1. Lecture 15 Regulatory variation and eQTLs Chris Cotsapas cotsapas@broadinstitute.org 6.047/6.878/HST.507 Computational Biology: Genomes, Networks, Evolution

3. Module 4: Population / Evolution / Phylogeny L15/16: Association mapping for disease and molecular traits –Statistical genetics: disease mapping in populations (Mark Daly) –Quantitative traits and molecular variation: eQTLs, cQTLs L17/18: Phylogenetics / Phylogenomics –Phylogenetics: Evolutionary models, Tree building, Phylo inference –Phylogenomics: gene/species trees, coalescent models, populations L19/20: Human history, Missing heritability –Measuring natural selection in human populations –The missing heritability in genome-wide associations And done! Last pset Nov 11 (no lab), In-class quiz on Nov 20 –No lab 4! Then entire focus shifts to projects, Thanksgiving, Frontiers

4. Today: Regulatory variation and eQTLs 1.Quantitative Trait Loci (QTLs), Regulatory Variation – Molecular phenotypes as QTs: expression, chromatin… – Discretization: a GWAS for each gene. Cis-/Trans-eQTLs – Underlying regulatory variation: eQTLs, GWAS, cis-eQTL 2.Finding trans-eQTLs (distal from gene that varies) – Challenges: Power, structure, sample size – Cross-phenotype analysis: trans QTLs affect many genes 3.Identifying underlying regulatory mechanisms – Cis-eQTLs: TSS-distance, cell type specificity – eQTLs vs. GWAS: Expression as intermediate trait 4.Population differences, emerging efforts – Shared associations, SNP-gene pairs, allelic direction – Confound: environment, preparation, batch, ancestry

5. Quantitative traits - weight, height - anything measurable - today: gene expression QTLs (QT Loci) - The loci that control quantitative traits

6. Regulatory variation What do trait-associated variants do? Genetic changes to: – Coding sequence ** – Gene expression levels – Splice isomer levels – Methylation patterns – Chromatin accessibility – Transcription factor binding kinetics – Cell signaling – Protein-protein interactions Regulatory

7. BASIC CONCEPTS History, eQTL, mQTL, others

9. Within a population Damerval et al 1994 42/72 protein levels differ in maize 2D electrophoresis, eyeball spot quantitation Problems: – genome coverage – quantitation – post-translational modifications Solution: use expression levels instead!

10. Usual mapping tools available Discretization approach

11. gene 3 Whole-genome eQTL analysis is an independent GWAS for expression of each gene gene 2gene N gene 5 gene 4 gene 1

12. cis-eQTL –The position of the eQTL maps near the physical position of the gene. –Promoter polymorphism? –Insertion/Deletion? –Methylation, chromatin conformation? trans-eQTL –The position of the eQTL does not map near the physical position of the gene. –Regulator? –Direct or indirect? Modified from Cheung and Spielman 2009 Nat Gen Genetics of gene expression (eQTL)

16. eQTL – THE ARRAY ERA yeast, mouse, maize, human

17. Yeast Brem et al Science 2002 Linkage in 40 offspring of lab x wild strain cross 1528/6215 DE between parents 570 map in cross – multiple QTLs – 32% of 570 have cis linkage 262 not DE in parents also map

18. trans hotspots Brem et al Science 2002

19. Yvert et al Nat Genet 2003

20. Mammals I F2 mice on atherogenic diet Expression arrays; WG linkage Schadt et al Nature 2003

21. Mammals II Chesler et al Nat Genet 2005 10% !!

22. Mammals III No major trans loci in humans – Cheung et al Nature 2003 – Monks et al AJHG 2004 – Stranger et al PLoS Genet 2005, Science 2007

