1 / 43

Rare and common variants: twenty arguments G.Gibson

Rare and common variants: twenty arguments G.Gibson. Homework 3 Mylène Champs Marine Flechet Mathieu Stifkens. Introduction Summary Rare allele model Infinitesimal model Conclusion. Content : Rare and common variants. Introduction Summary Rare allele model

duante
Download Presentation

Rare and common variants: twenty arguments G.Gibson

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Rare and common variants: twenty arguments G.Gibson Homework 3 Mylène Champs Marine Flechet Mathieu Stifkens Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  2. Introduction • Summary • Rare allele model • Infinitesimal model • Conclusion Content : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  3. Introduction • Summary • Rare allele model • Infinitesimal model • Conclusion Content : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  4. Genome-wide association studies (GWASs) identify genetic factors that influence health and disease. • First model used : CDCV (Common disease Common variant) = a small number of common variants can explain the percentage of disease risk. • This model is not used anymore because of the “missing heritability problem”. A few loci with moderate effect cannot explain several percent of disease susceptibility. Introduction: Rare and common variants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  5. Introduction • Summary • Rare allele model • Infinitesimal model • Conclusion Content : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  6. Rare allele model • Presentation of the model • Arguments « in favour » • Arguments « against » Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  7. Rare allele model – Presentation   • Model known as « many rare alleles of large effect ». • The variance for a disease is due to rare variants (allele frequency<1%) which are highly penetrant (large effect). • Example: Schizophrenia = collection of many similar conditions that are attributable to rare variants. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  8. Rare allele model – Presentation Causal variant effects (yellow dots) may be large in a few individuals but are not common enough to represent a “hit”in a GWAS. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  9. Rare allele model • Presentation of the model • Arguments « in favour » • Arguments « against » Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  10. Rare allele model – « In favour » • Evolutionnarytheorypredictsthatdiseaseallelesshouldbe rare[1] ; • Empirical population genetic data shows thatdeleteriousvariants are rare[1] ; • Rare copy numbervariantscontribute to severalcomplexpsychologicaldisorders[1] ; • Many rare familial disorders are due to rare alleles of large effects[1]; • Synthetic association mayexplaincommonvariantseffects[1] . Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  11. Rare allele model – « In favour » • Evolutionnarytheorypredictsthatdiseaseallelesshouldbe rare[1] ; • Empirical population genetic data shows thatdeleteriousvariants are rare[1] ; • Rare copy numbervariantscontribute to severalcomplexpsychologicaldisorders[1] ; • Many rare familial disorders are due to rare alleles of large effects[1]; • Synthetic association mayexplaincommonvariantseffects[1] . Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  12. Evolutionnarytheorypredictsthatdiseaseallelesshouldbe rare[1] : • Deleteriousalleles are • created by mutation; • removed by purifyingselection. • Rate(creation) > rate (removal) Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  13. Rare copy numbervariantscontribute to severalcomplexpsychologicaldisorders[1] : • CNVs : hemizygousdeletion – local duplication; • Promotediseasesuch as schyzophrenia and autism and modifyitsseverity . Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  14. Synthetic association mayexplaincommonvariantseffects[1]: LD Data [2] Summary : Rare and commonvariants For commonvariantswhich do not explainmuchpercentage of the diseasesusceptibility Rare variantsincreasethis case risk. Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  15. Rare allele model • Presentation of the model • Arguments « in favour » • Arguments « against » Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  16. Rare allele model – « Against » • Analysis of GWAS data is not consistent with rare variantsexplanations[1] ; • Sibling recurrence rates are greaterthanwouldbeexpected by the postulatedeffectsizes of rare variants[1] ; • Rare variants do not obviously have additive effects[1] ; • Epidemiological transitions cannotbeattributed to rare variants[1] ; • GWAS associations are consistent across populations[1] ; Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  17. Rare allele model – « Against » • Analysis of GWAS data is not consistent with rare variantsexplanations[1] ; • Sibling recurrence rates are greaterthanwouldbeexpected by the postulatedeffectsizes of rare variants[1] ; • Rare variants do not obviously have additive effects[1] ; • Epidemiological transitions cannotbeattributed to rare variants[1] ; • GWAS associations are consistent across populations[1] ; Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  18. Analysis of GWAS data is not consistent with rare variants explanations[1] • Rare variants cannot be the predominant source of heritabilily; • There should be many of them with large size and effect. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  19. Rare variants do not obviously have additive effects[1] • Genetic associations are known to be additive whereas rare variants interact multiplicatively and they have dominant effect; • However on the statistical side rare variants induce additivity effects. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  20. Epidemiological transitions cannot be attributed to rare variants[1] • The change of prevalence of some diseases is too fast; • The model can not explain the influence of environmental variable. