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

Content : Rare and common variants Introduction Summary ◦Rare allele model ◦Infinitesimal model Conclusion Bioinformatics - GBIO K.Van Steen University of Liège2

Content : Rare and common variants Introduction Summary ◦Rare allele model ◦Infinitesimal model Conclusion Bioinformatics - GBIO K.Van Steen University of Liège3

Introduction: Rare and common variants Introduction: Rare and common variants ◦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. Bioinformatics - GBIO K.Van Steen University of Liège4

Content : Rare and common variants Introduction Summary ◦Rare allele model ◦Infinitesimal model Conclusion Bioinformatics - GBIO K.Van Steen University of Liège5

Summary : Rare and common variants Rare allele model ◦Presentation of the model ◦Arguments « in favour » ◦Arguments « against » Bioinformatics - GBIO K.Van Steen University of Liège6

Summary : Rare and common variants 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. Bioinformatics - GBIO K.Van Steen University of Liège7

Summary : Rare and common variants 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. Bioinformatics - GBIO K.Van Steen University of Liège8

Summary : Rare and common variants Rare allele model ◦Presentation of the model ◦Arguments « in favour » ◦Arguments « against » Bioinformatics - GBIO K.Van Steen University of Liège9

Summary : Rare and common variants Rare allele model – « In favour » ◦Evolutionnary theory predicts that disease alleles should be rare [1] ; ◦Empirical population genetic data shows that deleterious variants are rare [1] ; ◦Rare copy number variants contribute to several complex psychological disorders [1] ; ◦Many rare familial disorders are due to rare alleles of large effects [1] ; ◦Synthetic association may explain common variants effects [1]. Bioinformatics - GBIO K.Van Steen University of Liège10

Summary : Rare and common variants Rare allele model – « In favour » ◦Evolutionnary theory predicts that disease alleles should be rare [1] ; ◦Empirical population genetic data shows that deleterious variants are rare [1] ; ◦Rare copy number variants contribute to several complex psychological disorders [1] ; ◦Many rare familial disorders are due to rare alleles of large effects [1] ; ◦Synthetic association may explain common variants effects [1]. Bioinformatics - GBIO K.Van Steen University of Liège11

Summary : Rare and common variants Evolutionnary theory predicts that disease alleles should be rare [1] : ◦Deleterious alleles are  created by mutation;  removed by purifying selection. ◦Rate(creation) > rate (removal) Bioinformatics - GBIO K.Van Steen University of Liège12

Summary : Rare and common variants Rare copy number variants contribute to several complex psychological disorders [1] : ◦CNVs : hemizygous deletion – local duplication ; ◦Promote disease such as schyzophrenia and autism and modify its severity. Bioinformatics - GBIO K.Van Steen University of Liège13

Summary : Rare and common variants Synthetic association may explain common variants effects [1] : Bioinformatics - GBIO K.Van Steen University of Liège14 LD Data [2] For common variants which do not explain much percentage of the disease susceptibility Rare variants increase this case risk.

Summary : Rare and common variants Rare allele model ◦Presentation of the model ◦Arguments « in favour » ◦Arguments « against » Bioinformatics - GBIO K.Van Steen University of Liège15

Summary : Rare and common variants Rare allele model – « Against » ◦Analysis of GWAS data is not consistent with rare variants explanations [1] ; ◦Sibling recurrence rates are greater than would be expected by the postulated effect sizes of rare variants [1] ; ◦Rare variants do not obviously have additive effects [1] ; ◦Epidemiological transitions cannot be attributed to rare variants [1] ; ◦GWAS associations are consistent across populations [1] ; Bioinformatics - GBIO K.Van Steen University of Liège16

Summary : Rare and common variants Rare allele model – « Against » ◦Analysis of GWAS data is not consistent with rare variants explanations [1] ; ◦Sibling recurrence rates are greater than would be expected by the postulated effect sizes of rare variants [1] ; ◦Rare variants do not obviously have additive effects [1] ; ◦Epidemiological transitions cannot be attributed to rare variants [1] ; ◦GWAS associations are consistent across populations [1] ; Bioinformatics - GBIO K.Van Steen University of Liège17

Summary : Rare and common variants 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. Bioinformatics - GBIO K.Van Steen University of Liège18

Summary : Rare and common variants 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. Bioinformatics - GBIO K.Van Steen University of Liège19

Summary : Rare and common variants 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. Bioinformatics - GBIO K.Van Steen University of Liège20

Content : Rare and common variants Introduction Summary ◦Rare allele model ◦Infinitesimal model Conclusion Bioinformatics - GBIO K.Van Steen University of Liège21

