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Bioinformatics Toolbox for Finding QTL Genes

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Bioinformatics Toolbox for Finding QTL Genes

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    1. Bioinformatics Toolbox for Finding QTL Genes HLB Course 2006 Luanne L. Peters The Jackson Laboratory

    3. Survey inbred strains for trait differences Characterize parents and F1 progeny Decide on type and size of cross Carry out cross, phenotype and genotype progeny Carry out statistical analysis for QTL Narrow the region - genetically *Narrow the region - bioinformatics, statistics *Find the gene *Prove the gene

    4. Comparative genomics Combining crosses Haplotype analysis In silico QTL mapping Testing the reduced list of genes for sequence and expression differences

    5. Rodent QTL and human QTL are found in homologous positions (Concordance). Homology can help to narrow QTL. Homology can be used to test QTL genes.

    7. QTL: Mouse-Human Genes Ctla4 - type I diabetes (Ueda et al, 2003) Angptl3 - atherosclerosis (Korstanje et al, 2004) Engrailed 2 - autism (Gharani et al, 2004) Ox40l - atherosclerosis (Wang et al, 2005) Podocin - hypertension (DiPetrillo et al, in press) Trhr - hypertension (DiPetrillo et al, in press) Tim1 - asthma (McIntire et al, 2001) C5 - liver fibrosis (Hildebrandt et al, 2005)

    8. Homology Can Narrow QTL

    9. Raw data from 2 or more crosses are recoded and crosses are combined into one dataset Increased significance of QTLs Narrows QTL regions May divide a broad QTL into separate peaks Li, Lyons, Wittenburg, Paigen, Churchill - Genetics, 2005

    11. HDL-C Chr 4

    12. Chr 6 QTL for Gallstones

    13. Combining Crosses Splits a Broad QTL into Two Peaks

    15. Bioinformatics Toolbox- Haplotyping Based on shared ancestors among common inbred strains so they have regions of DNA that are identical by descent QTL genes are highly unlikely to occur in regions IBD (therefore, look for regions that differ). Especially useful when multiple crosses find the same QTL

    27. Ath17- differs between B6 and 129

    28. SNP Distribution and Ath17

    29. Haplotype Analysis of Ath17 Reduced the number of candidate genes 179 in the original region 20 in region of high SNP density

    30. Combined Cross/Haplotype Analysis Leads to Candidate Gene

    31. Bone Density QTL on Chr 15 Mouse:Human:Mouse

    33. Trps1 Basis of Mendelian trait called trichorhinopharangeal syndrome 1 Patients are short and have stubby fingers Gene expression databases show Trps1 is expressed in bone

    34. Limitations of Haplotyping Assumption that QTLs in different crosses are due to the same gene may be wrong May eliminate a region that appears to be identical by descent but really is different (insufficient SNPs) May reach a gene rich region These limitations result in not finding a gene, unlikely to cause the incorrect gene to be identified

    35. Resolution of Haplotyping Almost always < 5 Mb Frequently < 1 Mb Sometimes down to a few genes The more crosses, the better the resolution

    37. Now called genome wide haplotype association mapping (HAM) An extension of haplotyping Search for an association of haplotype with phenotype over multiple strains Recent attempt using 25 strains and 12,000 SNPs showed reasonable agreement between predicted QTLs and QTLs found in crosses Pletcher Plos Biology 2004

    40. RBC Analysis (Sarah Burgess, TJL) In silico performed using 42 strains “Wild” strains included No recombinant inbred lines No congenics or consomics Males: 1 peak above highest threshold Females: 3 peaks above highest threshold

    43. RBC QTL Study 3 in silico QTL peaks predicted 2 QTL peaks significantly linked to phenotype detected in the cross Chr 2, 125.6 MB Chr 12, 66-68 MB Non-significant peak in LD with significant QTL Chr 7, 13.5 MB ? Chr 12, 66-68 MB Therefore, Chr 2 especially promising, and a cursory examination reveals…

    44. RBC: Chr 2 gene list

    49. Bioinformatic tools can narrow QTLs substantially. Additional SNPs and sequencing will accelerate gene finding even more. Haplotyping and in silico mapping are quite powerful.

    50. A QTL results from a base change in DNA. This could be in the coding region and change function. It could be in the regulatory regions and change message level, in the UTRs which affect message stability, or the splice junctions.

    51. 1. Polymorphisms in coding or regulatory regions 2. Some evidence linking function of gene and the QTL 3. In vitro studies showing differences in activity of alleles 4. Transgenics 5. Knockouts/knockins

    52. 6. Mutational analysis (mutations of candidate change trait) 7. Homology between human and animal model (Bev’s addition) 8. Distribution of alleles of candidate gene accounts for finding the QTL or failing to find the QTL in multiple crosses. For Apoa2, explained 18 crosses For Abca1, explained 9 crosses

    53. Importance of bioinformatics Importance of combining animal models and human data

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