Lecture 24: Asscociation Genetics November 20, 2015

Last Time uCoalescence and human origins uHuman origins: Neanderthals and Denisovans uCoalescent simulations and hybridization uAdaptive significance of ancient introgression

Today  Quantitative traits  Genetic basis  Heritability  Linking phenotype to genotype  QTL analysis introduction  Limitations of QTL  Association genetics

Quantitative trait Height Mendelian trait Individual Genotype = Allele A1 Allele A2 Courtesy of Glenn Howe

Hartl and Clark 2007  3 loci, 2 additive alleles  Uppercase alleles contribute 1 unit to phenotype (e.g., shade of color) Hartl, D A primer of Population Genetics.

Quantitative traits are polygenic Students at Connecticut Agricultural College, 1914 As the number of loci controlling a trait increases, the distribution of trait values in a population becomes bell-shaped

Height vs GDP ( ) Baten Schilling et al Amer. Stat. 56: Influence of Environment on Human Height By Country Mean = 67  2.7 in. Mean = 70  3 in. 6:5 4:10

Environment + Phenotype = Genotype The phenotype is the outward manifestation of the genotype σ2Pσ2P σ2Eσ2E σ2Gσ2G Courtesy of Glenn Howe

Types of genetic variance (σ 2 G )  Additive (σ 2 A ): effects of individual alleles  Dominance (σ 2 D ): effects of allele interactions within locus  Interaction (σ 2 I ): effects of interactions among loci (epistasis) σ 2 G = σ 2 A + σ 2 D + σ 2 I Non-additive Main cause for resemblance between relatives

Heritability  Phenotype vs Genotype Var(phenotype) = Var(genotype) + Var(environment)  Heritability: Var(genotype) / Var(phenotype)  Two types of heritability  Broad-Sense Heritability includes all genetic effects: dominance, epistasis, and additivity − For example, the degree to which clones or monozygotic twins have the same phenotype  Narrow-Sense Heritability includes only additive effects − For example, degree to which offspring resemble their parents

Heritability (continued)  Characteristic of a trait measured in a particular population in a particular environment  Best estimated in experiments (controlled environments)  Estimated from resemblance between relatives  The higher the heritability, the better the prediction of genotype from phenotype (and vice versa) h² = 0.1 h² = 0.5h² = PPP G G G

Identifying Genes Underlying Quantitative Traits  Many individual loci are responsible for quantitative traits, even those with high heritability  Identification of these loci is a major goal of breeding programs  Allows mechanistic understanding of adaptive variation  Methods usually rely on correlations between molecular marker polymorphisms and phenotypes

Quantitative Trait Locus Mapping HEIGHT GENOTYPE BBBbbb          modified from D. Neale abcabc ABCABC ABCABC Parent 1Parent 2 X abcabc F1F1 F1F1 X ABCABC abcabc ABCABC abcabc ABcABc aBcaBc aBcaBc AbcAbc ABcABc aBcaBc AbcAbc AbcAbc abcabc AbcAbc ABCABC ABcABc AbcAbc aBcaBc aBcaBc AbcAbc aBcaBc aBcaBc BbBb BbBB bb BBBb

Quantitative Trait Locus Analysis  Step 1: Make a controlled cross to create a large family (or a collection of families)  Parents should differ for phenotypes of interest  Segregation of trait in the progeny  Step 2: Create a genetic map  Large number of markers phenotyped for all progeny  Step 3: Measure phenotypes  Need phenotypes with high heritability

Step 1: Construct Pedigree  Cross two individuals with contrasting characteristics  Create population with segregating traits  Ideally: inbred parents crossed to produce F1s, which are intercrossed to produce F2s  Recombinant Inbred Lines created by repeated intercrossing  Allows precise phenotyping, isolation of allelic effects Grisel 2000 Alchohol Research & Health 24:169

Step 2: Construct Genetic Map  Number of recombinations between markers is a function of map distance  Gives overview of structure of entire genome  Anonymous markers are cheap and efficient: AFLP, Genotyping by Sequencing  Codominant markers much more informative: SSR, SNP  Genotyping by Sequencing gives best of both worlds: cheap, abundant, codominant markers!