Summary : Rare and common variants Infinitesimal model ◦Presentation of the model ◦Arguments « in favour » ◦Arguments « against » Bioinformatics - GBIO K.Van Steen University of Liège22

Summary : Rare and common variants 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 ». Bioinformatics - GBIO K.Van Steen University of Liège23

Summary : Rare and common variants Infinitesimal model – Presentation Bioinformatics - GBIO K.Van Steen University of Liège24 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 common variants Infinitesimal model ◦Presentation of the model ◦Arguments « in favour » ◦Arguments « against » Bioinformatics - GBIO K.Van Steen University of Liège25

Summary : Rare and common variants Infinitesimal model – « In favour » ◦The infinitesimal model underpins standard quantitative genetic theory [1] ; ◦Common variants collectively capture the majority of the genetic variance in GWASs [1] ; ◦Variation in endophenotypes is almost certainly due to common variants [1] ; ◦Model organism research supports common variants contributions to complex phenotypes [1] ; ◦GWASs have successfully identified thousands of common variants [1]. Bioinformatics - GBIO K.Van Steen University of Liège26

Summary : Rare and common variants Infinitesimal model – « In favour » ◦The infinitesimal model underpins standard quantitative genetic theory [1] ; ◦Common variants collectively capture the majority of the genetic variance in GWASs [1] ; ◦Variation in endophenotypes is almost certainly due to common variants [1] ; ◦Model organism research supports common variants contributions to complex phenotypes [1] ; ◦GWASs have successfully identified thousands of common variants [1]. Bioinformatics - GBIO K.Van Steen University of Liège27

Summary : Rare and common variants The infinitesimal model underpins standard quantitative genetic theory [1] : ◦High heritability ; ◦No results were against the infinitesimal model. Bioinformatics - GBIO K.Van Steen University of Liège28

Summary : Rare and common variants Common variants collectively capture the majority of the genetic variance in GWASs [1] : Capture more of the genetic variance by using all significant SNPs; Variance is attributed to hundreds of loci. Bioinformatics - GBIO K.Van Steen University of Liège29

Summary : Rare and common variants GWASs have successfully identified thousands of common variants [1] : ◦Unrealistic assumptions of the effect size ; ◦Increasing samples allows to determine more loci. Bioinformatics - GBIO K.Van Steen University of Liège30

Summary : Rare and common variants Infinitesimal model ◦Presentation of the model ◦Arguments « in favour » ◦Arguments « against » Bioinformatics - GBIO K.Van Steen University of Liège31

Summary : Rare and common variants Infinitesimal model – « Against » ◦The QTL paradox [1] ; ◦The abscence of blending inheritence [1] ; ◦Demographic phenomena suggest more than one simple common-variant model [1] ; ◦Very few common variants for disease have been functionnaly validated [1] ; ◦What accounts for the missing heritability [1] ? Bioinformatics - GBIO K.Van Steen University of Liège32

Summary : Rare and common variants Infinitesimal model – « Against » ◦The QTL paradox [1] ; ◦The abscence of blending inheritence [1] ; ◦Demographic phenomena suggest more than one simple common-variant model [1] ; ◦Very few common variants for disease have been functionnaly validated [1] ; ◦What accounts for the missing heritability [1] ? Bioinformatics - GBIO K.Van Steen University of Liège33

Summary : Rare and common variants 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. Bioinformatics - GBIO K.Van Steen University of Liège34

Summary : Rare and common variants 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 some family complex phenotype traits are recurrent. Bioinformatics - GBIO K.Van Steen University of Liège35

Summary : Rare and common variants What accounts for the missing heritability [1] ? ◦The model does not take into account the missing heritability problem; ◦But the problem really exists ! Bioinformatics - GBIO K.Van Steen University of Liège36

Content : Rare and common variants Introduction Summary ◦Rare allele model ◦Infinitesimal model Conclusion Bioinformatics - GBIO K.Van Steen University of Liège37

Conclusion : Rare and common variants Which model would you choose ? Bioinformatics - GBIO K.Van Steen University of Liège38

Conclusion : Rare and common variants 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. Bioinformatics - GBIO K.Van Steen University of Liège39

Conclusion : Rare and common variants Bioinformatics - GBIO K.Van Steen University of Liège40 The common variants establish the background liability to a disease and this liability can be modified by the rare variants with large effects [1].

Thank you for your attention ! Bioinformatics - GBIO K.Van Steen University of Liège41

References : Bioinformatics - GBIO K.Van Steen University of Liège42 [1] G. GIBSON : Rare and common variants: twenty arguments. Nat. Rev. Genet., 13(2):135145, Feb [2] Bioinformatics course – GWAS studies, K. VAN STEEN

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