Detection of Large Structural Variants Causing Inherited Retinal Diseases.

Current application of next-generation sequencing (NGS) leads to detection of the underlying disease-causing gene and mutation or mutations in from 60% to 85% of patients with inherited retinal diseases (IRDs), depending on the methods used, disease type, and population tested. In a cohort of 320 families with autosomal dominant retinitis pigmentosa (adRP), we have detected the mutation in 82% of cases using a variety of methods, leaving more than 50 families with "elusive" disease genotypes. All of the remaining families have been screened for mutations in known IRD genes using retinal-targeted-capture NGS, and most have been tested by whole-exome NGS. Linkage mapping has been conducted in several large families. In one of these families, with DNA samples from ten affected family members and six unaffected, linking members, we observed substantial maximum two-point LOD scores for linkage to both chromosomes 2 and 4. Subsequent 10X Genomics Chromium™ sequencing, which facilitates linked-read, phase-known chromosomal analysis, revealed a balanced translocation of the q terminus arms of chromosomes 2 and 4 involving 35 Mb and 73 Mb of 2 and 4, respectively. The balanced translocation is present in all affected family members and absent from all unaffected individuals. Family histories suggest multiple miscarriages are associated with the translocation. The breakpoint on chromosome 4 is within or 5' to the LRAT gene, whereas the chromosome 2 break is in a gene-poor region. We conclude that the balanced translocation is the cause of adRP in this family, which may lead to dysregulation of the LRAT gene. Since multiple miscarriages are a hallmark of balanced translocations, this possibility should be considered in evaluating family histories. Further, large structural variants, which are not easily detected by conventional sequencing methods, may account for a significant fraction of the remaining unsolved families.

Adjusting family relatedness in data-driven burden test of rare variants.

Family data represent a rich resource for detecting association between rare variants (RVs) and human traits. However, most RV association analysis methods developed in recent years are data-driven burden tests which can adaptively learn weights from data but require permutation to evaluate significance, thus are not readily applicable to family data, because random permutation will destroy family structure. Direct application of these methods to family data may result in a significant inflation of false positives. To overcome this issue, we have developed a generalized, weighted sum mixed model (WSMM), and corresponding computational techniques that can incorporate family information into data-driven burden tests, and allow adaptive and efficient permutation test in family data. Using simulated and real datasets, we demonstrate that the WSMM method can be used to appropriately adjust for genetic relatedness among family members and has a good control for the inflation of false positives. We compare WSMM with a nondata-driven, family-based Sequence Kernel Association Test (famSKAT), showing that WSMM has significantly higher power in some cases. WSMM provides a generalized, flexible framework for adapting different data-driven burden tests to analyze data with any family structures, and it can be extended to binary and time-to-onset traits, with or without covariates.

DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome.

Acute myeloid leukaemia is a highly malignant haematopoietic tumour that affects about 13,000 adults in the United States each year. The treatment of this disease has changed little in the past two decades, because most of the genetic events that initiate the disease remain undiscovered. Whole-genome sequencing is now possible at a reasonable cost and timeframe to use this approach for the unbiased discovery of tumour-specific somatic mutations that alter the protein-coding genes. Here we present the results obtained from sequencing a typical acute myeloid leukaemia genome, and its matched normal counterpart obtained from the same patient's skin. We discovered ten genes with acquired mutations; two were previously described mutations that are thought to contribute to tumour progression, and eight were new mutations present in virtually all tumour cells at presentation and relapse, the function of which is not yet known. Our study establishes whole-genome sequencing as an unbiased method for discovering cancer-initiating mutations in previously unidentified genes that may respond to targeted therapies.

The functional spectrum of low-frequency coding variation.

BACKGROUND: Rare coding variants constitute an important class of human genetic variation, but are underrepresented in current databases that are based on small population samples. Recent studies show that variants altering amino acid sequence and protein function are enriched at low variant allele frequency, 2 to 5%, but because of insufficient sample size it is not clear if the same trend holds for rare variants below 1% allele frequency. RESULTS: The 1000 Genomes Exon Pilot Project has collected deep-coverage exon-capture data in roughly 1,000 human genes, for nearly 700 samples. Although medical whole-exome projects are currently afoot, this is still the deepest reported sampling of a large number of human genes with next-generation technologies. According to the goals of the 1000 Genomes Project, we created effective informatics pipelines to process and analyze the data, and discovered 12,758 exonic SNPs, 70% of them novel, and 74% below 1% allele frequency in the seven population samples we examined. Our analysis confirms that coding variants below 1% allele frequency show increased population-specificity and are enriched for functional variants. CONCLUSIONS: This study represents a large step toward detecting and interpreting low frequency coding variation, clearly lays out technical steps for effective analysis of DNA capture data, and articulates functional and population properties of this important class of genetic variation.