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Hae Kyung Im, PhD

Functional Genomics to Inform GWAS - QTL Analysis

April 20, 2022

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4911 distinct loci associated with cancer traits are available as of 4/20/2022

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The Perils of Polygenicity

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Modified from https://twitter.com/DPosthu/status/1328662943739867137

Cancer Biology

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Most GWAS catalog variants are non-coding

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Altered Protein Levels Influence Disease Risk

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Albert & Kruglyak 2015 NGReviews

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Total mRNA and Splicing Affect Complex Traits

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2016

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17,382 tissue samples
54 tissues
838 donors
mRNA-seq
WGS 30x

Goals

characterize genetic effects on the transcriptome across human tissues
link these regulatory mechanisms to trait and disease associations

The GTEx Consortium, 2020, Science 369:1318-1330

https://www.science.org/doi/10.1126/science.aaz1776

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The GTEx Consortium, 2020, Science 369:1318-1330

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Expression Quantitative Trait Loci

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https://gtexportal.org/home/locusBrowserPage/ACTN3

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Expression and Splicing QTL as Function of Sample Size

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Allelic Heterogeneity

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The GTEx Consortium, 2020, Science 369:1318-1330

QTL discovery. (A) The number of genes with a cis-eQTL (eGenes) or cis-sQTL (sGenes) per tissue, as a function of sample size. See Fig. 1A for the legend of tissue colors. (B) Allelic heterogeneity of cis-eQTLs depicted as proportion of eGenes with one or more independent cis-eQTLs (blue stacked bars; left y axis) and as a mean number of cis-eQTLs per gene (red dots; right y axis). The tissues are ordered by sample size. (C) The number of genes with a trans-eQTL as a function of the number of cis-eGenes. (D) Sex-biased cis-eQTL for AURKA in skeletal muscle, where rs2273535-T is associated with increased AURKA expression in males (P = 9.02 × 10−27) but not in females (P = 0.75). (E) Population-biased cis-eQTL for SLC44A5 in esophagus mucosa [aFC = −2.85 and −4.82 and in African Americans (AA) and European Americans (EA), respectively; permutation P value = 1.2 × 10−3]. TPM, transcripts per million.

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Trans-QTLs

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The GTEx Consortium, 2020, Science 369:1318-1330

QTL discovery. (A) The number of genes with a cis-eQTL (eGenes) or cis-sQTL (sGenes) per tissue, as a function of sample size. See Fig. 1A for the legend of tissue colors. (B) Allelic heterogeneity of cis-eQTLs depicted as proportion of eGenes with one or more independent cis-eQTLs (blue stacked bars; left y axis) and as a mean number of cis-eQTLs per gene (red dots; right y axis). The tissues are ordered by sample size. (C) The number of genes with a trans-eQTL as a function of the number of cis-eGenes. (D) Sex-biased cis-eQTL for AURKA in skeletal muscle, where rs2273535-T is associated with increased AURKA expression in males (P = 9.02 × 10−27) but not in females (P = 0.75). (E) Population-biased cis-eQTL for SLC44A5 in esophagus mucosa [aFC = −2.85 and −4.82 and in African Americans (AA) and European Americans (EA), respectively; permutation P value = 1.2 × 10−3]. TPM, transcripts per million.

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Sex-biased eQTL

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The GTEx Consortium, 2020, Science 369:1318-1330

QTL discovery. (A) The number of genes with a cis-eQTL (eGenes) or cis-sQTL (sGenes) per tissue, as a function of sample size. See Fig. 1A for the legend of tissue colors. (B) Allelic heterogeneity of cis-eQTLs depicted as proportion of eGenes with one or more independent cis-eQTLs (blue stacked bars; left y axis) and as a mean number of cis-eQTLs per gene (red dots; right y axis). The tissues are ordered by sample size. (C) The number of genes with a trans-eQTL as a function of the number of cis-eGenes. (D) Sex-biased cis-eQTL for AURKA in skeletal muscle, where rs2273535-T is associated with increased AURKA expression in males (P = 9.02 × 10−27) but not in females (P = 0.75). (E) Population-biased cis-eQTL for SLC44A5 in esophagus mucosa [aFC = −2.85 and −4.82 and in African Americans (AA) and European Americans (EA), respectively; permutation P value = 1.2 × 10−3]. TPM, transcripts per million.

