Genome-wide association studies. Gene regulation and the genomics of complex traits. Relating Variation to Phenotype. Relating Variation to Phenotype

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Genome-wide association studies Gene regulation and the genomics of complex traits Eric R. Gamazon Vanderbilt University; Academic Medical Center, University of Amsterdam Nancy J.

heritability to such traits Importantly, they have not always led to better understanding of the mechanism underlying the traits Relating Variation to Phenotype Genotyping Assay known variants in

1 Genome-wide association studies Gene regulation and the genomics of complex traits Eric R. Gamazon Vanderbilt University; Academic Medical Center, University of Amsterdam Nancy J. Cox Vanderbilt University June 27, 2016 January 27, 2016 Thousands of loci have been reproducibly associated with common diseases through GWAS Together they account for only a small proportion of the heritability to such traits Importantly, they have not always led to better understanding of the mechanism underlying the traits Relating Variation to Phenotype Genotyping Assay known variants in high-throughput (beads, chips) Focused largely on common variation (which will be largely non-coding) Inexpensive ~$65 / 2.1M variants Soon will have $30-40 (all in) Sequencing Assay all variation in exomes (coding sequences) ~$400 + or whole genomes ~$1200 +, costs continue to decline Relating Variation to Phenotype Genome wide association studies Genotyping 1M variants allows good interrogation (through imputation) of most common variation Usually conducted in unrelated cases with disease and controls without disease (or unphenotyped individuals) Predicated on assumption that common diseases will be driven by common variation Using Correlation Structure of Variation in Human Genome

understand our signals; transcriptomes are a great place to start We

information to get maximum utility Integration of Function Testing

Testing whether functional variants concentrate heritability Testing

2 The Real Problem? Missing Biology Two Primary Themes We need functional information to understand our signals; transcriptomes are a great place to start We need to integrate the functional information with the association information to get maximum utility Integration of Function Testing whether there is enrichment of functional variants among top signals Testing whether functional variants concentrate heritability Testing whether imputed transcript levels are associated with phenotypes Genome Variation: Regulatory Genome Variation: Splicing

Natural Variation in Transcriptional Activity eqtl analysis investigates variation in expression

genome) associated with variation in RNA expression expression ~millions of markers genotypes -

- Association of a SNP far from the gene is a trans-acting eqtl expression genotype Gene

mechanistic basis for the observed associations Annotation with eqtl may improve our ability to

with disease and likely to be eqtls that can be identified through GWAS?

sequencing of subjects; RNAseq on ~50 tissues The Genotype-Tissue Expression (GTEx) project. Nat.

3 Natural Variation in Transcriptional Activity eqtl analysis investigates variation in expression due to genetic regulation, and downstream phenotypic effects An eqtl is a locus (position on genome) associated with variation in RNA expression expression ~millions of markers genotypes - A GWAS for each gene (~10 10 tests) - Association of a SNP nearby the gene is a cis-acting eqtl - Association of a SNP far from the gene is a trans-acting eqtl expression genotype Gene regulation and GWAS Expression QTL and GWAS SNP co-localization may provide insights into the mechanistic basis for the observed associations Annotation with eqtl may improve our ability to distinguish associations likely to be replicated Are there many more common variants associated with disease and likely to be eqtls that can be identified through GWAS? GTEx: Genotype Tissue Expression Subjects at autopsy, tissue / organ donation Whole genome sequencing of subjects; RNAseq on ~50 tissues The Genotype-Tissue Expression (GTEx) project. Nat. Gen eqtl studies within and across tissues and a variety of applications The Genotype-Tissue Expression (GTEx) project. Nat. Gen. 2013

Eligibility Donors of any racial ethnic group and sex of age 21-70 in whom biospecimen collection can start within 24 hours of death

radiation therapy in past 2 years Blood transfusion in past 48 hours BMI index >= 35 or <=18.

eqtl analyses, both single-tissue and multitissue, are conducted The Geno

4 Eligibility Donors of any racial ethnic group and sex of age in whom biospecimen collection can start within 24 hours of death are eligible Medical exclusion criteria HIV infection or high risk behaviors Viral hepatitis Metastatic cancer Chemotherapy or radiation therapy in past 2 years Blood transfusion in past 48 hours BMI index >= 35 or <=18.5 GTEx Blood samples are collected for SNP and CNV genotyping and to establish LCLs RNA quantification by massively parallel sequencing eqtl analyses, both single-tissue and multitissue, are conducted The Genotype-Tissue Expression (GTEx) project. Nat. Gen Sample clustering based on gene expression and exon splicing profiles. The GTEx Consortium Science 2015;348: Published by AAAS PEER factors and influence on detection Published by AAAS GTEx

5 Expression QTLs in blood GTEx Hypertension and Adipose eqtls MAGIC: HOMA-IR (all SNPs) MAGIC: HOMA-IR Definition of splicing events GTEx

6 Splicing quantitative trait locus (sqtl) Splice-junction QTLs in blood Splice-junction QTL: a genetic variant associated with changes in exon junction abundance (calculated using Altrans [Roderic s group]) Splicing-isoform ratio QTL: a genetic variant associated with changes in the relative abundances of gene transcript isoforms (detected using sqtlseeker [Roderic s group]) Transcript QTL: a genetic variant associated with the absolute abundance of a single isoform of a gene Splicing-isoform ratio QTLs in blood Splice-junction QTL GTEx. Science Splicing-isoform ratio QTL eqtl Discovery is a Bunch of GWAS Measured transcript levels for each gene in a given tissue from each person is just a quantitative trait like any other We can test the association of each SNP with the transcript level of each gene. If we have a matrix of genome variation and a matrix of transcriptome variation GTEx. Science 2015.

