Transcript Personal Genomics

Genomics & Medicine
http://biochem158.stanford.edu/
Personal Genomics
http://biochem158.stanford.edu/Personal%20Genomics.html
The Lancet 2010, 375: 1525-1535.
Doug Brutlag
Professor Emeritus of Biochemistry & Medicine
Stanford University School of Medicine
Doug Brutlag 2011
Odds Ratio
Low Heritability of Common SNPs
• Rare High Penetrance Variants Carry High Risk
• Common SNPs Carry Low Risk
• Multiple Variants May Increase Risk Synergistically
• Common SNPs Associated with Genes Containing High Risk Alleles
• Common SNPs Associations can Suggest Regions to Sequence in Cohorts or
Trios or Subpopulations
Manolio et al. Nature 461, 747-753
(2009)
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2011
Disease Genes are Often Enriched in
Subpopulations
• Subpopulations are often enriched for disease
alleles
• Subpopulations can cause synthetic SNP
associations
• Focusing on a subpopulations will eliminate synthetic
SNP associations
• Focusing on subpopulations eliminates need for
population stratification adjustments
• Egypt is a haplotype heaven!
– Highest frequency of genetic (SNP) variations
– High numbers of genetic subpopulations due to multiple
migrations and invasions
– Greeks, Romans, Turks, Persians etc.
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Summary of
Genome-Wide Association Studies
• Genome-wide association studies make no assumptions about
disease mechanism or cause
• Genome-wide association studies usually discover only genetic
correlations, not causal mutations
• Genome-wide associations suggest:
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Genes and regions one must analyze by re-sequencing for causal alleles
Subpopulations that may be enriched for causal or preventive alleles
Genes and gene products for functional and structural studies
Genes to examine for regulatory studies
• Genome-wide association studies coupled with proper
biological and structural studies can lead to:
– Unexpected causes for disease
– Novel mechanisms for disease (missense mutations, regulatory changes,
alternative splicing, copy number variation etc.)
– Multiple genes and multiple pathways involved in disease
– Novel diagnostics and prognosis
– Novel treatments
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Genetic Loci Associated with Hypertriglyceridemia
http://www.ncbi.nlm.nih.gov/pubmed/20657596
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Novel Rare Variants in GWAS Genes for Hypertriglyceridemia
http://www.ncbi.nlm.nih.gov/pubmed/20657596
Excess of rare variants in genes identified by genome-wide
association study of hypertriglyceridemia
© 2010 Nature America, Inc. All rights reserved.
Christopher T Johansen1, Jian Wang1, Matthew B Lanktree1, Henian Cao1, Adam D McIntyre1, Matthew R Ban1,
Rebecca A Martins1, Brooke A Kennedy1, Reina G Hassell 1, Maartje E Visser 2,3, Stephen M Schwartz4,
Benjamin F Voight 5,6, Roberto Elosua7, Veikko Salomaa8, Christopher J O’Donnell 9–11, Geesje M Dallinga-Thie2,3,
Sonia S Anand12, Salim Yusuf 12, Murray W Huff 1,13, Sekar Kathiresan5,6 & Robert A Hegele1,13
Genome-wide association studies (GWAS) have identified
multiple loci associated with plasma lipid concentrations1–5.
Common variants at these loci together explain <10%
of variation in each lipid trait4,5. Rare variants with large
individual effects may also contribute to the heritability of
lipid traits6,7; however, the extent to which rare variants affect
lipid phenotypes remains to be determined. Here we show an
accumulation of rare variants, or a mutation skew, in GWASidentified genes in individuals with hypertriglyceridemia
(HTG). Through GWAS, we identified common variants
in APOA5, GCKR, LPL and APOB associated with HTG.
Resequencing of these genes revealed a significant burden of
154 rare missense or nonsense variants in 438 individuals with
HTG, compared to 53 variants in 327 controls (P = 6.2 × 10−8),
corresponding to a carrier frequency of 28.1% of affected
individuals and 15.3% of controls (P = 2.6 × 10−5). Considering
rare variants in these genes incrementally increased the
proportion of genetic variation contributing to HTG.
GWAS have identified novel and known loci associated with
population-based plasma lipid concentrations1–5. Despite the
robustness of these associations, the proportion of variability
explained by GWAS-identified loci is relatively modest, <10% in
most studies4,5. Although vastly expanded study sample sizes continue to reveal new associations, each newly associated variant has
an incrementally smaller effect size and contributes only marginally
to the cumulative variation of each lipid phenotype6. This suggests
that GWASof population-based subjects may be reaching the limits
of their ability to reveal genetic variation underlying complex traits.
A question that has arisen is whether additional forms of genetic
variation, such as rare variants with large individual effects, could
contribute to the heritability of complex traits such as plasma lipid
concentrations6,7. Although the mechanistic basis for the association between lipid traits and most of the common variants discovered in GWAS is still largely unknown, it remains possible that rare
variants in GWAS-identified genes may contribute significantly to
lipid phenotypes.
Studying subjects at the extremes of a quantitative phenotype
distribution hasproven useful in identifying functional rarevariants8–12.
