A statistical procedure to map high-order epistasis for complex traits

Pang, Xiaoming; Wang, Zhong; Yap, John Stephen; Wang, Jianxin; Zhu, Junjia; Bo, Wenhao; Lv, Yafei; Xu, Fang; Zhou, Tao; Peng, Shaofeng; Shen, Dengfeng; Wu, Rongling

doi:10.1093/bib/bbs027

Cited by 14 publications

(4 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By analogy with pairwise interactions, third-order interactions capture how the function of a pair of species, (e.g., the amylolytic rate of the enzymes secreted by the pair) is altered when a third species is present. This simple idea, based on the study of fitness landscapes and complex interactions in genetics [41,64,65], allowed us to decompose the function of a community into the contributions of single species and the interactions that modulate these contributions and can be used to shed light onto the role played by HOIs in community function. As we have shown here and others have shown before in different contexts [41], a null model of how the functional contributions of multiple species should combine to determine the community function is essential to unequivocally identify interactions through this approach.…”

Section: Discussionmentioning

confidence: 99%

High-order interactions distort the functional landscape of microbial consortia

et al. 2019

View full text Add to dashboard Cite

Understanding the link between community composition and function is a major challenge in microbial population biology, with implications for the management of natural microbiomes and the design of synthetic consortia. Specifically, it is poorly understood whether community functions can be quantitatively predicted from traits of species in monoculture. Inspired by the study of complex genetic interactions, we have examined how the amylolytic rate of combinatorial assemblages of six starch-degrading soil bacteria depend on the separate functional contributions from each species and their interactions. Filtering our results through the theory of biochemical kinetics, we show that this simple function is additive in the absence of interactions among community members. For about half of the combinatorially assembled consortia, the amylolytic function is dominated by pairwise and higher-order interactions. For the other half, the function is additive despite the presence of strong competitive interactions. We explain the mechanistic basis of these findings and propose a quantitative framework that allows us to separate the effect of behavioral and population dynamics interactions. Our results suggest that the functional robustness of a consortium to pairwise and higher-order interactions critically affects our ability to predict and bottom-up engineer ecosystem function in complex communities.

show abstract

Section: Discussionmentioning

confidence: 99%

High-order interactions distort the functional landscape of microbial consortia

et al. 2019

View full text Add to dashboard Cite

show abstract

“…By simultaneously analyzing multiple traits, this issue can be resolved from framework of function mapping, such as proposing system mapping based on functional mapping (Wu et al, 2011). In addition, our new method was associated with single SNP, but the main-effects model is likely too simple to be used for characterizing genetic variants in quantitative traits (Pang et al, 2012; Mackay, 2014). We plan to integrate high-order QTL–QTL interactions into our new method.…”

Section: Discussionmentioning

confidence: 99%

Mapping Covariation Quantitative Trait Loci That Control Organ Growth and Whole-Plant Biomass

et al. 2019

View full text Add to dashboard Cite

Covariation between organ growth and biomass accumulation plays an important role in plants. Plant to capture optimal fitness in nature, which depend coordinate and interact for distinct organs such as leaves, stems, and roots. Although many studies have focused on plant growth or biomass allocation, detailed information on the genetic mechanism of coordinated variation is lacking. Here, we expand a new mapping model based on functional mapping to detect covariation quantitative trait loci (QTLs) that govern development of plant organs and whole biomass, which, via a series of hypothesis tests, allows quantification of how QTLs regulate covariation between organ growth and biomass accumulation. The model was implemented to analyze leaf number data and the whole dry weight of recombinant inbred lines (RILs) of Arabidopsis . Two key QTLs related to growth and biomass allocation that reside within biologically meaningful genes, CRA1 and HIPP25 , are characterized. These two genes may control covariation between two traits. The new model will enable the elucidation of the genetic architecture underlying growth and biomass accumulation, which may enhance our understanding of fitness development in plants.

show abstract

“…Interactions between genes can be defined both biologically (regulatory networks among genes) and statistically (interactions between additive and/or dominance effects of different loci), with the main difference being that biological epistasis is allele frequency independent, whereas statistical epistasis is highly related to allele frequency (Mackay and Moore 2014). Empirical experiments and high-throughput gene-gene or protein-protein interaction detection technologies (Gao et al 2014;Kadarmideen and Carmelo 2021;Ning et al 2018;Pang et al 2013;Upstill-Goddard et al 2013) provided plenty of gene interaction networks in the public domain such as KEGG pathway database (Kanehisa and Goto 2000). From the perspective of statistical definition, a number of statistical methods/algorithms have been established with the aim of estimating the amount of epistasis variance or detecting interactive loci.…”

Section: Introductionmentioning

confidence: 99%

Capturing biological interactions improves predictive ability of complex traits via epistatic model

Gao,

Teng,

et al. 2024

Preprint

View full text Add to dashboard Cite

Recent studies have emphasized the significance of epistatic effects in unraveling the underlying genetic architectures of complex traits. However, the rich landscape of biological interactions is often disregarded when statistically modeling epistatic effects for the purpose of dissecting and predicting complex traits/diseases. In this study, we utilize KEGG pathways as carriers of biological interactions, thereby bridging the gap between statistical and biological interactions and enable to capture the epistatic effects of complex traits. We introduce biBLUP (biological interaction best linear unbiased prediction) model for modelling the epistatic effects among genes from the KEGG pathways. Simulation experiments demonstrate that biBLUP effectively captures two- and three-dimensional interaction effects underlying complex traits across diverse scenarios, resulting in a maximum increase of 62% in the predictive accuracy of the simulated complex traits. In a specific application on data consisted of 6,642 yeast lines, biBLUP successfully links complex phenotypes to biological pathways. By modeling the genetic interaction effects within the KEGG pathway associated with allantoin utilization, biBLUP achieves 40.36% improvement in accuracy for predicting the growth rate of yeast. Additionally, our study demonstrates that the incorporation of KEGG into biBLUP effectively captures the validated epistatic effects related to the flowering time of rice. This integration results in an improvement of 16.29% in the accuracy for predicting the flowering time of rice using biBLUP. In summary, this study enhances our understanding of the genetic architecture underlying complex traits and improves our ability to predict and study complex phenotypes more comprehensively.

show abstract

A statistical procedure to map high-order epistasis for complex traits

Cited by 14 publications

References 50 publications

High-order interactions distort the functional landscape of microbial consortia

High-order interactions distort the functional landscape of microbial consortia

Mapping Covariation Quantitative Trait Loci That Control Organ Growth and Whole-Plant Biomass

Capturing biological interactions improves predictive ability of complex traits via epistatic model

Contact Info

Product

Resources

About