1. Research proposal 2009 信息技术会议 Bioinformatics Analysis & Identification of non-Synonymous SNPs in Candidate Genes for Ascites College of Animal Husbandry & Veterinary Medicine, Shenyang Agricultural University China P. R. Dr. Li-mei HAN 汉丽梅

2. Contents * Introduction * Materials and Methods * Results * Discussion and Conclusions

3. Introduction to Bioinformatics Management of biological information using information technology. Development of new tools for the analysis of genetic and molecular biological data. Current efforts in molecular biology are producing an abundance of data and increasing problem of information overload. * Bioinformatics:

4. Many studies on PHS have been done in other species, such as in human and mouse. Available resources Previous studies have produced a large number of SNPs data-set and have identified a number of QTL regions for ascites. Protein database, text data-mining tool, SIFT,

5. * SIFT a sequence homology-based tool that Sorts Intolerant From Tolerant amino acid substitutions predicts whether an amino acid substitution in a protein is likely to have a phenotypic effect.

6. How does SIFT work? searches for similar sequences in a database chooses closely related sequences that may share similar function to the query sequence obtains the alignment of these chosen sequences predicts whether amino acid substitution is tolerant or intolerant by calculating normalized probabilities.

7. Introduction t o ascites Ascites syndrome in broilers has increasingly become a significant problem for poultry producers throughout the world within the last decades. Particularly in regions of low temperature and/or high altitude. * Background

8. Introduction for ascites Multiple factors: Internal factors (genetics), External factors (environment ). * Contributing Factors

9. Materials and Methods 1.Files : with large data-sets about gene and SNP information of all chicken genes. 2.Data-mining tools: http://www.genome-informatics.net/nutsdieren,http://www.genome-informatics.net/nutsdieren http://www.cmbi.kun.nl/geneseeker/ 3.Program:PERL programming language, SIFT, pregap4 &gap4. ORF finder 4. Chicken Population G2: parents of Wageningen ascites QTL mapping cross. Materials :

10. Methods (bioinformatics) files Generate the input file for SIFT & run SIFT for prediction Perl scripts files Perl scripts Select nsSNPs in potential candidate gene 1.Extract Information & potential candidate gene selection Data-mining

11. Methods (lab) Sequencing the SNP region in potential candidate genes SNP detection CN/CS identification by ORF finder and standard genetic code SIFT prediction for new SNPs found and Swiss Prot model searching for conserved residues. 2.Lab test

12. Results 1. EnsemblSNP: EnsemblSNP: GN(trans_id) & CN Chicken_buildChicken_build: trans_id, peptide sequence Input file for SIFT: CN, peptide sequence PERL scripts Input file Input file for SIFT in right format EnsemblCN: EnsemblCN: & CN

13. Results 2.Candidate gene selection EnsemblSNP:28417 SelectCNrecord:8485 GgaHsaGeneList: 17708 Genes:408Data-mining result :956 Potential candidate genes: 5 Filter for Ascites QTL regions

14. Potential candidate genes ALB, Albumin ( nsSNP:1) PKD2, polycystic kidney disease 2 ( nsSNPs:2) FAH, fumarylacetoacetate hydrolase ( nsSNP:1) AGA, aspartylglucosaminidase ( nsSNP:1) MUC1, mucin 1, transmembrane ( nsSNPs:6)

15. Results 3. 11nsSNPs/ 9 fragments Sequencing on parents of WUR QTL cross chicken population (20 animals) In total 36 SNPs 19 SNPs in exon, 17 in intron 6 nsSNPs of the original 9 nsSNPs

16. Discussion and Conclusions The results of the SIFT prediction : a reference for the future studies on chicken. The 9 nsSNPs were successfully tested in the lab Six nsSNPs :are segregating in our population candidates to test for association with ascites. 66% of the nsSNPs :confirmed in our population.

17. Discussion and conclusions The nsSNPs in ALB gene are very promising to ascites syndrome. Because one of the amino acid altered is very conserved in the ALB family according to the information from Swiss Prot model. One nsSNP in PKD2 gene is interesting because SIFT predicts that the mutation is deleterious.

18. Thanks ! ! !

19. EnsemblSNP

20. EnsemblCN

21. Chicken_build >ENSGALP00000016478 trans_id=ENSGALT00000016497 gene_id=ENSGALG00000010147 chr=Z_random start=4447 end=23480 strand=-1 DEDCPDLVPIDVGIVQDSEPGSGRKIPVTIITGYLGAGKTTLLNYILTEQHSKRIAVILN EFGEGSALEKSLAISQGGELYEEWLELRNGCLCCSVKDNGVKAIENLMQKRGKFDYILLE TTGLADPGAVASMFWVDSELGSDIYLDGIVSVVDAKHGLQHLTEEKPEGLVNEAARQVAL ADLIIINKTDLVSGEELNKVRASVRSINGLVKILETQRSSLQKKLENMKTAHAHLDKGIV TVTFEVLGNIKEENLNLFIQNLLWEKNVKDKTGRTMDVIRLKGLVSIQGKSHQVIVQGVH ELYDLEETAVAWKEDEKRTNRLVLIGRNLNKEIIKEVFIETVSE >ENSGALP00000016477 trans_id=ENSGALT00000016496 gene_id=ENSGALG00000010147 chr=Z_random start=4447 end=23480 strand=-1 EDCPDLVPIDVGIVQDSEPGSGRKIPVTIITGYLGAGKTTLLNYILTEQHSKRIAVILNE FGEGSALEKSLAISQGGELYEEWLELRNGCLCCSVKDNGVKAIENLMQKRGKFDYILLET TGLADPGAVASMFWVDSELGSDIYLDGIVSVVDAKHGLQHLTEEKPEGLVNEAARQVALA DLIIINKTDLVSGEELNKVRASVRSINGLVKILETQRSRVDLSNVLDLHAFDSLSGISLQ KKLENMKTAHAHLDKGIVTVTFEVLGNIKEENLNLFIQNLLWEKNVKDKTGRTMDVIRLK GLVSIQGKSHQVIVQGVHELYDLEETAVAWKEDEKRTNRLVLIGRNLNKEIIKEVFIETV SEKHESS

22. Input file for SIFT