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Lecture 15 Regulatory variation and eQTLs

6.047/6.878/HST.507 Computational Biology: Genomes, Networks, Evolution. Lecture 15 Regulatory variation and eQTLs. Chris Cotsapas cotsapas@broadinstitute.org. Module 4: Population / Evolution / Phylogeny. L15/16: Association mapping for disease and molecular traits

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Lecture 15 Regulatory variation and eQTLs

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

  2. 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

  3. Today: Regulatory variation and eQTLs • 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 • Finding trans-eQTLs (distal from gene that varies) • Challenges: Power, structure, sample size • Cross-phenotype analysis: trans QTLs affect many genes • Identifying underlying regulatory mechanisms • Cis-eQTLs: TSS-distance, cell type specificity • eQTLs vs. GWAS: Expression as intermediate trait • Population differences, emerging efforts • Shared associations, SNP-gene pairs, allelic direction • Confound: environment, preparation, batch, ancestry

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

  5. 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

  6. History, eQTL, mQTL, others Basic Concepts

  7. Within a population • Damervalet 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!

  8. Usual mapping tools available • Discretization approach

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

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

  11. yeast, mouse, maize, human eqtl – the array era

  12. 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

  13. transhotspots Brem et al Science 2002

  14. Yvert et al Nat Genet 2003

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

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

  17. Mammals III • No major trans loci in humans • Cheung et al Nature2003 • Monks et al AJHG 2004 • Stranger et alPLoS Genet 2005, Science 2007

  18. Today: Regulatory variation and eQTLs • 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 • Finding trans-eQTLs (distal from gene that varies) • Challenges: Power, structure, sample size • Cross-phenotype analysis: trans QTLs affect many genes • Identifying underlying regulatory mechanisms • Cis-eQTLs: TSS-distance, cell type specificity • eQTLs vs. GWAS: Expression as intermediate trait • Population differences, emerging efforts • Shared associations, SNP-gene pairs, allelic direction • Confound: environment, preparation, batch, ancestry

  19. Open question Where are the trans eQTLS?

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

  21. 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

  22. But… • Assume that transeQTLs affect many genes… • …and you can use cross-trait methods!

  23. Association data

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

  25. CPMA detects trans mixtures

  26. 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?

  27. Today: Regulatory variation and eQTLs • 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 • Finding trans-eQTLs (distal from gene that varies) • Challenges: Power, structure, sample size • Cross-phenotype analysis: trans QTLs affect many genes • Identifying underlying regulatory mechanisms • Cis-eQTLs: TSS-distance, cell type specificity • eQTLs vs. GWAS: Expression as intermediate trait • Population differences, emerging efforts • Shared associations, SNP-gene pairs, allelic direction • Confound: environment, preparation, batch, ancestry

  28. Can we learn regulatory variation from eQTL?

  29. 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 ciseQTLs perturb promoter elements? • Do trans perturb TFs? Signaling cascades?

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

  31. 69-80% of cis associations are cell type-specific Cell type-specific and cell type-shared gene associations (0.001 permutation threshold) 262 268 271 82 73 85 86 86 86 No. of cell types with gene association cell type • 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 Dimas et alScience 2009

  32. Open research questions • Do ciseQTLs 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?

  33. Candidate genes, perturbations underlying organismal phenotypes Application to GWAS

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

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

  36. Barrett et al 2008 de Jageret al 2007

  37. Frankeet al 2010 Anderson et al 2011

  38. Today: Regulatory variation and eQTLs • 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 • Finding trans-eQTLs (distal from gene that varies) • Challenges: Power, structure, sample size • Cross-phenotype analysis: trans QTLs affect many genes • Identifying underlying regulatory mechanisms • Cis-eQTLs: TSS-distance, cell type specificity • eQTLs vs. GWAS: Expression as intermediate trait • Population differences, emerging efforts • Shared associations, SNP-gene pairs, allelic direction • Confound: environment, preparation, batch, ancestry

  39. Population differences

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

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

  42. Direction of allelic effectsame SNP-gene combination across populations Population 1 Population 2 AGREEMENT log2 expression log2 expression OPPOSITE log2 expression log2 expression Stranger et al., PLoS Gen 2012

  43. Slide courtesy Alkes Price

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