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Population-biased eQTL

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The GTEx Consortium, 2020, Science 369:1318-1330

QTL discovery. (A) The number of genes with a cis-eQTL (eGenes) or cis-sQTL (sGenes) per tissue, as a function of sample size. See Fig. 1A for the legend of tissue colors. (B) Allelic heterogeneity of cis-eQTLs depicted as proportion of eGenes with one or more independent cis-eQTLs (blue stacked bars; left y axis) and as a mean number of cis-eQTLs per gene (red dots; right y axis). The tissues are ordered by sample size. (C) The number of genes with a trans-eQTL as a function of the number of cis-eGenes. (D) Sex-biased cis-eQTL for AURKA in skeletal muscle, where rs2273535-T is associated with increased AURKA expression in males (P = 9.02 × 10−27) but not in females (P = 0.75). (E) Population-biased cis-eQTL for SLC44A5 in esophagus mucosa [aFC = −2.85 and −4.82 and in African Americans (AA) and European Americans (EA), respectively; permutation P value = 1.2 × 10−3]. TPM, transcripts per million.

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Fine-mapping

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Regional Plot (Locus Zoom)

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Fine Mapping

Fine mapping: searching for the causal variant underlying an observed association
An association observed in GWAS often involves a large number of associated variants in LD. The causal variant is not obvious.
To “fine map” we need association results for ALL variants that could possibly be the causal variant, either by imputation or “fill-in” genotyping
For a given locus, we can build “credible sets” of SNPs that are 95% (or 99% or 99.9%) likely to contain the causal variant (based on Bayes Factors or Likelihoods) assuming a single causal variant (Maller et al, Nat Genet 2012)
or Posterior Probability of Causality (pip=posterior inclusion prob.)

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Posterior Inclusion Probability: P(Causal given Data)

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red circles: p values

bars: posterior prob. causal

yellow bar: causal SNPs

W. Chen et al, “Fine mapping causal variants with an approximate Bayesian method using marginal test statistics,” Genetics, 2015.

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Example Top SNP Not Causal

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SMA: strongest marginal association

SNP1: causal SNP

SNP2: causal SNP

https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12388

Fine mapping example to illustrate that the IBSS (Iterative Bayesian Stepwise Selection) algorithm can deal with a challenging case: the results are from a simulated data set with p=1000 variables (SNPs); some of these variables are very strongly correlated (Fig. A1 in the on‐line appendix); two out of the 1000 variables are effect variables (image, labelled ‘SNP 1’ and ‘SNP 2’ in (a)); we chose this example from our simulations because the strongest marginal association is a non‐effect variable (image, labelled ‘SMA’ in (a)); after one iteration ((b)), IBSS incorrectly identifies a credible set containing the strongest marginal association and no effect variable (image); however, after 10 iterations (and also at convergence) the IBSS algorithm has corrected itself ((c)), finding two 95% credible sets (image, image), each containing a true effect variable; additionally, neither credible set contains the strongest marginal association variable; one credible set (image) contains only three SNPs (purity 0.85), whereas the other credible set (image) contains 37 very highly correlated variables (purity 0.97); in the latter credible set, the individual PIPs are small, but the inclusion of the 37 variables in this credible set indicates, correctly, high confidence in at least one effect variable among them

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Fine Mapping Methods

CAVIAR (Hormozdiari et al, Bioinformatics 2015)
Finemap (Benner et al, Bioinformatics 2016)
fastPAINTOR (Kichaev et al, Bioinformatics 2016)
DAP-G (Wen et al, AJHG 2016)
SUSIER (Wang et al, J. R. Stat. Soc. B, 2020)

