Hunting Disease Genes

1. Hunting Disease Genes Richard A. Spritz, M.D. April 13, 2015 richard.spritz@ucdenver.edu 303-724-3107 HMGP

2. Why Find Disease Genes? Accelerated by finding the disease gene

3. 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

4. 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

5. 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

6. Hunting for Disease Genes • 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

7. 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 How Do You Find Disease Genes?

8. Disease Gene Identification—“Functional Cloning” vs. “Positional Cloning”

9. Positional Cloning: Determine a Disease Gene’s Genomic Position, and then Identify the Gene Obviated by Human Genome Project

10. 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)

11. The First Goal of the HGP was to Assemble a High-Density Genome Map of Polymorphic Markers

12. 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 • Most hypotheses wrong! How Do You Find Disease Genes?

13. 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)

14. 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 Some Frequent - between 2 loci Linkage Tight Some Absent Absent Recombination 0% 1-49% 50% 50% • Unit of genetic “distance” is centiMorgan (cM) = 1% recombination/meiosis; ~ 1 Mb

15. Genetic Linkage Analysis • Statistical measure is LOD (log of odds) score • Significance level: LOD >3.0 for Mendelian trait LOD >3.3 for Polygenic trait Likelihood of data if loci linked at  LOD = Log10 Likelihood of data if loci unlinked

16. 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

18. “Genetic linkage analysis”Co-segregation of disease gene in “multiplex families” with alleles of polymorphic DNA “markers” (initially RFLPs)

19. “Microsatellites” (SSLPs; STRPs, SSRs) [multi-allelic; ~ 1/30,000 bp; mostly used for linkage analysis, forensics] ggctgcacacacacacacacacacacacatgctt ggctgcacacacacacacacacacacatgctt ggctgcacacacacacacacacacatgctt ggctgcacacacacacacacacatgctt ggctgcacacacacacacacatgctt

20. Can follow “segregation” of ancestral “haplotypes” of linked marker alleles along a chromosome through families

21. Recombination events prune marker haplotypes, defining “genetic interval” that must contain the disease gene

22. 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)

23. Haplotype Map of Human GenomeInternational 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 differentpopulations to characterize haplotype block distributions

24. 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

25. 1000 Genomes Project, UK10K ProjectInternational 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

26. 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 • Most hypotheses wrong! How Do You Find Disease Genes?

27. Common, Complex Diseases • Asthma • Autism • Obesity • Preterm birth • Cleft lip/palate • IBD • Diabetes • Cancers • Common traits like height

28. Common, Complex Diseases Utility of Experimental Approaches Common RISK ALLELE FREQUENCY Rare GWAS Re-Sequencing Linkage Small EFFECT SIZE (OR) Large

29. 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 How Do You Find Disease Genes?

30. 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! Hypothesis-Driven Approaches

31. 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 Study

32. 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 allele-specific effects (i.e. “complex”, polygenic traits) • Most common published type of “genetic study” • Most hypotheses wrong! • Most (~96%) such published studies wrong!!

33. 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

34. “Population stratification” and false-positive case-control genetic association studies Population 1Population 2 blue/green just indicates overall genetic background Disease Admixed Study Population 1/2 Prof. Wizard’s Case-Control Study CasesControls Eureka!

35. 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

36. 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 How Do You Find Disease Genes?

37. 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 Hypothesis-free approaches

38. The Genomewide Association Study (GWAS) Manolio TA. N Engl J Med 2010;363:166-176.

39. Meta-Analysis of Multiple Genomewide Association Studies

40. 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)

41. 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

42. 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

43. Hypotheses of Common, “Complex” Disease • Common disease, common variant hypothesis (Reich & Lander, 2001) versus • Rare variant hypothesis (Pritchard, 2001; Prixhard and Cox, 2002)

44. Complex Diseases Utility of Experimental Approaches Common RISK ALLELE FREQUENCY Rare GWAS Re-Sequencing Linkage Small EFFECT SIZE (OR) Large

45. Deep re-sequencing Combined hypothesis-based and hypothesis-free approaches • 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

46. 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 How Do You Find Disease Genes?

47. Exome/Genome sequencing Hypothesis-free approach • 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

48. E Exome Sequencing in Mendelian Diseases Method Exome = Gene coding regions; ~ 3 Mb (1% of genome)

49. E 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!!

50. 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 Variant Filtering in Exome/Genome Sequencing