Symposium review: Genetics, genome-wide association study, and genetic improvement of dairy fertility traits

Ma, Li; Cole, John B.; Da, Yang; VanRaden, P.M.

doi:10.3168/jds.2018-15269

Cited by 92 publications

(80 citation statements)

References 90 publications

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“…The sample for GWAS analysis contained 294,079 first lactation Holstein cows with phenotypic observations for five milk production traits (milk, fat and protein yields, and fat and protein percentages), three fertility traits (daughter pregnancy rate, cow conception rate, and heifer conception rate), and somatic cell score. Daughter pregnancy rate is the percentage of cows that become pregnant during each 21-d period, and cow and heifer conception rate each is defined as percentage pregnancy at each service (Ma et al, 2018). The number of phenotypic observations ranged from 294,079 for milk yield to 186,188 for cow conception rate (Table S1).…”

Section: Methodsmentioning

confidence: 99%

“…GWAS in several dairy cattle breeds have reported a large number of QTL effects on dairy traits (Bolormaa et al, 2010; Pryce et al, 2010, 2014; Cole et al, 2011; Guo et al, 2012; Ma et al, 2012; Rothammer et al, 2013; Raven et al, 2014; Littlejohn et al, 2016; Jiang et al, 2017; Sanchez et al, 2017; Bouwman et al, 2018; Weller et al, 2018). However, the number of confirmed QTL effects across studies is low (Ma et al, 2018; Weller et al, 2018), and only limited understanding of the genetic mechanism of the QTL effects is available. The DGAT1 gene was widely confirmed to have the most significant effects for milk production (Grisart et al, 2002; Spelman et al, 2002; Schennink et al, 2007; Cole et al, 2011; Ma et al, 2012; Jiang et al, 2017), and previously was shown to have antagonistic pleiotropy effects between fat yield and milk and protein yields based on candidate causal variants (Thaller et al, 2003) or causal alleles (da Silva et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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A Large-Scale Genome-Wide Association Study in U.S. Holstein Cattle

et al. 2019

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Genome-wide association study (GWAS) is a powerful approach to identify genomic regions and genetic variants associated with phenotypes. However, only limited mutual confirmation from different studies is available. We conducted a large-scale GWAS using 294,079 first-lactation Holstein cows and identified new additive and dominance effects on five production traits, three fertility traits, and somatic cell score. Four chromosomes had the most significant SNP effects on the five production traits, a Chr14 region containing DGAT1 mostly had positive effects on fat yield and negative effects on milk and protein yields, the 88.07–89.60 Mb region of Chr06 with SLC4A4, GC, NPFFR2 , and ADAMTS3 for milk and protein yields, the 30.03–36.67 Mb region of Chr20 with C6 and GHR for milk yield, and the 88.19–88.88 Mb region with ABCC9 as well as the 91.13–94.62 Mb region of Chr05 with PLEKHA5, MGST1, SLC15A5 , and EPS8 for fat yield. For fertility traits, the SNP in GC of Chr06, and the SNPs in the 65.02–69.43 Mb region of Chr01 with COX17, ILDR1 , and KALRN had the most significant effects for daughter pregnancy rate and cow conception rate, whereas SNPs in AFF1 of Chr06, the 47.54–52.79 Mb region of Chr07, TSPAN4 of Chr29, and NPAS1 of Chr18 had the most significant effects for heifer conception rate. For somatic cell score, GC of Chr06 and PRLR of Chr20 had the most significant effects. A small number of dominance effects were detected for the production traits with far lower statistical significance than the additive effects and for fertility traits with similar statistical significance as the additive effects. Analysis of allelic effects revealed the presence of uni-allelic, asymmetric, and symmetric SNP effects and found the previously reported DGAT1 antagonism was an extreme antagonistic pleiotropy between fat yield and milk and protein yields among all SNPs in this study.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Large-Scale Genome-Wide Association Study in U.S. Holstein Cattle

et al. 2019

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“…Genome-wide association studies (GWAS) have been successful at interrogating the genetic basis of complex traits and diseases in cattle [7][8][9][10]. Because complex traits are influenced by many genes, their interactions, and environment and due to the high level of linkage disequilibrium (LD) between genomic variants, pinpointing causal variants of complex traits has been challenging [11].…”

Section: Introductionmentioning

confidence: 99%

GWAS and fine-mapping of livability and six disease traits in Holstein cattle

et al. 2020

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Background: Health traits are of significant economic importance to the dairy industry due to their effects on milk production and associated treatment costs. Genome-wide association studies (GWAS) provide a means to identify associated genomic variants and thus reveal insights into the genetic architecture of complex traits and diseases. The objective of this study is to investigate the genetic basis of seven health traits in dairy cattle and to identify potential candidate genes associated with cattle health using GWAS, fine mapping, and analyses of multi-tissue transcriptome data. Results: We studied cow livability and six direct disease traits, mastitis, ketosis, hypocalcemia, displaced abomasum, metritis, and retained placenta, using de-regressed breeding values and more than three million imputed DNA sequence variants. After data edits and filtering on reliability, the number of bulls included in the analyses ranged from 11,880 (hypocalcemia) to 24,699 (livability). GWAS was performed using a mixed-model association test, and a Bayesian fine-mapping procedure was conducted to calculate a posterior probability of causality to each variant and gene in the candidate regions. The GWAS detected a total of eight genome-wide significant associations for three traits, cow livability, ketosis, and hypocalcemia, including the bovine Major Histocompatibility Complex (MHC) region associated with livability. Our fine-mapping of associated regions reported 20 candidate genes with the highest posterior probabilities of causality for cattle health. Combined with transcriptome data across multiple tissues in cattle, we further exploited these candidate genes to identify specific expression patterns in diseaserelated tissues and relevant biological explanations such as the expression of Group-specific Component (GC) in the liver and association with mastitis as well as the Coiled-Coil Domain Containing 88C (CCDC88C) expression in CD8 cells and association with cow livability. Conclusions: Collectively, our analyses report six significant associations and 20 candidate genes of cattle health. With the integration of multi-tissue transcriptome data, our results provide useful information for future functional studies and better understanding of the biological relationship between genetics and disease susceptibility in cattle.

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“…Increasing the proportion of the herd calving earlier in the year could be achieved through optimized reproductive management together with astute breeding decisions. Excellent reproductive performance is therefore key; considerable gains in performance have been achieved in most global dairy cow populations owing to genetic gain achieved following the consideration of reproductive performance in dairy cow breeding goals (Berry et al, 2014;Ma et al, 2019). In Ireland, for example, between the years 2010 and 2018, inclusive, there was an 8.2 percentage unit increase in the proportion of Irish dairy cows calving in January and February (ICBF, 2019).…”

Section: Management Factorsmentioning

confidence: 99%