Download - Medical variations Gabor T. Marth Boston College Biology Department BI543 Fall 2013 February 5, 2013
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Medical variations
Gabor T. MarthBoston College Biology Department
BI543 Fall 2013February 5, 2013
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Medical variations
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Phenotypic effects are often caused by genetic variants
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Many SNPs have phenotypic effects
Badano and Katsanis, NRG 2002
Some notable genetic diseases:
cystic fibrosis (Mendelian recessive)
sickle-cell anemia(Mendelian recessive)
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Genetic variants may affect drug metabolism: Pharmacogenetics
Evans and Relling, Science 1999
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Genetic variants in Pharmacogenetics
Evans and Rellig, Science 1999
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Finding variants that cause genetic disease
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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
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Mendelian diseases have simple inheritance
genotype inheritance
Mendelian diseases have simple relationship betweengenotype + phenotype inheritance
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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
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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.
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Population genetics continued…
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• because of recombination, DNA sequences may not have a unique common ancestor, hence phylogenetic analysis may not apply
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Genetic mapping
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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
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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
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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)
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Where is the missing heritability of disease?
Manolio et al. Nature 2009
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Variant discovery in population sequencing data
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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
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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
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Sequencing strategies
Low-coverage whole-genome data
Deep-coverage whole-exome data
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1000 Genome Project variants
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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
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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
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Deep exome vs. low-cov. WG sequencing
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Properties of low-frequency variation
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Rare SNPs enriched for functional variants
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Challenges for finding rare disease variants
Bansal et al. NRG 2010
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Concepts for method development
Bansal et al. NRG 2010
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Concepts for method development
Bansal et al. NRG 2010
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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
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Systems bringing high-res genetic knowledge to the “bedside”