Unravelling the genomic architecture of bull fertility in Holstein cattle

Han, Yi; Peñagaricano, Francisco

doi:10.1186/s12863-016-0454-6

Cited by 96 publications

(92 citation statements)

References 61 publications

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“…Previous studies have successfully identified potential genes and pathways affecting service sire fertility in Holstein breed (e.g., Blaschek et al . ; Han & Peñagaricano ; Whiston et al . ; Nicolini et al .…”

Section: Discussionmentioning

confidence: 99%

“…Our group has been investigating potential genetic factors underlying the observed variation in SCR in dairy cattle. We have identified regions on BTA21 and BTA25 that explain a significant amount of additive genetic variance (Han & Peñagaricano ). In addition, we recently reported significant non‐additive effects on BTA8, BTA9, BTA13 and BTA17 (Nicolini et al .…”

Section: Introductionmentioning

confidence: 99%

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Genetic dissection of bull fertility in US Jersey dairy cattle

2018

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SummaryThe service sire has been recognized as an important factor affecting herd fertility in dairy cattle. Recent studies suggest that genetic factors explain part of the difference in fertility among Holstein sires. The main objective of this study was to dissect the genetic architecture of sire fertility in US Jersey cattle. The dataset included 1.5 K Jersey bulls with sire conception rate (SCR) records and 96 K single nucleotide polymorphism (SNP) markers spanning the whole genome. The analysis included whole‐genome scans for both additive and non‐additive effects and subsequent functional enrichment analyses using KEGG Pathway, Gene Ontology (GO) and Medical Subject Headings (MeSH) databases. Ten genomic regions located on eight different chromosomes explained more than 0.5% of the additive genetic variance for SCR. These regions harbor genes, such as PKDREJ,EPB41L2,PDGFD,STX2,SLC25A20 and IP6K1, that are directly implicated in testis development and spermatogenesis, sperm motility and the acrosome reaction. In addition, the genomic scan for non‐additive effects identified two regions on BTA11 and BTA25 with marked recessive effects. These regions harbor three genes—FER1L5,CNNM4 and DNAH3—with known roles in sperm biology. Moreover, the gene‐set analysis revealed terms associated with calcium regulation and signaling, membrane fusion, sperm cell energy metabolism, GTPase activity and MAPK signaling. These gene sets are directly implicated in sperm physiology and male fertility. Overall, this integrative genomic study unravels genetic variants and pathways affecting Jersey bull fertility. These findings may contribute to the development of novel genomic strategies for improving sire fertility in Jersey cattle.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Genetic dissection of bull fertility in US Jersey dairy cattle

2018

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“…Genome-wide association testing between microarray-derived genotypes and semen characteristics uncovered quantitative trait loci (QTL) that are associated with male reproduction in cattle (e.g., [27][28][29][30][31][32][33]). These studies provided evidence that inherited differences in semen quality are amenable to genome-wide association testing.…”

Section: Introductionmentioning

confidence: 99%

Activation of cryptic splicing in bovineWDR19is associated with reduced semen quality and male fertility

