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DAE

Seminar

Estimating statistical significance of exome sequencing data for rare mendelian disorders using population-wide linkage analysis

Albers, K (Haematology)
Monday 26 September 2011, 10:10-10:20

Seminar Room 1, Newton Institute

Abstract

Exome sequencing of a small number of unrelated affected individuals has proved to be a highly effective approach for identifying causative genes of rare mendelian diseases. A widely used strategy is to consider as candidate causative mutations only those variants that have not been seen previously in other individuals, and those variants predicted to affect protein sequence, e.g. non-synonymous variants or stop-codons.

For the recessive disorder Gray Platelet Syndrome we identified 7 novel coding mutations in 4 affected individuals, all in different locations in one gene and absent from 994 individuals from the 1000 Genomes project; intuitively a highly significant result (Albers et al. Nat Genet 2011). However, in the case where the candidate causative mutations segregate at low frequency in the general population the significance may be less obvious. This raises a number of questions: what is the statistical significance of such findings in small numbers of affected individuals? If we would assume that the causative mutations are not necessarily in coding sequence, would these results be genome-wide significant? Motivated by these issues, we are developing a statistical model based on the idea that filtering out previously seen variants can be thought of as performing a whole-population parametric linkage analysis, whereby the individuals carrying previously seen variants represent the unaffected individuals.

We use the coalescent, a mathematical description of the notion that ultimately all individuals in a population are descendants of a single common ancestor, to model the unknown pedigree shared by the affected individuals and the unaffected individuals. I will discuss implications of population stratification, false positive variant calls and variation in coverage for singleton rates and significance estimates.

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