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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. Low Heritability of Common SNPs.

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genomics medicine http biochem158 stanford edu
Genomics & Medicinehttp://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

low heritability of common snps
Low Heritability of Common SNPs

Odds Ratio

  • 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)

disease genes are often enriched in subpopulations
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.
summary of genome wide association studies
Summary ofGenome-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:
    • 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
genetic loci associated with hypertriglyceridemia http www ncbi nlm nih gov pubmed 20657596
Genetic Loci Associated with Hypertriglyceridemiahttp://www.ncbi.nlm.nih.gov/pubmed/20657596
slide6
Novel Rare Variants in GWAS Genes for Hypertriglyceridemia http://www.ncbi.nlm.nih.gov/pubmed/20657596
rare variant accumulation in hypertriglyceridemia http www ncbi nlm nih gov pubmed 20657596
Rare Variant Accumulation in Hypertriglyceridemia http://www.ncbi.nlm.nih.gov/pubmed/20657596
so what can we learn from personal genomics
So What Can We Learn fromPersonal Genomics?
  • Disease risk for common diseases
    • 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
    • Premarital genetic counseling
    • Preimplantation genetic diagnosis
    • Neonatal diagnosis
      • Amniocentesis
      • Chorion villus sampling (CVS)
      • Fetal cells in pregnant mothers blood
  • Familial traits, diseases and relationships
    • 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
    • Efficacy of common drugs
    • Adverse reactions to common drugs
  • Ancestry
    • 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)
choice of gwas studies
Choice of GWAS Studies
  • Common traits of broad interest
    • Prevalence of > 1%
    • Report Mendelian traits when possible
    • Focus on drug responses
  • Avoid false discoveries
    • 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.
informed for 23andme customers http informeddna com index php 23andme schedule appointment 23 html
INFORMED for 23andMe Customershttp://informeddna.com/index.php/23andme/schedule-appointment-23.html
dnadirect clinical genetic testing
DNAdirect: Clinical Genetic Testing

http://www.dnadirect.com/

dnadirect clinical genetic testing48
DNAdirect: Clinical Genetic Testing

http://www.dnadirect.com/web/

personal genomics references
Personal Genomics References
  • Clinical Assessment Incorporating a Personal Genome. Ashley, E. et al. (2010)

Lancet 375, 1525-1535.

  • Emerging genomic applications in coronary artery disease. Damani SB,

Topal EJ, JACC Cardiovasc. Intervention (2011). 4:473-482.

  • 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.

  • Clinical pharmacogenetics and potential application in personalized medicine.

Zhou et al., (2008) Curr Drug Metab. 9(8):738-84.

  • 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.

  • Web-based, participant-driven studies yield novel genetic associations for

common traits. Eriksson et al. (2010) PLoS Genetics 6, e1000993.

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