HisCoM-GGI: Hierarchical structural component analysis of gene–gene interactions

2018 ◽  
Vol 16 (06) ◽  
pp. 1840026 ◽  
Author(s):  
Sungkyoung Choi ◽  
Sungyoung Lee ◽  
Yongkang Kim ◽  
Heungsun Hwang ◽  
Taesung Park
Keyword(s):  
Structural Component ◽  
Complex Traits ◽  
Latent Variable ◽  
Association Studies ◽  
Component Analysis ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Multiple Testing Correction ◽  
Genetic Mechanisms

Although genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with common diseases, these observations are limited for fully explaining “missing heritability”. Determining gene–gene interactions (GGI) are one possible avenue for addressing the missing heritability problem. While many statistical approaches have been proposed to detect GGI, most of these focus primarily on SNP-to-SNP interactions. While there are many advantages of gene-based GGI analyses, such as reducing the burden of multiple-testing correction, and increasing power by aggregating multiple causal signals across SNPs in specific genes, only a few methods are available. In this study, we proposed a new statistical approach for gene-based GGI analysis, “Hierarchical structural CoMponent analysis of Gene–Gene Interactions” (HisCoM-GGI). HisCoM-GGI is based on generalized structured component analysis, and can consider hierarchical structural relationships between genes and SNPs. For a pair of genes, HisCoM-GGI first effectively summarizes all possible pairwise SNP–SNP interactions into a latent variable, from which it then performs GGI analysis. HisCoM-GGI can evaluate both gene-level and SNP-level interactions. Through simulation studies, HisCoM-GGI demonstrated higher statistical power than existing gene-based GGI methods, in analyzing a GWAS of a Korean population for identifying GGI associated with body mass index. Resultantly, HisCoM-GGI successfully identified 14 potential GGI, two of which, (NCOR2 [Formula: see text] SPOCK1) and (LINGO2 [Formula: see text] ZNF385D) were successfully replicated in independent datasets. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand the biological genetic mechanisms of complex traits. We conclude that HisCoM-GGI method may be a valuable tool for genome to identify GGI in missing heritability, allowing us to better understand biological genetic mechanisms of complex traits. An implementation of HisCoM-GGI can be downloaded from the website ( http://statgen.snu.ac.kr/software/hiscom-ggi ).

scholarly journals HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

2020 ◽  
Vol 21 (18) ◽  
pp. 6724
Author(s):  
Sungkyoung Choi ◽  
Sungyoung Lee ◽  
Iksoo Huh ◽  
Heungsun Hwang ◽  
Taesung Park
Keyword(s):  
Structural Component ◽  
Latent Variable ◽  
Association Studies ◽  
Component Analysis ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Genome Wide ◽  
Missing Heritability Problem

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

scholarly journals A Comparative Study on Multifactor Dimensionality Reduction Methods for Detecting Gene-Gene Interactions with the Survival Phenotype

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Seungyeoun Lee ◽  
Yongkang Kim ◽  
Min-Seok Kwon ◽  
Taesung Park
Keyword(s):  
Dimensionality Reduction ◽  
Genetic Variants ◽  
Multifactor Dimensionality Reduction ◽  
Association Studies ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Analytical Strategy ◽  
Reduction Methods ◽  
Missing Heritability Problem

Genome-wide association studies (GWAS) have extensively analyzed single SNP effects on a wide variety of common and complex diseases and found many genetic variants associated with diseases. However, there is still a large portion of the genetic variants left unexplained. This missing heritability problem might be due to the analytical strategy that limits analyses to only single SNPs. One of possible approaches to the missing heritability problem is to consider identifying multi-SNP effects or gene-gene interactions. The multifactor dimensionality reduction method has been widely used to detect gene-gene interactions based on the constructive induction by classifying high-dimensional genotype combinations into one-dimensional variable with two attributes of high risk and low risk for the case-control study. Many modifications of MDR have been proposed and also extended to the survival phenotype. In this study, we propose several extensions of MDR for the survival phenotype and compare the proposed extensions with earlier MDR through comprehensive simulation studies.

scholarly journals Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models

Genes ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 87
Author(s):  
Sean M. Burnard ◽  
Rodney A. Lea ◽  
Miles Benton ◽  
David Eccles ◽  
Daniel W. Kennedy ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Complex Traits ◽  
Multiple Testing ◽  
Large Scale ◽  
Disease Risk ◽  
Association Studies ◽  
Meta Analysis ◽  
Elastic Net ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.

