Dataset Information

Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle: A Simulation Study.

ABSTRACT: Sequence data are expected to increase the reliability of genomic prediction by containing causative mutations directly, especially in cases where low linkage disequilibrium between markers and causative mutations limits prediction reliability, such as across-breed prediction in dairy cattle. In practice, the causative mutations are unknown, and prediction with only variants in perfect linkage disequilibrium with the causative mutations is not realistic, leading to a reduced reliability compared to knowing the causative variants. Our objective was to use sequence data to investigate the potential benefits of sequence data for the prediction of genomic relationships, and consequently reliability of genomic breeding values. We used sequence data from five dairy cattle breeds, and a larger number of imputed sequences for two of the five breeds. We focused on the influence of linkage disequilibrium between markers and causative mutations, and assumed that a fraction of the causative mutations was shared across breeds and had the same effect across breeds. By comparing the loss in reliability of different scenarios, varying the distance between markers and causative mutations, using either all genome wide markers from commercial SNP chips, or only the markers closest to the causative mutations, we demonstrate the importance of using only variants very close to the causative mutations, especially for across-breed prediction. Rare variants improved prediction only if they were very close to rare causative mutations, and all causative mutations were rare. Our results show that sequence data can potentially improve genomic prediction, but careful selection of markers is essential.

SUBMITTER: van den Berg I

PROVIDER: S-EPMC4978908 | biostudies-other | 2016 Aug

REPOSITORIES: biostudies-other

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Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle: A Simulation Study.

van den Berg Irene I Boichard Didier D Guldbrandtsen Bernt B Lund Mogens S MS

G3 (Bethesda, Md.) 20160809 8

Sequence data are expected to increase the reliability of genomic prediction by containing causative mutations directly, especially in cases where low linkage disequilibrium between markers and causative mutations limits prediction reliability, such as across-breed prediction in dairy cattle. In practice, the causative mutations are unknown, and prediction with only variants in perfect linkage disequilibrium with the causative mutations is not realistic, leading to a reduced reliability compared ...[more]

PMID: 27317779

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Project description:BackgroundGenome wide association studies (GWAS) in most cattle breeds result in large genomic intervals of significant associations making it difficult to identify causal mutations. This is due to the extensive, low-level linkage disequilibrium within a cattle breed. As there is less linkage disequilibrium across breeds, multibreed GWAS may improve precision of causal variant mapping. Here we test this hypothesis in a Holstein and Jersey cattle data set with 17,925 individuals with records for production and functional traits and 632,003 SNP markers.ResultsBy using a cross validation strategy within the Holstein and Jersey data sets, we were able to identify and confirm a large number of QTL. As expected, the precision of mapping these QTL within the breeds was limited. In the multibreed analysis, we found that many loci were not segregating in both breeds. This was partly an artefact of power of the experiments, with the number of QTL shared between the breeds generally increasing with trait heritability. False discovery rates suggest that the multibreed analysis was less powerful than between breed analyses, in terms of how much genetic variance was explained by the detected QTL. However, the multibreed analysis could more accurately pinpoint the location of the well-described mutations affecting milk production such as DGAT1. Further, the significant SNP in the multibreed analysis were significantly enriched in genes regions, to a considerably greater extent than was observed in the single breed analyses. In addition, we have refined QTL on BTA5 and BTA19 to very small intervals and identified a small number of potential candidate genes in these, as well as in a number of other regions.ConclusionWhere QTL are segregating across breed, multibreed GWAS can refine these to reasonably small genomic intervals. However, such QTL appear to represent only a fraction of the genetic variation. Our results suggest a significant proportion of QTL affecting milk production segregate within rather than across breeds, at least for Holstein and Jersey cattle.

Project description:BackgroundGenome-wide association studies (GWAS) were performed at the sequence level to identify candidate mutations that affect the expression of six major milk proteins in Montbéliarde (MON), Normande (NOR), and Holstein (HOL) dairy cattle. Whey protein (α-lactalbumin and β-lactoglobulin) and casein (αs1, αs2, β, and κ) contents were estimated by mid-infrared (MIR) spectrometry, with medium to high accuracy (0.59 ≤ R2 ≤ 0.92), for 848,068 test-day milk samples from 156,660 cows in the first three lactations. Milk composition was evaluated as average test-day measurements adjusted for environmental effects. Next, we genotyped a subset of 8080 cows (2967 MON, 2737 NOR, and 2306 HOL) with the BovineSNP50 Beadchip. For each breed, genotypes were first imputed to high-density (HD) using HD single nucleotide polymorphisms (SNPs) genotypes of 522 MON, 546 NOR, and 776 HOL bulls. The resulting HD SNP genotypes were subsequently imputed to the sequence level using 27 million high-quality sequence variants selected from Run4 of the 1000 Bull Genomes consortium (1147 bulls). Within-breed, multi-breed, and conditional GWAS were performed.ResultsThirty-four distinct genomic regions were identified. Three regions on chromosomes 6, 11, and 20 had very significant effects on milk composition and were shared across the three breeds. Other significant effects, which partially overlapped across breeds, were found on almost all the autosomes. Multi-breed analyses provided a larger number of significant genomic regions with smaller confidence intervals than within-breed analyses. Combinations of within-breed, multi-breed, and conditional analyses led to the identification of putative causative variants in several candidate genes that presented significant protein-protein interactions enrichment, including those with previously described effects on milk composition (SLC37A1, MGST1, ABCG2, CSN1S1, CSN2, CSN1S2, CSN3, PAEP, DGAT1, AGPAT6) and those with effects reported for the first time here (ALPL, ANKH, PICALM).ConclusionsGWAS applied to fine-scale phenotypes, multiple breeds, and whole-genome sequences seems to be effective to identify candidate gene variants. However, although we identified functional links between some candidate genes and milk phenotypes, the causality between candidate variants and milk protein composition remains to be demonstrated. Nevertheless, the identification of potential causative mutations that underlie milk protein composition may have immediate applications for improvements in cheese-making.

