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Fertility traits are economically important in cattle breeding programs. Scrotal circumference (SC) measures are repeatable, easily obtained, highly heritable, and positively correlated with female fertility traits and sperm quality traits in males. A useful approach to summarize SC measures over time is using nonlinear models, which summarize specific measures of SC in a few parameters with biological interpretation. This approach facilitates the selection of bulls with larger SC and maturity index (K), that is, early maturing animals. Because SC is a sex-limited trait, identifying the underlying genomics of growth curve parameters will allow selection across both males and females. We reported the first multitrait genomewide association study (GWAS) of estimated growth curve parameters for SC data in Brahman cattle. Five widely used nonlinear models were tested to fit a total of 3,612 SC records, measured at 6, 12, 18, and 24 mo of age. The von Bertalanffy model, individually fitted for each animal, best fit this SC data. Parameter estimates SC at maturity (A) and K as well as SC at all ages were jointly analyzed in a GWAS to identify 1-Mb regions most strongly associated with each trait. Heritabilities were 0.25 for K and 0.32 for A and ranged from 0.51 to 0.72 for SC at 6 (SC6), 12 (SC12), 18 (SC18), and 24 mo of age (SC24). An overlapping window on chromosome 14 explaining around 0.8% of genetic variance for K, SC12, SC18, and SC24 was observed. The major positional candidate genes within 1 Mb upstream and downstream of this overlapping window were LYN, CHD7, RAB2A, and ARFGEF1. Windows of 1 Mb explaining more than 0.4% of each trait on chromosomes 1, 3, 6, 7, 14, 17, 18, 24, 25, and 26 were identified. Pathways and net-work analyses were indicated through transcription factors playing a role on fertility traits: SOX9, BRCA1, STAT3, NFE2L2, EGR1, PPARG, and NFKB1. Further validation studies on larger populations or other breeds are required to validate these findings and to improve our understanding of the biology and complex genetic architecture of traits associated with scrotal growth and male fertility in cattle. [ABSTRACT FROM AUTHOR]

The advent of economically viable high-throughput genetic and genomic techniques has equipped animal geneticists with an unprecedented ability to generate massive amounts of molecular data. As a result, large lists of genes differentially expressed in many experimental conditions of interests have been reported and, likewise, the association of an ever growing number of DNA variants with phenotypes of importance is now a routine endeavor. Although these studies have greatly improved our understanding of the genetic basis of complex phenotypes, they have also revealed the difficulty in explaining more than a fraction of the genetic variance. Inspired by this data-rich and knowledge-poor dichotomy, systems biology aims at the formal integration of seemingly disparate datasets allowing for a holistic view where key properties of the systems emerge as an intuitive feature and enable the generation of testable hypotheses. Herein, we present 2 examples of integrating molecular data anchored in the power of gene network inference. The first example is concerned with the onset of puberty in Bos indicus-influenced cows bred in Australia. Using the results from genomewide association studies across a range of phenotypes, we developed what we termed an association weight matrix to generate a gene network underlying phenotypes of puberty in cattle. The network was mined for the minimal set of transcription factor genes whose predicted target spanned the majority of the topology of the entire network. The second example deals with piebald, a pigmentation phenotype in Merino sheep. Two networks were developed: a regulatory network and an epistatic network. The former is inferred based on promoter sequence analysis of differentially expressed genes. The epistatic network is built from 2-locus models among all pairwise associated polymorphisms. At the intersection between these 2 networks, we revealed a set of genes and gene-gene interactions of validated and de novo predicted relevance to the piebald phenotype. We argue that these new approaches are holistic and therefore more appropriate than traditional approaches for investigating genetic mechanisms underlying complex phenotypes of importance in livestock species. [ABSTRACT FROM AUTHOR]

Many traits affecting profitability and sustainability of meat, milk, and fiber production are polygenic, with no single gene having an overwhelming influence on observed variation. No knowledge of the specific genes controlling these traits has been needed to make substantial improvement through selection. Significant gains have been made through phenotypic selection enhanced by pedigree relationships and continually improving statistical methodology. Genomic selection, recently enabled by assays for dense SNP located throughout the genome, promises to increase selection accuracy and accelerate genetic improvement by emphasizing the SNP most strongly correlated to phenotype although the genes and sequence variants affecting phenotype remain largely unknown. These genomic predictions theoretically rely on linkage disequilibrium (LD) between genotyped SNP and unknown functional variants, but familial linkage may increase effectiveness when predicting individuals related to those in the training data. Genomic selection with functional SNP genotypes should be less reliant on LD patterns shared by training and target populations, possibly allowing robust prediction across unrelated populations. Although the specific variants causing polygenic variation may never be known with certainty, a number of tools and resources can be used to identify those most likely to affect phenotype. Associations of dense SNP genotypes with phenotype provide a 1-dimensional approach for identifying genes affecting specific traits; in contrast, associations with multiple traits allow defining networks of genes interacting to affect correlated traits. Such networks are especially compelling when corroborated by existing functional annotation and established molecular pathways. The SNP occurring within network genes, obtained from public databases or derived from genome and transcriptome sequences, may be classified according to expected effects on gene products. As illustrated by functionally informed genomic predictions being more accurate than naive whole-genome predictions of beef tenderness, coupling evidence from livestock genotypes, phenotypes, gene expression, and genomic variants with existing knowledge of gene functions and interactions may provide greater insight into the genes and genomic mechanisms affecting polygenic traits and facilitate functional genomic selection for economically important traits. [ABSTRACT FROM AUTHOR]

Polymorphisms located in the genes ABCG2, DGAT1, LEP, PRLR, RORC, CAPN1 and CAST previously have been associated with milk or meat production traits. In this study, these polymorphisms were examined for significant effects on reproductive traits [age at puberty (AGECL), post-partum anoestrus interval (PPAI) and the ability ovulate prior to weaning (PW)] and on a panel of correlated traits such as weight, growth and serum concentration of insulin-like growth factor I. The effects of the polymorphisms were examined in two samples of tropically adapted beef cattle: Brahman ( N = 932) and Tropical Composites ( N = 1097). A polymorphism in the gene DGAT1 was associated with age at puberty in the combined sample ( P = 0.042), and two polymorphisms in CAPN1 were associated with PPAI ( P = 0.033) and with the ability ovulate PW ( P = 0.017). The favourable allele for reproductive traits was not always the favourable allele associated with production traits. The effects of these polymorphisms on reproductive traits were small compared to their effects on the traits for which they were originally discovered. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]