Hiltpold

Niu²,

Kadri

et al. 2020

Preprint

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Cattle are uniquely suited to investigate the genetics of male reproduction disorders, as semen quality and fertility are recorded for all ejaculates of artificial insemination bulls. Using 26,090 ejaculates of 794 Brown Swiss bulls, we analysed ejaculate volume, sperm concentration, sperm motility, sperm head and tail anomalies, and bull fertility. The heritability of the six semen traits was between 0 and 0.26. Genome-wide association testing between semen quality and 607,511 SNPs reveaed a QTL on bovine chromosome 6 that was associated with sperm motility (P = 2.5 x 10 -27 ), head (P = 2.0 x 10 -44 ) and tail anomalies (P = 7.2 x 10 -49 ) and insemination success (P = 9.9 x 10 -13 ). The QTL harbors a recessive allele that compromises semen quality and male fertility. We replicated the effect of the QTL on fertility (P = 7.1 x 10 -32 ) in 2481 bulls from an independent cohort of Brown Swiss bulls. The analysis of whole-genome sequencing data revealed that a synonymous variant (BTA6:58373887C>T, rs474302732) in WDR19 encoding WD repeat-containing protein 19 was in linkage disequilibrium with the fertility-associated haplotype. WD repeat-containing protein 19 is a constituent of the intraflagellar transport complex that is essential for the physiological function of motile cilia and flagella. Bioinformatic and transcription analyses showed that the BTA6:58373887 T-allele activates a cryptic splice site that eliminates three evolutionarily conserved amino acids from the WD-repeat domain, thus reducing semen quality and fertility. Western blot analysis demonstrated that the BTA6:58373887 T-allele decreases the WDR19 protein expression. We make the remarkable observation that, in spite of negative effects on semen quality and bull fertility, the deleterious allele has a frequency of 24 % in the Brown Swiss population. Our findings are the first to uncover a variant that controls quantitative variation in semen quality and male fertility in cattle. Author summaryMale reproductive disorders occur in many species. In cattle farming, artificial insemination is widely used. To ensure high fertilization rates, the quality of each ejaculate is recorded and the insemination success is monitored for every artificial insemination bull. We analyse semen quality and microarray-called genotypes at more than 600,000 genome-wide SNP markers in 794 bulls to identify a recessive allele that compromises semen quality and fertility. We take advantage of whole-genome sequencing to pinpoint a variant in the coding sequence of WDR19 encoding WD repeat-containing protein 19 that activates a novel exonic splice site. Our results indicate that cryptic splicing in WDR19 is associated with reduced male reproductive performance in cattle. This is the first report to reveal a variant associated with quantitative variation in bovine semen quality.

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“…The so-called single step methods (i.e., Single Step GBLUP (SSGBLUP) and Single Step SNP-BLUP (SSSNP-BLUP)) project enotypes into phenotyped individuals, using pedigree relationships [13–16]. These “single step methods” allow the estimation of breeding values and also marker effects [15,17,18], and the latter have been used for GWAS analysis [19–21], typically by size of estimated marker effects or a similar measure such as the proportion of genetic variance explained by a marker or segment. A common pitfall of GWAS methods based on size of effects or variances explained is the lack of a formal framework for acceptance/rejection of hypothesis – in particular, there is no clearly defined statistic to be used for hypothesis testing, and there is no empirical statistical law or closed-form solution under the null hypothesis.…”

Section: Introductionmentioning

confidence: 99%

“…[22]. For instance, [19] studied “the 20 largest explanatory loci”, [20] studied “the 10 windows explaining the largest amount of genomic variance for gene annotation, gene network and pathway analyses” whereas [21] considered “1.5 Mb SNP windows that explained more than 0.50 % of the genetic variance”. Thus, there is no formal hypothesis testing.…”

Section: Introductionmentioning

confidence: 99%

Exact p-values for large-scale single step genome-wide association, with an application for birth weight in American Angus

Aguilar

Legarra

Cardoso

et al. 2019

Preprint

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BACKGROUND Single Step GBLUP (SSGBLUP) is the most comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for Genome Wide Association Studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for GWAS studies in the ssGBLUP framework, showing algorithms, computational procedures, and an application to a large beef cattle population. METHODS P-values were obtained based on the prediction error (co)variance for SNP, which uses the inverse of the coefficient matrix and formulas to compute SNP effects. RESULTS Computation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation was observed. The SNP passing the Bonferroni threshold of 5.9 in the −log10 scale were the same as those that explained the highest proportion of additive genetic variance, but the latter was penalized (as GWAS signal) by low allele frequency. CONCLUSION The exact p-value for SSGWAS is a very general and efficient strategy for QTL detection and testing. It can be used in complex data sets such as used in animal breeding, where only a proportion of pedigreed animals are genotyped.

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Unravelling the genomic architecture of bull fertility in Holstein cattle

Cited by 96 publications

References 61 publications

Genetic dissection of bull fertility in US Jersey dairy cattle

Genetic dissection of bull fertility in US Jersey dairy cattle

Activation of cryptic splicing in bovineWDR19is associated with reduced semen quality and male fertility

Exact p-values for large-scale single step genome-wide association, with an application for birth weight in American Angus

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