Project description:Domestic animals can serve as model systems of adaptive introgression and their genomic signatures. In part, their usefulness as model systems is due to their well-known histories. Different breeding strategies such as introgression and artificial selection have generated numerous desirable phenotypes and superior performance in domestic animals. The modern Danish Red Dairy Cattle is studied as an example of an introgressed population. It originates from crossing the traditional Danish Red Dairy Cattle with the Holstein and Brown Swiss breeds, both known for high milk production. This crossing happened, among other things due to changes in the production system, to raise milk production and overall performance. The genomes of modern Danish Red Dairy Cattle are heavily influenced by regions introgressed from the Holstein and Brown Swiss breeds and under subsequent selection in the admixed population. The introgressed proportion of the genome was found to be highly variable across the genome. Haplotypes introgressed from Holstein and Brown Swiss contained or overlapped known genes affecting milk production, as well as protein and fat content (CD14, ZNF215, BCL2L12, and THRSP for Holstein origin and ITPR2, BCAT1, LAP3, and MED28 for Brown Swiss origin). Genomic regions with high introgression signals also contained genes and enriched QTL associated with calving traits, body confirmation, feed efficiency, carcass, and fertility traits. These introgressed signals with relative identity-by-descent scores larger than the median showing Holstein or Brown Swiss introgression are mostly significantly correlated with the corresponding test statistics from signatures of selection analyses in modern Danish Red Dairy Cattle. Meanwhile, the putative significant introgressed signals have a significant dependency with the putative significant signals from signatures of selection analyses. Artificial selection has played an important role in the genomic footprints of introgression in the genome of modern Danish Red Dairy Cattle. Our study on a modern cattle breed contributes to an understanding of genomic consequences of selective introgression by demonstrating the extent to which adaptive effects contribute to shape the specific genomic consequences of introgression.

Project description:The Lundehund is an old dog breed with remarkable anatomical features including polydactyly in all four limbs and extraordinary flexibility of the spine. We genotyped 28 Lundehund using the canine Illumina high density beadchip to estimate the effective population size (Ne) and inbreeding coefficients as well as to identify potential regions of positive selection. The decay of linkage disequilibrium was slow with r2=0.95 in 50 kb distance. The last 7-200 generations ago, Ne was at 10-13. An increase of Ne was noted in the very recent generations with a peak value of 19 for Ne at generation 4. The FROH estimated for 50-, 65- and 358-SNP windows were 0.87, 087 and 0.81, respectively. The most likely estimates for FROH after removing identical-by-state segments due to linkage disequilibria were at 0.80-0.81. The extreme loss of heterozygosity has been accumulated through continued inbreeding over 200 generations within a probably closed population with a small effective population size. The mean inbreeding coefficient based on pedigree data for the last 11 generations (FPed=0.10) was strongly biased downwards due to the unknown coancestry of the founders in this pedigree data. The long-range haplotype test identified regions with genes involved in processes of immunity, olfaction, woundhealing and neuronal development as potential targets of selection. The genes QSOX2, BMPR1B and PRRX2 as well as MYOM1 are candidates for selection on the Lundehund characteristics small body size, increased number of digits per paw and extraordinary mobility, respectively. The objectives of the present study were to genotype 28 Lundehund using the canine Illumina high density beadchip (Illumina, San Diego, CA, USA) to estimate the effective population size (Ne) from data on linkage disequilibria (LD). We identified runs of homozygosity (ROH) as regions with a local loss of genetic variation. Inbreeding coefficients were calculated on the basis of pedigree data (FPed) and genotype information (FROH, FIS) to compare the results of these different methods for this population. We applied the long-range haplotype test to search for potential selective sweeps.

Dataset Information

Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle: A Simulation Study.

Publications

Using Sequence Variants in Linkage Disequilibrium with Causative Mutations to Improve Across-Breed Prediction in Dairy Cattle: A Simulation Study.

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