Search

In studies of families with rare disease, it is common to screen for de novo mutations, as well as recessive or dominant variants that explain the phenotype. However, the filtering strategies and software used to prioritize high-confidence variants vary from study to study. In an effort to establish recommendations for rare disease research, we explore effective guidelines for variant (SNP and INDEL) filtering and report the expected number of candidates for de novo dominant, recessive, and autosomal dominant modes of inheritance. We derived these guidelines using two large family-based cohorts that underwent whole-genome sequencing, as well as two family cohorts with whole-exome sequencing. The filters are applied to common attributes, including genotype-quality, sequencing depth, allele balance, and population allele frequency. The resulting guidelines yield ~10 candidate SNP and INDEL variants per exome, and 18 per genome for recessive and de novo dominant modes of inheritance, with substantially more candidates for autosomal dominant inheritance. For family-based, whole-genome sequencing studies, this number includes an average of three de novo, ten compound heterozygous, one autosomal recessive, four X-linked variants, and roughly 100 candidate variants following autosomal dominant inheritance. The slivar software we developed to establish and rapidly apply these filters to VCF files is available at https://github.com/brentp/slivar under an MIT license, and includes documentation and recommendations for best practices for rare disease analysis.

In the clinical setting, exome sequencing has become standard-of-care in diagnosing rare genetic disorders, however many patients remain unsolved. Trio sequencing has been demonstrated to produce a higher diagnostic yield than singleton (proband-only) sequencing. Parental sequencing is especially useful when a disease is suspected to be caused by a de novo variant in the proband, because parental data provide a strong filter for the majority of variants that are shared by the proband and their parents. However the additional cost of sequencing the parents makes the trio strategy uneconomical for many clinical situations. With two thirds of the sequencing budget being spent on parents, these are funds that could be used to sequence more probands. For this reason many clinics are reluctant to sequence parents.Here we propose a pooled-parent strategy for exome sequencing of individuals with likely de novo disease. In this strategy, DNA from all the parents of a cohort of unrelated probands is pooled together into a single exome capture and sequencing run. Variants called in the proband can then be filtered if they are also found in the parent pool, resulting in a shorter list of prioritised variants. To evaluate the pooled-parent strategy we performed a series of simulations by combining reads from individual exomes to imitate sample pooling. We assessed the recall and false positive rate and investigated the trade-off between pool size and recall rate. We compared the performance of GATK HaplotypeCaller individual and joint calling, and FreeBayes to genotype pooled samples. Finally, we applied a pooled-parent strategy to a set of real unsolved cases and showed that the parent pool is a powerful filter that is complementary to other commonly used variant filters such as population variant frequencies.

Rapid molecular typing of bacterial pathogens is critical for public health epidemiology, surveillance and infection control, yet routine use of whole genome sequencing (WGS) for these purposes poses significant challenges. Here we present SRST2, a read mapping-based tool for fast and accurate detection of genes, alleles and multi-locus sequence types (MLST) from WGS data. Using >900 genomes from common pathogens, we show SRST2 is highly accurate and outperforms assembly-based methods in terms of both gene detection and allele assignment. We include validation of SRST2 within a public health laboratory, and demonstrate its use for microbial genome surveillance in the hospital setting. In the face of rising threats of antimicrobial resistance and emerging virulence among bacterial pathogens, SRST2 represents a powerful tool for rapidly extracting clinically useful information from raw WGS data. Source code is available from http://katholt.github.io/srst2/. Electronic supplementary material The online version of this article (doi:10.1186/s13073-014-0090-6) contains supplementary material, which is available to authorized users.