Weighted single-step GWAS identified candidate genes associated with semen traits in a Duroc boar population

Gao, Ning; Chen, Yilong; Liu, Xiaohong; Zhao, Yunxiang; Zhu, Lin; Liu, Ali; Jiang, Wei; Peng, Xing; Zhang, Conglin; Tang, Zhenshuang; Li, Xinyun; Chen, Yaosheng

doi:10.1186/s12864-019-6164-5

Cited by 38 publications

(30 citation statements)

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“…Investigating the genomic regions and molecular processes that control sperm quality has become a focus of interest in humans and in livestock including swine, in the latter case for its relevance on the sustainability of pig breeding and production [5,6,[10][11][12]. In fact, our results and those obtained by other groups [5][6][7][8], have shown that boar sperm quality has a genetic basis, which means that it can be selected for in breeding strategies.…”

Section: Discussionmentioning

confidence: 54%

“…Marques et al [6] detected 16 and six QTL in Large White and Landrace pigs, respectively, associated with sperm motility, number of cells per ejaculate and morphological abnormalities. More recently, several QTL have been identified in a Duroc population associated to number of sperm cells, sperm motility, sperm progressive motility, total morphological abnormalities, coiled tail, bent tail, proximal droplets, distal droplets and distal midpiece reflex [11,12].…”

Section: Introductionmentioning

confidence: 99%

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A systems biology framework integrating GWAS and RNA-seq to shed light on the molecular basis of sperm quality in swine

et al. 2020

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Background Genetic pressure in animal breeding is sparking the interest of breeders for selecting elite boars with higher sperm quality to optimize ejaculate doses and fertility rates. However, the molecular basis of sperm quality is not yet fully understood. Our aim was to identify candidate genes, pathways and DNA variants associated to sperm quality in swine by analysing 25 sperm-related phenotypes and integrating genome-wide association studies (GWAS) and RNA-seq under a systems biology framework. Results By GWAS, we identified 12 quantitative trait loci (QTL) associated to the percentage of head and neck abnormalities, abnormal acrosomes and motile spermatozoa. Candidate genes included CHD2, KATNAL2, SLC14A2 and ABCA1. By RNA-seq, we identified a wide repertoire of mRNAs (e.g. PRM1, OAZ3, DNAJB8, TPPP2 and TNP1) and miRNAs (e.g. ssc-miR-30d, ssc-miR-34c, ssc-miR-30c-5p, ssc-miR-191, members of the let-7 family and ssc-miR-425-5p) with functions related to sperm biology. We detected 6128 significant correlations (P-value ≤ 0.05) between sperm traits and mRNA abundances. By expression (e)GWAS, we identified three trans-expression QTL involving the genes IQCJ, ACTR2 and HARS. Using the GWAS and RNA-seq data, we built a gene interaction network. We considered that the genes and interactions that were present in both the GWAS and RNA-seq networks had a higher probability of being actually involved in sperm quality and used them to build a robust gene interaction network. In addition, in the final network we included genes with RNA abundances correlated with more than four semen traits and miRNAs interacting with the genes on the network. The final network was enriched for genes involved in gamete generation and development, meiotic cell cycle, DNA repair or embryo implantation. Finally, we designed a panel of 73 SNPs based on the GWAS, eGWAS and final network data, that explains between 5% (for sperm cell concentration) and 36% (for percentage of neck abnormalities) of the phenotypic variance of the sperm traits. Conclusions By applying a systems biology approach, we identified genes that potentially affect sperm quality and constructed a SNP panel that explains a substantial part of the phenotypic variance for semen quality in our study and that should be tested in other swine populations to evaluate its relevance for the pig breeding sector.

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Section: Discussionmentioning

confidence: 54%

Section: Introductionmentioning

confidence: 99%

A systems biology framework integrating GWAS and RNA-seq to shed light on the molecular basis of sperm quality in swine

et al. 2020

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“…Genomic windows that explained higher than 1.0% of the total genetic variance were selected as candidate QTL regions associated with growth traits in this study, which was also used in previous studies [ 8 , 13 ]. Since the 0.8 Mb window explained on–average 0.0473% (100% divided by 2115 genomic regions) of the genetic variance, the 1% threshold is over 20 times the expected average genetic variance explained by the 0.8 Mb window.…”