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  21. Introduction • Summary • Rare allele model • Infinitesimal model • Conclusion Content : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  22. Infinitesimal model • Presentation of the model • Arguments « in favour » • Arguments « against » Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  23. Infinitesimal model – Presentation • Known as « common » model or many common variants of small effects. • This is the model used in GWASs. • Common variants are the major source of genetic variance for disease susceptibility. • Hundreds or thousands of loci of small effect contribute in each case. • Example : Height or BMI studies, hundred of loci have been found but they don’t explain all of the genetic variance. This problem is called the « missing heritability problem ». Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  24. Infinitesimal model – Presentation Significant “hits” of common variants with small effects. Several SNPs within a linkage disequilibrium (LD) block are associated with the trait [1]. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  25. Infinitesimal model • Presentation of the model • Arguments « in favour » • Arguments « against » Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  26. Infinitesimal model– « In favour » • The infinitesimal model underpins standard quantitative genetictheory[1] ; • Common variantscollectively capture the majority of the genetic variance in GWASs[1] ; • Variation in endophenotypesisalmostcertainly due to commonvariants[1] ; • Model organismresearch supports commonvariants contributions to complexphenotypes[1] ; • GWASs have successfullyidentifiedthousands of commonvariants[1] . Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  27. Infinitesimal model– « In favour » • The infinitesimal model underpins standard quantitative genetictheory[1] ; • Common variantscollectively capture the majority of the genetic variance in GWASs[1] ; • Variation in endophenotypesisalmostcertainly due to commonvariants[1] ; • Model organismresearch supports commonvariants contributions to complexphenotypes[1] ; • GWASs have successfullyidentifiedthousands of commonvariants[1]. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  28. The infinitesimal model underpins standard quantitative genetic theory[1] : • High heritability ; • No results were against the infinitesimal model. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  29. Common variantscollectively capture the majority of the genetic variance in GWASs[1]: • Capture more of the genetic variance by using all significantSNPs; • Variance isattributed to hundreds of loci. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  30. GWASs have successfullyidentifiedthousands of commonvariants[1] : • Unrealisticassumptions of the effect size ; • Increasingsamplesallows to determine more loci. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  31. Infinitesimal model • Presentation of the model • Arguments « in favour » • Arguments « against » Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  32. Infinitesimal model – « Against » • The QTL paradox[1] ; • The abscence of blendinginheritence[1] ; • Demographicphenomenasuggest more than one simple common-variant model[1] ; • Very few commonvariants for disease have been functionnalyvalidated[1] ; • Whataccounts for the missingheritability[1] ? Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  33. Infinitesimal model – « Against » • The QTL paradox[1] ; • The abscence of blendinginheritence[1] ; • Demographicphenomenasuggest more than one simple common-variant model[1] ; • Very few commonvariants for disease have been functionnalyvalidated[1] ; • Whataccounts for the missingheritability[1] ? Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  34. The QTL paradox[1] • We cannot find QTLs detected in pedigrees and in experimental crosses; • Explanations:-> QTLs are rare variants that only contribute in that cross.-> Each cross captures only a small fraction of genetic variance in a population. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  35. The abscence of blending inheritence[1] • The granularity in the distribution of risks and phenotypic trait variation should decrease with the crossing of two unrelated poeple; • However we observe higher risks than the model predicted; • For example : • We can observe that in somefamilycomplexphenotype traits are recurrent. Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  36. What accounts for the missing heritability[1]? • The model does not take into account the missing heritability problem; • But the problem reallyexists ! Summary : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  37. Introduction • Summary • Rare allele model • Infinitesimal model • Conclusion Content : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  38. Which model would you choose ? Conclusion : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  39. Which model would you choose ? • Both ! • We should learn how to use the two models together because they both have their place in the current research. • Idea : Integrate rare and common variants effects together. Conclusion : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  40. The common variants establish the background liability to a disease and this liability can be modified by the rare variants with large effects [1]. Conclusion : Rare and commonvariants Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  41. Thankyou for your attention ! Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  42. [1] G. GIBSON : Rare and common variants: twenty arguments. Nat. Rev. Genet., 13(2):135145, Feb 2012. [2] Bioinformatics course – GWAS studies, K. VAN STEEN References : Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

  43. Do you have any question(s) ? Bioinformatics - GBIO0009-1 - K.Van Steen University of Liège

More Related