Genes and Pathways Affecting Sheep Productivity Traits: Genetic Parameters, Genome-Wide Association Mapping, and Pathway Enrichment Analysis

Abstract

Ewe productivity is a composite and maternal trait that is considered the most important economic trait in sheep meat production. The objective of this study was the application of alternative genome-wide association study (GWAS) approaches followed by gene set enrichment analysis (GSEA) on the ewes’ genome to identify genes affecting pregnancy outcomes and lamb growth after parturition in Iranian Baluchi sheep. Three maternal composite traits at birth and weaning were considered. The traits were progeny birth weight, litter mean weight at birth, total litter weight at birth, progeny weaning weight, litter mean weight at weaning, and total litter weight at weaning. GWASs were performed on original phenotypes as well as on estimated breeding values. The significant SNPs associated with composite traits at birth were located within or near genes RDX, FDX1, ARHGAP20, ZC3H12C, THBS1, and EPG5. Identified genes and pathways have functions related to pregnancy, such as autophagy in the placenta, progesterone production by the placenta, placental formation, calcium ion transport, and maternal immune response. For composite traits at weaning, genes (NR2C1, VEZT, HSD17B4, RSU1, CUBN, VIM, PRLR, and FTH1) and pathways affecting feed intake and food conservation, development of mammary glands cytoskeleton structure, and production of milk components like fatty acids, proteins, and vitamin B-12, were identified. The results show that calcium ion transport during pregnancy and feeding lambs by milk after parturition can have the greatest impact on weight gain as compared to other effects of maternal origin.

Genome-wide association mapping and genomic prediction of agronomical traits and breeding values in Iranian wheat under rain-fed and well-watered conditions

“…The MTAs detected in this study are added to the previous pool of candidate genes and markers. However, it is a challenging task to align our results with earlier works because of the use of disparate reference genomes than the IWGSC Ref.Seq, the lack of accurate genomic locations, or the utilization of various markers (GBS-derived SNP vs. SSR and DART) [ 2 , 3 , 5 , 9 ]. Of course, detection of MTAs on the same chromosome as previous projects increases the assurance of these MTAs.…”

“…In addition to trait mean-based GWAS (pGWAS), there is a chance to estimate breeding values by some methods such as BRR (bayesian ridge regression), gBLUP (genomic best linear unbiased prediction), and rrBLUP (ridge regression-best linear unbiased prediction) and use them in association mapping (i.e., eGWAS). There is a lack of certainty on the best algorithm when utilizing a multiple-regression model in genomic selection and GWAS since the structure of the population and the architecture of the trait have a remarkable effect on identifying marker impacts [ 9 ]. As a result, it is imperative to compare the findings from the various algorithms when dissecting the genetic basis of a complicated trait in a crop population for the first time.…”

Non-parametric GWAS: Another View on Genome-wide Association Study

“…The pig data were downloaded from https://doi.org/10.6084/m9.figshare.8019551.v1 (Zhuang et al, 2019b), containing 2150 Canadian origin Duroc pig populations (Sus scrofa) with 36,740 informative SNPs. The sheep data were downloaded from https://doi.org/10.6084/m9.figshare.11859996.v1 (Esmaeili-Fard et al, 2021), comprised of 91 sheep panel (Ovis aries) with 45,342 SNPs. The chicken data were downloaded from https://www.animalgenome.org/repository/pub/CAU2018.0208/ (Jiang et al, 2018).…”

“…The sheep data were downloaded from https://doi.org/10.6084/m9. figshare.11859996.v1 (Esmaeili-Fard et al, 2021), comprised of 91 sheep panel (Ovis aries) with 45,342 SNPs. The chicken data were downloaded from https://www.animalgenome.org/ repository/pub/CAU2018.0208/ (Jiang et al, 2018).…”

Genome-wide association analysis and pathway enrichment provide insights into the genetic basis of photosynthetic responses to drought stress in Persian walnut

“…However, for complex or polygenic traits, the statistical power of GWAS for identifying variants of small effect is restricted by the stringent levels set for significance threshold and by insufficient numbers of high-frequency polymorphisms identified in most panels [ 26 , 27 ]. So, many small effect SNP markers are always ignored and most of the genetic variants contributing to the trait remains hidden [ 28 ]. Further, since many associated SNPs are noncoding it can be problematic to identify the molecular mechanisms by which they may act.…”