BackgroundSpinal muscular atrophy (SMA) is the most common pan-ethnic cause of early childhood death due to mutations in a single gene, SMN1. Most chromosome 5 homologs have a functional gene and dysfunctional copy, SMN2, with a single synonymous base substitution that results in faulty RNA splicing. However, the copy number of SMN1 and SMN2 is highly variable, and one in 60 adults worldwide are SMA carriers. Although population-wide screening is recommended, current SMA carrier tests have not been incorporated into targeted gene panels.MethodsHere we describe a novel computational protocol for determining SMA carrier status based solely on individual exome data. Our method utilizes a Bayesian hierarchical model to quantify an individual’s carrier probability given only his or her SMN1 and SMN2 reads at six loci of interest.ResultsWe find complete concordance with results obtained with the current qPCR-based testing standard in known SMA carriers and affecteds. We applied our protocol to the phase 3 cohort of the 1,000 Genomes Project and found carrier frequencies in multiple populations consistent with the present literature.ConclusionOur process is a convenient, robust alternative to qPCR, which can easily be integrated into the analysis of large multi-gene NGS carrier screens.Electronic supplementary materialThe online version of this article (doi:10.1186/s12881-015-0246-2) contains supplementary material, which is available to authorized users.

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Aims: DNA-based carrier screening is a standard component of donor eligibility protocols practiced by U.S. sperm banks. Applicants who test positive for carrying a recessive disease mutation are typically disqualified. The aim of our study was to examine the utility of a range of screening panels adopted by the industry and the effectiveness of the screening paradigm in reducing a future child's risk of inheriting disease. Methods: A cohort of 27 donor applicants, who tested negative on an initial cystic fibrosis carrier test, was further screened with three expanded commercial carrier testing panels. These results were then compared to a systematic analysis of the applicants' DNA using next-generation sequencing (NGS) data. Results: The carrier panels detected serious pediatric disease mutations in one, four, or six donor applicants. Because each panel screens distinct regions of the genome, no single donor was uniformly identified as carrier positive by all three panels. In contrast, systematic NGS analysis identified all donors as carriers of one or more mutations associated with severe monogenic pediatric disease. These included 30 variants classified as “pathogenic” based on clinical observation and 66 with a high likelihood of causing gene dysfunction. Conclusion: Despite tremendous advances in variant identification, understanding, and analysis, the vast majority of disease-causing mutation combinations remain undetected by commercial carrier screening panels, which cover a narrow, and often distinct, subset of genes and mutations. The biological reality is that all donors and recipients carry serious recessive disease mutations. This challenges the utility of any screening protocol that anchors donor eligibility to carrier status. A more effective approach to reducing recessive disease risk would consider joint comprehensive analysis of both donor and recipient disease mutations. This type of high-resolution recessive disease risk analysis is now available and affordable, but industry practice must be modified to incorporate its use.

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