Step 3: Determine Phenotypes of Offspring  Phenotype must be segregating in pedigree  Must differentiate genotype and environment effects  How?  Works best with phenotypes with high heritability

Step 4: Detect Associations between Markers and Phenotypes  Single-marker associations are simplest  Simple ANOVA, correcting for multiple comparisons  Log likelihood ratio: LOD (Log 10 of odds)  If QTL is between two markers, situation more complex  Recombination between QTL and markers (genotype doesn't predict phenotype)  'Ghost' QTL due to adjacent QTL  Use interval mapping or composite interval mapping  Simultaneously consider pairs of loci across the genome

Step 5: Identify underlying molecular mechanisms QTG: Quantitative Trait Gene QTN: Quantitative Trait Nucleotide chromosome Genetic Marker Adapted from Richard Mott, Wellcome Trust Center for Human Genetics QTL

QTL Limitations  Huge regions of genome underly QTL, usually hundreds of genes  How to distinguish among candidates?  Biased toward detection of large-effect loci  Need very large pedigrees to do this properly  Limited genetic base: QTL may only apply to the two individuals in the cross!  Genotype x Environment interactions rampant: some QTL only appear in certain environments

Linkage Disequilibrium and Quantitative Trait Mapping  Linkage and quantitative trait locus (QTL) analysis  Need a pedigree and moderate number of molecular markers  Very large regions of chromosomes represented by markers  Association Studies with Natural Populations  No pedigree required  Need large numbers of genetic markers  Small chromosomal segments can be localized  Many more markers are required than in traditional QTL analysis Cardon and Bell 2001, Nat. Rev. Genet. 2: 91-99

Association Mapping ancestral chromosomes * T G recombination through evolutionary history present-day chromosomes in natural population * T G * T A C G C A * T G CA Slide courtesy of Dave Neale HEIGHT GENOTYPE CCTCTT         

Next-Generation Sequencing and Whole Genome Scans  The $1000 genome is here  Current cost with Illumina HiSeq X10 is about $1000 for 30X depth  Tens of thousands of human genomes have now been sequenced at low depth  Can detect most polymorphisms with frequency >0.01  True whole genome association studies now possible at a very large scale  Direct to Consumer Genomics: 23 & Me and other genotyping services

Commercial Services for Human Genome-Wide SNP Characterization NATURE|Vol 437|27 October 2005  Assay 1.2 million “tag SNPs” scattered across genome using Illumina BeadArray technology  Ancestry analyses and disease/behavioral susceptibility

Identifying genetic mechanisms of simple vs. complex diseases  Simple (Mendelian) diseases: Caused by a single major gene  High heritability; often can be recognized in pedigrees  Example: Huntington’s, Achondroplasia, Cystic fibrosis, Sickle Cell Anemia  Tools: Linkage analysis, positional cloning  Over 2900 disease-causing genes have been identified thus far: Human Gene Mutation Database:  Complex (non-Mendelian) diseases: Caused by the interaction between environmental factors and multiple genes with minor effects  Interactions between genes, Low heritability  Example: Heart disease, Type II diabetes, Cancer, Asthma  Tools: Association mapping, SNPs !!  Over 35,000 SNP associations have been identified thus far: Slide adapted from Kermit Ritland

Complicating factor: Trait Heterogeneity Same phenotype has multiple genetic mechanisms underlying it Slide adapted from Kermit Ritland

Case-Control Example: Diabetes  Knowler et al. (1988) collected data on 4920 Pima and Papago Native American populations in Southwestern United States  High rate of Type II diabetes in these populations  Found significant associations with Immunoglobin G marker (Gm)  Does this indicate underlying mechanisms of disease? Knowler et al. (1988) Am. J. Hum. Genet. 43: 520

Type 2 DiabetespresentabsentTotal present82937 absent Total Gm Haplotype (1) Test for an association  2 1 = (ad - bc) 2 N. (a+c)(b+d)(a+b)(c+d) Case-control test for association (case=diabetic, control=not diabetic) Question: Is the Gm haplotype associated with risk of Type 2 diabetes??? (2) Chi-square is significant. Therefore presence of GM haplotype seems to confer reduced occurence of diabetes = [(8x71)-(29x92)] 2 (200) (100)(100)(37)(163) = Slide adapted from Kermit Ritland

Index of indian Heritage Gm Haplotype Percent with diabetes 0Present Absent Present Absent Present Absent Case-control test for association (continued) Question: Is the Gm haplotype actually associated with risk of Type 2 diabetes??? The real story: Stratify by American Indian heritage 0 = little or no indian heritage; 8 = complete indian heritage Conclusion:The Gm haplotype is NOT a risk factor for Type 2 diabetes, but is a marker of American Indian heritage Slide adapted from Kermit Ritland

 Assume populations are historically isolated  One has higher disease frequency by chance  Unlinked loci are differentiated between populations also  Unlinked loci show disease association when populations are lumped together Population structure and spurious association Alleles at neutral locus Alleles causing susceptibility to disease Population with low disease frequency Population with high disease frequency Gene flow barrier

Association Study Limitations  Population structure: differences between cases and controls  Genetic heterogeneity underlying trait  Random error/false positives  Inadequate genome coverage  Poorly-estimated linkage disequilibrium