Epistasis Detection in Genome-Wide Screening for Complex Human Diseases in Structured Populations

Abegaz, Fentaw; Lishout, François Van; John, Jestinah Mahachie; Chiachoompu, Kridsadakorn; Bhardwaj, Anu; Gusareva, Elena S.; Wei, Zhi; Hákonarson, Hákon; Steen, Kristel Van

doi:10.1089/sysm.2019.0003

Cited by 10 publications

(6 citation statements)

References 33 publications

(49 reference statements)

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“…They introduced Model-Based Multifactor Dimensionality Reduction-Principal Component (MBMDR-PC) for Structured Populations and applied this method on International Inflammatory Bowel Disease Genetics Consortium Crohn’s disease (CD) data from 15 countries. SNX20 was identified in 4 of the 8 pairs of potentially significant interacting genes which 36 of 109 interacting pairs of SNPs were mapped to [47].…”

Section: Resultsmentioning

confidence: 99%

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Bian¹,

Li²,

Leung³

et al. 2023

Preprint

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Transcriptome-wide association study (TWAS) is an emerging model leveraging gene expressions to direct genotype-phenotype association mapping. A key component in TWAS is the prediction of gene expressions; and many statistical approaches have been developed along this line. However, a problem is that many genes have low expression heritability, limiting the performance of any predictive model. In this work, hypothesizing that appropriate denoising may improve the quality of expression data (including heritability), we propose AE-TWAS, which adds a transformation step before conducting standard TWAS. The transformation is composed of two steps by first splitting the whole transcriptome into co-expression networks (modules) and then using autoencoder (AE) to reconstruct the transcriptome data within each module. This transformation removes noise (including nonlinear ones) from the transcriptome data, paving the path for downstream TWAS. We applied AE-TWAS to the GTEx whole blood transcriptome data and GWAS data of five human diseases, showing two inspiring properties of AE-TWAS: (1) After transformation, the transcriptome data enjoy higher expression heritability at the low-heritability spectrum and possess higher connectivity within the modules. (2) The transferred transcriptome indeed enables better performance of TWAS; and moreover, the newly formed highly connected genes (i.e., hub genes) are more functionally relevant to diseases, evidenced by their functional annotations and overlap with TWAS hits. Taking together, we show that autoencoder transformation produces better transcriptome, which in turn enables improved expression-assisted genotype-phenotype association mapping. The impact of this work may be beyond the field of gene mapping: AE can be deemed as a nonlinear extension of principal component analysis (PCA) that is used for removing artifacts in expression data routinely. As such, this work may inspire more expression-based applications to be carried out after an appropriate AE-transformation, unlocking the use of AE-denoised transcriptome in many fields.

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Section: Resultsmentioning

confidence: 99%

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Bian¹,

Li²,

Leung³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…The newly adjusted phenotype Y adj is taken as input to classic MB-MDR, in an attempt to capture genetic interactions that are not spurious due to inadequate handling of population structures. Detail of the MBMDR-PC approach is outlined in [ 29 , 30 ].…”

Section: Methodsmentioning

confidence: 99%

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure

et al. 2021

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Background In genome-wide association studies the extent and impact of confounding due to population structure have been well recognized. Inadequate handling of such confounding is likely to lead to spurious associations, hampering replication, and the identification of causal variants. Several strategies have been developed for protecting associations against confounding, the most popular one is based on Principal Component Analysis. In contrast, the extent and impact of confounding due to population structure in gene-gene interaction association epistasis studies are much less investigated and understood. In particular, the role of nonlinear genetic population substructure in epistasis detection is largely under-investigated, especially outside a regression framework. Methods To identify causal variants in synergy, to improve interpretability and replicability of epistasis results, we introduce three strategies based on a model-based multifactor dimensionality reduction approach for structured populations, namely MBMDR-PC, MBMDR-PG, and MBMDR-GC. Results Simulation results comparing the performance of various approaches show that in the presence of population structure MBMDR-PC and MBMDR-PG consistently better control type I error rate at the nominal level than MBMDR-GC. Moreover, our proposed three methods of population structure correction outperform MDR-SP in terms of statistical power. Conclusion We demonstrate through extensive simulation studies the effect of various degrees of genetic population structure and relatedness on epistasis detection and propose appropriate remedial measures based on linear and nonlinear sample genetic similarity.

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“…One example, the multifactor dimensionality reduction (MDR) technique finds multilocus genotypes that have high or low association with disease and defines new variables that explain the relationship of both loci [6,29,30]. MDR can be combined with other machine learning methods and has been extended to handle population structure [31].…”

Section: Computational Challenges In Detecting Epistasismentioning

confidence: 99%

Computational framework for statistical epistasis supports XOR penetrance function in a living system

Batista

Madar²,

Freda

et al. 2023

Preprint

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Statistical epistasis has been studied extensively because of its potential to provide evidence for genetic interactions for phenotypes, but there have been methodological limitations to its exhaustive, widespread application. We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied encodings for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided.We also present a new efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10000 loci and 1000 samples each, using the standard Cartesian encoding and the XOR penetrance function for the interactions, to test for evidence of statistical epistasis for the phenotype of body mass index. This study revealed that the XOR penetrance function found greater evidence for statistical epistasis in many more pairs of loci in both data sets and in the rat data set, those pairs of loci found using the XOR penetrance function are enriched for biologically relevant pathways.Author summaryEpistasis, the interaction between two or more genes, is likely integral to the study of genetics and present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects (such as GWAS), partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Many current methods for epistasis detection only consider a Cartesian encoding for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genes, some non-linear. In this work we describe computationally efficient algorithms for the detection of statistical epistasis that allow for varied interaction encodings for modeling epistasis. Our methodology efficiently detects pairwise and three-way epistatic interactions in two closely related species (rat and mouse) under both Cartesian and XOR interaction encodings. Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction encoding was applied providing evidence that more varied models for interaction may need to be applied to describe epistasis that occurs in living systems.

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Epistasis Detection in Genome-Wide Screening for Complex Human Diseases in Structured Populations

Cited by 10 publications

References 33 publications

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Autoencoder-transformed transcriptome improves genotype-phenotype association studies

Performance of model-based multifactor dimensionality reduction methods for epistasis detection by controlling population structure

Computational framework for statistical epistasis supports XOR penetrance function in a living system

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