High-resolution haplotype block structure in the cattle genome

<p>Abstract</p> <p>Background</p> <p>The Bovine HapMap Consortium has generated assay panels to genotype ~30,000 single nucleotide polymorphisms (SNPs) from 501 animals sampled from 19 worldwide taurine and indicine breeds, plus two outgroup species (Anoa and Water Buffalo). Within the larger set of SNPs we targeted 101 high density regions spanning up to 7.6 Mb with an average density of approximately one SNP per 4 kb, and characterized the linkage disequilibrium (LD) and haplotype block structure within individual breeds and groups of breeds in relation to their geographic origin and use.</p> <p>Results</p> <p>From the 101 targeted high-density regions on bovine chromosomes 6, 14, and 25, between 57 and 95% of the SNPs were informative in the individual breeds. The regions of high LD extend up to ~100 kb and the size of haplotype blocks ranges between 30 bases and 75 kb (10.3 kb average). On the scale from 1–100 kb the extent of LD and haplotype block structure in cattle has high similarity to humans. The estimation of effective population sizes over the previous 10,000 generations conforms to two main events in cattle history: the initiation of cattle domestication (~12,000 years ago), and the intensification of population isolation and current population bottleneck that breeds have experienced worldwide within the last ~700 years. Haplotype block density correlation, block boundary discordances, and haplotype sharing analyses were consistent in revealing unexpected similarities between some beef and dairy breeds, making them non-differentiable. Clustering techniques permitted grouping of breeds into different clades given their similarities and dissimilarities in genetic structure.</p> <p>Conclusion</p> <p>This work presents the first high-resolution analysis of haplotype block structure in worldwide cattle samples. Several novel results were obtained. First, cattle and human share a high similarity in LD and haplotype block structure on the scale of 1–100 kb. Second, unexpected similarities in haplotype block structure between dairy and beef breeds make them non-differentiable. Finally, our findings suggest that ~30,000 uniformly distributed SNPs would be necessary to construct a complete genome LD map in <it>Bos taurus </it>breeds, and ~580,000 SNPs would be necessary to characterize the haplotype block structure across the complete cattle genome.</p

Statistical method on nonrandom clustering with application to somatic mutations in cancer

<p>Abstract</p> <p>Background</p> <p>Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention.</p> <p>Results</p> <p>We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC) database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and <it>β</it>-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors.</p> <p>Conclusions</p> <p>Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.</p

Ionotropic Glutamate Receptor AMPA 1 Is Associated with Ovulation Rate

Ionotropic glutamate receptors mediate most excitatory neurotransmission in the central nervous system by opening ion channels upon the binding of glutamate. Despite the essential roles of glutamate in the control of reproduction and anterior pituitary hormone secretion, there is a limited understanding of how glutamate receptors control ovulation. Here we reveal the function of the ionotropic glutamate receptor AMPA-1 (GRIA1) in ovulation. Based on a genome-wide association study in Bos taurus, we found that ovulation rate is influenced by a variation in the N-terminal leucine/isoleucine/valine-binding protein (LIVBP) domain of GRIA1, in which serine is replaced by asparagine. GRIA1Asn has a weaker affinity to glutamate than GRIA1Ser, both in Xenopus oocytes and in the membrane fraction of bovine brain. This single amino acid substitution leads to the decreased release of gonadotropin-releasing hormone (GnRH) in immortalized hypothalamic GT1-7 cells. Cows with GRIA1Asn have a slower luteinizing hormone (LH) surge than cows with GRIA1Ser. In addition, cows with GRIA1Asn possess fewer immature ovarian follicles before superovulation and have a lower response to hormone treatment than cows with GRIA1Ser. Our work identified that GRIA1 is a critical mediator of ovulation and that GRIA1 might be a useful target for reproductive therapy