Hunting Disease Genes HMGP Richard A. Spritz, M.D. April 13, 2015 [email protected] 303-724-3107 Why Find Disease Genes? Accelerated by finding the disease gene.
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Hunting Disease Genes HMGP Richard A. Spritz, M.D. April 13, 2015 [email protected] 303-724-3107 Why Find Disease Genes? Accelerated by finding the disease gene Why Find Disease Genes? • Virtually all diseases result from a combination of genes and environmental factors • We have no systematic ways to discover environmental risk factors • We do have systematic ways discover disease genes • Discovery of disease genes will provide clues to pathogenic mechanisms, new approaches to treatment, inference of environmental risk factors, and ultimately disease prevention • Personalized medicine ( = “Precision Medicine”) The Holy Grail Personalized/Precision Medicine Paradigm • Discover risk genes for common diseases, specific risk variants, high-risk combinations • Carry out accurate DNA-based predictive diagnostics of disease susceptibilities based on individualized genetic risks • Apply optimized individualized treatment or prevention based on genetic diagnosis of disease susceptibilities and pharmacogenetic • analysis of optimized drug efficacy/specificity • This is why there was a Human Genome Project Personalized/Precision Medicine Paradigm--Problems • For most common complex traits, individual genes/variants confer low odds ratio OR = Risk of disease having a given gene variant / Risk of disease not having variant Population/study wide; no meaning at level of individual • We do not yet know how to do “combinatorial” complex trait risk prediction Genetic risk scores • For most complex diseases it has been hard to account for much of the ‘heritability’ of the trait H2 = (Var G) / (Var P) • Low positive predictive value of genetic tests for complex traits significant non-genetic component late onset Hunting for Disease Genes 1. In a “Mendelian”, single-gene trait, one gene is sufficient to cause (most of) the disease phenotype 2. In a polygenic/multifactorial, “complex” trait, no one gene is sufficient to cause the disease phenotype How Do You Find Disease Genes? I. Hypothesis-driven approaches Candidate gene association Candidate gene sequencing II. Hypothesis-free approaches Genomewide linkage Genomewide association (Genomewide expression) Genomewide sequencing Exome Full-genome Disease Gene Identification— “Functional Cloning” vs. “Positional Cloning” Positional Cloning: Determine a Disease Gene’s Genomic Position, and then Identify the Gene Obviated by Human Genome Project Gene Mapping Technology Polymorphic DNA Markers • • • • You can only track/measure differences between people and through families Polymorphic DNA markers constitute any scorable differences at known genomic positions Surrogates for disease mutations; some polymorphisms cause disease; most don’t Most commonly used marker types: – microsatellites – single-nucleotide polymorphisms (SNPs) – copy-number variations (CNVs) The First Goal of the HGP was to Assemble a High-Density Genome Map of Polymorphic Markers How Do You Find Disease Genes? I. Hypothesis-driven approaches Candidate gene association Candidate gene sequencing II. Hypothesis-free approaches Genomewide linkage Genomewide association (Genomewide expression) Genomewide sequencing Exome Full-genome Genetic Linkage Studies •Studies families •Search for regions of genome that are systematically co-inherited along with disease on passage through families •Requires families with multiple affected relatives (multiplex families) •Best at detecting genes with Mendelian effects (uncommon alleles with strong effects) •Unit of genetic linkage is LOD (“Log of the Odds) score (>3) Principle of genetic linkage— Loci close by on a chromosome tend not to be separated by recombination vs. loci far apart Loci on the same chromosome Loci on different chromosomes Very close Nearby Far Apart Freq. of crossover Rare between 2 loci Some Frequent - Linkage Tight Some Absent Absent 0% 1-49% 50% Recombination 50% • Unit of genetic “distance” is centiMorgan (cM) = 1% recombination/meiosis; ~ 1 Mb Genetic Linkage Analysis • Statistical measure is LOD (log of odds) score LOD = Log10 Likelihood of data if loci linked at Likelihood of data if loci unlinked • Significance level: LOD >3.0 for Mendelian trait LOD >3.3 for Polygenic trait Restriction Fragment Length Polymorphism (RFLP) EcoRI Allele 1 AGAGCCTCAACTTGAATTCGTTTAGTAA Allele 2 AGAGCCTCAACTTGAATTTGTTTAGTAA Restriction enzyme EcoRI cuts at sequence 5’-GAATTC-3’ Allele 1 has an EcoRI cut site; Allele 2 does not • This RFLP is assaying a SNP “Genetic linkage analysis” Co-segregation of disease gene in “multiplex families” with alleles of polymorphic DNA “markers” (initially RFLPs) “Microsatellites” (SSLPs; STRPs, SSRs) [multi-allelic; ~ 1/30,000 bp; mostly used for linkage analysis, forensics] ggctgcacacacacacacacacacacacatgctt ggctgcacacacacacacacacacacatgctt ggctgcacacacacacacacacacatgctt