Kevlar: A Mapping-Free Framework for Accurate Discovery of De Novo Variants.

De novo genetic variants are an important source of causative variation in complex genetic disorders. Many methods for variant discovery rely on mapping reads to a reference genome, detecting numerous inherited variants irrelevant to the phenotype of interest. To distinguish between inherited and de novo variation, sequencing of families (parents and siblings) is commonly pursued. However, standard mapping-based approaches tend to have a high false-discovery rate for de novo variant prediction. Kevlar is a mapping-free method for de novo variant discovery, based on direct comparison of sequences between related individuals. Kevlar identifies high-abundance k-mers unique to the individual of interest. Reads containing these k-mers are partitioned into disjoint sets by shared k-mer content for variant calling, and preliminary variant predictions are sorted using a probabilistic score. We evaluated Kevlar on simulated and real datasets, demonstrating its ability to detect both de novo single-nucleotide variants and indels with high accuracy

Using GWAS Data to Identify Copy Number Variants Contributing to Common Complex Diseases

Copy number variants (CNVs) account for more polymorphic base pairs in the human genome than do single nucleotide polymorphisms (SNPs). CNVs encompass genes as well as noncoding DNA, making these polymorphisms good candidates for functional variation. Consequently, most modern genome-wide association studies test CNVs along with SNPs, after inferring copy number status from the data generated by high-throughput genotyping platforms. Here we give an overview of CNV genomics in humans, highlighting patterns that inform methods for identifying CNVs. We describe how genotyping signals are used to identify CNVs and provide an overview of existing statistical models and methods used to infer location and carrier status from such data, especially the most commonly used methods exploring hybridization intensity. We compare the power of such methods with the alternative method of using tag SNPs to identify CNV carriers. As such methods are only powerful when applied to common CNVs, we describe two alternative approaches that can be informative for identifying rare CNVs contributing to disease risk. We focus particularly on methods identifying de novo CNVs and show that such methods can be more powerful than case-control designs. Finally we present some recommendations for identifying CNVs contributing to common complex disorders.Comment: Published in at http://dx.doi.org/10.1214/09-STS304 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Accumulation of rare coding variants in genes implicated in risk of human cleft lip with or without cleft palate.

Cleft lip with/without cleft palate (CLP) is a common craniofacial malformation with complex etiologies, reflecting both genetic and environmental factors. Most of the suspected genetic risk for CLP has yet to be identified. To further classify risk loci and estimate the contribution of rare variants, we sequenced the exons in 49 candidate genes in 323 CLP cases and 211 nonmalformed controls. Our findings indicated that rare, protein-altering variants displayed markedly higher burdens in CLP cases at relevant loci. First, putative loss-of-function mutations (nonsense, frameshift) were significantly enriched among cases: 13 of 323 cases (~4%) harbored such alleles within these 49 genes, versus one such change in controls (p = 0.01). Second, in gene-level analyses, the burden of rare alleles showed greater case-association for several genes previously implicated in cleft risk. For example, BHMT displayed a 10-fold increase in protein-altering variants in CLP cases (p = .03), including multiple case occurrences of a rare frameshift mutation (K400 fs). Other loci with greater rare, coding allele burdens in cases were in signaling pathways relevant to craniofacial development (WNT9B, BMP4, BMPR1B) as well as the methionine cycle (MTRR). We conclude that rare coding variants may confer risk for isolated CLP

arrEYE : a customized platform for high-resolution copy number analysis of coding and noncoding regions of known and candidate retinal dystrophy genes and retinal noncoding RNAs

Purpose: Our goal was to design a customized microarray, arrEYE, for high-resolution copy number variant (CNV) analysis of known and candidate genes for inherited retinal dystrophy (iRD) and retina expressed noncoding RNAs (ncRNAs). Methods: arrEYE contains probes for the full genomic region of 106 known iRD genes, including those implicated in retinitis pigmentosa (RP) (the most frequent iRD), cone rod dystrophies, macular dystrophies, and an additional 60 candidate iRD genes and 196 ncRNAs. Eight CNVs in iRD genes identified by other techniques were used as positive controls. The test cohort consisted of 57 patients with autosomal dominant, X-linked, or simplex RP. Results: In an RP patient, a novel heterozygous deletion of exons 7 and 8 of the HGSNAT gene was identified: c.634-408_820+338delins AGAATATG, p.(G1u2 I 2Glyfs*2). A known variant was found on the second allele: c.1843G>A, p.(A1a615Thr). Furthermore, we expanded the allelic spectrum of USH2A and RCBTB1 with novel CNVs. Conclusion: The arrEYE platform revealed subtle single-exon to larger CNVs in iRD genes that could be characterized at the nucleotide level, facilitated by the high resolution of the platform. We report the first CNV in HGSNAT that, combined with another mutation, leads to RP, further supporting its recently identified role in nonsyndromic iRD