Abstract P455: Epistatic Regulation of Hematological-related Traits and Gene Expression in Mice Reveals Additional Heritability Missed by Standard GWAS-type Analyses

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Anna Miller ◽  
Anlu Chen ◽  
David Buchner ◽  
Scott Williams
Keyword(s):  
Gene Expression ◽  
Complex Traits ◽  
Association Studies ◽  
Additive Models ◽  
Blood Analysis ◽  
Gene Interactions ◽  
Genome Wide Association Studies ◽  
Epistatic Qtls ◽  
Distribution Width ◽  
Additive Qtls

The genetic contribution of additive versus non-additive (epistasis) effects in the regulation of hematologic and other complex traits is unclear. Although many variants have been associated with a range of complex traits via genome wide association studies (GWAS), these loci combined in additive models do not account for most of the trait heritability. GWAS-type analyses typically ignore gene-gene interactions, in part because of the difficulty in detecting them in complex multicellular organisms, especially humans. We have previously shown that mouse chromosome substitution strains (CSSs) are a powerful model for detecting epistasis, and that for certain complex traits the relative contribution of epistasis to heritability is as important as additivity. We have now applied the use of these CSSs to identify and map additive and epistatic loci that regulate a range of hematological-related traits and hepatic gene expression levels. A modified backcross was performed with CSS strains carrying the A/J-derived substituted chromosomes 4 and 6 on an otherwise C57BL/6J genetic background. By analyzing the transcriptomes of offspring from this cross, we identified and mapped additive quantitative trait loci (QTLs) that regulated the expression of 770 genes, and epistatic QTLs for 802 genes. Similarly we performed a complete blood analysis of offspring from the cross and identified additive QTLs for platelets and percentage of granulocyte in the blood as well as epistatic QTLs controlling the percentage of lymphocytes in the blood (rs13477644, rs13478739; LOD = 3.4) and red cell distribution width (rs13477864, rs13478802; LOD = 3.7). The variance attributable to the epistatic QTLs was approximately equal to that of the additive QTLs, highlighting the importance of identifying genetic interactions. Of note, even the SNPs associated with the most significant epistatic interactions were undetected in our single loci GWAS-like association analyses, demonstrating the need to specifically test for gene-gene interactions in studies of complex traits. In summary, our studies identified epistatic loci in mice that are important regulators of hematological-related traits and gene expression. Additionally, our studies call attention to the importance of extending single loci GWAS-type analyses to include analyses of gene-gene interactions to improve our ability to identify genetic variants that regulate complex traits.

scholarly journals networkGWAS: A network-based approach for genome-wide association studies in structured populations

2021 ◽  
Author(s):  
Giulia Muzio ◽  
Leslie O'Bray ◽  
Laetitia Meng-Papaxanthos ◽  
Juliane Klatt ◽  
Karsten Borgwardt
Keyword(s):  
Genetic Markers ◽  
Complex Traits ◽  
Multiple Testing ◽  
Association Studies ◽  
Search Space ◽  
Structured Populations ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Genome Wide

While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose <tt>networkGWAS</tt>, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. <tt>networkGWAS</tt> successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

scholarly journals Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning

2020 ◽  
Vol 36 (12) ◽  
pp. 3811-3817 ◽  
Author(s):  
Songshan Yang ◽  
Jiawei Wen ◽  
Scott T Eckert ◽  
Yaqun Wang ◽  
Dajiang J Liu ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Complex Traits ◽  
Association Studies ◽  
Superior Performance ◽  
Supplementary Information ◽  
Tuning Parameter ◽  
Snp Analysis ◽  
Genome Wide Association Studies ◽  
Multiple Testing Correction ◽  
Wide Range

