Using genetic and transcriptomics data to (help) understand disease - - PowerPoint PPT Presentation

Introduction Finding effects Leveraging information

Using genetic and transcriptomics data to (help) understand disease aetiology

Joseph Powell

The University of Queensland Diamantina Institute and Queensland Brain Institute

Introduction Finding effects Leveraging information

Phenotypes

Introduction Finding effects Leveraging information

Phenotypes

Introduction Finding effects Leveraging information

Phenotypes

Introduction Finding effects Leveraging information

Phenotypes

Introduction Finding effects Leveraging information

Phenotypes

Introduction Finding effects Leveraging information

Heritability

σ2

P = σ2 G + σ2 E

σ2

G = σ2 A + σ2 D + σ2 I

H2 = σ2

G/σ2 P

h2 = σ2

A/σ2 P

σ2

P = Phenotypic Variance

σ2

G = Genetic Variance

σ2

E = Environmental Variance

σ2

A = Additive Genetic Variance

σ2

D = Dominance Genetic Variance

σ2

I = Interaction Genetic Variance

Introduction Finding effects Leveraging information

Heritability

Introduction Finding effects Leveraging information

Outline

Genetic control of gene expression eQTL Additive and alternative genetics Leveraging eQTL and GWAS information Differential gene expression Network analysis Pathway analysis GO term enrichment

Introduction Finding effects Leveraging information

Systems Genetics

Introduction Finding effects Leveraging information

eQTLs

Introduction Finding effects Leveraging information

Systems Genetics

Introduction Finding effects Leveraging information

Starting simple

Introduction Finding effects Leveraging information

GWAS / eQTL

Introduction Finding effects Leveraging information

GWAS / eQTL

Introduction Finding effects Leveraging information

GWAS / eQTL

Introduction Finding effects Leveraging information

Additive

Assumptions Additive Constant variance Extensions Covariates Multivariate Statistics Non-additive effects (dominance and interactions) Variance heterogeneity y = µ + bx + e

Introduction Finding effects Leveraging information

Non-additive

Re-parameterized linear or logistic models Double generalised linear models

Introduction Finding effects Leveraging information

So you’ve done an eQTL analysis...

Introduction Finding effects Leveraging information

Tissue specifc transcriptionally active regions

Introduction Finding effects Leveraging information

Chromosome interactions

Introduction Finding effects Leveraging information

SNP colocalisation with genomic features

Introduction Finding effects Leveraging information

Phenotype - Expression Correlations

Test statistical significance of the correlation Large number of tests Correlations due to genetic and environmental factors Correlate expression with phenotype

Introduction Finding effects Leveraging information

Phenotype - Expression Correlations

Null Hypothesis: expression is not correlated with the phenotype Statistical test for deviation in the p value distribution from null Select the top x percent of genes most correlated genes (Corr Regions) P-value distribution from the correlations

Introduction Finding effects Leveraging information

Phenotype - Genotype Correlations

What are the p values for SNPs in the Corr Regions? Are they different from non-correlated regions? If Pheno - Exp Cor are due to environmental factors SNP values distributions will be equal

Introduction Finding effects Leveraging information

Acknowledgements

Complex Trait Genomics Group Peter Visscher Naomi Wray Jian Yang Gib Hemani Anita Goldinger Allan Mcrae Kostya Shakhbazov Hong Lee Qinggyi Zhao Anna Vinkhuyzen Guo-Bo Chen Beben Benyamin Gerhard Moser Zong Zhang Zhihong Zhu Jake Gratten Marie-Jo Brion John Witte Lars Ronnegard Collaborators Grant Montgomery (QIMR) Nick Martin (QIMR) Greg Gibson (Georgia Tech) Manolis Dermitzakis (Uni of Geneva) Lude Franke (Uni of Groningen) Tim Spector (KCL) Kerrin Small (KCL) Visit us at www.complextraitgenomics.com