Short communication: Replication of genome-wide association studies for milk production traits in Chinese Holstein by an efficient rotated linear mixed model

Wang, Dan; Ning, Chao; Liu, Jianfeng; Zhang, Qin; Jiang, Li

doi:10.3168/jds.2018-15298

Cited by 26 publications

(21 citation statements)

References 30 publications

(36 reference statements)

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“…This region contains several genes including MIR2308, CYHR1, FOXH1, COMMD5, TONSL, PPP1R16A, MFSD3, LRRC24, C14H8orf33, RECQL4, ARHGAP39, RPL8, GPT, LRRC14, ZNF34, C14H8orf82, ZNF7 and KIFC2. The association of genes including TONSL, PPP1R16A, FOXH1, ARHGAP39, CYHR1 and ARHGAP39 with milk yield and milk composition has been reported in previous studies (Buitenhuis et al, ; Nayeri & Stothard, ; Ning et al, ; Wang et al, ). Nayeri et al () reported that highly significant SNP for milk yield in Canadian Holsteins were mapped inside genes including CPSF1 , DGAT1 , TONSL , CYHR1 , FOXH1 and PPP1R16A .…”

Section: Discussionsupporting

confidence: 59%

“…This region contains several genes including MIR2309, MIR1839, OPLAH, HGH1, GRINA, PARP10, MAF1, SHARPIN, CYC1, GPAA1, MROH1, EXOSC4 and SPATC1. The association of genes including OPLAH, GRINA and MF1 with milk yield and lactation performance has been reported in previous studies (Kolbehdari et al, 2009;Wang, Ning, Liu, Zhang, & Jiang, 2019); however, the association of the remaining identified genes inside this region with lactation performance in dairy cows has not been reported previously.…”

Section: T a B L E 5 Gene Ontologies (Go)mentioning

confidence: 64%

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Genome‐wide association for milk production and lactation curve parameters in Holstein dairy cows

Atashi

Salavati

Koster

et al. 2019

J Animal Breeding Genetics

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The aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.

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Section: Discussionsupporting

confidence: 59%

Section: T a B L E 5 Gene Ontologies (Go)mentioning

confidence: 64%

Genome‐wide association for milk production and lactation curve parameters in Holstein dairy cows

Atashi

Salavati

Koster

et al. 2019

J Animal Breeding Genetics

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“…Genetic improvement can gradually induce smaller but permanent trait modifications [1], and genetic analyses have shown heritable variations in milk, fat and protein yields, and fat and protein percentages of bovine [2]. Genome-wide studies in dairy cows have allowed researchers to detect quantitative trait locus (QTL) harboring candidate polymorphic genes that are involved in milk production [3,4,5]. Moreover, studies have shown that the single nucleotide polymorphisms (SNPs) in the genes clearly influenced the milk yield and composition in Holsteins [6,7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Genetic Effects of LPIN1 Polymorphisms on Milk Production Traits in Dairy Cattle

Han

Yuán

Liang

et al. 2019

Genes

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Our initial RNA sequencing work identified that lipin 1 (LPIN1) was differentially expressed during dry period, early lactation, and peak of lactation in dairy cows, and it was enriched into the fat metabolic Gene Ontology (GO) terms and pathways, thus we considered LPIN1 as the candidate gene for milk production traits. In this study, we detected the polymorphisms of LPIN1 and verified their genetic effects on milk yield and composition in a Chinese Holstein cow population. We found seven SNPs by re-sequencing the entire coding region and partial flanking region of LPIN1, including one in 5′ flanking region, four in exons, and two in 3′ flanking region. Of these, four SNPs, c.637T > C, c.708A > G, c.1521C > T, and c.1555A > C, in the exons were predicted to result in the amino acid replacements. With the Haploview 4.2, we found that seven SNPs in LPIN1 formed two haplotype blocks (D′ = 0.98–1.00). Single-SNP association analyses showed that SNPs were significantly associated with milk yield, fat yield, fat percentage, or protein yield in the first or second lactation (p = < 0.0001–0.0457), and only g.86049389C > T was strongly associated with protein percentage in both lactations (p = 0.0144 and 0.0237). The haplotype-based association analyses showed that the two haplotype blocks were significantly associated with milk yield, fat yield, protein yield, or protein percentage (p = < 0.0001–0.0383). By quantitative real-time PCR (qRT-PCR), we found that LPIN1 had relatively high expression in mammary gland and liver tissues. Furthermore, we predicted three SNPs, c.637T > C, c.708A > G, and c.1521C > T, using SOPMA software, changing the LPIN1 protein structure that might be potential functional mutations. In summary, we demonstrated the significant genetic effects of LPIN1 on milk production traits, and the identified SNPs could serve as genetic markers for dairy breeding.

