Multimarker and rare variants genomewide association studies for bone weight in Simmental cattle

Miao, Jianyin; Wang, X; Bao, Jinsong; Jin, Sheng Chih; Chang, Tianpeng; Xia, Jiangwei; Liu, Yang; Zhu, Bo; Xu, Lei; Zhang, L; Gao, Xue; Chen, Y; Li, J; Gao, Huijiang

doi:10.1111/jbg.12326

Cited by 10 publications

(12 citation statements)

References 31 publications

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“…This result is completely consistent with Chang et al [25]. Moreover, the LCORL and RIMS2 genes found in this study also appeared in Miao et al’s study [26]. Clearly, Fast-EB-LMM performed well when real data were used.…”

Section: Discussionsupporting

confidence: 93%

A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies

Chang

Wei

Liang

et al. 2019

Animals

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Linear mixed model (LMM) is an efficient method for GWAS. There are numerous forms of LMM-based GWAS methods. However, improving statistical power and computing efficiency have always been the research hotspots of the LMM-based GWAS methods. Here, we proposed a fast empirical Bayes method, which is based on linear mixed models. We call it Fast-EB-LMM in short. The novelty of this method is that it uses a modified kinship matrix accounting for individual relatedness to avoid competition between the locus of interest and its counterpart in the polygene. This property has increased statistical power. We adopted two special algorithms to ease the computational burden: Eigenvalue decomposition and Woodbury matrix identity. Simulation studies showed that Fast-EB-LMM has significantly increased statistical power of marker detection and improved computational efficiency compared with two widely used GWAS methods, EMMA and EB. Real data analyses for two carcass traits in a Chinese Simmental beef cattle population showed that the significant single-nucleotide polymorphisms (SNPs) and candidate genes identified by Fast-EB-LMM are highly consistent with results of previous studies. We therefore believe that the Fast-EB-LMM method is a reliable and efficient method for GWAS.

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Section: Discussionsupporting

confidence: 93%

A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies

Chang

Wei

Liang

et al. 2019

Animals

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“…LAP3, as a member of LAPs family involved in cell maintenance and growth development, had been suggested that it has potential function on body weight in sheep (La et al, 2019) and body measurements traits in cattle (An et al, 2019). Moreover, previous studies also reported that LAP3 is the potential candidate gene for feed efficiency component traits (Lindholm-Perry et al, 2011;Zhang et al, 2016), carcass merit, and internal organ traits including kidney weight and spleen weight (Song et al, 2016;Xia et al, 2017;Miao et al, 2018). MED28, a gene involved in the regulation of cell proliferation and cycle (Cho et al, 2019), was captured to associate with body weight, intramuscular fat content, and yearling weight in cattle (Santiago et al, 2017a;Santiago et al, 2017b;Anton et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…To explore evidence of the identified variants and genes from previous studies, we summarized the association results obtained by different methods based on Illumina BovineHD array including single marker association, multi-marker association, and gene-based association, and we compared their results with the finding in our study ( Xia et al, 2017 ; Chang et al, 2018 ; Miao et al, 2018 ; Chang et al, 2019 ; An et al, 2020 ). The coordination of candidate SNPs or regions was determined according to the UMD 3.1 genome assembly.…”

Section: Methodsmentioning

confidence: 99%

“…Many previous studies have explored the genetic basis of bone weight using Illumina BovineHD SNP BeadChip based on different models. Several candidate genes including LCORL , NCAPG , LAP3 , RIMS2 , and SLIT2 were detected for bone weight ( Xia et al, 2017 ; Chang et al, 2018 ; Miao et al, 2018 ; Chang et al, 2019 ; An et al, 2020 ). These genes have also been identified for some other traits relevant to stature size, such as live weight, carcass weight, and body measurements ( Song et al, 2016 ; An et al, 2018 ; Zhang W. et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

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Identification of Candidate Variants Associated With Bone Weight Using Whole Genome Sequence in Beef Cattle

Niu

Zhang

et al. 2021

Front. Genet.

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Bone weight is critical to affect body conformation and stature in cattle. In this study, we conducted a genome-wide association study for bone weight in Chinese Simmental beef cattle based on the imputed sequence variants. We identified 364 variants associated with bone weight, while 350 of them were not included in the Illumina BovineHD SNP array, and several candidate genes and GO terms were captured to be associated with bone weight. Remarkably, we identified four potential variants in a candidate region on BTA6 using Bayesian fine-mapping. Several important candidate genes were captured, including LAP3, MED28, NCAPG, LCORL, SLIT2, and IBSP, which have been previously reported to be associated with carcass traits, body measurements, and growth traits. Notably, we found that the transcription factors related to MED28 and LCORL showed high conservation across multiple species. Our findings provide some valuable information for understanding the genetic basis of body stature in beef cattle.

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“…FGF5 had relationships with embryonic development, cell growth, morphogenesis, tissue repair, tumor growth, and invasion [29], and it may be a potential candidate gene associated with CW. Several studies have reported that RIMS2 is associated with carcass weight in Simmental beef cattle [30][31][32]. This gene was also reported as a candidate gene associated with Paget's disease of bone [33].…”

Section: Gwas With Additive and Dominance Effectsmentioning

confidence: 99%

Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Liu

Wang

et al. 2019

Animals

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Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

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Multimarker and rare variants genomewide association studies for bone weight in Simmental cattle

Cited by 10 publications

References 31 publications

A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies

A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies

Identification of Candidate Variants Associated With Bone Weight Using Whole Genome Sequence in Beef Cattle

Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

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