A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values

Ning, Chao; Wang, Dan; Kang, Hao; Mrode, Raphael; Zhou, Lei; Xu, Shizhong; Liu, Jianfeng

doi:10.1093/bioinformatics/bty017

Cited by 25 publications

(35 citation statements)

References 40 publications

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“…As expected from previous statistical epistatic methods that have heavily studied this data [49,55,56,108,109], LT-MAPIT most noticeably identified the major histocompatibility complex (MHC) region on chromosome 6 in RA and T1D as having many genetic variants that are most likely involved in prominent pairwise interactions. This is clearly visible in Figures S7 and S8 which display manhattan plots of our marginal epistatic genome-wide scans for these two traits.…”

Section: Practical Application To Wtccc Datasupporting

confidence: 66%

Genome-wide Marginal Epistatic Association Mapping in Case-Control Studies

Crawford

Zhou

2018

Preprint

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Epistasis, commonly defined as the interaction between genetic loci, is an important contributor to the genetic architecture underlying many complex traits and common diseases. Most existing epistatic mapping methods in genome-wide association studies explicitly search over all pairwise or higher-order interactions. However, due to the potentially large search space and the resulting multiple testing burden, these conventional approaches often suffer from heavy computational cost and low statistical power. A recently proposed attractive alternative for mapping epistasis focuses instead on detecting marginal epistasis, which is defined as the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact -thus, potentially alleviating much of the statistical and computational burden associated with conventional epistatic mapping procedures. However, previous marginal epistatic mapping methods are based on quantitative trait models. As we will show here, these lack statistical power in case-control studies. Here, we develop a liability threshold mixed model that extends marginal epistatic mapping to case-control studies. Our method properly accounts for case-control ascertainment and the binary nature of casecontrol data. We refer to this method as the liability threshold marginal epistasis test (LT-MAPIT). With simulations, we illustrate the benefits of LT-MAPIT in terms of providing effective type I error control, and being more powerful than both existing marginal epistatic mapping methods and conventional explicit search-based approaches in case-control data. We finally apply LT-MAPIT to identify both marginal and pairwise epistasis in seven complex diseases from the Wellcome Trust Case Control Consortium (WTCCC) 1 study.

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Section: Practical Application To Wtccc Datasupporting

confidence: 66%

Genome-wide Marginal Epistatic Association Mapping in Case-Control Studies

Crawford

Zhou

2018

Preprint

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“…However, the calculation of V is inefficient due to high-dimensional M aa , M ad and M dd . According to previous studies (Ning, et al, 2018; Su, et al, 2012; VanRaden, 2008; Xu, 2013), and can be used to respectively reflect the additive, dominance, additive by additive, additive by dominance and dominance by dominance genomic relationship among individuals with the following equation. Where, λ a = ∑2 p i (1 − p i ), λ d = ∑ 2 p i (1 − p i )(1 − 2 p i (1 − p i )).…”

Section: Methodsmentioning

confidence: 99%

“…The rheumatoid arthritis (RA) and type-I diabetes (T1D) traits were analyzed for the WTCCC data. After quality control of genotypes following Ning, et al (2018), 4,961 individuals genotyped by 353,859 SNPs for RA and 4,962 individuals genotyped by 353,751 for T1D were remained for subsequent analysis. As no individuals has repeated records, p is removed from model and Z is the identity matrix.…”

Section: Methodsmentioning

confidence: 99%

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Rapid Epistatic Mixed Model Association Studies by Controlling Multiple Polygenic Effects

Wang

Tang

Liu

et al. 2020

Preprint

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SummaryWe have developed a rapid mixed model algorithm for exhaustive genome-wide epistatic association analysis by controlling multiple polygenic effects. Our model can simultaneously handle additive by additive epistasis, dominance by dominance epistasis and additive by dominance epistasis, and account for intrasubject fluctuations due to individuals with repeated records. Furthermore, we suggest a simple but efficient approximate algorithm, which allows examination of all pairwise interactions in a remarkably fast manner of linear with population size. Application to publicly available yeast and human data has showed that our mixed model-based method has similar performance with simple linear model-based Plink on computational efficiency. It took less than 40 hours for the pairwise analysis of 5,000 individuals genotyped with roughly 350,000 SNPs with five threads on Intel Xeon E5 2.6GHz CPU.Availability and implementationSource codes are freely available at https://github.com/chaoning/GMAT.

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“…Early algorithms that were able to detect epistasis could only make use of small datasets and were too cumbersome to analyse the many gene‐by‐gene interactions to be found in diversity panels. New efficient ones are being developed that will make such analyses accessible to research groups without sophisticated high‐performance computing infrastructure (Ning et al ., ; Tsai et al ., ; Zhu and Fang, ), which should lead to a more complete comprehension of highly complex traits such as photosynthesis.…”

Section: Genetic Analysis Of Photosynthesis Phenomics Datamentioning

confidence: 98%

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

et al. 2019

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SummaryIn recent years developments in plant phenomic approaches and facilities have gradually caught up with genomic approaches. An opportunity lies ahead to dissect complex, quantitative traits when both genotype and phenotype can be assessed at a high level of detail. This is especially true for the study of natural variation in photosynthetic efficiency, for which forward genetics studies have yielded only a little progress in our understanding of the genetic layout of the trait. High‐throughput phenotyping, primarily from chlorophyll fluorescence imaging, should help to dissect the genetics of photosynthesis at the different levels of both plant physiology and development. Specific emphasis should be directed towards understanding the acclimation of the photosynthetic machinery in fluctuating environments, which may be crucial for the identification of genetic variation for relevant traits in food crops. Facilities should preferably be designed to accommodate phenotyping of photosynthesis‐related traits in such environments. The use of forward genetics to study the genetic architecture of photosynthesis is likely to lead to the discovery of novel traits and/or genes that may be targeted in breeding or bio‐engineering approaches to improve crop photosynthetic efficiency. In the near future, big data approaches will play a pivotal role in data processing and streamlining the phenotype‐to‐gene identification pipeline.

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A rapid epistatic mixed-model association analysis by linear retransformations of genomic estimated values

Cited by 25 publications

References 40 publications

Genome-wide Marginal Epistatic Association Mapping in Case-Control Studies

Genome-wide Marginal Epistatic Association Mapping in Case-Control Studies

Rapid Epistatic Mixed Model Association Studies by Controlling Multiple Polygenic Effects

Converging phenomics and genomics to study natural variation in plant photosynthetic efficiency

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