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“…Additional large set of candidate genes (LRRC24, LRRC14, RECQL4, HEATR7A, MFSD3, GPT, PPP1R16A, FOXH1, CYHR1, SLC39A4, CPSF1, ADCK5, FBXL6, BOP1, SCRT1, DGAT1, GPAA1, EXOSC4, PARP10, HSF1, OPLAH, and GRINA), associated with milk production traits in Kholmogor cattle was found on BTA 14. These genes were shown to be involved in regulation of milk fatty acid composition [113][114][115][116], milk yield [117,118], milk fat yield, and milk fat percentage [119][120][121][122][123]. In contrast to Kholmogor cattle, we identified only two candidate genes on BTA 5, which were associated with milk production traits in Yaroslavl cattle, including TMCC3 and DDX10 [124,125].…”

Section: Plos Onementioning

confidence: 81%

Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis

et al. 2020

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Native cattle breeds can carry specific signatures of selection reflecting their adaptation to the local environmental conditions and response to the breeding strategy used. In this study, we comprehensively analysed high-density single nucleotide polymorphism (SNP) genotypes to characterise the population structure and detect the selection signatures in Russian native Yaroslavl and Kholmogor dairy cattle breeds, which have been little influenced by introgression with transboundary breeds. Fifty-six samples of pedigree-recorded purebred animals, originating from different breeding farms and representing different sire lines, of the two studied breeds were genotyped using a genome-wide bovine genotyping array (Bovine HD BeadChip). Three statistical analyses—calculation of fixation index (FST) for each SNP for the comparison of the pairs of breeds, hapFLK analysis, and estimation of the runs of homozygosity (ROH) islands shared in more than 50% of animals—were combined for detecting the selection signatures in the genome of the studied cattle breeds. We confirmed nine and six known regions under putative selection in the genomes of Yaroslavl and Kholmogor cattle, respectively; the flanking positions of most of these regions were elucidated. Only two of the selected regions (localised on BTA 14 at 24.4–25.1 Mbp and on BTA 16 at 42.5–43.5 Mb) overlapped in Yaroslavl, Kholmogor and Holstein breeds. In addition, we detected three novel selection sweeps in the genome of Yaroslavl (BTA 4 at 4.74–5.36 Mbp, BTA 15 at 17.80–18.77 Mbp, and BTA 17 at 45.59–45.61 Mbp) and Kholmogor breeds (BTA 12 at 82.40–81.69 Mbp, BTA 15 at 16.04–16.62 Mbp, and BTA 18 at 0.19–1.46 Mbp) by using at least two of the above-mentioned methods. We expanded the list of candidate genes associated with the selected genomic regions and performed their functional annotation. We discussed the possible involvement of the identified candidate genes in artificial selection in connection with the origin and development of the breeds. Our findings on the Yaroslavl and Kholmogor breeds obtained using high-density SNP genotyping and three different statistical methods allowed the detection of novel putative genomic regions and candidate genes that might be under selection. These results might be useful for the sustainable development and conservation of these two oldest Russian native cattle breeds.

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Short communication: Replication of genome-wide association studies for milk production traits in Chinese Holstein by an efficient rotated linear mixed model

Cited by 26 publications

References 30 publications

Genome‐wide association for milk production and lactation curve parameters in Holstein dairy cows

Genome‐wide association for milk production and lactation curve parameters in Holstein dairy cows

Genetic Effects of LPIN1 Polymorphisms on Milk Production Traits in Dairy Cattle

Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis

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