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Fine-mapping of cis-eQTLs in GTEx

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The GTEx Consortium, 2020, Science 369:1318-1330

Fine-mapping of cis-eQTLs. (A) Number of eGenes per tissue with variants fine-mapped with >0.5 posterior probability of causality, using three methods. The overall number of eGenes with at least one fine-mapped eVariant increases with sample size for all methods. However, this increase is in part driven by better statistical power to detect small effect size cis-eQTLs (aFC ≤ 1 in log2 scale; see also fig. S14) with larger sample sizes, and the proportion of well fine-mapped eGenes with small effect sizes increases more modestly with sample size (bottom versus top panels), indicating that such cis-eQTLs are generally more difficult to fine-map. (B) Enrichment of variants among experimentally validated regulatory variants, shown for the cis-eVariant with the best P value (top eVariant), and those with posterior probability of causality >0.8 according to each of the three methods individually or all of them (consensus). Error bars: 95% confidence interval (CI). (C) The cis-eQTL signal for CBX8 is fine-mapped to a credible set of three variants (red and purple diamonds), of which rs9896202 (purple diamond) overlaps a large number of transcription factor binding sites in ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) data and disrupts the binding motif of EGR1. (D) The potential role of EGR1 binding driving this cis-eQTL is further supported by correlation between EGR1 expression and the CBX8 cis-eQTL effect size across tissues.

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Fine-mapping EGR1 eQTL

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The GTEx Consortium, 2020, Science 369:1318-1330

Fine-mapping of cis-eQTLs. (A) Number of eGenes per tissue with variants fine-mapped with >0.5 posterior probability of causality, using three methods. The overall number of eGenes with at least one fine-mapped eVariant increases with sample size for all methods. However, this increase is in part driven by better statistical power to detect small effect size cis-eQTLs (aFC ≤ 1 in log2 scale; see also fig. S14) with larger sample sizes, and the proportion of well fine-mapped eGenes with small effect sizes increases more modestly with sample size (bottom versus top panels), indicating that such cis-eQTLs are generally more difficult to fine-map. (B) Enrichment of variants among experimentally validated regulatory variants, shown for the cis-eVariant with the best P value (top eVariant), and those with posterior probability of causality >0.8 according to each of the three methods individually or all of them (consensus). Error bars: 95% confidence interval (CI). (C) The cis-eQTL signal for CBX8 is fine-mapped to a credible set of three variants (red and purple diamonds), of which rs9896202 (purple diamond) overlaps a large number of transcription factor binding sites in ENCODE chromatin immunoprecipitation sequencing (ChIP-seq) data and disrupts the binding motif of EGR1. (D) The potential role of EGR1 binding driving this cis-eQTL is further supported by correlation between EGR1 expression and the CBX8 cis-eQTL effect size across tissues.

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Enrichment of Functional Annotation for cis and trans QTLs

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The GTEx Consortium, 2020, Science 369:1318-1330

Functional mechanisms of genetic regulatory effects. QTL enrichment in functional annotations for (A) cis-eQTLs and cis-sQTLs and for (B) trans-eQTLs. cis-QTL enrichment is shown as mean ± SD across tissues; trans-eQTL enrichment as 95% CI. UTR, untranslated region. (C) Enrichment of lead trans-eVariants or trans-sVariants that have been tested for cis-QTL effects also being significant cis-eVariants or cis-sVariants in the same tissue, respectively. Asterisk denotes significant enrichment, P < 10−21. (D) Proportion of trans-eQTLs that are significant cis-eQTLs or mediated by cis-eQTLs. (E) Trans associations of cis-mediating genes identified through colocalization (PP4 > 0.8 and nominal association with discovery trans-eVariant P < 10−5). (Top) Associations for four thyroid cis-eQTLs (indicated by gene names); (bottom) cis-mediating genes with five or more colocalizing trans-eQTLs.

