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Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies.

ABSTRACT: Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased probability of a rare variant being pathogenic. Also, for the first time we assess the predictive power of seven prediction methods (including SIFT, PolyPhen2, CONDEL, and logit) in predicting pathogenic nsSNVs from other rare variants, which reflects the situation after MAF filtering is done in exome-sequencing studies. We found that a logit model combining all or some original prediction methods outperforms other methods examined, but is unable to discriminate between autosomal dominant and autosomal recessive disease mutations. Finally, based on the predictions of the logit model, we estimate that an individual has around 5% of rare nsSNVs that are pathogenic and carries ~22 pathogenic derived alleles at least, which if made homozygous by consanguineous marriages may lead to recessive diseases.

SUBMITTER: Li MX

PROVIDER: S-EPMC3547823 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies.

Li Miao-Xin MX Kwan Johnny S H JS Bao Su-Ying SY Yang Wanling W Ho Shu-Leong SL Song Yong-Qiang YQ Sham Pak C PC

PLoS genetics 20130117 1

Exome sequencing is becoming a standard tool for mapping Mendelian disease-causing (or pathogenic) non-synonymous single nucleotide variants (nsSNVs). Minor allele frequency (MAF) filtering approach and functional prediction methods are commonly used to identify candidate pathogenic mutations in these studies. Combining multiple functional prediction methods may increase accuracy in prediction. Here, we propose to use a logit model to combine multiple prediction methods and compute an unbiased p ...[more]

PMID: 23341771

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Project description:BACKGROUND:Although mosaic variation has been known to cause disease for decades, high-throughput sequencing technologies with the analytical sensitivity to consistently detect variants at reduced allelic fractions have only recently emerged as routine clinical diagnostic tests. To date, few systematic analyses of mosaic variants detected by diagnostic exome sequencing for diverse clinical indications have been performed. METHODS:To investigate the frequency, type, allelic fraction, and phenotypic consequences of clinically relevant somatic mosaic single nucleotide variants (SNVs) and characteristics of the corresponding genes, we retrospectively queried reported mosaic variants from a cohort of ~?12,000 samples submitted for clinical exome sequencing (ES) at Baylor Genetics. RESULTS:We found 120 mosaic variants involving 107 genes, including 80 mosaic SNVs in proband samples and 40 in parental/grandparental samples. Average mosaic alternate allele fraction (AAF) detected in autosomes and in X-linked disease genes in females was 18.2% compared with 34.8% in X-linked disease genes in males. Of these mosaic variants, 74 variants (61.7%) were classified as pathogenic or likely pathogenic and 46 (38.3%) as variants of uncertain significance. Mosaic variants occurred in disease genes associated with autosomal dominant (AD) or AD/autosomal recessive (AR) (67/120, 55.8%), X-linked (33/120, 27.5%), AD/somatic (10/120, 8.3%), and AR (8/120, 6.7%) inheritance. Of note, 1.7% (2/120) of variants were found in genes in which only somatic events have been described. Nine genes had recurrent mosaic events in unrelated individuals which accounted for 18.3% (22/120) of all detected mosaic variants in this study. The proband group was enriched for mosaicism affecting Ras signaling pathway genes. CONCLUSIONS:In sum, an estimated 1.5% of all molecular diagnoses made in this cohort could be attributed to a mosaic variant detected in the proband, while parental mosaicism was identified in 0.3% of families analyzed. As ES design favors breadth over depth of coverage, this estimate of the prevalence of mosaic variants likely represents an underestimate of the total number of clinically relevant mosaic variants in our cohort.

Project description:BackgroundThe targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed.ResultsUsing down-sampling of 30 deeply sequenced exomes and a set of gold-standard single nucleotide variant (SNV) genotype calls for each sample, we developed an empirical model relating the read depth at a polymorphic site to the probability of calling the correct genotype at that site. We find that measured sensitivity in SNV detection is substantially worse than that predicted from the naive expectation of sampling from a binomial. This calibrated model allows us to produce single nucleotide resolution SNV sensitivity estimates which can be merged to give summary sensitivity measures for any arbitrary partition of the target sequences (nucleotide, exon, gene, pathway, exome). These metrics are directly comparable between platforms and can be combined between samples to give "power estimates" for an entire study. We estimate a local read depth of 13X is required to detect the alleles and genotype of a heterozygous SNV 95% of the time, but only 3X for a homozygous SNV. At a mean on-target read depth of 20X, commonly used for rare disease exome sequencing studies, we predict 5-15% of heterozygous and 1-4% of homozygous SNVs in the targeted regions will be missed.ConclusionsNon-reference alleles in the heterozygote state have a high chance of being missed when commonly applied read coverage thresholds are used despite the widely held assumption that there is good polymorphism detection at these coverage levels. Such alleles are likely to be of functional importance in population based studies of rare diseases, somatic mutations in cancer and explaining the "missing heritability" of quantitative traits.

Dataset Information

Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies.

Publications

Predicting mendelian disease-causing non-synonymous single nucleotide variants in exome sequencing studies.

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