ggctgcacacacacacacacacatgctt ggctgcacacacacacacacatgctt Can follow “segregation” of ancestral “haplotypes” of linked marker alleles along a chromosome through families Recombination events prune marker haplotypes, defining “genetic interval” that must contain the disease gene Single-Nucleotide Polymorphisms (SNPs) [bi-allelic; ~1/50-300 bp; mostly used for association analysis] SNP1 Allele 1 CCGAGATCCAGAAATCCTGAACATAA SNP1 Allele 2 CTGAGATCCAGAAATCCTGAACATAA SNP2 Allele 1 CCGAGATCCAGAAATCCTGAACATAA SNP2 Allele 2 CCGAGATCCAGAAAGCCTGAACATAA • Occurrence/allele frequencies differ in different ethic groups/populations • Can be in genes (~4,000,000) on not (~8,000,000), can result in amino acid substitutions or not • Each occurs in local context (haplotype) of surrounding SNPs (in example above, SNP2 is on background of SNP1 C allele) Haplotype Map of Human Genome International HapMAP Project •Recombination breaks macro-patterns of polymorphic genotypes on the same chromosome into haplotypes •Recombination is not truly random, so very close polymorphism genotypes on the same chromosome cluster into ~10-50 kb haplotype blocks in which SNP alleles are in linkage disequilibrium (marker alleles within blocks tend to be co-inherited, because recombination within blocks is uncommon) •Blocks smaller in African than Caucasian or Asian pops. because African pop. is more ancient •HapMap genotyped SNPs in different populations to characterize haplotype block distributions Copy-Number Variants (CNVs) [bi-allelic] Basically are common genomic deletions, hundreds to tens of thousands of nucleotides in size May be detected by LD with local SNP patterns: Allele --1---1---1----1---2----1----2----1-----1----2----1----1----1----1--Allele --2---2---2----1---1----2----2----1-----1----2----2----2----1----2--CNV Allele --1---1—[ ]--1----2--- • Tens of thousands known • Like SNPS, occurrence/allele frequencies differ in different ethic groups/populations • Individually most are rare (< 1%), collectively common • Can be in genes or not, can include genes • NOT commonly definitively causal for human disease 1000 Genomes Project, UK10K Project International projects to sequence 1000/10000 genomes from different ethnic groups • Catalog human genetic variations (particularly SNPs, indels) – ~60,000,000 SNPs now known – Essential for sequence-based analysis of rare variants that may be causal for common diseases How Do You Find Disease Genes? I. Hypothesis-driven approaches Candidate gene association Candidate gene sequencing II. Hypothesis-free approaches Genomewide linkage Genomewide association (Genomewide expression) Genomewide sequencing Exome Full-genome Common, Complex Diseases • • • • • • • • • Asthma Autism Obesity Preterm birth Cleft lip/palate IBD Diabetes Cancers Common traits like height Common, Complex Diseases Utility of Experimental Approaches Common GWAS RISK ALLELE FREQUENCY Rare Re-Sequencing Linkage Small EFFECT SIZE (OR) Large How Do You Find Disease Genes? I. Hypothesis-driven approaches Candidate gene association Candidate gene sequencing II. Hypothesis-free approaches Genomewide linkage Genomewide association (Genomewide expression) Genomewide sequencing Exome Full-genome Hypothesis-Driven Approaches Candidate genes Depends on: biological hypothesis (biological candidate) positional hypothesis / information (positional candidate) Sometimes successful in Mendelian disorders Low yield in polygenic, multifactorial (“complex”) disorders—pathogenic sequence variants not obvious, often present in normal individuals Most hypotheses wrong! Candidate Gene Association Study Concept: Causal disease variation in gene suggested by known biology ‘tagged’ by nearby polymorphic DNA markers; test for co-occurrence. Because: DNA sequence variations very close together on the same piece of DNA will tend to not be separated by recombination over long periods, and so will be non-randomly co-inherited even on a populationbasis (“linkage disequilibrium”). Most hypotheses wrong! Candidate Gene Association Studies Compares SNP allele frequencies in cases versus controls (“case-control” study design) Easy statistics (Fisher exact test, Chi-square) Must Bonferroni correct for multiple-testing Must ethnically match cases and controls Easy, cheap Most powerful for common risk alleles Can detect common alleles with small allelespecific effects (i.e. “complex”, polygenic traits) Most common published type of “genetic study” Most hypotheses wrong! Most (~96%) such published studies wrong!! Three Fatal Flaws in Gene-by-Gene Case-Control Design • Must apply multiple-testing correction; true denominator often not known • Must ethnically match cases & controls; otherwise, differences in allele frequencies may reflect different genetic backgrounds of cases vs. controls • Positive studies result in publication bias “Population stratification” and false-positive case-control genetic association studies Population 1 Population 2 blue/green just indicates overall genetic