Understanding the Genetic Architecture of Complex Human Diseases: Biomedicine, Statistics, and Large Genomic Data Sets Josée Dupuis

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1 Understanding the Genetic Architecture of Complex Human Diseases: Biomedicine, Statistics, and Large Genomic Data Sets Josée Dupuis Professor and Interim Chair Department of Biostatistics Boston University School of Public Health

2 Outline What are Complex Genetic Diseases Research Questions related to Complex Genetic Diseases Type of Data Collected Type of Statistical Analyses Performed Example Results Future Research 2

3 Genetic Diseases In the last decades, great successes has been achieved in the identification of genes responsible for Mendelian Human Diseases Mendelian diseases are caused by a single gene E.g.: Cystic Fibrosis, Huntington's disease, Hemophilia, Sickle Cell Anemia Finding the genetic causes of complex human diseases has been more challenging 3

4 Complex Genetic Diseases Heritable Runs in family, i.e. more likely to get the disease if one or both of your parents are affected E.g. The risk of type-2 diabetes is doubled in individuals with a parent affected by type-2 diabetes Caused by multiple genes Influenced by both genetic and environmental factors 4

5 Research Questions What genes and pathways are implicated in disease development Are the genes and pathway drugable, i.e. can we use the information on genes to develop therapies? Can I use the knowledge to predict who will develop the disease and target prevention intervention? Genetic variants can be measured at birth; environmental factors changes over time How do these genes interact to increase disease susceptibility? What are the function of the implicated genes? Can I use this information to develop better therapies? How is the role of environmental factors in disease development? Are the genetic effect modified by environmental factors? 5

6 Type of Data Data collected on human participants from casecontrol or cohort studies, such as the Framingham Heart Study participants Disease related traits Disease status E.g. Type-2 diabetes, obesity, cardiovascular disease Traits related to disease status E.g. Fasting glucose Levels (Type-2 diabetes); body mass index (obesity); EKG (cardiovascular disease) May have multiple (longitudinal) measurements 6

7 Type of Data Risk factors and other relevant variables E.g. age, smoking status, weight, diet, physical activity Can be measured once or multiple times over many years 32 exams for the Framingham Heart Study participants who were part of the original cohort recruited in 1948! 7

8 Type of Data Genetic and Genomic data Genetic variants Human genome contains ~ 3 billions base pairs of DNA Most of our DNA is identical among humans What matter are the differences, DNA sites that are called genetic polymorphisms or variants Can measure millions of genetic variants for each participant Can infer information at millions more variants by using correlation between genetic variants and available databases of human genetic polymorphisms Bottom line: ~ 80 million variables measured on each participant! Genetic variants can influence disease risk 8

9 Type of Data Genetic and Genomic data Gene expression Can measure the level of gene expression in a particular tissue for ~ 20,000 human genes Most arrays have multiple probes per genes Can summarize expression at the gene level or a the exon level Can measure in a single tissue (e.g. blood) or in multiple tissues (e.g. bone, nasal cells, etc.) Can measure expression with multiple technologies Micro-arrays, RNA-seq Gene expression can influence disease susceptibility 9

10 Type of Data Genetic and Genomic data Methylation data DNA methylation can be measured genome-wide and in multiple tissues DNA methylation can alter the function of genes DNA methylation can increase/decrease disease susceptibility 10

11 Type of Statistical Analyses Analyses range from very simple to more sophisticated Most basic analysis: genome-wide association analysis (GWAS) Test each genetic variant/gene expression/methylation level for association with disease status/quantitative trait using regression models Get thousands to millions of statistical test results! More sophisticated: systems biology approach that models all data types jointly and may incorporate prior information on gene/variant functionality from available databases Involves multi-disciplinary team of clinicians, molecular biologists, bioinformaticians, statisticians to appropriately model available data 11

12 Statistical Analysis Challenges Correlation between observations Multiple correlated measures of disease status E.g. lipid levels (total cholesterol, HDL, LDL, triglycerides), blood pressure (systolic and diastolic), obesity (BMI, % body fat) E.g. Multiple measures taken over time Correlation among genetic variants Finding multiple associations with disease status within one gene may be the result of correlation between genetic variants located in the same genomic region 12

13 Statistical Analysis Challenges Correlation between observations Correlation between data types Gene expression influenced by genetic variants Correlation among participants Related participants may be correlated in their disease risk, risk factors and genetic variants May genetic studies include related participants, including the three generation Framingham Heart Study 13

14 Statistical Analysis Challenges Multiple Statistical Tests Performed Need to account for multiple testing because millions of statistical tests are performed Typical to use very stringent thresholds for statistical significance Need large sample sizes to have enough power to detect genetic association This needs for large samples led to the creation of large consortia that joint effort in their quest to elucidate the genetic architecture of complex diseases Results in papers with 100s of authors Hard to find qualified reviewer when everyone in the field is a co-author! 14

15 Statistical Tools To deal with multiple layers of correlation, often use linear mixed effect models Fixed effect of risk factors and genetic factors Random effect to account for correlation between repeated measures and related participants Use a combination of publicly available software and R functions Biostatistics Department faculty and PhD students have contributed a number of R packages for genetic analysis to the CRAN repository 15

16 Framingham Heart Study GWAS Results G6PC2 Fasting Glucose MTNR1B GCK 16

17 Consortium (MAGIC) GWAS Results G6PC2 MTNR1B GCKR PROX1 ADCY5 SLC2A2 CRY2 GCK ADRA2A DGKB SLC30A8 TCF7L2 GLIS3 MADD FADS1 FAM148B Note: Hits represented by closest mapping gene, but this does not imply causality Dupuis*, Langenberg*, Prokopenko*, Saxena*, Soranzo* et al. for MAGIC, Nat Genet (2010) 17

18 Whole Blood Gene Expression and Atrial Fibrillation: subnetwork derived from protein-protein interactions Lin, Yin, LuneMa, Dupuis, et al. PLOS One (2014) 18

19 Future Research Functionality of GWAS associated variants has not been established Team up with lab scientists to determine causal genes/variants Identified variants do not fully explain heritability Jointly analyze many types of genetic/genomic data to explain familial aggregation of disease Incorporate genomic and genetic information generated by other labs E.g. GO, ENCODE, Genotype-Tissue Expression (GTEx) Systems biology approach to incorporate ALL data available; requires multi-disciplinary team of scientists 19