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Genome-wide association studies: what they can and can't tell us about disease biology

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Genome-wide association studies: what they can and can't tell us about disease biology

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  1. ATGGATTCTGGTATGTTCTAGCGCTTGCACCATCCCATTTAACTGTAAGAAGAATTGCAC GGTCCCAATTGCTCGAGAGATTTCTCTTTTACCTTTTTTTACTATTTTTCACTCTCCCAT AACCTCCTATATTGACTGATCTGTAATAACCACGATATTATTGGAATAAATAGGGGCTTG AAATTTGGAAAAAAAAAAAAACTGAAATATTTTCGTGATAAGTGATAGTGATATTCTTCT TTTATTTGCTACTGTTACTAAGTCTCATGTACTAACATCGATTGCTTCATTCTTTTTGTT GCTATATTATATGTTTAGAGGTTGCTGCTTTGGTTATTGATAACGGTTCTGGTATGTGTA AAGCCGGTTTTGCCGGTGACGACGCTCCTCGTGCTGTCTTCCCATCTATCGTCGGTAGACAAGACACCAAGGTATCATGGTCGGTATGGGTCAAAAAGACTCCTACGTTGGTGATGAA CTCAATCCAAGAGAGGTATCTTGACTTTACGTTACCCAATTGAACACGGTATTGTCACCA ACTGGGACGATATGGAAAAGATCTGGCATCATACCTTCTACAACGAATTGAGAGTTGCCC CAGAAGAACACCCTGTTCTTTTGACTGAAGCTCCAATGAACCCTAAATCAAACAGAGAAA AGATGACTCAAATTATGTTTGAAACTTTCAACGTTCCAGCCTTCTACGTTTCCATCCAAG CCGTTTTGTCCTTGTACTCTTCCGGTAGAACTACTGGTATTGTTTTGGATTCCGGTGATG GTGTTACTCACGTCGTTCCAATTTACGCTGGTTTCTCTCTACCTCACGCCATTTTGAGAA TCGATTTGGCCGGTAGAGATTTGACTGACTACTTGATGAAGATCTTGAGTGAACGTGGTT ACTCTTTCTCCACCACTGCTGAAAGAGAAATTGTCCGTGACATCAAGGAAAAACTATGTT ACGTCGCCTTGGACTTCGAACAAGAAATGCAAACCGCTGCTCAATCTTCTTCAATTGAAA AATCCTACGAACTTCCAGATGGTCAAGTCATCACTATTGGTAACGAAAGATTCAGAGCCC CAGAAGCTTTGTTCCATCCTTCTGTTTTGGGTTTGGAATCTGCCGGTATTGACCAAACTA CTTACAACTCCATCATGAAGTGTGATGTCGATGTCCGTAAGGAATTATACGGTAACATCG TTATGTCCGGTGGTACCACCATGTTCCCAGGTATTGCCGAAAGAATGCAAAAGGAAATCA CCGCTTTGGCTCCATCTTCCATGAAGGTCAAGATCATTGCTCCTCCAGAAAGAAAGTACT CCGTCTGGATTGGTGGTTCTATCTTGGCTTCTTTGACTACCTTCCAACAAATGTGGATCT CAAAACAAGAATACGACGAAAGTGGTCCATCTATCGTTCACCACAAGTGTTTCTAA Genome-wide association studies: what they can and can't tell us about disease biology Hunter Fraser

  2. Causes of disease • Environmental • Genetic

  3. Causes of disease • Environmental • Correlation vs. causation • Genetic

  4. Causes of disease • Environmental • Correlation vs. causation • Genetic

  5. Causes of disease • Environmental • Correlation vs. causation • Genetic

  6. Causes of disease • Environmental • Correlation vs. causation • Infinite possibilities • Genetic

  7. Polymorphisms • SNPs • Insertions/deletions • Copy-number variation • Inversions • Translocations

  8. Causes of disease • Environmental • Correlation vs. causation • Infinite possibilities • Genetic • Causality clear (in properly designed study)

  9. Causes of disease • Environmental • Correlation vs. causation • Infinite possibilities • Genetic • Causality clear (in properly designed study) • Finite number of polymorphisms (~107 common)

  10. Genome-wide association studies (GWAS) • Polymorphisms are the basis for the genetic component of disease risk variability • Genetic component (heritability) often explains >50% of disease incidence • Goal: For every polymorphism, determine what diseases are affected

  11. Genome-wide association studies (GWAS) Diseases . . . Polymorphisms

  12. Genome-wide association studies (GWAS) Diseases . . . Polymorphisms

  13. Genome-wide association studies (GWAS) Diseases . . . Polymorphisms

  14. Genome-wide association studies (GWAS) Diseases . . . Polymorphisms

  15. Genome-wide association studies (GWAS) • Main idea: look for genetic differences of people with vs. without a disease • First, need a method able to genotype thousands/millions of polymorphisms at once– microarrays • Second, need to know where are the common polymorphisms– HapMap project • Third, need HUGE cohorts of people to find subtle allele frequency differences

  16. Genome-wide association studies (GWAS)

  17. Genome-wide association studies (GWAS) Genomic position

  18. Genome-wide association studies (GWAS) • End result of a successful GWAS:

  19. Genome-wide association studies (GWAS) • End result of a successful GWAS: • What does this actually tell us?

  20. Genome-wide association studies (GWAS) • End result of a successful GWAS: • What does this actually tell us? • How to predict disease risk from genotype? • What polymorphisms cause disease? • What genes are involved?

