Genomic structure in a global collection of domesticated sheep reveals a history of artificial selection for horn loss and traits relating to pigmentation, reproduction, and body size.

The Bovine HapMap Consortium* The imprints of domestication and breed development on the genomes of livestock likely differ from those of companion animals. A deep draft sequence assembly of shotgun reads from a single Hereford female and comparative sequences sampled from six additional breeds were used to develop probes to interrogate 37,470 single-nucleotide polymorphisms (SNPs) in 497 cattle from 19 geographically and biologically diverse breeds. These data show that cattle have undergone a rapid recent decrease in effective population size from a very large ancestral population, possibly due to bottlenecks associated with domestication, selection, and breed formation. Domestication and artificial selection appear to have left detectable signatures of selection within the cattle genome, yet the current levels of diversity within breeds are at least as great as exists within humans.T he emergence of modern civilization was accompanied by adaptation, assimilation, and interbreeding of captive animals. In cattle (Bos taurus), this resulted in the development of individual breeds differing in, for example, milk yield, meat quality, draft ability, and tolerance or resistance to disease and pests. However, despite mapping and diversity studies (1-5) and the identification of mutations affecting some quantitative phenotypes (6-8), the detailed genetic structure and history of cattle are not known.Cattle occur as two major geographic types, the taurine (humpless-European, African, and Asian) and indicine (humped-South Asian, and East African), which diverged >250 thousand years ago (Kya) (3). We sampled individuals representing 14 taurine (n = 376), three indicine (n = 73) (table S1), and two hybrid breeds (n = 48), as well as two individuals each of Bubalus quarlesi and Bubalus bubalis, which diverged from Bos taurus~1.25 to 2.0 Mya (9, 10). All breeds except Red Angus (n = 12) were represented by at least 24 individuals. We preferred individuals that were unrelated for ≥4 generations; however, each breed had one or two sire, dam, and progeny trios to allow assessment of genotype quality.Single-nucleotide polymorphisms (SNPs) that were polymorphic in many populations were primarily derived by comparing whole-genome sequence reads representing five taurine and one indicine breed to the reference genome assembly obtained from a Hereford cow (10) (table S2). This led to the ascertainment of SNPs with high minor allele frequencies (MAFs) within the discovery breeds (table S5). Thus, as expected, with trio progeny removed, SNPs discovered within the taurine breeds had higher average MAFs

Polymorphisms that affect complex traits or quantitative trait loci (QTL) often affect multiple traits. We describe two novel methods (1) for finding single nucleotide polymorphisms (SNPs) significantly associated with one or more traits using a multi-trait, meta-analysis, and (2) for distinguishing between a single pleiotropic QTL and multiple linked QTL. The meta-analysis uses the effect of each SNP on each of n traits, estimated in single trait genome wide association studies (GWAS). These effects are expressed as a vector of signed t-values (t) and the error covariance matrix of these t values is approximated by the correlation matrix of t-values among the traits calculated across the SNP (V). Consequently, t'V−1t is approximately distributed as a chi-squared with n degrees of freedom. An attractive feature of the meta-analysis is that it uses estimated effects of SNPs from single trait GWAS, so it can be applied to published data where individual records are not available. We demonstrate that the multi-trait method can be used to increase the power (numbers of SNPs validated in an independent population) of GWAS in a beef cattle data set including 10,191 animals genotyped for 729,068 SNPs with 32 traits recorded, including growth and reproduction traits. We can distinguish between a single pleiotropic QTL and multiple linked QTL because multiple SNPs tagging the same QTL show the same pattern of effects across traits. We confirm this finding by demonstrating that when one SNP is included in the statistical model the other SNPs have a non-significant effect. In the beef cattle data set, cluster analysis yielded four groups of QTL with similar patterns of effects across traits within a group. A linear index was used to validate SNPs having effects on multiple traits and to identify additional SNPs belonging to these four groups.

A cattle genetic linkage map was constructed which marks about 90% of the expected length of the cattle genome. Over 200 DNA polymorphisms were genotyped in cattle families which comprise 295 individuals in full sibling pedigrees. One hundred and seventy-one loci were found linked to one other locus. Twenty nine of the 30 chromosome pairs are represented by at least one of the 36 linkage groups. Less than a 50 cM difference was found in the male and female genetic maps. The conserved loci on this map show as many differences in gene order compared to humans as is found between humans and mice. The conservation is consistent with the patterns of karyotypic evolution found in the rodents, primates and artiodactyls. This map will be important for localizing quantitative trait loci and provides a basis for further mapping.

