Heritability enrichment of differentially expressed genes. Hilary Finucane PGC Statistical Analysis Call January 26, 2016
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1 Heritability enrichment of differentially expressed genes Hilary Finucane PGC Statistical Analysis Call January 26,
2 Functional genomics + GWAS gives insight into disease relevant tissues Trynka et al Nature Genetics Farh et al Nature See also Maurano et al Science, Pickrell 2014 AJHG, Kichaev et al PLoS Genet. 2
3 LD score regression uses the whole genome, improving power for polygenic traits. Finucane*, Bulik Sullivan*, et al Nature Genetics 3
4 Gene expression + GWAS gives insight into disease relevant tissues Observed log 10 (p) Bipolar disorder Crohn s disease Type 1 diabetes Hu et al AJHG Observed log 10 (p) Observed log 10 (p) Type 2 diabetes Expected log 10 (p) Coronary artery disease Expected log 10 (p) Expected log 10 (p) 5 Observed log 10 (p) Whole blood Muscle Lung Nerve, tibial Thyroid Rheumatoid arthritis Hypertension Expected log 10 (p) Adipose Artery, tibial Heart Skin GTEx Consortium, 2015 Science 4
5 Our goal: combine ENCODE/Roadmap data with gene expression data to identify disease relevant tissues By combining regulatory marks with gene expression data, we should be able to: Replicate results in orthogonal data Increase power Using LD score regression will increase power for very polygenic traits. First step: how much signal is there in GTEx, and how can we get it out? 5
6 Outline Review of stratified LD score regression. Differentially expressed genes in GTEx. Validation: Brain v Blood in SCZ and RA. Results in 7 Brain related traits and RA. 6
7 Outline Review of stratified LD score regression. Differentially expressed genes in GTEx. Validation: Brain v Blood in SCZ and RA. Results in 7 Brain related traits and RA. 7
8 Stratified LD score regression Our model is Where Y is an individual s phenotype, X j is an individual s genotype at the j th SNP (normalized to mean 0 and variance 1), β j is the effect of SNP j, and ε is noise and random environmental effects. Finucane*, Bulik Sullivan* et al Nat Genet 8
9 Stratified LD score regression We model SNP effects as random and mean 0. For disjoint categories, where Intuition: LD to a category that is enriched for heritability will increase the chi square statistic of a SNP more than LD to other categories. Finucane*, Bulik Sullivan* et al Nat Genet 9
10 Stratified LD score regression with overlapping categories Var(β j ) = Expected per SNP heritability of SNP j. With disjoint categories, if SNP j is in category C, With overlapping categories, i.e., τ c is the contribution of category C to per SNP heritability of SNPs in category C, after controlling for all other categories in the model. Finucane*, Bulik Sullivan* et al Nat Genet 10
11 Stratified LD score regression with overlapping categories With overlapping categories, Stratified LD score regression: Estimate LD scores from a reference panel with matching LD. Perform (weighted) regression of chi square on LD scores to estimate the Optional: convert back to heritability. Finucane*, Bulik Sullivan* et al Nat Genet 11
12 Stratified LD score regression to identify disease relevant tissues Have a baseline model with many annotations (exon, DHS, H3K27ac, weak enhancer, etc.) Add tissue specific annotation; e.g. H3K27ac in Liver. Test for positive for this annotation. Finucane*, Bulik Sullivan* et al Nat Genet 12
13 Stratified LD score regression identifies disease relevant tissues Results with Roadmap/ENCODE data: To apply stratified LD score regression to GTEx, have to define new tissue specific annotations. Finucane*, Bulik Sullivan* et al Nat Genet 13
14 Outline Review of stratified LD score regression. Differentially expressed genes in GTEx. Validation: Brain v Blood in SCZ and RA. Results in 7 Brain related traits and RA. 14