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Trans-eQTLs Enriched among cis-eQTLs, likely mediators

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The GTEx Consortium, 2020, Science 369:1318-1330

Functional mechanisms of genetic regulatory effects. QTL enrichment in functional annotations for (A) cis-eQTLs and cis-sQTLs and for (B) trans-eQTLs. cis-QTL enrichment is shown as mean ± SD across tissues; trans-eQTL enrichment as 95% CI. UTR, untranslated region. (C) Enrichment of lead trans-eVariants or trans-sVariants that have been tested for cis-QTL effects also being significant cis-eVariants or cis-sVariants in the same tissue, respectively. Asterisk denotes significant enrichment, P < 10−21. (D) Proportion of trans-eQTLs that are significant cis-eQTLs or mediated by cis-eQTLs. (E) Trans associations of cis-mediating genes identified through colocalization (PP4 > 0.8 and nominal association with discovery trans-eVariant P < 10−5). (Top) Associations for four thyroid cis-eQTLs (indicated by gene names); (bottom) cis-mediating genes with five or more colocalizing trans-eQTLs.

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GWAS-Associated SNPs are Enriched among cis-e/sQTLs

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The GTEx Consortium, 2020, Science 369:1318-1330

Trans-eQTLs also enriched among trait-associated variants

Regulatory mechanisms of GWAS loci. (A) GWAS enrichment of cis-eQTLs, cis-sQTLs, and trans-eQTLs measured with different approaches: enrichment calculated from GWAS summary statistics of the most significant cis-QTL per eGene or sGene with QTLEnrich and LD score regression with all significant cis-QTLs (S-LDSC all QTLs), simple QTL overlap enrichment with all GWAS catalog variants, and LD score regression with fine-mapped cis-QTLs in the 95% credible set (S-LDSC credible set) and using posterior probability of causality as a continuous annotation (S-LDSC causal posterior). Enrichment is shown as mean and 95% CI. (B) Number of GWAS loci linked to eGenes or sGenes through colocalization (ENLOC) and association (PrediXcan), aggregated across tissues. (C) Concordance of mediated effects among independent cis-eQTLs for the same gene, shown for different levels of regional colocalization probability (RCP) (32), which is used as a proxy for the gene’s causality. As the null, we show the concordance for LD matched genes without colocalization. (D) Proportion of colocalized cis-eQTLs with a matching phenotype for genes with different levels of rare variant trait association in the UK Biobank (UKB). (E) Horizontal GWAS trait pleiotropy score distribution for cis-eQTLs that regulate multiple versus a single gene (left) and for cis-eQTLs that are tissue-shared versus specific.

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Concordance of Mediated Effects of cis-eQTLs on GWAS Traits

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The GTEx Consortium, 2020, Science 369:1318-1330

Given allelic heterogeneity, we tested whether the primary eQTL's mediated effect was similar to the secondary one

Regulatory mechanisms of GWAS loci. (A) GWAS enrichment of cis-eQTLs, cis-sQTLs, and trans-eQTLs measured with different approaches: enrichment calculated from GWAS summary statistics of the most significant cis-QTL per eGene or sGene with QTLEnrich and LD score regression with all significant cis-QTLs (S-LDSC all QTLs), simple QTL overlap enrichment with all GWAS catalog variants, and LD score regression with fine-mapped cis-QTLs in the 95% credible set (S-LDSC credible set) and using posterior probability of causality as a continuous annotation (S-LDSC causal posterior). Enrichment is shown as mean and 95% CI. (B) Number of GWAS loci linked to eGenes or sGenes through colocalization (ENLOC) and association (PrediXcan), aggregated across tissues. (C) Concordance of mediated effects among independent cis-eQTLs for the same gene, shown for different levels of regional colocalization probability (RCP) (32), which is used as a proxy for the gene’s causality. As the null, we show the concordance for LD matched genes without colocalization. (D) Proportion of colocalized cis-eQTLs with a matching phenotype for genes with different levels of rare variant trait association in the UK Biobank (UKB). (E) Horizontal GWAS trait pleiotropy score distribution for cis-eQTLs that regulate multiple versus a single gene (left) and for cis-eQTLs that are tissue-shared versus specific.