23. Today: Regulatory variation and eQTLs 1.Quantitative Trait Loci (QTLs), Regulatory Variation – Molecular phenotypes as QTs: expression, chromatin… – Discretization: a GWAS for each gene. Cis-/Trans-eQTLs – Underlying regulatory variation: eQTLs, GWAS, cis-eQTL 2.Finding trans-eQTLs (distal from gene that varies) – Challenges: Power, structure, sample size – Cross-phenotype analysis: trans QTLs affect many genes 3.Identifying underlying regulatory mechanisms – Cis-eQTLs: TSS-distance, cell type specificity – eQTLs vs. GWAS: Expression as intermediate trait 4.Population differences, emerging efforts – Shared associations, SNP-gene pairs, allelic direction – Confound: environment, preparation, batch, ancestry

24. WHERE ARE THE TRANS eQTLS? Open question

25. gene 3 Whole-genome eQTL analysis is an independent GWAS for expression of each gene gene 2gene N gene 5 gene 4 gene 1

26. Issues with trans mapping Power – Genome-wide significance is 5e -8 – Multiple testing on ~20K genes – Sample sizes clearly inadequate Data structure – Bias corrections deflate variance – Non-normal distributions Sample sizes – Far too small

27. But… Assume that trans eQTLs affect many genes… …and you can use cross-trait methods!

28. Association data Z 1,1 Z 1,2 ……Z 1,p Z 2,1 : : Z s,1 Z s,p

29. Cross-phenotype meta-analysis S CPMA ~ L(data | λ≠1) L(data | λ=1) Cotsapas et al, PLoS Genetics

30. CPMA detects trans mixtures

31. Open research questions Do trans effects exist? – Yes – heritability estimates suggest so. – Can we detect them? Larger cohorts? – Most eQTL studies ~50-500 individuals – See later, GTEx Project Better methods? – Collapsing data? – PCA, summary statistics, modeling?

32. Today: Regulatory variation and eQTLs 1.Quantitative Trait Loci (QTLs), Regulatory Variation – Molecular phenotypes as QTs: expression, chromatin… – Discretization: a GWAS for each gene. Cis-/Trans-eQTLs – Underlying regulatory variation: eQTLs, GWAS, cis-eQTL 2.Finding trans-eQTLs (distal from gene that varies) – Challenges: Power, structure, sample size – Cross-phenotype analysis: trans QTLs affect many genes 3.Identifying underlying regulatory mechanisms – Cis-eQTLs: TSS-distance, cell type specificity – eQTLs vs. GWAS: Expression as intermediate trait 4.Population differences, emerging efforts – Shared associations, SNP-gene pairs, allelic direction – Confound: environment, preparation, batch, ancestry

33. CAN WE LEARN REGULATORY VARIATION FROM eQTL?

34. First, let’s define the question Can we use genetic perturbations as a way to understand how genes are regulated? In what groups, in which tissues? To what stimuli/signaling events? Do cis eQTLs perturb promoter elements? Do trans perturb TFs? Signaling cascades?

35. Most significant SNP per gene 0.001 permutation threshold Significant associations are symmetrically distributed around TSS Stranger et al., PLoS Gen 2012

36. 268271 262 7385 82 86 Cell type-specific and cell type-shared gene associations (0.001 permutation threshold) cell type No. of cell types with gene association 69-80% of cis associations are cell type-specific cis association sharing increases slightly when significance thresholds are relaxed Cell type specificity verified experimentally for subset of eQTLs Dimas et al Science 2009 Slide courtesy Antigone Dimas

37. Open research questions Do cis eQTLs perturb functional elements? – Given each is independent, how can we know? Do tissue-specific effects correlate with the expression of a gene across tissues? Or a regulator? – Perhaps a gene is expressed, but in response to different regulators across tissues? If we ever find trans eQTLs… – Common regulators of coregulated genes? – Tissue specificity? – Mechanisms?

