Causes of regulatory variation in the human genome Manolis Dermitzakis The Wellcome Trust Sanger Institute Wellcome Trust Genome Campus Cambridge, UK md4@sanger.ac.uk

Human Genome: ~25,000 genes 1-1.5% of the human DNA is coding Is the remaining 98.5% “junk”

Gene expression as a phenotype Altered patterns of gene expression  disease. e.g., Type 1 diabetes, Burkitt’s lymphomas. Widespread intraspecific variation. Heritable genetic variation for transcript levels. Familial aggregation of expression profiles (Cheung et al. 2003). In humans, ~30% of surveyed loci exhibited a genetic component for expression differences (Monks et al. 2004; Schadt et al. 2003). Much of the influential variation is located cis- to the coding locus. In humans, mouse, and maize, 35%-50% of the genetic basis for intraspecific differences in transcription level are cis- to the coding locus (e.g. Morley et al. 2004; Schadt et al. 2003; Stranger et al. 2005; Cheung et al. 2005, etc.). As an introduction, I’d like to give you a couple of quick facts about gene expression: In general, normal cell function and many aspects of development are highly dependent on having the right genes transcribed at the right time and place…and certainly some diseases are associated with altered patterns of gene expression. Extreme effects or subtle changes in expression. However, in many species there is also quite a lot of variation among individuals with respect to gene expression patterns. Much of this variation has a genetic component, for example in humans, nearly 30% of surveyed loci exhibited a genetic component for expression differences. And more studies are showing that much of the genetic component influencing expression is located cis- to the coding locus, for example a survey of humans mouse and maize estimates that approx 30-50% is attributable to cis-located variants Stranger and Dermitzakis 2006

Why study gene expression Describe and dissect regulatory variation Annotate regulatory elements in the human genome Support disease studies to interpret statistical signals Distribution of molecular effects in the genome Natural selection

Outline Gene expression variation – recent studies Analysis of gene expression with HapMap phase II SNPs Update on CNV-expression associations Natural selection and cis regulatory effects

Nature of regulatory variation DNA REG GENE i) Pre-mRNA ii) mRNA iii) Protein iv) DNA Expression Stranger and Dermitzakis, Human Genomics 2005

Effects of Copy Number Variation on gene expression Copy number variation kb to Mb size variable DNA copy number contribute to disease heritable common in humans/recently appreciated

Gene expression association mapping AA AG GG Quantitative phenotype Stranger et al. PLoS Genet 2005

Whole-genome gene expression ~48,000 transcripts 24,000 RefSeq 24,000 other transcripts 270 HapMap individuals: CEU: 30 trios, 90 total CHB: 45 unrelated JPT: 45 unrelated YRI: 30 trios, 90 total 2 IVTs each person 2 replicate hybridizations each IVT Quantile normalization of all replicates of each individual. Median normalization across all individuals of a population. illumina Human 6 x 2 gene GEX arrays Cell line RNA IVT1 IVT2 rep1 rep2 rep3 rep4

HapMap SNPs 60 CEU 45 CHB 44 JPT 60 YRI 14,072 genes Phase I HapMap; MAF > 0.05 CEU: 762,447 SNPs CHB: 695,601 JPT: 689,295 YRI: 799,242 ~1/5kb The number of expression phenotypes is not a direct correlation to the number of genes in these regions because there were 2 probes per gene

Copy Number Variation dataset Genome Structural Variation Consortium Redon et al. Nature Nov 22, 2006 Array-CGH using a whole genome tile path array Median clone size ~170 kb All 270 HapMap individuals Quantitative values (log2 ratios) representing diploid genome copy number, not genotypes. 1117 CNVs called from log2 ratios Calls based on standard deviation of log2 ratios Many CNVs experimentally verified 26,563 clones 93.7% euchromatic genome

Linear regression for SNPs CNV and expression Clone signal (log2 ratio)

SNP cis-analysis: SNPs within 1Mb of probe midpoint 2Mb window probe gene SNPs

CNV cis-analysis: clone midpoint within 2Mb of probe midpoint 4Mb window probe gene clones

Permutation GENOTYPES GENE EXPRESSION g11 g12 g13 g14 … g1n gi1 gi2 gi3 gi4 … gin Exp1 Exp2 Exp3 … Expi permute - 10,000 permutations – each time keep lowest p-value - Null distribution of 10,000 extreme p-values - Compare observed p-values to the tails of the null Doerge and Churchill 1996

CNV vs. SNP associations Stranger et al. Science 2007

CNVs and SNPs mostly capture different effects Relative impact on gene expression: 82% SNPs 18% CNVs Only 13% of genes with CNV association also had a SNP association in the same population biased toward large effect size. CNV and SNP variation are highly correlated (p-value 0.001).