Abstract Motivation Large scale genome-wide association studies (GWAS) have resulted in the identification of a wide range of genetic variants related to a host of complex traits and disorders. Despite their success, the individual single-nucleotide polymorphism (SNP) analysis approach adopted in most current GWAS can be limited in that it is usually biologically simple to elucidate a comprehensive genetic architecture of phenotypes and statistically underpowered due to heavy multiple-testing correction burden. On the other hand, multiple-SNP analyses (e.g. gene-based or region-based SNP-set analysis) are usually more powerful to examine the joint effects of a set of SNPs on the phenotype of interest. However, current multiple-SNP approaches can only draw an overall conclusion at the SNP-set level and does not directly inform which SNPs in the SNP-set are driving the overall genotype–phenotype association. Results In this article, we propose a new permutation-assisted tuning procedure in lasso (plasso) to identify phenotype-associated SNPs in a joint multiple-SNP regression model in GWAS. The tuning parameter of lasso determines the amount of shrinkage and is essential to the performance of variable selection. In the proposed plasso procedure, we first generate permutations as pseudo-SNPs that are not associated with the phenotype. Then, the lasso tuning parameter is delicately chosen to separate true signal SNPs and non-informative pseudo-SNPs. We illustrate plasso using simulations to demonstrate its superior performance over existing methods, and application of plasso to a real GWAS dataset gains new additional insights into the genetic control of complex traits. Availability and implementation R codes to implement the proposed methodology is available at https://github.com/xyz5074/plasso. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Genome-wide detection of CNV regions and their potential association with growth and fatness traits in Duroc pigs

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yibin Qiu ◽  
Rongrong Ding ◽  
Zhanwei Zhuang ◽  
Jie Wu ◽  
Ming Yang ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Complex Traits ◽  
Copy Number ◽  
Association Studies ◽  
Average Daily Gain ◽  
Genome Wide Association Studies ◽  
Potential Candidate ◽  
Genome Wide ◽  
Number Variation ◽  
Genetic Mechanisms

Abstract Background In the process of pig breeding, the average daily gain (ADG), days to 100 kg (AGE), and backfat thickness (BFT) are directly related to growth rate and fatness. However, the genetic mechanisms involved are not well understood. Copy number variation (CNV), an important source of genetic diversity, can affect a variety of complex traits and diseases and has gradually been thrust into the limelight. In this study, we reported the genome-wide CNVs of Duroc pigs using SNP genotyping data from 6627 animals. We also performed a copy number variation region (CNVR)-based genome-wide association studies (GWAS) for growth and fatness traits in two Duroc populations. Results Our study identified 953 nonredundant CNVRs in U.S. and Canadian Duroc pigs, covering 246.89 Mb (~ 10.90%) of the pig autosomal genome. Of these, 802 CNVRs were in U.S. Duroc pigs with 499 CNVRs were in Canadian Duroc pigs, indicating 348 CNVRs were shared by the two populations. Experimentally, 77.8% of nine randomly selected CNVRs were validated through quantitative PCR (qPCR). We also identified 35 CNVRs with significant association with growth and fatness traits using CNVR-based GWAS. Ten of these CNVRs were associated with both ADG and AGE traits in U.S. Duroc pigs. Notably, four CNVRs showed significant associations with ADG, AGE, and BFT, indicating that these CNVRs may play a pleiotropic role in regulating pig growth and fat deposition. In Canadian Duroc pigs, nine CNVRs were significantly associated with both ADG and AGE traits. Further bioinformatic analysis identified a subset of potential candidate genes, including PDGFA, GPER1, PNPLA2 and BSCL2. Conclusions The present study provides a necessary supplement to the CNV map of the Duroc genome through large-scale population genotyping. In addition, the CNVR-based GWAS results provide a meaningful way to elucidate the genetic mechanisms underlying complex traits. The identified CNVRs can be used as molecular markers for genetic improvement in the molecular-guided breeding of modern commercial pigs.

