Medical variations

Gabor T. MarthBoston College Biology Department

BI543 Fall 2013February 5, 2013

Medical variations

Phenotypic effects are often caused by genetic variants

Many SNPs have phenotypic effects

Badano and Katsanis, NRG 2002

Some notable genetic diseases:

cystic fibrosis (Mendelian recessive)

sickle-cell anemia(Mendelian recessive)

Genetic variants may affect drug metabolism: Pharmacogenetics

Evans and Relling, Science 1999

Genetic variants in Pharmacogenetics

Evans and Rellig, Science 1999

Finding variants that cause genetic disease

Population genetics 101

• sequence variations are the result of mutation events TAAAAAT

TAACAAT

TAAAAAT TAAAAAT TAACAAT TAACAAT TAACAAT

TAAAAAT TAACAAT

TAAAAAT

MRCA• mutations are propagated down through generations

• and determine present-day variation patterns

Mendelian diseases have simple inheritance

genotype inheritance

Mendelian diseases have simple relationship betweengenotype + phenotype inheritance

Linkage analysis compares the transmission of marker genotype and phenotype in families

Sequence regions of the genome to determine which loci are linked with the trait.

Works well for Mendelian diseases

However, some diseases have complex inheritance

Badano and Katsanis, NRG 2002

A) Multiple genes may influence the trait.

B) E.g. retinitis pigmentosa requires heterozygosity for two genes.

Population genetics continued…

acggttatgtaga accgttatgtaga

accgttatgtaga

acggttatgtaga

acggttatgtaga

acggttatgtaga

acggttatgtaga

acggttatgtaga

acggttatgtaga

acggttatgtaga

accgttatgtaga

accgttatgtaga

accgttatgtaga

• because of recombination, DNA sequences may not have a unique common ancestor, hence phylogenetic analysis may not apply

Genetic mapping

Allelic association (linkage disequilibrium, LD)

• allelic association is the non-random assortment between alleles i.e. it measures how well knowledge of the allele state at one site permits prediction at another marker site functional site

• significant allelic association between a marker and a functional site permits localization (mapping) even without having the functional site in our collection

• allelic association, and the use of genetic markers is the basis for mapping functional alleles

Case-control association testing

• searching for markers with “significant” marker allele frequency differences between cases and controls; these marker signify regions of possible causative alleles

AF(cases)

AF(

contr

ols

)

clinical cases

clinical controls

• genotyping cases and controls at various polymorphisms

Genome-wide scans for human diseases

Klein et al, Science 2005

SNPs in Complement Factor H (CFH) gene are associated with Age-related Macular Degeneration (AMD)

Where is the missing heritability of disease?

Manolio et al. Nature 2009

Variant discovery in population sequencing data

Intro

• International project to construct a foundational data set for human genetics– Discover virtually all common human variations by investigating

many genomes at the base pair level– Consortium with multiple centers, platforms, funders

• Aims• Discover population level human genetic variations of all types

(95% of variation > 1% frequency)• Define haplotype structure in the human genome• Develop sequence analysis methods, tools, and other reagents

that can be transferred to other sequencing projects

EUROPE

AMERICAS

AFRICA SOUTH ASIA

LWKMSL ESN YRI GIH BEBSTU ITU PJL

ASW

MXL

PUR

CLM

PEL

EAST ASIA

CHB

JPT

CHS

CDX

KHV

TSIIBS GBR FIN

Spain

Finland

Beijing, China Tokyo, Japan

Yunnan, ChinaVietnam

Hunan & Fujian, China

Los Angeles,

USAPuerto Rico

Medellín, Colombia

Lima, Peru

PakistanBanglade

sh

Great Britain

Italy

ACB Barbados

Colorado, USA

Southwest, USA Houston, USA

The GambiaSierra, Leone

Kenya Nigeria

GWD

New 1000 Genomes Population

HapMap 3 PopulationInternational HapMap Population

1000 Genomes Project Populations

CEU

Utah, USA

~2,500 samples representing all continents

Sequencing strategies

Low-coverage whole-genome data

Deep-coverage whole-exome data

1000 Genome Project variants

We know 99% of SNP variants in any individual

Date Fraction not in dbSNP

February, 2000 98%

February, 2001 80%

April, 2008 10%

February, 2011 2%

May 2011 1%

Ryan Poplin, David Altshuler

38M SNPs are known as of Phase 1 of the 1000 Genomes Project

Newly discovered SNPs are mostly rare

(Ryan Poplin)

12M

10M

8M

4M

2M

0

6M

num

ber

of

site

s

frequency of alternate allele 0.001 0.01 0.1 1.0

Deep exome vs. low-cov. WG sequencing

Properties of low-frequency variation

Rare SNPs enriched for functional variants

Challenges for finding rare disease variants

Bansal et al. NRG 2010

Concepts for method development

Bansal et al. NRG 2010

Concepts for method development

Bansal et al. NRG 2010

A rare variant predictor (VAAST)

• Instead of individual variants, use a larger unit for comparison e.g. a gene

• Weight predicted impact of variant (e.g. non-synonymous change, large allele frequency difference etc.)

Yandell et al. GR 2011

Systems bringing high-res genetic knowledge to the “bedside”