Using missense-accumulation analysis in genes defined a priori as
likely to contain rare variants, studies can statistically quantify a
burden of mutations in subjects with severe phenotypes, before functional assessment of each variant. Primary HTG is one such complex
polygenic disease, broadly defined by fasting plasma triglyceride
concentrations above the ninety-fifth percentile13. Resequencing of
triglyceride-modulating candidate genes has implicated both common
and rare variants in HTG disease pathophysiology9,14–16; however,
the majority of phenotypic variation underlying severe HTG remains
unattributed17. Our objectives were (i) to perform an unbiased GWAS
of individuals with HTG to identify common variants associated with
HTG, and (ii) to resequence coding regions of candidate genes in loci
reaching genome-wide significance to evaluate the burden of rare
variants in individuals with HTG compared with controls. Here we
show that loci found to be associated with HTG by GWASalso harbor
a significant excess of rare variants.
In total, 555 individuals with HTG and 1,319 controls were included
in two cohorts of the study: the GWAS cohort included 463 affected
individuals and 1,197 controls, and the sequencing cohort included
438 affected individuals and 327 controls. Individuals with HTG were
unrelated subjects diagnosed with Fredrickson hyperlipoproteinemia
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Rare Variant Accumulation in Hypertriglyceridemia
http://www.ncbi.nlm.nih.gov/pubmed/20657596
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So What Can We Learn from
Personal Genomics?
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Disease risk for common diseases
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Genetic predisposition towards a disease (relative risk or odds ratio)
Genetic versus environmental contributions to disease (penetrance)
How to alter your environment and behavior to avoid the disease
Disease Carrier status
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Premarital genetic counseling
Preimplantation genetic diagnosis
Neonatal diagnosis
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Familial traits, diseases and relationships
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Known family diseases (breast cancers, colorectal cancer, lysosome storage diseases, etc.)
Paternity (10% of people do not know their true biological father)
Maternity (about 1% of people do not know their true biological mother)
Inbreeding and incest lead to increased homozygosity and recessive diseases
Orphans can find family relations
Pharmacogenomics and Pharmacogenetics: Drug susceptibility
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Amniocentesis
Chorion villus sampling (CVS)
Fetal cells in pregnant mothers blood
Efficacy of common drugs
Adverse reactions to common drugs
Ancestry
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One can follow maternal line using mitochondrial DNA SNPs
Males can follow paternal line using Y chromosome SNPs
Shared haplotypes with recent relatives (up to 5th cousins)
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23andMe
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23andMe Kit
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23andMe Spittoon
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23andMe Sample Tube
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23andMe Tube in Envelope
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23andMe Fedex Mailer
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Choice of GWAS Studies
• Common traits of broad interest
– Prevalence of > 1%
– Report Mendelian traits when possible
– Focus on drug responses
• Avoid false discoveries
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Large case-control studies > 750 cases
Highly significant expectation values (<0.01 errors)
Published in reputable journals
Studies that have been replicated
• May impute highly linked missing SNPs
• Calculate likelihood and odds ratio using
customers ethnicity as detected
• Distinguish preliminary studies (non-replicated or
smaller sample sizes) from established research.
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23andMe Login
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23andMe Disease Risks
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23andME Opt-In Statement
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23andMe Carrier Status
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23andMe Carrier Status for
Alpha-1 Antitrypsin Deficiency
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23andMe Drug Responses
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Clopidogrel (Plavix®) Efficacy
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23andMe Traits
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23andMe Traits
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23andMe Maternal Inheritance
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23andMe Paternal Inheritance
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23andMe Relative Finder
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What is a Fifth Cousin?
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23andMe Ancestry Painting
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23andMe Global Similarity
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23andMe Ancestry Labs
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23andWe Discoveries
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23andWe Discoveries
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INFORMED Medical Decisions
http://informeddna.com/
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INFORMED for 23andMe Customers
http://informeddna.com/index.php/23andme/schedule-appointment-23.html
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Navigenics
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Navigenics
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Navigenics
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Navigenics
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Navigenics
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Navigenics
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Navigenics
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Navigenics
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Navigenics
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Navigenics Compass Program
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Navigenics Conditions Covered
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DNAdirect: Clinical Genetic Testing
http://www.dnadirect.com/
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DNAdirect: Clinical Genetic Testing
http://www.dnadirect.com/web/
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Personal Genomics References
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Clinical Assessment Incorporating a Personal Genome. Ashley, E. et al. (2010)
Lancet 375, 1525-1535.
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Emerging genomic applications in coronary artery disease. Damani SB,
Topal EJ, JACC Cardiovasc. Intervention (2011). 4:473-482.
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Clinical applicability of sequence variations in genes related to drug
metabolism. Stojiljkovic M, Patrinos GP, Pavlovic S. (2011) Curr Drug Metab.
1;12(5):445-54.
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Clinical pharmacogenetics and potential application in personalized medicine.
Zhou et al., (2008) Curr Drug Metab. 9(8):738-84.
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Genes, mutations, and human inherited disease at the dawn of the age of
personalized genomics. Cooper et al (2010) Hum Mutat. 31(6):631-55.
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Web-based, participant-driven studies yield novel genetic associations for
common traits. Eriksson et al. (2010) PLoS Genetics 6, e1000993.
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