A Model-Based Analysis of GC-Biased Gene Conversion in the Human and Chimpanzee Genomes

GC-biased gene conversion (gBGC) is a recombination-associated process that favors the fixation of G/C alleles over A/T alleles. In mammals, gBGC is hypothesized to contribute to variation in GC content, rapidly evolving sequences, and the fixation of deleterious mutations, but its prevalence and general functional consequences remain poorly understood. gBGC is difficult to incorporate into models of molecular evolution and so far has primarily been studied using summary statistics from genomic comparisons. Here, we introduce a new probabilistic model that captures the joint effects of natural selection and gBGC on nucleotide substitution patterns, while allowing for correlations along the genome in these effects. We implemented our model in a computer program, called phastBias, that can accurately detect gBGC tracts about 1 kilobase or longer in simulated sequence alignments. When applied to real primate genome sequences, phastBias predicts gBGC tracts that cover roughly 0.3% of the human and chimpanzee genomes and account for 1.2% of human-chimpanzee nucleotide differences. These tracts fall in clusters, particularly in subtelomeric regions; they are enriched for recombination hotspots and fast-evolving sequences; and they display an ongoing fixation preference for G and C alleles. They are also significantly enriched for disease-associated polymorphisms, suggesting that they contribute to the fixation of deleterious alleles. The gBGC tracts provide a unique window into historical recombination processes along the human and chimpanzee lineages. They supply additional evidence of long-term conservation of megabase-scale recombination rates accompanied by rapid turnover of hotspots. Together, these findings shed new light on the evolutionary, functional, and disease implications of gBGC. The phastBias program and our predicted tracts are freely available. © 2013 Capra et al

EM-mosaic detects mosaic point mutations that contribute to congenital heart disease.

BackgroundThe contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined.MethodsWe developed a new computational method, EM-mosaic (Expectation-Maximization-based detection of mosaicism), to analyze mosaicism in exome sequences derived primarily from blood DNA of 2530 CHD proband-parent trios. To optimize this method, we measured mosaic detection power as a function of sequencing depth. In parallel, we analyzed our cohort using MosaicHunter, a Bayesian genotyping algorithm-based mosaic detection tool, and compared the two methods. The accuracy of these mosaic variant detection algorithms was assessed using an independent resequencing method. We then applied both methods to detect mosaicism in cardiac tissue-derived exome sequences of 66 participants for which matched blood and heart tissue was available.ResultsEM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The estimated true frequency of mosaic variants above 10% mosaicism was 0.14/person in blood and 0.21/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction.ConclusionsWe estimate that ~ 1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants with relatively higher allele fraction. Although blood is a readily available DNA source, cardiac tissues analyzed contributed ~ 5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses

The driver landscape of sporadic chordoma.

Chordoma is a malignant, often incurable bone tumour showing notochordal differentiation. Here, we defined the somatic driver landscape of 104 cases of sporadic chordoma. We reveal somatic duplications of the notochordal transcription factor brachyury (T) in up to 27% of cases. These variants recapitulate the rearrangement architecture of the pathogenic germline duplications of T that underlie familial chordoma. In addition, we find potentially clinically actionable PI3K signalling mutations in 16% of cases. Intriguingly, one of the most frequently altered genes, mutated exclusively by inactivating mutation, was LYST (10%), which may represent a novel cancer gene in chordoma.Chordoma is a rare often incurable malignant bone tumour. Here, the authors investigate driver mutations of sporadic chordoma in 104 cases, revealing duplications in notochordal transcription factor brachyury (T), PI3K signalling mutations, and mutations in LYST, a potential novel cancer gene in chordoma

A new targeted CFTR mutation panel based on next-generation sequencing technology

Searching for mutations in the cystic fibrosis transmembrane conductance regulator gene (CFTR) is a key step in the diagnosis of and neonatal and carrier screening for cystic fibrosis (CF), and it has implications for prognosis and personalized therapy. The large number of mutations and genetic and phenotypic variability make this search a complex task. Herein, we developed, validated, and tested a laboratory assay for an extended search for mutations in CFTR using a next-generation sequencing based method, with a panel of 188 CFTR mutations customized for the Italian population. Overall, 1426 dried blood spots from neonatal screening, 402 genomic DNA samples from various origins, and 1138 genomic DNA samples from patients with CF were analyzed. The assay showed excellent analytical and diagnostic operative characteristics. We identified and experimentally validated 159 (of 188) CFTR mutations. The assay achieved detection rates of 95.0% and 95.6% in two large-scale case series of CF patients from central and northern Italy, respectively. These detection rates are among the highest reported so far with a genetic test for CF based on a mutation panel. This assay appears to be well suited for diagnostics, neonatal and carrier screening, and assisted reproduction, and it represents a considerable advantage in CF genetic counseling