Matrix eqtl 2-3 orders of magnitude faster than conventional association analysis on transcriptome data Special

Supports additive linear and ANOVA models, covariates, correlated error Calculates false discovery rates

computational feasibility With covariate Gene Expression Matrix: Each row is expression measurement for a gene

7 Matrix eqtl 2-3 orders of magnitude faster than conventional association analysis on transcriptome data Special preprocessing allows the most computationally intensive parts of analysis to be done as large matrix operations Supports additive linear and ANOVA models, covariates, correlated error Calculates false discovery rates seperately for cis- and trans-eqtls One MAMMOTH Matrix Multiplication GS T, broken into 10K x 10K blocks for computational feasibility With covariate Gene Expression Matrix: Each row is expression measurement for a gene across columns of individuals Columns are identical sets of individuals Genotype Matrix: Each SNP is a row across columns of individuals

8 Huge Advantages to Fast Computation The analysis becomes an experiment that you can perform over and over to understand how different covariates affect results, and how different types of models perform Past studies often focused on only local SNPs as eqtls because using the whole genome was prohibitively computationally burdensome Writing p-values takes time (when you have 10 billion); they tally counts (for FDR, QQ plots, but save only values meeting a user-specified threshold Huge Advantages to Fast Computation The analysis becomes an experiment that you can perform over and over to understand how different covariates affect results, and how different types of models perform Past studies often focused on only local SNPs as eqtls because using the whole genome was prohibitively computationally burdensome Writing p-values takes time (when you have 10 billion); they tally counts (for FDR, QQ plots, but save only values meeting a user-specified threshold Example Application RNA from HapMap/1000 Genomes cell lines measured using RNA-Seq in the GEUVADIS study HEADS UP Data processing before these analyses is also very important: SNPs downloaded from 1000 Genomes Project data Chromosome 22 only Test different thresholds for reporting eqtls Test different models for cis- and trans- PEER factors, surrogate variable analysis to reduce batch effects If you choose PEERS to optimize cis-eqtl discovery, you will not be optimized for trans-eqtl discovery (networks of co-ordinately regulated genes picked up in higher order PEERS) If you want to focus on sex or tissue effects, how you choose to normalize data in advance of analysis will matter Polygenic Modeling Heritability is a population parameter that may elucidate the genetic architecture of complex traits Methods for obtaining estimates from pedigree data are well established Estimates may be derived from tag SNPs (e.g., on platforms) chip heritability Polygenic Modeling Relates phenotypic variation to many genetic variants Differs from traditional single-variant tests of association Estimating eqtl-derived heritability The proportion of variance captured by eqtl SNPs Predicting disease risk or therapeutic response on the basis of genotypes

9 Crohn s Disease Y = Xb + T g T + C + e Crohn s Disease Type 1 Diabetes Type 1 Diabetes Concentration of Heritability We are able to capture 20 to 30% of the heritability from genome-wide marker SNPs (~200,000 SNPs) with our cis eqtls far fewer SNPs. Tissue-specificity of eqtl contribution to heritability We can concentrate heritability with the use of cis eqtls.

PrediXcan Develop prediction models for expression Predict full transcriptome based on genetic data Quantify the

PrediXcan is More than Genetics GReX Genetically regulated expression Other factors Traitaltered component Figure 1:

(regulating gene expression) enriched in disease Transcriptome captures consequences of environmental exposures on

Learn relationship of genome variation to transcriptome in reference sample (GTEx) Store weights from prediction

Informative priors and groupings of functional units (on the basis of known pathways, for example) No actual

Apply to any existing data set with large-scale genome interrogation, such as those dbgap or other repositories.

Meta-analysis of gene-based results is simplified, as less stringent harmonization between studies is required.

