Screening for epistatic selection signatures: A simulation study

Parent-offspring genotyped trios unravelling genomic regions with gametic and genotypic epistatic transmission bias on the cattle genome

Several biological mechanisms affecting the sperm and ova fertility and viability at developmental stages of the reproductive cycle resulted in observable transmission ratio distortion (i.e., deviation from Mendelian expectations). Gene-by-gene interactions (or epistasis) could also potentially cause specific transmission ratio distortion patterns at different loci as unfavorable allelic combinations are under-represented, exhibiting deviation from Mendelian proportions. Here, we aimed to detect pairs of loci with epistatic transmission ratio distortion using 283,817 parent-offspring genotyped trios (sire-dam-offspring) of Holstein cattle. Allelic and genotypic parameterization for epistatic transmission ratio distortion were developed and implemented to scan the whole genome. Different epistatic transmission ratio distortion patterns were observed. Using genotypic models, 7, 19 and 6 pairs of genomic regions were found with decisive evidence with additive-by-additive, additive-by-dominance/dominance-by-additive and dominance-by-dominance effects, respectively. Using the allelic transmission ratio distortion model, more insight was gained in understanding the penetrance of single-locus distortions, revealing 17 pairs of SNPs. Scanning for the depletion of individuals carrying pairs of homozygous genotypes for unlinked loci, revealed 56 pairs of SNPs with recessive epistatic transmission ratio distortion patterns. The maximum number of expected homozygous offspring, with none of them observed, was 23. Finally, in this study, we identified candidate genomic regions harboring epistatic interactions with potential biological implications in economically important traits, such as reproduction.

Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits

Although the genetic correlations between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated. Yet, we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations (Brahman; BB, Tropical Composite; TC) to develop a novel framework termed correlation scan (CS). This framework was used to identify local regions associated with the genetic correlations between male and female fertility traits. Animals were genotyped with bovine high-density single nucleotide polymorphisms (SNPs) chip assay. The data used consisted of ~1000 individual records measured through frequent ovarian scanning for age at first corpus luteum (AGECL) and a laboratory assay for serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP effects in a 100-SNPs sliding window in each chromosome to identify local genomic regions that either drive or antagonize the genetic correlations between traits. We used Fisher's Z-statistics through a permutation method to confirm which regions of the genome harboured significant correlations. About 30% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two populations. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. For BB, the most important chromosome in terms of local regions is often located on bovine chromosome (BTA) 14. However, the important regions are spread across few different BTA's in TC. Quantitative trait loci (QTLs) and functional enrichment analysis revealed many significant windows co-localized with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to chromosome X. These results suggest regions of chromosome X for further investigation into the trade-offs between male and female fertility. We compared the CS with two other recently proposed methods that map local genomic correlations. Some genomic regions were significant across methods. Yet, many significant regions identified with the CS were overlooked by other methods.

GALLO: An R package for genomic annotation and integration of multiple data sources in livestock for positional candidate loci

BACKGROUND

The development of high-throughput sequencing and genotyping methodologies has enabled the identification of thousands of genomic regions associated with several complex traits. The integration of multiple sources of biological information is a crucial step required to better understand patterns regulating the development of these traits.

FINDINGS

Genomic Annotation in Livestock for positional candidate LOci (GALLO) is an R package developed for the accurate annotation of genes and quantitative trait loci (QTLs) located in regions identified in common genomic analyses performed in livestock, such as genome-wide association studies and transcriptomics using RNA sequencing. Moreover, GALLO allows the graphical visualization of gene and QTL annotation results, data comparison among different grouping factors (e.g., methods, breeds, tissues, statistical models, studies), and QTL enrichment in different livestock species such as cattle, pigs, sheep, and chickens.

CONCLUSIONS

Consequently, GALLO is a useful package for annotation, identification of hidden patterns across datasets, and data mining previously reported associations, as well as the efficient examination of the genetic architecture of complex traits in livestock.

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

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.

EpiGEN: an epistasis simulation pipeline

Summary

Simulated data are crucial for evaluating epistasis detection tools in genome-wide association studies. Existing simulators are limited, as they do not account for linkage disequilibrium (LD), support limited interaction models of single nucleotide polymorphisms (SNPs) and only dichotomous phenotypes or depend on proprietary software. In contrast, EpiGEN supports SNP interactions of arbitrary order, produces realistic LD patterns and generates both categorical and quantitative phenotypes.

Availability and implementation

EpiGEN is implemented in Python 3 and is freely available at https://github.com/baumbachlab/epigen.

Supplementary information

Supplementary data are available at Bioinformatics online.