2 batches of 60 CEU individuals Custom vs. Genome-wide [Stranger et al. 2005 PLoS Genet and Stranger et al. 2007 Science] 2 batches of 60 CEU individuals grown independently at two different labs RNA extraction and labelling by different labs and people Run in custom and gw illumina arrays 97% of associations at the 0.05 permutation threshold from the custom array analysis were also detected in gw analysis

HapMap phase II analysis ~ 4 million SNP genotypes made publicly available for the 270 HapMap individuals. Density: 1 SNP/ 700 bps Includes ~50% of expected common SNPs in these populations. 2.2 million SNPs analyzed (MAF>0.05)

Phase I vs. Phase II cis- significant genes (0.001) phase I HapMap both phase II HapMap CEU 286 258 299 CHB 317 269 318 JPT 337 297 341 YRI 356 310 394 90% 86% 85% 85% 87% 87% 87% 79%

Phase I vs. Phase II

Population sharing of cis- associations

Associated SNP position relative to TSS

Distribution of regulatory elements around the TSS ENCODE Nature 2007

Direction of allelic effect same SNP-gene combination across populations AGREEMENT log2 expression log2 expression OPPOSITE log2 expression log2 expression

Direction of allelic effect

Pooling populations Spurious associations Pop1 Pop2 Pop1 Pop2

Conditional permutations Permute data within each pop separately then perform test X 4

Multi-population analysis

Figure 2A Proportion of single pop cis associated genes detected in multi-population analysis Number of populations sharing association in cis: single population analysis

SGPP2

Trans- phase II HapMap association Biological hypotheses: functional categories Regulatory SNPs identified from cis- analysis (52%) Non-synonymous SNPs (39%) Splice site SNPs (7%) miRNA SNPs (1%) DNA REG GENE rSNPs nsSNPs spliceSNPs miRNA SNPs Genome-wide associations Network analysis GENE ~ 25,000 SNPs per population x 14,072 genes

Trans- associations 10-3 threshold correction at 0.001 15 genes estimated false positives FDR = 33%-39% correction at 0.01 150 genes estimated false positives FDR = 60%-75% 14,072 genes tested

Enrichment of regulatory SNPs and deficit of nsSNPs in trans- associations regulatory SNPs (cis 0.001) ns SNPs splice SNPs miRNA SNPs ratio p-value Ratio CEU 6.05 3.23E-24 0.15 1.22E-21 0.49 0.07 1 CHB 3.69 7.90E-10 0.24 1.91E-09 0.76 0.71 JPT 3.15 2.06E-07 0.31 8.82E-07 0.55 ! 3-6x more likely that a cis regulatory effect explains a trans regulatory effect

Multi-pop CNV analysis Combined 4 populations: 193 genes at 0.001 (48 overlap with the 99 from single population analysis) Combined 3 populations: 173 genes at 0.001 (42 overlap with the 99 from single population analysis)

CNV trans effects Variable expression Biological pathway Copy number variation kb to Mb size variable DNA copy number contribute to disease heritable common in humans/recently appreciated Biological pathway

Trans-position Copy number variation kb to Mb size variable DNA copy number contribute to disease heritable common in humans/recently appreciated

Trans effects - CEU

Trans effects - YRI

Gene expression and natural selection -logpval TSS TSS With Sridhar Kudaravalli and Jonathan Pritchard (unpublished)

Gene expression and natural selection With Sridhar Kudaravalli and Jonathan Pritchard (unpublished)

Co-segregating regulatory variants can drive differential isoform expression

SUMMARY Cis- and trans- acting genetic variation influencing mRNA levels. CNV effects detected are largely not captured by SNPs Structural variation (copy number polymorphism) influences transcript level variation. Many detected associations are shared across human populations – replication of effects Signal concentrated within 100 Kb from the promoter symmetrically Trans-acting effects of CNVs - interpretation Primary effects of trans associations are largely cis regulatory effects Cis regulatory effects under positive selection

Acknowledgements Cambridge University Stanford illumina Mark Dunning Natalie Thorne Simon Tavaré Barbara Stranger Alexandra Nica Antigone Dimas Christine Bird Matthew Forrest Catherine Ingle Claude Beazley Panos Deloukas Matt Hurles Stanford Daphne Koller illumina Jill Orwick Mark Gibbs Genome Structural Variation Consortium Richard Redon, Nigel Carter, Charles Lee, Chris Tyler-Smith, Stephen Scherer, The HapMap Consortium Wellcome Trust for funding