The aim of this study was to assess the accuracy of genomic predictions for 19 traits including feed efficiency, growth, and carcass and meat quality traits in beef cattle. The 10,181 cattle in our study had real or imputed genotypes for 729,068 SNP although not all cattle were measured for all traits. Animals included Bos taurus, Brahman, composite, and crossbred animals. Genomic EBV (GEBV) were calculated using 2 methods of genomic prediction [BayesR and genomic BLUP (GBLUP)] either using a common training dataset for all breeds or using a training dataset comprising only animals of the same breed. Accuracies of GEBV were assessed using 5-fold cross-validation. The accuracy of genomic prediction varied by trait and by method. Traits with a large number of recorded and genotyped animals and with high heritability gave the greatest accuracy of GEBV. Using GBLUP, the average accuracy was 0.27 across traits and breeds, but the accuracies between breeds and between traits varied widely. When the training population was restricted to animals from the same breed as the validation population, GBLUP accuracies declined by an average of 0.04. The greatest decline in accuracy was found for the 4 composite breeds. The BayesR accuracies were greater by an average of 0.03 than GBLUP accuracies, particularly for traits with known genes of moderate to large effect mutations segregating. The accuracies of 0.43 to 0.48 for IGF-I traits were among the greatest in the study. Although accuracies are low compared with those observed in dairy cattle, genomic selection would still be beneficial for traits that are hard to improve by conventional selection, such as tenderness and residual feed intake. BayesR identified many of the same quantitative trait loci as a genomewide association study but appeared to map them more precisely. All traits appear to be highly polygenic with thousands of SNP independently associated with each trait.

The genetic factors that contribute to efficient food conversion are largely unknown. Several physiological systems are likely to be important, including basal metabolic rate, the generation of ATP, the regulation of growth and development, and the homeostatic control of body mass. Using whole-genome association, we found that DNA variants in or near proteins contributing to the background use of energy of the cell were 10 times as common as those affecting appetite and body-mass homeostasis. In addition, there was a genic contribution from the extracellular matrix and tissue structure, suggesting a trade-off between efficiency and tissue construction. Nevertheless, the largest group consisted of those involved in gene regulation or control of the phenotype. We found that the distribution of micro-RNA motifs was significantly different for the genetic variants associated with residual feed intake than for the genetic variants in total, although the distribution of promoter sequence motifs was not different. This suggests that certain subsets of micro-RNA are more important for the regulation of this trait. Successful validation depended on the sign of the allelic association in different populations rather than on the strength of the initial association or its size of effect.

A cattle genetic linkage map was constructed which covers more than 95 percent of the bovine genome at medium density. Seven hundred and forty six DNA polymorphisms were genotyped in cattle families which comprise 347 individuals in full sibling pedigrees. Seven hundred and three of the loci are linked to at least one other locus. All linkage groups are assigned to chromosomes, and all are orientated with regards to the centromere. There is little overall difference in the lengths of the bull and cow linkage maps although there are individual differences between maps of chromosomes. One hundred and sixty polymorphisms are in or near genes, and the resultant genome-wide comparative analyses indicate that while there is greater conservation of synteny between cattle and humans compared with mice, the conservation of gene order between cattle and humans is much less than would be expected from the conservation of synteny. This map provides a basis for high-resolution mapping of the bovine genome with physical resources such as Yeast and Bacterial Artificial Chromosomes as well as providing the underpinning for the interpolation of information from the Human Genome Project.

The genetics of reproduction is poorly understood because the heritabilities of traits currently recorded are low. To elucidate the genetics underlying reproduction in beef cattle, we performed a genome-wide association study using the bovine SNP50 chip in 2 tropically adapted beef cattle breeds, Brahman and Tropical Composite. Here we present the results for 3 female reproduction traits: 1) age at puberty, defined as age in days at first observed corpus luteum (CL) after frequent ovarian ultrasound scans (AGECL); 2) the postpartum anestrous interval, measured as the number of days from calving to first ovulation postpartum (first rebreeding interval, PPAI); and 3) the occurrence of the first postpartum ovulation before weaning in the first rebreeding period (PW), defined from PPAI. In addition, correlated traits such as BW, height, serum IGF1 concentration, condition score, and fatness were also examined. In the Brahman and Tropical Composite cattle, 169 [false positive rate (FPR) = 0.262] and 84 (FPR = 0.581) SNP, respectively, were significant (P < 0.001) for AGECL. In Brahman, 41% of these significant markers mapped to a single chromosomal region on BTA14. In Tropical Composites, 16% of these significant markers were located on BTA5. For PPAI, 66 (FPR = 0.67) and 113 (FPR = 0.432) SNP were significant (P < 0.001) in Brahman and Tropical Composite, respectively, whereas for PW, 68 (FPR = 0.64) and 113 (FPR = 0.432) SNP were significant (P < 0.01). In Tropical Composites, the largest concentration of PPAI markers were located on BTA5 [19% (PPAI) and 23% (PW)], and BTA16 [17% (PPAI) and 18% (PW)]. In Brahman cattle, the largest concentration of markers for postpartum anestrus was located on BTA3 (14% for PPAI and PW) and BTA14 (17% PPAI). Very few of the significant markers for female reproduction traits for the Brahman and Tropical Composite breeds were located in the same chromosomal regions. However, fatness and BW traits as well as serum IGF1 concentration were found to be associated with similar genome regions within and between breeds. Clusters of SNP associated with multiple traits were located on BTA14 in Brahman and BTA5 in Tropical Composites.