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Trait Pleiotropy Associated with Allelic Heterogeneity and Cross-tissueness

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The GTEx Consortium, 2020, Science 369:1318-1330

Regulatory mechanisms of GWAS loci. (A) GWAS enrichment of cis-eQTLs, cis-sQTLs, and trans-eQTLs measured with different approaches: enrichment calculated from GWAS summary statistics of the most significant cis-QTL per eGene or sGene with QTLEnrich and LD score regression with all significant cis-QTLs (S-LDSC all QTLs), simple QTL overlap enrichment with all GWAS catalog variants, and LD score regression with fine-mapped cis-QTLs in the 95% credible set (S-LDSC credible set) and using posterior probability of causality as a continuous annotation (S-LDSC causal posterior). Enrichment is shown as mean and 95% CI. (B) Number of GWAS loci linked to eGenes or sGenes through colocalization (ENLOC) and association (PrediXcan), aggregated across tissues. (C) Concordance of mediated effects among independent cis-eQTLs for the same gene, shown for different levels of regional colocalization probability (RCP) (32), which is used as a proxy for the gene’s causality. As the null, we show the concordance for LD matched genes without colocalization. (D) Proportion of colocalized cis-eQTLs with a matching phenotype for genes with different levels of rare variant trait association in the UK Biobank (UKB). (E) Horizontal GWAS trait pleiotropy score distribution for cis-eQTLs that regulate multiple versus a single gene (left) and for cis-eQTLs that are tissue-shared versus specific.

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Tissue specificity of cis-QTLs

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The GTEx Consortium, 2020, Science 369:1318-1330

Tissue specificity of cis-QTLs. (A) Tissue clustering with pairwise Spearman correlation of cis-eQTL effect sizes. (B) Similarity of tissue clustering across core data types quantified using median pairwise Rand index calculated across tissues. (C) Tissue activity of cis expression and splicing QTLs, where an eQTL was considered active in a tissue if it had a mashr local false sign rate (LFSR, equivalent to FDR) of <5%. This is shown for all cis-QTLs and only those that could be tested in all 49 tissues (red and blue). (D) Spearman correlation (corr.) between cis-eQTL effect size and eGene expression level across tissues. cis-eQTL counts are shown for those not tested owing to low expression (low expr.) level; tested but without significant (FDR < 5%) correlation (uncorrelated); a significant correlation, but effect sizes crossed zero, which made the correlation direction unclear (uninterpretable); positively correlated; and negatively correlated. (E and F) The effect of genomic function on cis-QTL tissue sharing modeled using logistic regression with functional annotations (E) and chromatin state (F). CTCF peak, motif, TF peak, and DHS (DNase I hypersensitive site) indicate whether the cis-QTL lies in a region annotated as having one of these features in any of the Ensembl Regulatory Build tissues. For chromatin states, model coefficients are shown for the discovery and replication tissues that have the same or different chromatin states. INDEL, insertion or deletion; ZNF, zinc finger; TSS, transcription start site; Transcr., transcription; Enh, enhancers.

