Genome-wide association study reveals a quantitative trait locus and two candidate genes on Sus scrofa chromosome 5 affecting intramuscular fat content in Suhuai pigs

Wang, B.B.; Hou, Liming; Zhou, Wuduo; Liu, H.; Wei, Tao; Wu, Wangjun; Niu, Peipei; Zhang, Z.P.; Zhou, Juan; Li, Q.; Huang, Ruihua; Li, P.H.

doi:10.1016/j.animal.2021.100341

Cited by 16 publications

(14 citation statements)

References 30 publications

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“…One possible reason was that the genomic relationship matrices in the models were constructed with different SNP data sets. The higher p values could be also resulted from larger sample size, in our previous studies, a QTL on SSC5 affecting IFC was identified by using GWAS based on 80 K dataset in high ( n = 48) and low ( n = 48) IFC groups (Wang et al, 2021 ), and the rs1110770079 SNP located on FABP3 was significantly associated with IFC by association analysis in 330 Suhuai pigs (Wang et al, 2019 ). However, this significant signal on SSC5 and rs1110770079 SNP were not detected in current study, we speculated that the significant signals of early research were detected based on a limited sample size, and the phenotypic variations explained were small.…”

Section: Discussionmentioning

confidence: 96%

“…To facilitate the subsequent genotype imputation, we converted the physical positions of 80 K SNP chip data to the Sus scrofa 11.1 by USCS website ( https://genome.ucsc.edu/cgi‐bin/hgLiftOver ). These 80 K chip dataset of 482 samples included 96 sample SNP data in our previous study (Wang et al, 2021 ). Quality control of genotype data was conducted using PLINK (v1.90 beta) (Purcell et al, 2007 ) to detect and exclude unreliable genotypes.…”

Section: Methodsmentioning

confidence: 99%

“…The variance components of IFC were estimated and the IFC phenotype was adjusted for fixed effects (sex, batch, age and carcass weight) using the mixed linear model implemented in the DMU software (Madsen et al, 2014) based on 80 K SNP chip data, and the model was described previously (Wang et al, 2021). The phenotypic variance explained (PVE) by each locus was calculated using the equation

{PVE}_{i} = \frac{2 q_{i} ()(, 1 - q_{i}) b_{i}^{2}}{{normalσ}_{normalp}^{2}}

, where q i is the minor allele frequency of the i th SNP, b i is the allele substitution effect of the i th SNP and

σ_{p}^{2}

is the phenotypic variance of IFC.…”

Section: Methodsmentioning

confidence: 99%

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Genome‐wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole‐genome sequencing data

Wang

Hou

et al. 2022

Evolutionary Applications

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Integrating the single‐nucleotide polymorphisms (SNPs) significantly affecting target traits from imputed whole‐genome sequencing (iWGS) data into the genomic prediction (GP) model is an economic, efficient, and feasible strategy to improve prediction accuracy. The objective was to dissect the genetic architecture of intramuscular fat content (IFC) by genome wide association studies (GWAS) and to investigate the accuracy of GP based on pedigree‐based BLUP (PBLUP) model, genomic best linear unbiased prediction (GBLUP) models and Bayesian mixture (BayesMix) models under different strategies. A total of 482 Suhuai pigs were genotyped using an 80 K SNP chip. Furthermore, 30 key samples were selected for resequencing and were used as a reference panel to impute the 80 K chip data to the WGS dataset. The 80 K data and iWGS data were used to perform GWAS and test GP accuracies under different scenarios. GWAS results revealed that there were four major regions affecting IFC. Two important functional candidate genes were found in the two most significant regions, including protein kinase C epsilon (PRKCE) and myosin light chain 2 (MYL2). The results of the predictions showed that the PBLUP model had the lowest reliability (0.096 ± 0.032). The reliability (0.229 ± 0.035) was improved by replacing pedigree information with 80 K chip data. Compared with using 80 K SNPs alone, pruning iWGS SNPs with the R‐squared cutoff of linkage disequilibrium (0.55) led to a slight improvement (0.006), adding significant iWGS SNPs led to an improvement of reliability by 0.050 when using a one‐component GBLUP, a further increase of 0.033 when using a two‐component GBLUP model. For BayesMix models, compared with using 80 K SNPs alone, adding additional significant iWGS SNPs into one‐ or two‐component BayesMix models led to improvements of reliabilities for IFC by 0.040 and 0.089, respectively. Our results may facilitate further identification of causal genes for IFC and may be beneficial for the improvement of IFC in pig breeding programs.

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Section: Discussionmentioning

confidence: 96%

Section: Methodsmentioning

confidence: 99%

{PVE}_{i} = \frac{2 q_{i} ()(, 1 - q_{i}) b_{i}^{2}}{{normalσ}_{normalp}^{2}}

, where q i is the minor allele frequency of the i th SNP, b i is the allele substitution effect of the i th SNP and

σ_{p}^{2}

is the phenotypic variance of IFC.…”

Section: Methodsmentioning

confidence: 99%

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Genome‐wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole‐genome sequencing data

Wang

Hou

et al. 2022

Evolutionary Applications

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“…The GWAS was performed by linkage disequilibrium adjusted kinships (LDAK) software [ 17 ] to calculate the associations between genotypes and observed phenotypes ( Supplementary Material S4 ). The mixed linear model (Model 2) of the GWAS analysis is as follows:

where

is the vector of three phenotypes;

contains birth season and measurement batch and

is incidence matrix;

is the effect of SNP and

represents the genotype matrix of SNPs;

is polygenic effect; the meanings of

and

are the same as model 1;

is kinship matrix calculated by pruned marker genotypes from Chip data or imputed data (pruning threshold is 0.98 and the size of window is 100 Kb) and the codes referred to Wang et al [ 18 ]. In the mixed model, single SNP was fitted as both a fixed effect and a random effect, which would weaken the effect of a single SNP.…”