background Disease Admixed Study Population 1/2 Cases Prof. Wizard’s Case-Control Study Eureka! Controls Hypothesis-Free Approaches Genome-Wide Association Studies (GWAS) Relatively recent approach (>300 published): •Genotype hundreds of thousands to millions of SNPs across genome using microarrays; extremely expensive •Case-control or family-based (trio) design •Requires no hypotheses about pathogenesis; can discover new genes •Can discover common alleles with small effects •Can provide very fine localization How Do You Find Disease Genes? I. Hypothesis-driven approaches Candidate gene association Candidate gene sequencing II. Hypothesis-free approaches Genomewide linkage Genomewide association (Genomewide expression) Genomewide sequencing Exome Full-genome Hypothesis-free approaches Genome-wide association studies (GWAS) • Study self-contained; can apply appropriate multiple testing correction - “Genomewide significance” P < 5 x 10-8 • Still requires ethnic matching of cases and controls - Can correct for population stratification by “Principal components” analysis - Can correct for residual “Genomic inflation factor” by “genomic control” • Can discover new, unknown genes; power similar to candidate gene case-control study • Case-control “associations” require independent confirmation The Genomewide Association Study (GWAS) Manolio TA. N Engl J Med 2010;363:166-176. Meta-Analysis of Multiple Genomewide Association Studies Genome-Wide Association Studies “Manhattan plot” Per-SNP -log(P values) across genome for association of SNP allele freq. differences between patients with generalized vitiligo versus controls (all Caucasian) Genome-Wide Association Studies • Very large number of SNPs tested (500,000 – 2,000,000) presents huge multiple-testing problem; requires at least ~1000 cases and ~1000 controls • Many SNPs in linkage disequilibrium (i.e. correlated); simple Bonferroni correction too strict (assumes independence) •“Significant” associations require confirmation by independent follow-up association study of specific SNPs to reduce multiple-testing complexity Personalized Medicine The case of the ‘missing heritability’ • Disease risk genes found by GWAS account for only a small fraction of genetic risk >Type 1 diabetes-- ~100 genes, ~70% of genetic risk 50% of risk due to HLA class II • Are there a virtually unlimited number of additional genes, each conferring small additional risk? >Maybe • Have we under-estimated fraction of genetic risk already accounted for? >Maybe. GWAS misses rare risk alleles • Have we over-estimated total genetic component of risk? >Maybe, but not ten-fold Hypotheses of Common, “Complex” Disease • Common disease, common variant hypothesis (Reich & Lander, 2001) versus • Rare variant hypothesis (Pritchard, 2001; Prixhard and Cox, 2002) Complex Diseases Utility of Experimental Approaches Common GWAS RISK ALLELE FREQUENCY Rare Re-Sequencing Linkage Small EFFECT SIZE (OR) Large Combined hypothesis-based and hypothesis-free approaches Deep re-sequencing • High-throughput DNA sequencing • Biological candidate genes • GWAS signals (specific genes or genes within regions) • Must distinguish potentially causal variants from non-pathological variation (1000 Genomes Project data will help) • Prioritize for follow-up functional analyses How Do You Find Disease Genes? I. Hypothesis-driven approaches Candidate gene association Candidate gene sequencing II. Hypothesis-free approaches Genomewide linkage Genomewide association (Genomewide expression) Genomewide sequencing Exome Full-genome Hypothesis-free approach Exome/Genome sequencing • High-throughput DNA sequencing - Genome - Exome (1% of genome) • Must distinguish potentially causal variants from non-pathological variation (1000 Genomes Project data will help) - Predict based on Mendelian inheritance - Compare across unrelated families • Prioritize for follow-up functional analyses Exome Sequencing in Mendelian Diseases Method E Exome = Gene coding regions; ~ 3 Mb (1% of genome) How Do You Find Disease Genes? Exome/Genome Sequencing in Mendelian Diseases There is a lot of genomic ‘noise’ There is a lot of noise!! E Variant Filtering in Exome/Genome Sequencing • Missense (non-synonymous) substitutions - Most rare (<1%) missense may be deleterious • Nonsense, frameshift mutations • Splice junction mutations • Exonic splice enhancer mutations • INDELs, CNVs, translocations • Regulatory Feature variants How Do You Find Disease Genes? Exome/Genome Sequencing in Mendelian Diseases Filtering Schemes E How Do You Find Disease Genes? Exome/Genome Sequencing in Mendelian Diseases Exome sequencing is rapidly becoming a fairly routine clinical test, costing ~$1000, ordered in lieu of tens of thousands of dollars worth of functional clinical tests in a patient one believes might have a genetic (principally single-gene Mendelian) cause for their disorder. Who will do the interpretation of the data, how will “variants of unknown significance” (VUS) be addressed, and what will that cost?