Lecture 24 : Asscociation Genetics

November 20, 2015

Last Time

Coalescence and human origins

Human origins: Neanderthals and Denisovans

Coalescent simulations and hybridization

Adaptive significance of ancient introgression

Today Quantitative traits

Genetic basis

Heritability

Linking phenotype to genotype

QTL analysis introduction

Limitations of QTL

Association genetics

Quantitative traitQuantitative trait

16 64 76 8828 40 52Height

Mendelian traitMendelian traitIndividual

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12 11 22 22 11 22 12 11 22 12Genotype =

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. 1987. A primer of Population Genetics.

Quantitative traits are polygenic

Students at Connecticut Agricultural College, 1914

50 55 60 65 70 75 80 850

1

2

3

4

5

6

7x 10

4

As the number of loci controlling a trait increases, the distribution of trait values in a population becomes bell-shaped

Height vs GDP (1925-1949)

Baten 2006

1914

1996

Schilling et al. 2002. Amer. Stat. 56: 223-229

Influence of Environment on Human Height

By Country

Mean = 67 2.7 in.

Mean = 70 3 in.

6:54:10

Environment

+

Phenotype

=

Genotype

The phenotype is the outward manifestation of the genotype

The phenotype is the outward manifestation of the genotype

σ2P σ2

Eσ2G

Courtesy of Glenn Howe

Types of genetic variance (σ2G)

Additive (σ2A): effects of individual alleles

Dominance (σ2D): effects of allele interactions within

locus

Interaction (σ2I): effects of interactions among loci

(epistasis)

σ2G = σ2

A + σ2D + σ2

INon-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 = 0.5 h² = 0.9

http://psych.colorado.edu/~carey/hgss/hgssapplets/heritability/heritability1/heritability1.html

P P P

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

HEIG

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modified from D. Neale

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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

0.1

0.5

0.9

Step 4: Detect Associations between Markers and Phenotypes Single-marker associations are

simplest

Simple ANOVA, correcting for multiple comparisons

Log likelihood ratio: LOD (Log10 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

*TG

recombination throughevolutionary history

present-daychromosomesin natural population

*TG

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Slide courtesy of Dave Neale

HEIG

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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

http://www.1000genomes.org/

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: www.hgmd.cf.ac.uk

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:

http://www.snpedia.com

Slide adapted from Kermit Ritland

Complicating factor: Trait HeterogeneitySame 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 Diabetes present absent Total

present 8 29 37

absent 92 71 163

Total 100 100 200

Gm Haplotype

(1) Test for an association

21 = (ad - bc)2N .

(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)

= 14.62

Slide adapted from Kermit Ritland

Index of indian Heritage

Gm Haplotype

Percent with diabetes

0 Present

Absent

17.8

19.9

4 Present

Absent

28.3

28.8

8 Present

Absent

35.9

39.3

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

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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