38. APPLICATION TO GWAS Candidate genes, perturbations underlying organismal phenotypes

39. eQTLs as intermediate traits Schadt et al Nat Genet 2005

40. Modified from Nica and Dermitzakis Hum Mol Genet 2008 Exploring eQTLs in the relevant cell type is important for disease association studies cell type not relevant for disease relevant cell type for disease Importance of cataloguing regulatory variation in multiple cell types Slide courtesy Antigone Dimas

41. Barrett et al 2008 de Jager et al 2007

44. Franke et al 2010 Anderson et al 2011

45. Today: Regulatory variation and eQTLs 1.Quantitative Trait Loci (QTLs), Regulatory Variation – Molecular phenotypes as QTs: expression, chromatin… – Discretization: a GWAS for each gene. Cis-/Trans-eQTLs – Underlying regulatory variation: eQTLs, GWAS, cis-eQTL 2.Finding trans-eQTLs (distal from gene that varies) – Challenges: Power, structure, sample size – Cross-phenotype analysis: trans QTLs affect many genes 3.Identifying underlying regulatory mechanisms – Cis-eQTLs: TSS-distance, cell type specificity – eQTLs vs. GWAS: Expression as intermediate trait 4.Population differences, emerging efforts – Shared associations, SNP-gene pairs, allelic direction – Confound: environment, preparation, batch, ancestry

46. POPULATION DIFFERENCES

47. Shared association in 8 HapMap populations APOH: apolipoprotein H Stranger et al., PLoS Gen 2012

48. Number of genes with cis-eQTL associations 8 extended HapMap populations SRC: permutation threshold Stranger et al., PLoS Gen 2012

49. Direction of allelic effect same SNP-gene combination across populations AGREEMENT OPPOSITE Population 1Population 2 log 2 expression Stranger et al., PLoS Gen 2012

50. Slide courtesy Alkes Price

51. Population differences could have non-genetic basis Differences due to environment? (Idaghdour et al. 2008)‏ Differences in cell line preparation? (Stranger et al. 2007)‏ Differences due to batch effects? (Akey et al. 2007)‏ (Reviewed in Gilad et al. 2008)‏ Slide courtesy Alkes Price

52. Gene expression experiment Does gene expression in 60 CEU + 60 YRI vary with ancestry? Does gene expression in 89 AA vary with % Eur ancestry? 60 CEU + 60 YRI from HapMap, 89 AA from Coriell HD100AA Gene expression measurements at 4,197 genes obtained using Affymetrix Focus array c Slide courtesy Alkes Price

53. Gene expression differences in African Americans validate CEU-YRI differences c = 0.43 (± 0.02)‏ (P-value < 10 -25 )‏ 12% ± 3% in cis Slide courtesy Alkes Price

54. EMERGING EFFORTS RNAseq, GTEx

55. RNAseq questions Standard eQTLs – Montgomery et al, Pickrell et al Nature 2010 Isoform eQTLs – Depth of sequence! Long genes are preferentially sequenced Abundant genes/isoforms ditto Power!? Mapping biases due to SNPs

56. Strategies for transcript assembly Garber et al. Nat Methods 8:469 (2011)

57. GTEx – Genotype-Tissue EXpression An NIH common fund project Current: 35 tissues from 50 donors Scale up: 20K tissues from 900 donors. Novel methods groups: 5 current + RFA

58. RNAseq combined with other techs Regulons: TF gene sets via CHiP/seq – Look for trans effects Open chromatin states (Dnase I; methylation) – Find active genes – Changes in epigenetic marks correlated to RNA – Genetic effects RNA/DNA comparisons – Simultaneous SNP detection/genotyping – RNA editing ???

59. Today: Regulatory variation and eQTLs 1.Quantitative Trait Loci (QTLs), Regulatory Variation – Molecular phenotypes as QTs: expression, chromatin… – Discretization: a GWAS for each gene. Cis-/Trans-eQTLs – Underlying regulatory variation: eQTLs, GWAS, cis-eQTL 2.Finding trans-eQTLs (distal from gene that varies) – Challenges: Power, structure, sample size – Cross-phenotype analysis: trans QTLs affect many genes 3.Identifying underlying regulatory mechanisms – Cis-eQTLs: TSS-distance, cell type specificity – eQTLs vs. GWAS: Expression as intermediate trait 4.Population differences, emerging efforts – Shared associations, SNP-gene pairs, allelic direction – Confound: environment, preparation, batch, ancestry