  21. What do GWAS tell us? • How to predict disease risk from genotype? • Potentially yes, but in practice GWAS explain only a few percent of the genetic component (“Missing heritability”) • What polymorphisms cause disease? • No, GWAS use “tag SNPs” in linkage disequilibrium with causal polymorphisms

  22. What do GWAS tell us? • What genes are involved? • An important issue for our understanding of disease biology • Candidate genes are nearly always guessed, but this is biased by prior knowledge • Almost never any evidence implicating a particular gene, and most hits are intergenic • Transcriptional enhancers can act at long distance, making this a nontrivial problem

  23. Inferring disease genes • Need an unbiased, systematic method to infer disease genes from GWAS hits • One solution: integrate results with separate GWAS for gene expression “traits” (eQTL) DNA Genotyping array RNA Expression array

  24. eQTL mapping

  25. eQTL mapping • How does this tell us about disease genes? • Coincidence, or SNP X affects disease Z via its effect on gene Y; Y is a “disease gene” SNP X expr of gene Y disease Z

  26. Affymetrix exon arrays • ~6 million probes • Covers nearly all exons in human genome

  27. Alternative splicing

  28. The data set • Exon arrays on lymphoblastoid cell lines • 89 YRI (Yoruban from Nigeria) and 87 CEPH (European-American from Utah), genotyped at >3 million SNPs (HapMap)

  29. The analysis • Compute transcript variation for each exon • Compare transcript variation patterns to SNP genotypes • Integrate with disease GWAS results

  30. The analysis • Compute transcript variation for each exon • Compare transcript variation patterns to SNP genotypes • Integrate with disease GWAS results

  31. The analysis • Compute transcript variation for each exon • Compare transcript variation patterns to SNP genotypes • Integrate with disease GWAS results

  32. Comparing transcripts to SNPs • Compare each exon to all HapMap SNPs • Strongest correlations are local– SNPs nearby the exon(s) they affect

  33. Comparing transcripts to SNPs • Calculate correlation between each exon’s expression level and all SNPs within 100kb

  34. Comparing transcripts to SNPs • Calculate correlation between each exon’s expression level and all SNPs within 100kb

  35. Comparing transcripts to SNPs • Calculate correlation between each exon’s expression level and all SNPs within 100kb SNPs 100 kb 100 kb

  36. Significant associations: 1,061 exons, ~10 SNPs/exon

  37. Top hit: IRF5 • A transcription factor that acts downstream of Toll-like receptors, and a cause of lupus

  38. Top hit: IRF5 • A transcription factor that acts downstream of Toll-like receptors, and a cause of lupus probeset

  39. Top hit: IRF5 • A transcription factor that acts downstream of Toll-like receptors, and a cause of lupus probeset r = 0.97 No overlap between genotypes

  40. Second hit: OAS1 chr12 • 2’,5’-oligoadenylate synthetase 1 • Splice site mutations contribute to viral infection susceptibility and T1D (Bonnevie-Nielsen et al., 2005) • Three known splice variants of exons 5/6

  41. Second hit: OAS1 chr12 probeset

  42. Second hit: OAS1 chr12 probeset r = 0.93

  43. Second hit: OAS1 chr12 probeset probeset r = 0.93

  44. The analysis • Compute transcript variation for each exon • Compare transcript variation patterns to SNP genotypes • Integrate with disease GWAS results

  45. Genome-wide association studies • Could some disease-associated SNPs influence disease through splicing? • Compiled list of 68 disease SNPs

  46. Genome-wide association studies • Could some disease-associated SNPs influence disease through splicing? • Compiled list of 68 disease SNPs 4 overlaps with splicing SNPs (expect 0.1)

  47. Genome-wide association studies • Could some disease-associated SNPs influence disease through splicing? • Compiled list of 68 disease SNPs 4 overlaps with splicing SNPs (expect 0.1) • All 4 are from autoimmune diseases! 24 autoimmune SNPs, expect 0.04 overlaps • Suggests tissue specificity of polymorphic transcript variation

  48. Autoimmune-associated SNP • One SNP was associated with multiple autoimmune conditions (T1D, CD) • Located near PTPN2, a tyrosine phosphatase involved in immune regulation • PTPN2 known to have two splice forms, only one of which has an NLS (Ibarra-Sanchez et al., 2000) • Two isoforms have very different target proteins

  49. Autoimmune-associated SNP • Could this SNP cause autoimmune diseases by changing the ratio of PTPN2 splice forms? probeset PTPN2

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