Comments on Rare V ariants A nalyses

1. Comments on Rare Variants Analyses Ryo Yamada Kyoto University 2012/08/27 IBC2012@Kobe, Japan

2. Many difficulties to detect true signals in rare variant analyses Type 1 error, Power-control, Various Statistics with/without Weights, Data quality, Data load, Platform variations, Missing data, Permutations, Allele freq., Rares with Commons, Replication, Neutral or not,

3. Many difficulties to detect true signals in rare variant analyses Type 1 error, Power-control, Various Statistics with/without Weights, Data quality, Data load, Platform variations, Missing data, Permutations, Allele freq., Replication, Rares with Commons integrating all variations, So many....

4. Many difficulties to detect true signals in rare variant analyses • My discussing points • Some problems in rare variant analyses are the unsolved problems in common variant analyses.

5. Many difficulties to detect true signals in rare variant analyses • My discussing points • Some problems in rare variant analyses are the unsolved problems in common variant analyses.

6. Many difficulties to detect true signals in rare variant analyses • My discussing points • Some problems in rare variant analyses are the unsolved problems in common variant analyses. • Changes in genetic studies along with rare variant analyses • Next-generation sequencing technologies-driven changes

7. Common Variant Analyses • Pre kit-GWAS ~2005 • Exonic region-dominant • kit-GWAS 2005 ~ • Genome-wide

8. Common Variant Analyses • Pre kit-GWAS ~2005 • Exonic region-dominant • kit-GWAS 2005 ~ • Genome-wide Rare Variant Analyses • Exome • Whole-genome sequencing

9. Common Variant Analyses • Pre kit-GWAS ~2005 • Exonic region-dominant • kit-GWAS 2005 ~ • Genome-wide déjà vu Rare Variant Analyses • Exome • Whole-genome sequencing

10. What we can learn from SNP LD mapping • Hypothesis-free approach • All markers vs. 1 trait

11. What we can learn from SNP LD mapping • Hypothesis-free approach • All markers vs. 1 trait

12. What we can learn from SNP LD mapping • Hypothesis-free approach • All markers vs. 1 trait

13. Set of Hypotheses • Hypothesis-free approach • All markers vs. 1 trait • Almost all hypotheses are null. • They work as negative controls. • We obtain distribution under null hypothesis. • A few hypotheses are “positive”. • Pick up “outliers” from “null distribution” as positive signals

14. Many Positive Hypotheses • Many hypotheses are “truly positive”. • Transctiptome, microarray-chips • False discovery rate

15. Many Positive Hypotheses • Many hypotheses are “truly positive”. • Transctiptome, microarray-chips • False discovery rate

16. Many Positive Hypotheses • Many hypotheses are “truly positive”. • Transctiptome, microarray-chips • False discovery rate

17. Many Positive Hypotheses • Many hypotheses are “truly positive”. • Transctiptome, microarray-chips • False discovery rate • Different approach to multiple testings from GWAS

18. Many Positive Hypotheses • Many hypotheses are “truly positive”. • Transctiptome, microarray-chips • False discovery rate • Similar approach to multiple testings in GWAS?

19. Many Positive Hypotheses • Many hypotheses are “truly positive”. • Transctiptome, microarray-chips • False discovery rate • Similar approach in GWAS? • e-QTL and cis-effect of neighboring SNPs on gene expression Nature Genetics 43, 561–564 (2011)

20. Both are GWAS but different • Almost all hypotheses are null • Many positive hypotheses

21. Both are GWAS but different • Almost all hypotheses are null • All markers vs. 1 trait • Many positive hypotheses • All markers vs. genes (traits)

22. Both are GWAS but different • Almost all hypotheses are null • All markers vs. 1 trait • Many positive hypotheses • All markers vs. genes (traits) Many traits

23. Both are GWAS but different • Almost all hypotheses are null • All markers vs. 1 trait • Many positive hypotheses • All markers vs. genes (traits) Many traits Positives : Neighboring and cis effects Negatives : Remote or trans effects

24. Many Positive Hypotheseswith Rare Variant Analyses

25. Many Positive Hypotheseswith Rare Variant Analyses • Distribution of statistics is like what?

26. Many Positive Hypotheseswith Rare Variant Analyses • Distribution of statistics is like what? • Strategy to detect signals should be cared.

