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International audience; Functional annotation of genomes is a prerequisite for contemporary basic and applied genomic research, yet farmed animal genomics is deficient in such annotation. To address this, the FAANG (Functional Annotation of Animal Genomes) Consortium is producing genome-wide data sets on RNA expression, DNA methylation, and chromatin modification, as well as chromatin accessibility and interactions. In addition to informing our understanding of genome function, including comparative approaches to elucidate constrained sequence or epigenetic elements, these annotation maps will improve the precision and sensitivity of genomic selection for animal improvement. A scientific community-driven effort has already created a coordinated data collection and analysis enterprise crucial for the success of this global effort. Although it is early in this continuing process, functional data have already been produced and application to genetic improvement reported. The functional annotation delivered by the FAANG initiative will add value and utility to the greatly improved genome sequences being established for domesticated animal species.

Additional file 4. Analyses of variants located around previously reported wool and hair genes. A set of variants in and close to 453 previously reported genes associated with wool/fur/hair traits across mammals (Published-Gene set) were identified including 1619 coding variants and 11,712 variants up- and down-stream of the genes. A BayesR fine-mapping analysis identified which of these variants were most associated with wool traits and a GBLUP tested the proportion of genetic variance that the sequence variant set explained for each of the wool traits.

Additional file 2: Table S1. STAR parameters used for alignment.

Additional file 4: Table S2. RNA-seq library information. Mean, minimum, maximum and median values of the number of read pairs that pass trimming and filtering (clean reads), and are uniquely mapped reads, and the proportion of clean reads that map uniquely to the genome in liver and muscle samples. The distribution of the proportion of clean reads that are uniquely mapped in liver and muscle samples is also shown.

Additional file 1. Figure S1. PCA of the genetic distance across all samples of European, Asian indicine, African taurine and African indicine cattle based on their genome (a) and (b) or breed of origin (c) and (d). (a) PC1 and PC2 explaining 84.02% and 11.60% of the variability, respectively; (b) PC1 versus PC2 (77.74% and 7.92% of the total variability for PC1 and PC2, respectively); and (c) and (d) PC1 versus PC2 and PC1 versus PC3 colour coded according to breed, respectively. Figure S2. PCA of the genetic distance to assess the clustering of sequences according to their breed of origin. (a) PC1 (77.74% of the total variability) and PC2 (7.92% of the total variability); and (b) PC1 versus PC3 (5.45% of total variability). Figure S3. Heterozygosity levels (number of heterozygous sites/total number of sites) in Asiatic, admixed and European taurine breeds classified according to genome of origin (i.e. orange = indicine, green = admixed, blue = taurine) (a) or to breed (b); and in African cattle classified according to genome of origin (c) or according to breed (d). Figure S4. Inbreeding coefficient, F, in Asiatic, admixed and European taurine breeds classified according to genome of origin (i.e. orange = indicine, green = admixed, blue = taurine) (a) or to breed (b); and in African cattle classified according to genome of origin (c) or to breed (d). Figure S5. Genetic variation and divergence in African cattle. (a) Proportion and number of private and shared SNPs in a set of African whole-genome sequences corresponding to 12 N’Dama (taurine in blue), 26 Uganda-mixed, 5 Africander and 10 Ankole (Sanga, zebu-taurine in green) and 10 Oganden and 10 Boran (Zebu or indicine in orange) (b) Nucleotide diversity was estimated in 20-kb genomic intervals for N’Dama π = 0.17%, Sanga π = 0.29% and indicine π = 0.32% sequences. Correlations between estimated reference allele frequencies (RAF) between taurine and indicine (c), Sanga and indicine (d) and Sanga and taurine (e). Bins were estimated for each genome of origin or population, then compared between populations and visualised in heatmaps. The colors get warmer as the number of SNP counts increases. Figure S6. FST measure in 20-kb genome-wide overlapping bins with a 10 kb step size. Figure S7. Candidate selective sweeps in taurine and indicine in African cattle. (a) Population differentiation (FST) and relative nucleotide diversity between taurine and indicine cattle in genome-wide 20-kb genomic bins. (b) Genome-wide distribution of relative nucleotide diversity. Positive values represent candidate sweeps in taurine cattle and negative values in indicine. (c) FST measure in 20-kb genome-wide overlapping windows with a 10-kb step size. Figure S8. Genomic feature enrichment in selective sweeps. Strength of enrichment for 20 genomic features within 372 European taurine regions (a); 611 African indicine regions (b); 117 African taurine regions (c). Figure S9. Intersection of delta allele frequency (ΔAF) with different gene annotations. (a) Genome annotation derived and (b) using predicted [20] and experimental annotations in cattle [48, 49]. Figure S10. The 36 breeds in run6 of the 1000 Bull Genomes Project which present allele G at SNP rs447470311 (chr5:47726121) [24]. Figure S11. Regions that include selective sweeps in the cattle genome new assembly (ARS-UCD 1.2) at coordinates chr5: 47,481,051–47,520,235.

Using a combination of whole-genome resequencing and high-density genotyping arrays, genome-wide haplotypes were reconstructed for two of the most important bulls in the history of the dairy cattle industry, Pawnee Farm Arlinda Chief (“Chief”) and his son Walkway Chief Mark (“Mark”), each accounting for ∼7% of all current genomes. We aligned 20.5 Gbp (∼7.3× coverage) and 37.9 Gbp (∼13.5× coverage) of the Chief and Mark genomic sequences, respectively. More than 1.3 million high-quality SNPs were detected in Chief and Mark sequences. The genome-wide haplotypes inherited by Mark from Chief were reconstructed using ∼1 million informative SNPs. Comparison of a set of 15,826 SNPs that overlapped in the sequence-based and BovineSNP50 SNPs showed the accuracy of the sequence-based haplotype reconstruction to be as high as 97%. By using the BovineSNP50 genotypes, the frequencies of Chief alleles on his two haplotypes then were determined in 1,149 of his descendants, and the distribution was compared with the frequencies that would be expected assuming no selection. We identified 49 chromosomal segments in which Chief alleles showed strong evidence of selection. Candidate polymorphisms for traits that have been under selection in the dairy cattle population then were identified by referencing Chief’s DNA sequence within these selected chromosome blocks. Eleven candidate genes were identified with functions related to milk-production, fertility, and disease-resistance traits. These data demonstrate that haplotype reconstruction of an ancestral proband by whole-genome resequencing in combination with high-density SNP genotyping of descendants can be used for rapid, genome-wide identification of the ancestor’s alleles that have been subjected to artificial selection.