SyntenyTracker: a tool for defining homologous synteny blocks using radiation hybrid maps and whole-genome sequence

<p>Abstract</p> <p>Background</p> <p>The recent availability of genomic sequences and BAC libraries for a large number of mammals provides an excellent opportunity for identifying comparatively-anchored markers that are useful for creating high-resolution radiation-hybrid (RH) and BAC-based comparative maps. To use these maps for multispecies genome comparison and evolutionary inference, robust bioinformatic tools are required for the identification of chromosomal regions shared between genomes and to localize the positions of evolutionary breakpoints that are the signatures of chromosomal rearrangements. Here we report an automated tool for the identification of homologous synteny blocks (HSBs) between genomes that tolerates errors common in RH comparative maps and can be used for automated whole-genome analysis of chromosome rearrangements that occur during evolution.</p> <p>Findings</p> <p>We developed an algorithm and software tool (SyntenyTracker) that can be used for automated definition of HSBs using pair-wise RH or gene-based comparative maps as input. To verify correct implementation of the underlying algorithm, SyntenyTracker was used to identify HSBs in the cattle and human genomes. Results demonstrated 96% agreement with HSBs defined manually using the same set of rules. A comparison of SyntenyTracker with the AutoGRAPH synteny tool was performed using identical datasets containing 14,380 genes with 1:1 orthology in human and mouse. Discrepancies between the results using the two tools and advantages of SyntenyTracker are reported.</p> <p>Conclusion</p> <p>SyntenyTracker was shown to be an efficient and accurate automated tool for defining HSBs using datasets that may contain minor errors resulting from limitations in map construction methodologies. The utility of SyntenyTracker will become more important for comparative genomics as the number of mapped and sequenced genomes increases.</p

Fine Mapping and Evolution of a QTL Region on Cattle Chromosome 3

113 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.Using genotypes obtained from the offspring of three half sib families, paternal and maternal haplotypes of the offspring were reconstructed in addition to the six sire haplotypes. A 9.7 Mbp haplotype block likely to be identical by descent was identified in sires, S1 and S2. This shared haplotype block was found to be associated with high milk yield in families, F1 and F2, confirming previous studies. Maternal haplotypes of offspring were then used to define linkage disequilibrium (LD) within the QTL critical region. There were 15 LD blocks found to be shared among all three sires, covering 0.5 Mbp within the 9.7 Mbp region. These regions may contain QTL alleles carried by all three sires. Fst analysis and integrated haplotype scores (iHS) identified signatures of selection in a 3 Mbp subregion of the QTL critical region. For genes located in the QTL critical region, functions were predicted on the basis of human gene annotation. To identify orthologous genomic regions containing candidate genes, a bioinformatics tool, SyntenyTracker was developed. Within the QTL critical region, eight genes with well established functions related to metabolism, membrane transport or mammary gland function were identified. These results show that the combination of a high density marker map, haplotype analysis, characterization of LD, and identification of genomic regions and nucleotides under selection is a powerful approach for QTL fine mapping.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Multisite haplotype on cattle chromosome 3 is associated with quantitative trait locus effects on lactation traits

The goal of this study was to identify candidate genes and DNA polymorphisms for quantitative trait loci (QTL) affecting milk yield (MY), fat yield (FY), and protein yield (PY) previously mapped to bovine chromosome 3 (BTA3). To accomplish this, 373 half-siblings sired by three bulls previously shown to be segregating for lactation trait QTL, and 263 additional sires in the U.S. Dairy Bull DNA Repository (DBDR) were genotyped for 2,500 SNPs within a 16.3 Mbp QTL critical region on BTA3. Targeted resequencing of ∼1.8 Mbp within the QTL critical region of one of the QTL heterozygous sires identified additional polymorphisms useful for association studies. Twenty-three single nucleotide polymorphisms (SNPs) within a fine-mapped region were associated with effects on breeding values for MY, FY, or PY in DBDR sires, of which five SNPs were in strong linkage disequilibrium in the population. This multisite haplotype included SNPs located within exons or promoters of four tightly linked genes: RAP1A, ADORA3, OVGP1, and C3H1orf88. An SNP within RAP1A showed strong evidence of a recent selective sweep based on integrated haplotype score and was also associated with breeding value for PY. Because of its known function in alveolar lumen formation in the mammary gland, RAP1A is thus a strong candidate gene for QTL effects on lactation traits. Our results provide a detailed assessment of a QTL region that will be a useful guide for complex traits analysis in humans and other noninbred species.</jats:p

Assessing computational genomics skills: Our experience in the H3ABioNet African bioinformatics network

The H3ABioNet pan-African bioinformatics network, which is funded to support the Human Heredity and Health in Africa (H3Africa) program, has developed node-assessment exer�cises to gauge the ability of its participating research and service groups to analyze typical genome-wide datasets being generated by H3Africa research groups. We describe a frame�work for the assessment of computational genomics analysis skills, which includes standard operating procedures, training and test datasets, and a process for administering the exer�cise. We present the experiences of 3 research groups that have taken the exercise and the impact on their ability to manage complex projects. Finally, we discuss the reasons why many H3ABioNet nodes have declined so far to participate and potential strategies to encourage them to do so