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Genetic Analysis in Human Disease Learning Objectives Describe the differences between a linkage analysis and an association analysis Identify potentially confounding factors in a genetic study Define missing heritability Question: 1) You have a grant to do a genetics study of the disease of your choice. What are 3 aspects you need to consider when recruiting subjects? A) Phenotype, gender and age B) Phenotype, gender and income C) Gender, age and income D) Age, income and education Question: 2) You’ve analyzed 1,000 cases and 1,000 controls for an association study but found nothing significant. What went wrong? A) Recruited too many subjects B) Population was too homogeneous C) Not enough subjects D) Genotyped using only one platform Question: 3) You’ve made it to the big time. From your GWAS analysis you have significant hits in known genes. What’s the next step? A) End of story, move on to the next study B) Develop new drugs C) Replication/validation D) Patent the SNPs Power of Genetic Analysis Success stories Age-related Macular Degeneration Crohn’s Disease Allopecia Areata Type1 Diabetes Not so successful Ovarian Cancer Obesity The spectrum of genetic effects in complex diseases Getting Started Question to be answered Which gene(s) are responsible for genetic susceptibility for Disease A? What is the measurable difference Clinical phenotype biomarkers, drug response, outcome Who is affected Demographics male/female, ethnic/racial background, age Study Design Linkage (single gene diseases: cystic fibrosis, Huntington’s disease, Duchene's Muscular Dystrophy) Families Association (complex diseases: RA, SLE, breast cancer, autism, allopecia, AMD, Alzheimer’s) Case - control Linkage vs. Association Analysis 5M Linkage Studies- all in the family Family based method to map location of disease causing loci Families Multiplex Trios Sib pairs Staged Genetic Analysis - RA Linkage/Association/Candidate Gene Association Studies – numbers game Genome-Wide Association Studies (GWAS) Tests the whole genome for a statistical association between a marker and a trait in unrelated cases and controls Affecteds Controls Staged Genetic Analysis - RA Linkage/Association/Candidate Gene So you have a hit: p< 5 x10 Validation/ replication Dense mapping/Sequencing Functional Analysis -7 Validation Independent replication set Genotyping platform Same inclusion/exclusion subject criteria Sample size Same polymorphism Analysis Different ethnic group (added bonus) Staged Genetic Analysis - RA Linkage/Association/Candidate Gene Dense Mapping/Sequencing Identifies the boundaries of your signal close in on the target gene/ causal variant find other (common or rare) variants Functional Analysis Does your gene make sense? pathway function cell type expression animal models PTPN22: first non-MHC gene associated with RA (TCR signaling) Perfect vs Imperfect Worlds Perfect world Linkage and/or GWAS – identify causative gene polymorphism for your disease Publish Imperfect world nothing significant identify genes that have no apparent influence in your disease of interest Now what? What Happened? Disease has no genetic component. Genetic effect is small and your sample size wasn’t big enough to detect it. Too many outliers Wrong controls. CDCV vs CDRV Phenotype /or demographics too heterogeneous Viral, bacterial, environmental Population stratification; admixture Not asking the right question. wrong statistics, wrong model Meta-Analysis – Bigger is better Meta-analysis - combines genetic data from multiple studies; allows identification of new loci Rheumatoid Arthritis Lupus Crohn’s disease Alzheimer’s Schizophrenia Autism Influence of Admixture Not all Subjects are the same Missing heritability Except for a few diseases (AMD, T1D) genetics explains less than 50% of risk. Large number of genes with small effects Other influences? Other Contributors Any change in gene expression can influence disease state- not always related directly to DNA sequence Environmental Epigenetic MicroRNA Microbiome Copy Number Variation Gene-Gene Interactions Alternative splice sites/transcription start sites Genome-Wide Association Studies The promise Better understanding of biological processes leading to disease pathogenesis Development of new treatments Identify non-genetic influences of disease Better predictive models of risk GWAS – what have we found? 3800 SNPs identified for 427 diseases and traits Only 7% in coding regions >50% in DNAse sensitive sites, presumed regulatory regions Genome-Wide Association Studies The reality Few causal variants have been identified Clinical heterogeneity and complexity of disease Genetic results don’t account for all of disease risk Genome-Wide Association Studies The potential clinical applications Risk prediction Disease subtyping/classification MODY: HNF1A- C- reactive protein biomarker Drug development Type 1 Diabetes (MHC and 50 loci) Ribavirin- induced anemia: ITPA variants protective Drug toxicity/ adverse effects MCR4 SNPs and extreme SGA-induced weight gain (Manolio 2013) Question: 1) You have a grant to do a genetics study of the disease of your choice. What are 3 aspects you need to consider when recruiting subjects? A) Phenotype, gender and age B) Phenotype, gender and income C) Gender, age and income D) Age, income and education Answer: 1) You have a grant to do a genetics study of the disease of your choice. What are 3 aspects you need to consider when recruiting subjects? A) Phenotype, gender and age Question: 2) You’ve analyzed 1,000 cases and 1,000 controls for an association study but found nothing significant. What went wrong? A) Recruited too many subjects B) Population was too homogeneous C) Not enough subjects D) Genotyped using only one platform Answer: 2) You’ve analyzed 1,000 cases and 1,000 controls for an association study but found nothing significant. What went wrong? C) Not enough subjects Question: 3) You’ve made it to the big time. From your GWAS analysis you have significant hits in known genes. What’s the next step? A) End of story, move on to the next study B) Develop new drugs C) Replication/validation D) Patent the SNPs Answer: 3) You’ve made it to the big time. From your GWAS analysis you have significant hits in known genes. What’s the next step? C) Replication/validation