title: Can whole-exome sequencing data be used for linkage analysis?
abstract: Whole-exome sequencing (WES) has become the strategy of choice to identify causal variants in monogenic disorders. However, the list of candidate variants can be quite large, including false positives generated by sequencing errors. To reduce this list of candidate variants to the most relevant ones, a cost-effective strategy would be to focus on regions of linkage identified through linkage analysis conducted with common polymorphisms present in WES data. However, the non-uniform exon coverage of the genome and the lack of knowledge on the power of this strategy have largely precluded its use so far. To compare the performance of linkage analysis conducted with WES and SNP chip data in different situations, we performed simulations on two pedigree structures with, respectively, a dominant and a recessive trait segregating. We found that the performance of the two sets of markers at excluding regions of the genome were very similar, and there was no real gain at using SNP chip data compared with using the common SNPs extracted from WES data. When analyzing the real WES data available for these two pedigrees, we found that the linkage information derived from the WES common polymorphisms was able to reduce by half the list of candidate variants identified by a simple filtering approach. Conducting linkage analysis with WES data available on pedigrees and excluding among the candidate variants those that fall in excluded linkage regions is thus a powerful and cost-effective strategy to reduce the number of false-positive candidate variants.
authors.affiliation: INSERM, IAME, UMR 1137, Paris, France.|Plateforme de Génomique Constitutionnelle du GHU Nord, Assistance Publique des Hôpitaux de Paris (APHP), Hôpital Bichat, Paris, France.
authors.forename: Steven
authors.initials: S
authors.lastname: Gazal
authors.affiliation: INSERM, IAME, UMR 1137, Paris, France.|Univ Paris Diderot, IAME, UMR 1137, Sorbonne Paris Cité, Paris, France.
authors.forename: Simon
authors.initials: S
authors.lastname: Gosset
authors.affiliation: INSERM, UMR 1161, Paris, France.|Univ Paris-Diderot, Génétique et Physiopathologie des Maladies Cérébro-Vasculaires, UMR 1161, Sorbonne Paris Cité, Paris, France.
authors.forename: Edgard
authors.initials: E
authors.lastname: Verdura
authors.affiliation: INSERM, UMR 1161, Paris, France.|Univ Paris-Diderot, Génétique et Physiopathologie des Maladies Cérébro-Vasculaires, UMR 1161, Sorbonne Paris Cité, Paris, France.
authors.forename: Françoise
authors.initials: F
authors.lastname: Bergametti
authors.affiliation: INSERM, UMR 1161, Paris, France.|Univ Paris-Diderot, Génétique et Physiopathologie des Maladies Cérébro-Vasculaires, UMR 1161, Sorbonne Paris Cité, Paris, France.
authors.forename: Stéphanie
authors.initials: S
authors.lastname: Guey
authors.affiliation: INSERM, Genetic Variability and Human Diseases, UMR 946, Paris, France.|Univ Paris-Diderot, UMR 946, Sorbonne Paris Cité, Paris, France.
authors.forename: Marie-Claude
authors.initials: MC
authors.lastname: Babron
authors.affiliation: INSERM, UMR 1161, Paris, France.|Univ Paris-Diderot, Génétique et Physiopathologie des Maladies Cérébro-Vasculaires, UMR 1161, Sorbonne Paris Cité, Paris, France.|Assistance Publique des Hôpitaux de Paris, Hôpital Lariboisière, Paris, France.
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Tournier-Lasserve
mesh_terms: D002874:Chromosome Mapping / Q000379:methods*; D059472:Exome; D008040:Genetic Linkage; D060005:Genotyping Techniques / Q000379:methods*; D006801:Humans; D010375:Pedigree; D020641:Polymorphism, Single Nucleotide; D017422:Sequence Analysis, DNA / Q000379:methods*; D059629:Signal-To-Noise Ratio