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A linkage disequilibrium-based statistical test for Genome-Wide Epistatic Selection Scans in structured populations.

ABSTRACT: The quest for signatures of selection using single nucleotide polymorphism (SNP) data has proven efficient to uncover genes involved in conserved and/or adaptive molecular functions, but none of the statistical methods were designed to identify interacting alleles as targets of selective processes. Here, we propose a statistical test aimed at detecting epistatic selection, based on a linkage disequilibrium (LD) measure accounting for population structure and heterogeneous relatedness between individuals. SNP-based ([Formula: see text]) and window-based ([Formula: see text]) statistics fit a Student distribution, allowing to test the significance of correlation coefficients. As a proof of concept, we use SNP data from the Medicago truncatula symbiotic legume plant and uncover a previously unknown gene coadaptation between the MtSUNN (Super Numeric Nodule) receptor and the MtCLE02 (CLAVATA3-Like) signaling peptide. We also provide experimental evidence supporting a MtSUNN-dependent negative role of MtCLE02 in symbiotic root nodulation. Using human HGDP-CEPH SNP data, our new statistical test uncovers strong LD between SLC24A5 (skin pigmentation) and EDAR (hairs, teeth, sweat glands development) world-wide, which persists after correction for population structure and relatedness in Central South Asian populations. This result suggests that epistatic selection or coselection could have contributed to the phenotypic make-up in some human populations. Applying this approach to genome-wide SNP data will facilitate the identification of coadapted gene networks in model or non-model organisms.

SUBMITTER: Boyrie L

PROVIDER: S-EPMC7852595 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundSpurious associations between single nucleotide polymorphisms and phenotypes are a major issue in genome-wide association studies and have led to underestimation of type 1 error rate and overestimation of the number of quantitative trait loci found. Many authors have investigated the influence of population structure on the robustness of methods by simulation. This paper is aimed at developing further the algebraic formalization of power and type 1 error rate for some of the classical statistical methods used: simple regression, two approximate methods of mixed models involving the effect of a single nucleotide polymorphism (SNP) and a random polygenic effect (GRAMMAR and FASTA) and the transmission/disequilibrium test for quantitative traits and nuclear families. Analytical formulae were derived using matrix algebra for the first and second moments of the statistical tests, assuming a true mixed model with a polygenic effect and SNP effects.ResultsThe expectation and variance of the test statistics and their marginal expectations and variances according to the distribution of genotypes and estimators of variance components are given as a function of the relationship matrix and of the heritability of the polygenic effect. These formulae were used to compute type 1 error rate and power for any kind of relationship matrix between phenotyped and genotyped individuals for any level of heritability. For the regression method, type 1 error rate increased with the variability of relationships and with heritability, but decreased with the GRAMMAR method and was not affected with the FASTA and quantitative transmission/disequilibrium test methods.ConclusionsThe formulae can be easily used to provide the correct threshold of type 1 error rate and to calculate the power when designing experiments or data collection protocols. The results concerning the efficacy of each method agree with simulation results in the literature but were generalized in this work. The power of the GRAMMAR method was equal to the power of the FASTA method at the same type 1 error rate. The power of the quantitative transmission/disequilibrium test was low. In conclusion, the FASTA method, which is very close to the full mixed model, is recommended in association mapping studies.

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A linkage disequilibrium-based statistical test for Genome-Wide Epistatic Selection Scans in structured populations.

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