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Medical variations. Gabor T. Marth Boston College Biology Department BI543 Fall 2013 February 5, 2013. Medical variations. Phenotypic effects are often caused by genetic variants. Many SNPs have phenotypic effects. Some notable genetic diseases: cystic fibrosis - PowerPoint PPT Presentation
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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
acggttatgtagaacggttatgtaga
acggttatgtagaacggttatgtagaacggttatgtaga
accgttatgtagaaccgttatgtagaaccgttatgtaga
• 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(c
ontro
ls)
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 ASIALWKMSL ESN YRI GIH BEBSTU ITU PJL
ASW
MXL
PUR
CLM
PEL
EAST ASIACHB
JPT
CHS
CDX
KHV
TSIIBS GBR FIN
Spain
Finland
Beijing, China Tokyo, Japan
Yunnan, ChinaVietnam
Hunan & Fujian, China
Los Angeles,
USA Puerto RicoMedellín, 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 o
f 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”