associations between SNPs and egg-laying dates, no SNP reached genome-wide significance. We then explored whether SNP effects were modified by annual spring temperature by formally testing for an interaction between SNP effect and temperature. The models including the SNP∗temperature interaction performed consistently better although no SNP reached genome-wide significance. Our results suggest that the effects of genes shaping seasonal timing depended on annual spring temperature. Such environment-dependent effects are expected for any phenotypically plastic trait. Taking these effects into account will thus improve the success of detecting genes involved in phenotypically plastic traits, thereby leading to a better understanding of their evolutionary potential.

CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies

Detecting epistatic interactions in genome-wide association studies (GWAS) is a computational challenge. Such huge numbers of single-nucleotide polymorphism (SNP) combinations limit the some of the powerful algorithms to be applied to detect the potential epistasis in large-scale SNP datasets. We propose a new algorithm which combines the differential evolution (DE) algorithm with a classification based multifactor-dimensionality reduction (CMDR), termed DECMDR. DECMDR uses the CMDR as a fitness measure to evaluate values of solutions in DE process for scanning the potential statistical epistasis in GWAS. The results indicated that DECMDR outperforms the existing algorithms in terms of detection success rate by the large simulation and real data obtained from the Wellcome Trust Case Control Consortium. For running time comparison, DECMDR can efficient to apply the CMDR to detect the significant association between cases and controls amongst all possible SNP combinations in GWAS. DECMDR is freely available at https://goo.gl/p9sLuJ . chuang@isu.edu.tw or e0955767257@yahoo.com.tw. Supplementary data are available at Bioinformatics online.

A powerful and efficient two-stage method for detecting gene-to-gene interactions in GWAS.

For over a decade functional gene-to-gene interaction (epistasis) has been suspected to be a determinant in the "missing heritability" of complex traits. However, searching for epistasis on the genome-wide scale has been challenging due to the prohibitively large number of tests which result in a serious loss of statistical power as well as computational challenges. In this article, we propose a two-stage method applicable to existing case-control data sets, which aims to lessen both of these problems by pre-assessing whether a candidate pair of genetic loci is involved in epistasis before it is actually tested for interaction with respect to a complex phenotype. The pre-assessment is based on a two-locus genotype independence test performed in the sample of cases. Only the pairs of loci that exhibit non-equilibrium frequencies are analyzed via a logistic regression score test, thereby reducing the multiple testing burden. Since only the computationally simple independence tests are performed for all pairs of loci while the more demanding score tests are restricted to the most promising pairs, genome-wide association study (GWAS) for epistasis becomes feasible. By design our method provides strong control of the type I error. Its favourable power properties especially under the practically relevant misspecification of the interaction model are illustrated. Ready-to-use software is available. Using the method we analyzed Parkinson's disease in four cohorts and identified possible interactions within several SNP pairs in multiple cohorts.