BST227: Introduction to Statistical Genetics

Transcription

1 BST227: Introduction to Statistical Genetics Lecture 11: Heritability from summary statistics & epigenetic enrichments Guest Lecturer: Caleb Lareau

2 Success of GWAS EBI Human GWAS Catalog

3 As of this morning EBI Human GWAS Catalog

4 Questions of the Post-GWAS Era Can we identify other traits that share a similar genetic basis for the specific phenotype? Can we identify the cell types most important for disease (e.g. schizophrenia) and other traits (e.g. height) where variants are acting?

5 Tackling the big post-gwas questions Khan Academy; NIH Roadmap Website

6 Overview Part I: Omnigenic Model Part II: LD Score Regression <break> Part III: Epigenetic enrichment of GWAS Part IV: Improving precision of epigenetic enrichments ~ 1 hour ~ 20 minutes

7 Part I: Omnigenic Model

8 Questions: How many genes are important in a Mendelian disease (e.g. Sickle-Cell Disease)? How many genes are important in a non- Mendelian disease(e.g. schizophrenia)? How many genes are important in height?

9 Inflated summary statistics PGC 2014 Nature

10 Remove all green regions (+/- 1 Mb) PGC 2014 Nature

11 After removing all GWAS-hits PGC 2014 Nature

12

13 Omnigenic model Boyle et al Cell

14 Low et al 2010 PLoS One; PGC 2014 Nature Contrasting Models Polygenic Omnigenic

15 Question: If the omnigenic model is true, which chromosome should have the most heritability?

16 Omnigenic model validation Shi et al., 2016 AJHG

17 Part II: LD Score Regression

18 LD Score Regression can 1. Accurately distinguish polygenicity over confounding 2. Estimate heritability from summary statistics 3. Identify traits that share a genetic basis all of which you need to discuss in your project so ask questions!

19 LD Score Regression can 1. Accurately distinguish polygenicity over confounding 2. Estimate heritability from summary statistics 3. Identify traits that share a genetic basis

20 Omnigenic association vs. confounding Inflation: Confounding: No Yes *Simulated Data Bulik-Sullivan 2015 Nature Genetics

21 Definitions A standard model for GWAS is: (recall: need standardization) Heritability can be defined: Heritability of a category C is: Finucane 2014 AJHG

22 Polygenicity Polygenicity causes more chi-square statistic inflation in high LD regions than in low LD regions Finucane 2014 AJHG

23 Toy Illustration of the Genome Bulik-Sullivan 2015

24 Simulating a polygenic trait Bulik-Sullivan 2015

25 Simulating a polygenic trait Bulik-Sullivan 2015

26 Simulating a polygenic trait Bulik-Sullivan 2015

27 High-level overview 1. Separate the genome into bins 2. Compute the mean chi-squared statistic per bin 3. Compute the mean LD score per bin 4. Perform a regression of 2 & 3

28 LD Bins

29 LD Score Let C be the bin of genome of interest LD Score for SNP j Χ 2 statistic for SNP j (copy on board) Traylor et al PLoS Genetics

30 LD Score Regression each bin is a dot intercept is important Bulik-Sullivan et al 2015 Nature Genetics

31 pause, review last slides if needed

32 Confounding (Population Stratification) Bulik-Sullivan et al 2015 Nature Genetics

33 No Confounding (Omnigenic) Bulik-Sullivan et al 2015 Nature Genetics

34 Intercept matters

35 Real GWAS PGC 2014 Nature

36 Bulik-Sullivan et al 2015 Nature Genetics

37 LD Score Regression can 1. Accurately distinguish polygenicity over confounding 2. Estimate heritability from summary statistics 3. Identify traits that share a genetic basis

38 LD Score Regression Slope -> Slope is proportional to the heritability Write on the board

39 Recall Lecture 9 requires genotypes!!!

40 Key point: LD Score regression can compute heritability using summary statistics Why might this be important?

41 From LD Hub ldsc.broadinstitute.org

42 LD Score Regression can 1. Accurately distinguish polygenicity over confounding 2. Estimate heritability from summary statistics 3. Identify traits that share a genetic basis

43 Pleiotropy Pleiotropy := the production by a single gene (or genes!) of two or more apparently unrelated phenotypes or traits.

44 Single Trait Bulik-Sullivan 2015

45 Two Traits Bulik-Sullivan 2015

46 Pleiotropy using LD Score Z 1j and Z 2j are the z statistics of a single SNP j for two different traits Bulik-Sullivan et al., 2015 Nature Genetics

47 Genetic Correlations Cor = ~ 0 Cor = ~ 0.5 Bulik-Sullivan 2015

48 Many traits share a genetic basis! Bulik-Sullivan et al., 2015 Nature Genetics

49 LD Score isn t alone Bulik-Sullivan et al 2015 Nature Genetics

50 <break>

51 Part III: Epigenetic enrichment of GWAS

52 Epigenetics Encode Project Consortium 2012 Nature

53 What makes cells so different? NIH Roadmap Website

54 Epigenetic plots Buenrostro et al 2013 Nature Methods

55 Meyer and Liu 2014 Nature Reviews Genetics

56 Roadmap Project Roadmap Consortium 2015 Nature

57 Finding causal tissues for GWAS Intersecting with epigenetic annotations can find causal variants Intersecting GWAS with epigenetics can also find important tissues

58 Finding important tissue Encode Consortium 2012 Nature

59 Where is schizophrenia risk important? Boyle et al Cell

60 Stratified LD Score Regression Regular LD Score Regression: Stratified LD Score Regression (sldsc): Finucane 2014 AJHG

61 Stratifying the genome Encode Consortium 2012 Nature

62 Where is heritability localized? Finucane et al 2015 Nature Genetics

63 What cell types are important? Finucane et al 2015 Nature Genetics

64 LD Score Regression can 1. Accurately distinguish polygenicity over confounding 2. Estimate heritability from summary statistics 3. Identify traits that share a genetic basis 4. Identify cell types important for traits all of which you need to discuss in your project so ask questions!

65 Part IV: Improving precision of epigenetic enrichments

66 In collaboration with Jacob Ulirsch Harvard BBS Program Martin Aryee, PhD Massachusetts General Hospital Erik Bao Harvard Medical School Jason Buenrostro, PhD Broad Institute Vijay Sankaran, MD, PhD Boston Children s Hospital

67 LD Score Regression gets us in the right zip code Finucane et al 2017 Nature Genetics

68 Accessibility peaks are not the same!

69 Main Question: Can we develop a methodology that accurately identifies the causal tissue for GWAS traits? Can we apply this approach to single cells?

70 Human Hematopoiesis

71 New method: gchromvar 1. Use quantitative genetic information about the core gene associations 2. Use quantitative epigenetic information about chromatin locations

72 Human hematopoietic traits are heritable h 2

73 sldsc vs. gchromvar reticulocyte count (-log 10 p-value)

74 New method: gchromvar 1. Use quantitative genetic information about the core gene associations 2. Use quantitative epigenetic information about chromatin locations

75 gchromvar Results

76 Can we apply gchromvar to single cells?

77 Single Cell ATAC ~2,200 cells assayed

78 scatac + gchromvar

79 Pseudotime

80 Platelet count single cell GWAS Enrichment

81 Ongoing efforts Pinpoint the precise cell types and stage of development where GWAS seems to matter most for a trait Our approach, gchromvar, is more sensitive at distinguishing enrichments in closelyrelated cell types.

82 More information EPI511 Offered Spring of 2019 Supplemental reading on the course webpage Homework 5, final projects will require running and interpreting LD Score Regression

83 Thanks!