An Efficient Genome-Wide Multilocus Epistasis Search

Kärkkäinen, Hanni P.; Li, Zitong; Sillanpää, Mikko J.

doi:10.1534/genetics.115.182444

Cited by 14 publications

(29 citation statements)

References 30 publications

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“…Large numbers of variants, each present at a low frequency, create a major challenge, leading to low statistical confidence in the level of epistasis. Thanks to recently proposed efficient computing algorithms (Hemani et al ., ; Gyenesei et al ., ; Lishout et al ., ; Zhang et al ., ; Cowman and Koyuturk, ) and alternative, non‐exhaustive modeling approaches (Guo et al ., ; Leem et al ., ; Karkkainen et al ., ; Wang et al ., 2015a; Zhang et al ., ; Mathew et al ., ), both the computational cost and multiple‐testing burden can be effectively addressed and even high‐order interactions can be uncovered, provided there is a sufficient number of individuals in the population under study. Still, because of the need to test alleles pairwise, those with relatively high frequency will provide the greatest probability of discovering epistasis, and low‐frequency alleles at either locus will reduce the statistical power.…”

Section: Epistasis: Negligible or Neglected?mentioning

confidence: 99%

Crop genome‐wide association study: a harvest of biological relevance

2018

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Summary With the advent of rapid genotyping and next‐generation sequencing technologies, genome‐wide association study (GWAS) has become a routine strategy for decoding genotype–phenotype associations in many species. More than 1000 such studies over the last decade have revealed substantial genotype–phenotype associations in crops and provided unparalleled opportunities to probe functional genomics. Beyond the many ‘hits’ obtained, this review summarizes recent efforts to increase our understanding of the genetic architecture of complex traits by focusing on non‐main effects including epistasis, pleiotropy, and phenotypic plasticity. We also discuss how these achievements and the remaining gaps in our knowledge will guide future studies. Synthetic association is highlighted as leading to false causality, which is prevalent but largely underestimated. Furthermore, validation evidence is appealing for future GWAS, especially in the context of emerging genome‐editing technologies.

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Section: Epistasis: Negligible or Neglected?mentioning

confidence: 99%

Crop genome‐wide association study: a harvest of biological relevance

2018

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“…If so, they need to use ad hoc procedures, such as multi-stage analysis, by screening major markers [16]. …”

Section: Discussionmentioning

confidence: 99%

“…The searching strategy for epistasis has been proposed by several authors in genome-wide studies to incorporate its effects into the model [16, 28, 31, 32, 38, 40]. However, several of these methods are based on undirected epistasis estimates for multistage strategies; in these circumstances, the genetic architecture may not be correctly depicted.…”

Section: Discussionmentioning

confidence: 99%

“…In these models, the pairwise epistasis effects are ignored, and only the genomic epistatic values are taken into account for genomic prediction [13, 22, 27, 35]. Other approaches have estimated epistasis using markers with high marginal effects, which cannot explore minor epistatic interactions and the universal epistasis hypothesis [16]. …”

Section: Introductionmentioning

confidence: 99%

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Bayesian reversible-jump for epistasis analysis in genomic studies

Balestre

Souza

2016

BMC Genomics

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BackgroundThe large amount of data used in genomic analysis has allowed geneticists to achieve some understanding of the genetic architecture of complex traits. Although the information gathered by molecular markers has permitted gains in predictive accuracy and gene discovery, epistatic effects have been ignored based on exhaustive searches requesting estimates of its effects on the whole genome. In this work, we propose the reversible-jump technique to estimate epistasis in the genome without drastically altering the model dimension. To this end, we used a real maize dataset based on 256 F2:3 progenies plus a simulation data set based on 300 F2 individuals. In the simulation scenario, six QTL presenting main effects (additive and dominance) were combined with seven other epistatic effects totaling 13 QTL controlling the trait.ResultsOur model explored 18,624 candidate epistases, but even in this vast space, only one spurious interaction was found. The three epistases selected by our model, named here as 18x26, 56x68 and 59x93, were very close to simulated ones (19x25, 54x72, 59x91 and 59x94). In the real dataset, we estimate 33,024 epistatic effects, and several minor epistatic combinations were found to explain a significant proportion of the genetic variance. The broad participation of epistasis in the real dataset may indicate the presence of pervasive epistasis acting on maize grain yield.ConclusionsThe power of selecting true epistasis in thousands of possible combinations suggests the attractiveness of our model to handle genomic dataElectronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3342-6) contains supplementary material, which is available to authorized users.

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“…However, genes with strong main effects are linearly related to the phenotype by which high and low groups are formed implying that this equality is not valid anymore. Another problem is that the proportion of the phenotypic variation explained by the gene-gene interaction might be insufficient for the interaction terms to be identifiable with small sample sizes [65][66][67].…”

Section: Violation Of the Main Effect Assumptionmentioning

confidence: 99%

Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways

Kontio

Pyhäjärvi

Sillanpää

2020

Preprint

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A wide variety of parametric approaches and co-expression networks have been developed for finding gene-by-gene interactions underlying complex traits from expression data. However, a little is known about the practical correspondence and synergistic potential of these different schemes. We provide a framework for parallel consideration of parametric interaction models with quantitative traits and co-expression networks based on a previously uncharacterized link between them. Resulting trait-specific co-expression network estimation method 1) serves to enhance the interpretation of biological networks in a more parametric sense and 2) exploits the underlying parametric model itself in the estimation process. It is tailored for simultaneous identification and classification of molecular interactions and pathways regulating complex traits by accounting for common characteristics of genetic architectures due to which the mainstream methods often lack efficiency. A remarkable advance over the state-of-art methods is illustrated theoretically and through comprehensive simulated scenarios. In particular, prognostically important novel findings in acute myeloid leukemia analysis demonstrate the method's immediate practical relevance.

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An Efficient Genome-Wide Multilocus Epistasis Search

Cited by 14 publications

References 30 publications

Crop genome‐wide association study: a harvest of biological relevance

Crop genome‐wide association study: a harvest of biological relevance

Bayesian reversible-jump for epistasis analysis in genomic studies

Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways

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