15 GTEx tissues Brain Amygdala Brain Anterior cingulate cortex (BA24) Brain Caudate (basal ganglia) Brain Cerebellar Hemisphere Brain Cerebellum Brain Cortex Brain Frontal Cortex (BA9) Brain Hippocampus Brain Hypothalamus Brain Nucleus accumbens (basal ganglia) Brain Putamen (basal ganglia) Brain Spinal cord (cervical c 1) Brain Substantia nigra Adipose Subcutaneous Adipose Visceral (Omentum) Adrenal Gland Artery Aorta Artery Coronary Artery Tibial Breast Mammary Tissue Cells EBV transformed lymphocytes Cells Leukemia cell line (CML) Cells Transformed fibroblasts Colon Transverse Esophagus Mucosa Esophagus Muscularis Fallopian Tube Heart Atrial Appendage Heart Left Ventricle Kidney Cortex Liver Lung Muscle Skeletal Nerve Tibial Ovary Pancreas Pituitary Prostate Skin Not Sun Exposed (Suprapubic) Skin Sun Exposed (Lower leg) Stomach Testis Thyroid Uterus Vagina Whole Blood 46 tissues total Average of 66 samples per tissue min = 1 sample max = 607 samples Of these, 13 are brain tissues Average of 42 samples per tissue min = 28 samples max = 60 samples 15
16 Defining the annotation For each tissue: Rank genes by differential expression. Choose the top X genes in this ranking. Include these genes, with a window of Y kb. 16
17 Defining the annotation For each tissue: Rank genes by differential expression. Choose the top X genes in this ranking. Include these genes, with a window of Y kb. Choices: How to rank? What should X (# genes) be? What should Y (window size around gene) be? 17
18 Defining the annotation For each tissue: Rank genes by differential expression. Choose the top X genes in this ranking. Include these genes, with a window of Y kb. Choices: How to rank? What should X (# genes) be? What should Y (window size around gene) be? 18
19 We rank genes using HKNorm and a t test for differential expression. We normalize RPKM using the HKNorm algorithm [Byrnes et al. in prep]. For each gene, we compute a t statistic for whether HKNorm expression levels are different for samples in the tissue vs. not in the tissue. Rank by the t statistic. 19
20 Defining the annotation For each tissue: Rank genes by differential expression. Choose the top X genes in this ranking. Include these genes, with a window of Y kb. Choices: How to rank? What should X (# genes) be? What should Y (window size around gene) be? 20
21 Choosing #genes and window size. How many genes to choose? We try top 2%, 5%, 10% of all genes. After QC, this gives us 369, 8921, and 1842 genes, respectively. What window size? We try 20kb and 100kb from transcribed region. For all genes, this gives us 45% and 58% of the genome, respectively. In total, we try 3 x 2 = 6 parameter settings. Note: For LD score, want an annotation that covers at least roughly 1% of the genome. 21
22 Outline Review of stratified LD score regression. Differentially expressed genes in GTEx. Validation: Brain v Blood in SCZ and RA. Results in 7 Brain related traits and RA. 22
23 Brain annotation Brain = union of 13 brain tissues GO enrichment (10% set): Blood annotation Blood = Whole blood and LCLs GO enrichment (10% set): 23
24 Brain v Blood, RA v SCZ: should use 10% of genes with a 100kb window 24
25 Outline Review of stratified LD score regression. Differentially expressed genes in GTEx. Validation: Brain v Blood in SCZ and RA. Results in 7 Brain related traits and RA. 25
26 Brain vs. Blood in many traits also identifies Brain for Bipolar Disorder 26
27 Schizophrenia 27
28 Bipolar Disorder 28
29 BMI 29
30 Age at menarche 30
31 Rheumatoid Arthritis 31
32 Brain vs Blood in many traits also identifies Brain for Bipolar Disorder 32
33 Previous anlaysis with histone marks has higher power 33
34 Gene sets derived from histone marks have similar power to gene sets derived from GTEx. 34
35 Future directions Improve power by combining signal from multiple tissue relevant annotations (histone marks, gene sets, etc.). Still method in progress. One idea: for a single tissue, add histone marks, gene expression, etc. to the model together. Test for enrichment using the sum of the coefficients. (Or some other linear combination.) Difficulty: need data on the same cell types, or to group cell types together. 35
36 Acknowledgements Alkes Price Ben Neale Andrea Byrnes Verneri Anttila Samuela Pollack Yakir Reshef The GTEx Consortium 36
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