Cell type interaction cis-eQTLs and cis-sQTLs. (A) Number of cell type interaction cis-eQTLs and cis-sQTLs (ieQTLs and isQTLs, respectively) discovered in seven tissue–cell type pairs, with shading indicating whether the ieGene or isGene was discovered by cis-eQTL or cis-sQTL analysis in bulk tissue. Colored dots are proportional to sample size. (B) Functional enrichment of neutrophil ieQTLs and isQTLs compared with cis-eQTLs and cis-sQTLs from whole blood. (C) Proportion of conditionally independent cis-eQTLs per eGene, for eGenes that do or do not have ieQTLs in GTEx, and for eGenes that have shared (=eQTLs) or nonshared (≠eQTLs) cis-eQTLs across five sorted blood cell types. (D) Whole blood cis-eQTL P value landscape for NCOA4, for the standard analysis (unconditional; top row) and for two independent cis-eQTLs (bottom rows). In a dataset of five sorted cell types (56), analyses of all cell types yielded a lead eVariant, rs2926494 (left), which is in high LD with the first independent cis-eQTL but not the second. The lead variant in monocyte cis-eQTL analysis, rs10740051, is in high LD with the second conditional cis-eQTL, indicating that this cis-eQTL is active specifically in monocytes. Thus, the full GTEx whole blood cis-eQTL pattern and allelic heterogeneity is composed of cis-eQTLs that are active in different cell types. (E) COLOC posterior probability (PP4) of GWAS colocalization with whole blood ieQTLs and eQTLs of the same eGene. Three hundred forty-nine gene-trait combinations across 132 genes and 36 GWAS traits showed evidence of colocalization (PP4 > 0.5) with an ieQTL and/or eQTL.

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ENCODE (Encyclopedia of DNA Elements)

Pilot phase started in 2003
Goal is to catalog all the functional elements in the human genome including

elements that act at the protein and RNA levels,
regulatory elements that control cells and circumstances in which a gene is active

Characterize functional elements of the Genome and Epigenome

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ENCODE (Encyclopedia of DNA Elements)

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This provides information on regions, not specific variants

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Partitioning Heritability

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Partitioning Heritability into Classes

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Variance of each component is computed using

REML: Restricted Maximum Likelihood

GRM of each class is computed using only SNPs in the class

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Partitioning Heritability by Chromosome

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J. Yang,et al, “Genome partitioning of genetic variation for complex traits using common SNPs”, May 2011.

Height

BMI

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Partitioning Heritability by Chromosome

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vWF

QTi

Von Willebrand factor (vWF) is a blood glycoprotein involved in hemostasis

QT interval is a measure of the time between the start of the Q wave and the end of the T wave in the heart's electrical cycle.

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MAF-stratified Heritability Estimates

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J. Yang, A. Bakshi, Z. Zhu, G. Hemani, A. A. E. Vinkhuyzen, S. H. Lee, M. R. Robinson, J. R. B. Perry, I. M. Nolte, J. V. Van Vliet-Ostaptchouk, H. Snieder, T. Esko, L. Milani, R. Mägi, A. Metspalu, A. Hamsten, P. K. E. Magnusson, N. L. Pedersen, E. Ingelsson, N. Soranzo, M. C. Keller, N. R. Wray, M. E. Goddard, and P. M. Visscher, “Genetic variance estimation with imputed variants finds negligible missing heritability for human height and body mass index,” Nat Genet, vol. 47, no. 10, pp. 1114–1120, Aug. 2015.

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Functional Partitioning of Heritability

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Important to use imputed genotypes

A. Gusev et al, “Partitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases,” AJHG 2014.

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Partitioning with LD Score Regression

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GWAS results (effect size / standard error )^2 is Chi2 under Null of no association

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How Do You Calculate Chi2 Statistic From Summary Results?

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Stratified LD score regression

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Finucane et al, “Partitioning heritability by functional annotation using genome-wide association summary statistics,” Nat Genet, Sep. 2015.

Combination of nine phenotypes with low phenotypic correlation and sample overlap

height, BMI, age at menarche, LDL levels, coronary artery disease, schizophrenia, educational attainment, smoking behavior, and rheumatoid arthritis

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Gene Expression Traits: Functional Annotations Enrichment

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Enrichment of Tissue Types

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Finucane et al, “Partitioning heritability by functional annotation using genome-wide association summary statistics,” Nat Genet, Sep. 2015.

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Examples of Partitioning by Functional Classes

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