scholarly journals Inferring the Nature of Missing Heritability in Human Traits Using Data from the GWAS Catalog

Genetics ◽  
2019 ◽  
Vol 212 (3) ◽  
pp. 891-904 ◽  
Author(s):  
Eugenio López-Cortegano ◽  
Armando Caballero
Keyword(s):  
Complex Traits ◽  
Statistical Power ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Narrow Sense Heritability ◽  
Additive Contribution ◽  
Heritability Estimates ◽  
Using Data ◽  
Gwas Catalog

Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBI GWAS Catalog in order to explore the observed distribution of locus effects for a set of 42 complex traits and to quantify their contribution to narrow-sense heritability. With the data at hand, we were able to predict the expected distribution of locus effects for 16 traits and diseases, their expected contribution to heritability, and the missing number of loci yet to be discovered to fully explain the familial heritability estimates. Our results indicate that, for 6 out of the 16 traits, the additive contribution of a great number of loci is unable to explain the familial (broad-sense) heritability, suggesting that the gap between GWAS and familial estimates of heritability may not ever be closed for these traits. In contrast, for the other 10 traits, the additive contribution of hundreds or thousands of loci yet to be found could potentially explain the familial heritability estimates, if this were the case. Computer simulations are used to illustrate the possible contribution from nonadditive genetic effects to the gap between GWAS and familial estimates of heritability.

scholarly journals The contribution of rare whole genome sequencing variants to plasma protein levels and to the missing heritability

2021 ◽  
Author(s):  
Marcin Kierczak ◽  
Nima Rafati ◽  
Julia Höglund ◽  
Hadrien Gourle ◽  
Daniel Schmitz ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Complex Traits ◽  
Rare Variants ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Missing Heritability ◽  
Common Genetic Variants

Abstract Despite the success in identifying effects of common genetic variants, using genome-wide association studies (GWAS), much of the genetic contribution to complex traits remains unexplained. Here, we analysed high coverage whole-genome sequencing (WGS) data, to evaluate the contribution of rare genetic variants to 414 plasma proteins. The frequency distribution of genetic variants was skewed towards the rare spectrum, and damaging variants were more often rare. However, only 2.24% of the heritability was estimated to be explained by rare variants. A gene-based approach, developed to also capture the effect of rare variants, identified associations for 249 of the proteins, which was 25% more as compared to a GWAS. Out of those, 24 associations were driven by rare variants, clearly highlighting the capacity of aggregated tests and WGS data. We conclude that, while many rare variants have considerable phenotypic effects, their contribution to the missing heritability is limited by their low frequencies.

scholarly journals A Two-State Epistasis Model Reduces Missing Heritability of Complex Traits

2015 ◽  
Author(s):  
Kerry L. Bubb ◽  
Christine Queitsch
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Additive Model ◽  
Twin Studies ◽  
Additive Models ◽  
Genome Wide Association Studies ◽  
Missing Heritability ◽  
Epistatic Interactions ◽  
Epistasis Model ◽  
Genome Wide

ABSTRACTDespite decade-long efforts, the genetic underpinnings of many complex traits and diseases remain largely elusive. It is increasingly recognized that a purely additive model, upon which most genome-wide association studies (GWAS) rely, is insufficient. Although thousands of significant trait-associated loci have been identified, purely additive models leave much of the inferred genetic variance unexplained. Several factors have been invoked to explain the ‘missing heritability’, including epistasis. Accounting for all possible epistatic interactions is computationally complex and requires very large samples. Here, we propose a simple two-state epistasis model, in which individuals show either high or low variant penetrance with respect to a certain trait. The use of this model increases the power to detect additive trait-associated loci. We show that this model is consistent with current GWAS results and improves fit with heritability observations based on twin studies. We suggest that accounting for variant penetrance will significantly increase our power to identify underlying additive loci.

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