Genomic analysis of inherited hearing loss in the Palestinian population

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Abstract

The genetic characterization of a common phenotype for an entire population reveals both the causes of that phenotype for that place and the power of family-based, population-wide genomic analysis for gene and mutation discovery. We characterized the genetics of hearing loss throughout the Palestinian population, enrolling 2,198 participants from 491 families from all parts of the West Bank and Gaza. In Palestinian families with no prior history of hearing loss, we estimate that 56% of hearing loss is genetic and 44% is not genetic. For the great majority (87%) of families with inherited hearing loss, panel-based genomic DNA sequencing, followed by segregation analysis of large kindreds and transcriptional analysis of participant RNA, enabled identification of the causal genes and mutations, including at distant noncoding sites. Genetic heterogeneity of hearing loss was striking with respect to both genes and alleles: The 337 solved families harbored 143 different mutations in 48 different genes. For one in four solved families, a transcription-altering mutation was the responsible allele. Many of these mutations were cryptic, either exonic alterations of splice enhancers or silencers or deeply intronic events. Experimentally calibrated in silico analysis of transcriptional effects yielded inferences of high confidence for effects on splicing even of mutations in genes not expressed in accessible tissue. Most (58%) of all hearing loss in the population was attributable to consanguinity. Given the ongoing decline in consanguineous marriage, inherited hearing loss will likely be much rarer in the next generation.

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Copy number variation detection and genotyping from exome sequence data

Niklas Krumm, Peter Sudmant, Arthur Ko … (2012)

While exome sequencing is readily amenable to single-nucleotide variant discovery, the sparse and nonuniform nature of the exome capture reaction has hindered exome-based detection and characterization of genic copy number variation. We developed a novel method using singular value decomposition (SVD) normalization to discover rare genic copy number variants (CNVs) as well as genotype copy number polymorphic (CNP) loci with high sensitivity and specificity from exome sequencing data. We estimate the precision of our algorithm using 122 trios (366 exomes) and show that this method can be used to reliably predict (94% overall precision) both de novo and inherited rare CNVs involving three or more consecutive exons. We demonstrate that exome-based genotyping of CNPs strongly correlates with whole-genome data (median r 2 = 0.91), especially for loci with fewer than eight copies, and can estimate the absolute copy number of multi-allelic genes with high accuracy (78% call level). The resulting user-friendly computational pipeline, CoNIFER ( co py n umber i nference f rom e xome r eads), can reliably be used to discover disruptive genic CNVs missed by standard approaches and should have broad application in human genetic studies of disease.

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Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth.

Menachem Fromer, Jennifer Moran, Kimberly Chambert … (2012)

Sequencing of gene-coding regions (the exome) is increasingly used for studying human disease, for which copy-number variants (CNVs) are a critical genetic component. However, detecting copy number from exome sequencing is challenging because of the noncontiguous nature of the captured exons. This is compounded by the complex relationship between read depth and copy number; this results from biases in targeted genomic hybridization, sequence factors such as GC content, and batching of samples during collection and sequencing. We present a statistical tool (exome hidden Markov model [XHMM]) that uses principal-component analysis (PCA) to normalize exome read depth and a hidden Markov model (HMM) to discover exon-resolution CNV and genotype variation across samples. We evaluate performance on 90 schizophrenia trios and 1,017 case-control samples. XHMM detects a median of two rare (<1%) CNVs per individual (one deletion and one duplication) and has 79% sensitivity to similarly rare CNVs overlapping three or more exons discovered with microarrays. With sensitivity similar to state-of-the-art methods, XHMM achieves higher specificity by assigning quality metrics to the CNV calls to filter out bad ones, as well as to statistically genotype the discovered CNV in all individuals, yielding a trio call set with Mendelian-inheritance properties highly consistent with expectation. We also show that XHMM breakpoint quality scores enable researchers to explicitly search for novel classes of structural variation. For example, we apply XHMM to extract those CNVs that are highly likely to disrupt (delete or duplicate) only a portion of a gene. Copyright © 2012 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

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An increased specificity score matrix for the prediction of SF2/ASF-specific exonic splicing enhancers.

Philip J Smith, Chaolin Zhang, Jinhua Wang … (2006)

Numerous disease-associated point mutations exert their effects by disrupting the activity of exonic splicing enhancers (ESEs). We previously derived position weight matrices to predict putative ESEs specific for four human SR proteins. The score matrices are part of ESEfinder, an online resource to identify ESEs in query sequences. We have now carried out a refined functional SELEX screen for motifs that can act as ESEs in response to the human SR protein SF2/ASF. The test BRCA1 exon under selection was internal, rather than the 3'-terminal IGHM exon used in our earlier studies. A naturally occurring heptameric ESE in BRCA1 exon 18 was replaced with two libraries of random sequences, one seven nucleotides in length, the other 14. Following three rounds of selection for in vitro splicing via internal exon inclusion, new consensus motifs and score matrices were derived. Many winner sequences were demonstrated to be functional ESEs in S100-extract-complementation assays with recombinant SF2/ASF. Motif-score threshold values were derived from both experimental and statistical analyses. Motif scores were shown to correlate with levels of exon inclusion, both in vitro and in vivo. Our results confirm and extend our earlier data, as many of the same motifs are recognized as ESEs by both the original and our new score matrix, despite the different context used for selection. Finally, we have derived an increased specificity score matrix that incorporates information from both of our SF2/ASF-specific matrices and that accurately predicts the exon-skipping phenotypes of deleterious point mutations.