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Genetics in Epidemiology Nazarbayev University July 2012 Jan Dorman, PhD University of Pittsburgh Pittsburgh, PA, USA [email protected] Genetics in Epidemiology • Is important because – It focuses on heritable & non-modifiable determinants of disease – It allows examination of gene-gene & geneenvironment interactions – It can contribute to personalized medicine • Is being transformed because – Human Genome Project is complete – Genetic variation can be now examined across the entire genome at a very low cost – Contribution of GWAS has been enormous in terms of identifying disease-susceptibility genes Human Genome Project • February 2010 marked the 10th anniversary of the completion of the human genome project • Initial sequence was finished early because of advancements in genome sequence technology • Resulted in drastically reduced labor & delivery costs Human Genome Sequencing Costs • 2000 – Human Genome Project – $3 billion • 2007 – James Watson – $2 million • 2009 – Illumina & Helicos – $50,000 • 2010 – Illumina HiSeq – $10,000 • 2014 – Multiple companies – $1,000 Genetics in Epidemiology • Is there evidence of familial aggregation of the disorder (phenotype)? – Is a positive family history an independent risk factor for the disorder? • For many chronic disorders, a positive family history is associated with odds ratios between 2-6 • Is there evidence of heritability? – A heritability of 50% indicates that ~ ½ of the variation in disease risk in a population is due to genetics J Intern Med 2008;263:16 Candidate Gene Approach • Are there potential candidate genes? – Genes that are selected based on known biological, physiological, or functional relevance to the phenotype under investigation – Approach is limited by its reliance on existing knowledge about the biology of disease – Associations may be population-specific • E.g., type 2 diabetes – Genes encoding molecules known to primarily influence pancreatic β-cell or insulin action • ABCC8 (sulphonylurea receptor), INS, INSR, etc. PLOS Bio 2003;1:41 Alternative Approach • Genome-wide association studies (GWAS) – Hypothesis: common genetic variants (>5%) ; common diseases (traits) • Limited number of variants, each with a small effect • No a priori hypotheses • Power to identify rare variants (1-5%) is limited – First publication was in 2005 • Complement factor H & age-related macular degeneration – Require • Large, well-characterized populations • Genotyping across the entire genome • Sophisticated data analysis – collaborate on this!! Monogenetic vs. Common Disorders GWAS • 2 tiered approach – 1st tier: genotyping identifies the ‘discovery set’ – 2nd tier: discovery set genotyped in another population • Replication is a requirement for publication – 3rd tier: rule out false positives & false negatives • Requires consortia • Possible because – High-density genotype platforms • By 2007 – chips contained 500,000 – 1,000,000 markers – DNA samples were available from wellcharacterized epidemiological cohorts GWAS Example NEJM 2010; 362:166 GWAS Example NEJM 2010; 362:166 GWAS Example NEJM 2010; 362:166 GWAS • Have identified novel gene-disease (trait) associations – Most alleles are common (>5%) – Most have small effect sizes (OR ~1.5) • Are providing insights into pathways of complex diseases Published Genome-Wide Associations published for 249 traits NHGRI GWA Catalog www.genome.gov/GWAStudies Genetics Review Anatomy of the Cell Chromosomes, Genes & DNA • Somatic cells are diploid - 46 chromosomes – 22 pairs autosomes; 1 pair sex chromosomes • Each pair of autosomes is homologous – Contains the same genes in the same order – 1 is maternal, the other is paternal • Chromosome are composed of deoxyribonucleic acid (DNA) – Genome contains 3 billion base pairs (haploid) – ~1% encode proteins • Genes are located on chromosomes Human Karyogram Figure of a Chromosome DNA Double Helix Base Pairs of a Double Helix T C A G Structure of a Gene A gene is a functional unit that includes introns, exons enhancer & promoter sequences & untranslated sequences at the 5’ & 3’ ends Transcription Results in mRNA Primary Transcript mRNA Processing From Genes to Proteins via mRNA • Proteins consist of 1+ polypeptide chains • Polypeptides chains are made of amino acids • There are 20 amino acids – Their order in is determined by the mRNA sequence read in triplet • Genetic code – 64 combinations of 3 bases called codons – 3 are stop codons (UAA, UGA, UAG) • Genetic code is degenerate • Genetic code is universal Genetic Code mRNA Determines AA Sequence Translation is Protein Synthesis Post-Translation Modifications Advancements in Biotechnology Original Method for DNA Sequencing Polymerase Chain Reaction (PCR) • Revolutionized molecular genetics • Exploits the in vivo processes of DNA replication to copy short DNA fragments in vitro within a few hours • Exponential increase of target DNA sequences • Highly sensitive – need small amount of template DNA • DNA ‘photocopier’ PCR - Cycle 1 5’ A C G T T A C C G T G A A C G T C T T A 3’ Denaturation, ~30 seconds H bonds dissolve at 95oC 3’ T G C A A T G G C A C T T G C A G A A T 5’ PCR - Cycle 1 5’ A C G T T A C C G T G A A C G T C T