27. Both are GWAS but different • Almost all hypotheses are null • All markers vs. 1 trait • Many Positive Hypotheses • All markers vs. genes (traits) Many traits Positives : Neighboring and cis effects Negatives : Remote or trans effects

28. Same GWAS but difference • Almost all hypotheses are null • All markers vs. 1 phenotype • Many Positive Hypotheses • All markers vs. genes (traits) Many traits Positives : Neighboring Negatives : Trans / Remote genes

29. Same GWAS but difference Where are many traits? • Almost all hypotheses are null • All markers vs. 1 phenotype • Many Positive Hypotheses • All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes

30. Same GWAS but difference Where are many traits? • Biobankprojects with multiple traits • EHR (Electrical Health Record)-driven genetic studies • Very many phenotypes Nature Reviews Genetics12, 417-428 (June 2011)  • Almost all hypotheses are null • All markers vs. 1 phenotype • Many Positive Hypotheses • All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes

31. Same GWAS but difference Where are many traits? • Biobankprojects with multiple traits • EHR (Electrical Health Record)-driven genetic studies • Very many phenotypes Nature Reviews Genetics12, 417-428 (June 2011)  • Almost all hypotheses are null • All markers vs. 1 phenotype • Many Positive Hypotheses • All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes

32. Same GWAS but difference Where are many traits? • Physical / pathological conditions vary. • Developmental stages vary. • Cell types/ tissue types/ organs vary. • Almost all hypotheses are null • All markers vs. 1 phenotype • Many Positive Hypotheses • All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes

33. Same GWAS but difference Where are many traits? • Physical / pathological conditions vary. • Developmental stages vary. • Cell types/ tissue types/ organs vary. • Almost all hypotheses are null • All markers vs. 1 phenotype • Many Positive Hypotheses • All markers vs. cis-neighboring genes Many traits Positives : Neighboring Negatives : Trans / Remote genes

34. Same GWAS but difference Where are many traits? • Physical / pathological conditions vary. • Developmental stages vary. • Cell types/ tissue types/ organs vary. Intra-individual diversity Many traits Positives : Neighboring Negatives : Trans / Remote genes

35. Same GWAS but difference Many traits

36. Same GWAS but difference Change view points Many traits

37. Same GWAS but difference Many genotypes

38. Same GWAS but difference Where are many genotypes? Many genotypes

39. Same GWAS but difference Where are many genotypes? • Physical / pathological conditions vary. • Developmental stages vary. • Cell types/ tissue types/ organs vary. Many genotypes

40. Same GWAS but difference Where are many genotypes? • Physical / pathological conditions vary. • Developmental stages vary. • Cell types/ tissue types/ organs vary. Intra-individual diversity Again Many genotypes

41. Same GWAS but difference Where are many genotypes? • Physical / pathological conditions vary. • Developmental stages vary. • Cell types/ tissue types/ organs vary. Intra-individual diversity Many genotypes Next generation sequencing technology makes these possible.

42. Changes by Next Generation Sequencing Technologies • Individual cell-sequencing detects • Hereditary • Variants from parents to offsprings • Non-hereditary but genetic • de novo mutations in gamates • Somatic mutations in fetus • Somatic mutations after birth

43. Changes by Next Generation Sequencing Technologies • Individual cell-sequencing detects • Hereditary • Variants from parents to offsprings • Non-hereditary but genetic • de novo mutations in gamates • Somatic mutations in fetus • Somatic mutations after birth Before birth After birth

44. Changes by Next Generation Sequencing Technologies • Individual cell-sequencing detects • Hereditary • Variants from parents to offsprings • Non-hereditary but genetic • de novo mutations in gamates • Somatic mutations in fetus • Somatic mutations after birth Before birth After birth Hereditary ~ Genetic ~ Somatic Discriminations are becoming vague.

46. “Collapsing methods”

47. “Collapsing methods” • Patterns of possession of rare variants vary with phenotypes. • “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure

48. “Collapsing methods” • Patterns of possession of rare variants vary with phenotypes. • “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure

49. “Collapsing methods” • Patterns of possession of rare variants vary with phenotypes. • “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure • Information on functionality of variants might be used when collapse to modify the “measure”.

50. “Collapsing methods” • Patterns of possession of rare variants vary with phenotypes. • “Collapsing methods” make the patterns into a style that can be tested easily; one-dimensional measure • Is this problem NEW to rare variant analyses?