Section: Methodsmentioning

confidence: 99%

Genome-Wide Association Study Identified a Quantitative Trait Locus and Two Candidate Genes on Sus scrofa Chromosome 2 Affecting Vulvar Traits of Suhuai Pigs

Yin

Hou

Liu

et al. 2022

Genes

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Vulvar size and angle are meaningful traits in pig production. Sows with abnormal vulva generally show reproductive disorders. In order to excavate candidate loci and genes associated with pig’s vulvar traits, 270 Suhuai pigs with vulvar phenotype were genotyped by a porcine single nucleotide polymorphisms (SNP) Chip. Then, Chip data were imputed using resequenced data of 30 Suhuai pigs as a reference panel. Next, we estimated the heritability and performed a genome-wide association study (GWAS) for vulvar traits. The heritabilities for the traits vulvar length (VL), vulvar width (VW) and vulvar angle (VA) in this pig population were 0.23, 0.32 and 0.22, respectively. GWAS based on Chip data identified nine significant SNPs on the Sus scrofa chromosomes (SSC) 2, 7, 9 and 13 for VL or VW. GWAS based on imputed data identified 11 new quantitative trait loci (QTL) on SSC1, 2, 7, 8, 9, 11, 13, 16 and 17 for VL or VW. The most significant QTL for VL on SSC2 were refined to a 3.48–3.97 Mb region using linkage disequilibrium and linkage analysis (LDLA). In this refined region, FGF19 and CCND1, involved in the development of the reproductive tract, cell growth and vulvar cancer, could be new candidate genes affecting VL. Our results provided potential genetic markers for the breeding of vulvar traits in pigs and deepened the understanding of the genetic mechanism of vulvar traits.

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“…Indices of sows' reproductive ability largely determine the level of overall efficiency of pig breeding technology (Peltoniemi et al, 2019;Stoyanovskyy et al, 2020;Muro et al, 2022). In recent decades, methodological approaches to the evaluation of DNA polymorphisms have made it possible to significantly accelerate the speed of selection in pig breeding (Rexroad et al, 2019;Wang et al, 2021;Dilger et al, 2022). It should be noted that the most well-known candidate genes responsible for the expression of main features of the reproductive qualities of sows are the estrogen receptor (ESR1) and prolactin (PRLR) genes (Balatsky et al, 2015;Vashchenko et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The effect of the ryanodine receptor gene on the reproductive traits of Welsh sows

Zhukorskyi¹,

Tsereniuk

Vashchenko

et al. 2022

Regul. Mech. Biosyst.

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The reproductive performance of sows largely determines the efficiency of the entire pig farming industry. The purpose of our work is the evaluation of polymorphism of the ryanodine receptor gene and its impact on the reproductive traits of sows of the Welsh breed of pigs. For this study, 148 pigs of the Welsh breed were used. The reproductive traits of sows were evaluated in two adjacent generations. We conducted a comprehensive assessment of the reproductive ability of sows using the SIRQS (selection index of reproductive qualities of sows), determined phenotypic consolidation coefficients and assessed the genetic potential of the animals’ productivity. The polymorphism of the RYR1 gene was determined using polymerase chain reaction-restriction fragment length polymorphism analysis (PCR-RFLP). Data processing was performed using single-factor analysis of variance (ANOVA). Polymorphism of the ryanodine receptor gene in sows of the Welsh pig breed was evaluated. 8.0% of the animals were identified as carriers of the mutant allele of the RYR1 gene. However, no homozygous RYR1-nn animals were found. Pigs of the maternal generation carrying the homozygous NN genotype had better reproductive performance in all indicators. Sows that were carriers of the mutant allele were characterized by lower values of the genetic productivity potential compared with the entire estimated population for all productive traits. Sows which were free of the mutant allele of the RYR1 gene were characterized by large values of the SIRQS index. The values of the coefficients of phenotypic consolidation of the number of live born piglets in sows without the mutant allele were lower than in sows with the mutant allele n. Better performance of sows free of the mutant allele of the RYR1 gene was established over sows carrying it in all evaluated traits of reproductive capacity (for different traits P ranged from 0.021 to 1.0*10–4), except for number of piglets born alive per sow in the daughter generation. Sows with the NN genotype had better selection index values by 15.7% in the maternal generation and by 10.2% in the daughter generation. In order to increase the reproductive ability of sows in the studied population of Welsh pigs and achieve similar results in other herds of this breed, animals free from the mutant allele of the RYR1 gene should be selected for further reproduction in the process of breeding, while on the contrary, carriers of this gene should be gradually eliminated from the herd. To carry out breeding work, further research is needed on the entire population of Welsh pigs for the RYR1 gene.

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Genome-wide association study reveals a quantitative trait locus and two candidate genes on Sus scrofa chromosome 5 affecting intramuscular fat content in Suhuai pigs

Cited by 16 publications

References 30 publications

Genome‐wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole‐genome sequencing data

Genome‐wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole‐genome sequencing data

Genome-Wide Association Study Identified a Quantitative Trait Locus and Two Candidate Genes on Sus scrofa Chromosome 2 Affecting Vulvar Traits of Suhuai Pigs

The effect of the ryanodine receptor gene on the reproductive traits of Welsh sows

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