T A 3’ 3’ C A G A AT 5’ Anneal primers, ~30 seconds at 35-65oC Temperature determined by sequence / length 5’ A C G T T A 3’ 3’ T G C A A T G G C A C T T G C A G A A T 5’ PCR - Cycle 1 5’ A C G T T A C C G T G A A C G T C T T A 3’ T G C A A T G G C A C T T G C A G A A T 5’ Extension of Primers, ~30 seconds at 70-75oC Taq polymerase - thermostable 5’ A C G T T A C C G T G A A C G T C T T A 3’ T G C A A T G G C A C T T G C A G A A T 5’ Post-Genome Era Human Genetic Variation • Single nucleotide polymorphisms (SNPs) • Tandem repeat Sequences – Microsatellites (<8 bp) – Minisatellites (VNTRs; 8-100 bp) • Copy number variants (CNVs; 1Kb – 1Mb) • Insertions – deletions (indels; 100bp – 1Kb) • Note: size limitations are arbitrary – no biological basis & definitions are not consistent across studies SNPs • • • • ~10 million SNPs in human genome & counting Most common type of genetic variation 2 alleles; e.g., A → T Occurs across the entire genome & in stable regions • Many SNPs are in linkage disequilibrium – SNPs close together are more likely to travel together in a block than SNPs far apart – Can use 1 ‘tagging’ SNP per block – cost effective Linkage Disequilibrium Haplotype Block NEJM 2007; 356:1094 SNPs ‘Tag’ Haplotype Blocks NEJM 2007; 356:1094 International HapMap • Emerged as next logical step after sequencing human genome • Goal was to create a public genome-wide database of common genetic variants • Genotyped SNPs from 270 samples from: – Nigeria, Utah, Han Chinese, Japanese • Phase I – Typed 1 million common SNPs (>5%) to characterize LD patterns • Phase II – Typed 3 million rare SNPs (1-5%) DNA Microarray Used to genotype 500,000 – 1+ million SNPs International HapMap • Where are the SNPs? – – – – – 12% occur in protein coding regions 8% occur in gene regulatory regions 40% occur in non-coding introns 40% occur in intergenic sequences Regions of high linkage disequilibrium are similar across populations • HapMap was instrumental in facilitating GWAS Tandem Repeat Sequences • 100,000+ TRSs in human genome • Microsatellites (VNTRs) – Repeat units (8 – 100 bp) • Minisatellites – Repeat units (2 – 8 bp) – Eg., CAGCAGCAGCAGCAGCGACAG • More than 200 diseases genes indentified – E.g., Huntington’s disease, Fragile X syndrome Copy Number Variants • Size is 1 Kb to 1 Mb – Duplications or deletions • Less is known about CNV – Term was introduced in 2004 • Are ubiquitous & reflect 12% of human genome • May span multiple genes • May change gene dosage or effect transcription and translation • Are creating a CNV map along with HapMap • Associated with autism, schizophrenia, lupus, Crohn’s disease, rheumatoid arthritis Copy Number Variants Indels • Insertions & deletions – Size 100 bp to 1 Kb – Millions in genome – Introduced in 2006 • Phenotype may depend on gene dosage • May occur within genes or in promoter • Also creating an indel map Consequences of Genetic Variation • No change – In a non-coding region – In a coding region - genetic code is degenerate • • • • Change in 1 amino acid of a protein Change in multiple amino acids of a protein A truncated protein Change in gene expression – In a regulatory region or splice site • Next generation GWAS will be based on markers other than SNPs – Tandem repeats, CNV, indels Genetic Variation Databases Database Content Address dbSNP SNPs covering the human genome http://www.ncbi.nlm.nih. gov/projects/SNPs HapMap Catalog of variants from HapMap Project http://hapmap.org 1000 Genome Project Extension of HapMap – www.1000genomes.org aim to catalog 95% of variants with 1% freq UCSC Genome Bioinformatics Reference human genome sequence with annotation http://genome.ucsc.edu Ensembl Genome browser, annotation, comparative genomics http://www.ensembl.org /index.html Genetic Variation Databases Database Content Address GeneCards Database of human http://www.genecards.or genes linked to relevant g databases PharmGKB SNPs involved in drug metabolism DGV Database of Genomic http://projects.tcag.ca/var Variants, including CNV iation SCAN SNP & CNV annotation http://www.scandb.org/n based on gene function ewinterface & expression OMIM Online Mendelian Inheritance in Man – over 12,000 genes http://www.pharmgkb.org http://www.ncbi.nlm.nih.g ov/sites/entrez?db=omim Genetic Variation Databases Database HuGE navigator Content Human genome epidemiology knowledge base Address http://hugenavigator.net/ HuGENavigator/home.do Best Pract Res Clin Endo Metab, 2012. 26:119. Collecting DNA • Sources of DNA – – – – Blood samples Buccal brushes Saliva samples Dried blood spots • Depends on – – – – – Conditions at time of collection Resources available to process samples What other biological samples will be collected Long & short term storage Quality control Saliva vs. Blood Samples • Considerations – – – – – – Lower cost More convenient & acceptable to patients Increases compliance Lower mean yield of DNA But quality is comparable No difference in success from high throughput genotyping Other Considerations • Informed consent What analysis can be performed now? What analysis can be performed in the future? Who has control of the specimen? Do you need to ‘re-consent’ the participants due to IRB changes? - Will you inform participants of results? -