Section: Methodsmentioning

confidence: 99%

“…In that case, the wssGWAS can be adopted, which weighs SNPs according to their effects that were calculated genomic estimated breeding values (GEBVs) via ssGBLUP. The wssGWAS method has been successfully applied to detect supplementary QTLs and candidate genes in domestic and aquaculture animals, such as carcass traits in Nellore cattle [ 11 ], growth and carcass traits in rainbow trout [ 12 ], and reproductive traits in pigs [ 13 ]. However, to our knowledge, few wssGWASs have been performed to study growth traits in purebred Duroc pigs.…”

Section: Introductionmentioning

confidence: 99%

Weighted Single-Step GWAS Identified Candidate Genes Associated with Growth Traits in a Duroc Pig Population

Ruan

Zhuang

Ding

et al. 2021

Genes

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Growth traits are important economic traits of pigs that are controlled by several major genes and multiple minor genes. To better understand the genetic architecture of growth traits, we performed a weighted single-step genome-wide association study (wssGWAS) to identify genomic regions and candidate genes that are associated with days to 100 kg (AGE), average daily gain (ADG), backfat thickness (BF) and lean meat percentage (LMP) in a Duroc pig population. In this study, 3945 individuals with phenotypic and genealogical information, of which 2084 pigs were genotyped with a 50 K single-nucleotide polymorphism (SNP) array, were used for association analyses. We found that the most significant regions explained 2.56–3.07% of genetic variance for four traits, and the detected significant regions (>1%) explained 17.07%, 18.59%, 23.87% and 21.94% for four traits. Finally, 21 genes that have been reported to be associated with metabolism, bone growth, and fat deposition were treated as candidate genes for growth traits in pigs. Moreover, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses implied that the identified genes took part in bone formation, the immune system, and digestion. In conclusion, such full use of phenotypic, genotypic, and genealogical information will accelerate the genetic improvement of growth traits in pigs.

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“…GWAS have a greater capability than QTL mapping to detect causal SNPs in a smaller genetic range 12 . In recent years, many genes and molecular markers, regulate important traits, were identi ed using GWAS in livestock animals like pigs, cattle, sheep, and chickens 13,14 . Despite its unique potential and increased contribution to food security, comparatively less attention has been paid to camels compared to other livestock species 3 .…”

Section: Introductionmentioning

confidence: 99%

Genomic Prediction for Growth Using Low-Density SNP Panel in Dromedary Camels

Sani

Harofte

Banabazi

et al. 2020

Preprint

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For thousands of years, camels have produced meat, milk, and fiber in harsh desert conditions. For a sustainable development to provide protein resources from desert areas, it is necessary to pay attention to genetic improvement in camel breeding. By using genotyping-by-sequencing (GBS) method we produced over 14,500 genome wide markers to conduct a genome- wide association study (GWAS) for investigating the birth weight, daily gain, and body weight of 96 dromedaries in the Iranian central desert. A total of 99 SNPs were associated with birth weight, daily gain, and body weight (p-value < 0.002). Genomic breeding values (GEBVs) were estimated with the BGLR package using (i) all 14,522 SNPs and (ii) the 99 SNPs by GWAS. Twenty-eight SNPs located in 36 candidate genes were associated with birth weight, daily gain, and body weight (p-value < 0.001). The accuracy of GEBVs was more than 0.65 based on all 14,522 SNPs, but the regression coefficients for birth weight, daily gain, and body weight were 0.39, 0.20, and 0.23, respectively. Because of low sample size, the GEBVs were predicted using the associated SNPs from GWAS. The accuracy of GEBVs based on the 99 associated SNPs was 0.62, 0.82, and 0.57 for birth weight, daily gain, and body weight. This report is the first GWAS using GBS on dromedary camels and identifies markers associated with growth traits that could help to plan breeding program to genetic improvement. Further researches using larger sample size and collaboration of the camel farmers and more profound understanding will permit verification of the associated SNPs identified in this project. The preliminary results of study show that genomic selection could be the appropriate way to genetic improvement of body weight in dromedary camels, which is challenging due to a long generation interval, seasonal reproduction, and lack of records and pedigrees.

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Weighted single-step GWAS identified candidate genes associated with semen traits in a Duroc boar population

Cited by 38 publications

References 62 publications

A systems biology framework integrating GWAS and RNA-seq to shed light on the molecular basis of sperm quality in swine

A systems biology framework integrating GWAS and RNA-seq to shed light on the molecular basis of sperm quality in swine

Weighted Single-Step GWAS Identified Candidate Genes Associated with Growth Traits in a Duroc Pig Population

Genomic Prediction for Growth Using Low-Density SNP Panel in Dromedary Camels

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