Genome-wide mapping of 10 calving and fertility traits in Holstein dairy cattle with special regard to chromosome 18

Abstract

Over the last decades, a dramatic decrease in reproductive performance has been observed in Holstein cattle and fertility problems have become the most common reason for a cow to leave the herd. The premature removal of animals with high breeding values results in both economic and breeding losses. For efficient future Holstein breeding, the identification of loci associated with low fertility is of major interest and thus constitutes the aim of this study. To reach this aim, a genome-wide combined linkage disequilibrium and linkage analysis (cLDLA) was conducted using data on the following 10 calving and fertility traits in the form of estimated breeding values: days from first service to conception of heifers and cows, nonreturn rate on d 56 of heifers and cows, days from calving to first insemination, days open, paternal and maternal calving ease, paternal and maternal stillbirth. The animal data set contained 2,527 daughter-proven Holstein bulls from Germany that were genotyped with Illumina's BovineSNP50 BeadChip (Illumina Inc., San Diego, CA). For the cLDLA, 41,635 sliding windows of 40 adjacent single nucleotide polymorphisms (SNP) were used. At each window midpoint, a variance component analysis was executed using ASReml. The underlying mixed linear model included random quantitative trait locus (QTL) and polygenic effects. We identified 50 genome-wide significant QTL. The most significant peak was detected for direct calving ease at 59,179,424 bp on chromosome 18 (BTA18). Next, a mixed-linear model association (MLMA) analysis was conducted. A comparison of the cLDLA and MLMA results with special regard to BTA18 showed that the genome-wide most significant SNP from the MLMA was associated with the same trait and located on the same chromosome at 57,589,121 bp (i.e., about 1.5 Mb apart from the cLDLA peak). The results of 5 different cLDLA and 2 MLMA models, which included the fixed effects of either SNP or haplotypes, suggested that the cLDLA method outperformed the MLMA in accuracy and precision. The haplotype-based cLDLA method allowed for a more precise mapping and the definition of ancestral and derived QTL alleles, both of which are essential for the detection of underlying quantitative trait nucleotides.

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

“…Although this QTL has been detected by several studies, the causative gene or mutation is still missing. A few candidate genes are located in this QTL region, including clusters of sialic acid binding Ig-like lectin genes and zinc finger protein genes; however, several studies have failed to conclusively identify the causal genetic variant (Cole et al, 2009;Seidenspinner et al, 2009;Purfield et al, 2015;Mao et al, 2016;Müller et al, 2017). Future studies are still needed to pinpoint the candidate genes and mutations underlying this large QTL on fertility.…”

Genetic selection for bovine chromosome 18 haplotypes associated with divergent somatic cell score affects postpartum reproductive and metabolic performance

“…Genetic selection offers a sustainable tool to improve dairy cattle farming via the selection of animals with reduced susceptibility toward diseases, which in turn results in improved periparturient health. Until now, association and linkage studies have revealed promising results concerning bovine chromosome 18 (BTA18) and performance traits (Mao et al, 2016;Abo-Ismail et al, 2017;Müller et al, 2017;Wu et al, 2017). Concerning udder health, Kühn et al (2008) showed that the prediction of half-sib heifers with a high or low SCS was possible through marker-assisted selection regarding a confirmed QTL for SCS in the telomeric region of BTA18.…”

Symposium review: The choice and collection of new relevant phenotypes for fertility selection

“…The availability of genomic data, such as SNP markers and whole-genome sequence, have provided additional opportunities to improve dairy cattle fertility. Several studies identified SNP and deleterious haplotypes related to fertility and reproduction traits Nayeri et al, 2016;Parker-Gaddis et al, 2016;Müller et al, 2017). The inclusion of genomic information, such as SNP markers and variants from whole-genome sequence data, has provided better reliability estimates for fertility traits (Wiggans et al, 2011;Ortega et al, 2016;VanRaden et al, 2017).…”