In general, larger sample sizes for the training set will be needed to achieve good prediction models with rare

10 PrediXcan Develop prediction models for expression Predict full transcriptome based on genetic data Quantify the association between genetically predicted levels of gene expression and phenotype (disease or QT) Transcriptome PrediXcan is More than Genetics GReX Genetically regulated expression Other factors Traitaltered component Figure 1: Regulatory Mechanism Tested by PrediXcan Rich data for capturing Trait consequences of environmental exposures Gamazon ER et al. Nature Genetics Gene Expression Decomposition Use of Transcriptome in Prediction Transcriptome captures important genome variation (regulating gene expression) enriched in disease Transcriptome captures consequences of environmental exposures on gene expression Example use: serial transcriptomes in watchful waiting compared with imaging Analogous to Imputation Learn relationship of genome variation to transcriptome in reference sample (GTEx) Store weights from prediction equations Apply to any dataset with genome interrogation Reduced multiple-testing burden Advantages of Framework Informative priors and groupings of functional units (on the basis of known pathways, for example) No actual transcriptome data are required, as the predicted expression levels are a function of genetic variation alone. Apply to any existing data set with large-scale genome interrogation, such as those dbgap or other repositories. Reverse causality is not a major concern; disease status or drug treatment does not alter germline genomic variation. Meta-analysis of gene-based results is simplified, as less stringent harmonization between studies is required. The approach can be applied to common or rare variants. In general, larger sample sizes for the training set will be needed to achieve good prediction models with rare variants. Advantages of Framework We iteratively use more and more of what we do know to figure out what we most want to learn for new discovery Signals come at the levels of the gene, improving the ability to do pathway/network analyses Sets up a natural framework for the unified analysis of whole genome sequence data

WTCCC RA outside HLA Resources for EMR-based research at Vanderbilt The Synthetic Derivative A de-identified and continuously-updated image of the EMR: 2,500,000 subjects BioVU Subjects with DNA:

the EMR: 3,000,000 subjects BioVU Subjects with DNA: >225,000 Dense genotypes: >100,000 Whole genome or exome sequencing: ~1000 s The genome-wide association study Target phenotype association P

11 WTCCC RA outside HLA Resources for EMR-based research at Vanderbilt The Synthetic Derivative A de-identified and continuously-updated image of the EMR: 2,500,000 subjects BioVU Subjects with DNA: >214,000 Dense (GWAS-level) genotypes: ~20,000 Exome chip data: 42,000 Resources for EMR-based research at Vanderbilt 2017 The Synthetic Derivative A de-identified and continuously-updated image of the EMR: 3,000,000 subjects BioVU Subjects with DNA: >225,000 Dense genotypes: >100,000 Whole genome or exome sequencing: ~1000 s The genome-wide association study Target phenotype association P value chromosomal location The phenome-wide association study Target genotype association P value BioVU X PrediXcan: Gene-based PheWAS BioVU An in silico Discovery Engine diagnosis code PheWAS requirement: A large cohort of patients with genotype data and many diagnoses The First Gene X Medical Phenome Catalog

Deviation in the Transcriptome Across 13K BioVU subjects, the number of PheWAS codes is significantly (p = 8.

12 cat a log ˈkadlˌôɡ/ noun noun: catalogue; plural noun: catalogues; noun: catalog; plural noun: catalogs 1. a complete list of items, typically one in alphabetical or other systematic order, in particular. The Burden of Medical Disease vs. Deviation in the Transcriptome Across 13K BioVU subjects, the number of PheWAS codes is significantly (p = 8.5 x 10-4 ) correlated with the number of genes with predicted transcript levels +/- 5 SD from the mean Consistently observed across tissues Significant even at +/- 3 SD What s the Best Measure of Burden of Disease? What s the Best Measure of Transcriptome Deviance? Capture number of codes and severity? Other variables? Raw numbers of genes, or weight more connected genes more highly? More heritable genes more highly? Reduced Predicted Expression GRIK5 An Eye Super Gene? Zebrafish studies conducted in the Zebrafish Aquatic Facility by Ela Knapik, and students Daniel Levin, Gokhan Unlu, and Jessica Brown

13 GRIK5 expression in the zebrafish eye OK, GRIK5 is involved in normal eye development, but that does not fully explain how it can contribute to so many different eye phenotypes that were not previously thought to be related 3 dpf Lens RPE GCL PCL OPL INL Optic Nerve RPE Retinal Pigment Epithelium PCL Photoreceptor Cell Layer OPL Outer Plexiform Layer INL Inner Nuclear Layer GCL Ganglion Cell Layer Nuclei (DAPI) GRIK5 Results on ~500 genes in 5000 individuals Mega Project Team Results on all genes in 13,000 Results on all genes in 25,000 Results in 100,000+, 200,000+, Jim Sutcliffe Cara Sutcliffe Sarah Collier Lana Olson Janey Wang Vanderbilt Zebrafish Aquatic Facility Ela Knapik Gokhan Unlu Jess Brown Daniel Levic VICTR Vanderbilt Institute for Clinical and Translational Research Gordon Bernard 79

14 Our GTEx Team at University of Chicago CDR PRC Dan Nicolae Lin Chen Hae Kyung (Haky) Im Barbara Stranger Virginia Beach Roanoke Richmond Kaanan Shah Jason Torres Keston Aquino-Michaels Data analysis egtex BSS OPO CBR CDR Brain bank Inactive site LDACC ELSI NIH

Whole Transcriptome Analysis of Illumina RNA- Seq Data. Ryan Peters Field Application Specialist

Whole Transcriptome Analysis of Illumina RNA- Seq Data Ryan Peters Field Application Specialist Partek GS in your NGS Pipeline Your Start-to-Finish Solution for Analysis of Next Generation Sequencing Data