A comprehensive framework for prioritizing variants in exome sequencing studies of Mendelian diseases

Exome sequencing strategy is promising for finding novel mutations of human monogenic disorders. However, pinpointing the casual mutation in a small number of samples is still a big challenge. Here, we propose a three-level filtration and prioritization framework to identify the casual mutation(s) in exome sequencing studies. This efficient and comprehensive framework successfully narrowed down whole exome variants to very small numbers of candidate variants in the proof-of-concept examples. The proposed framework, implemented in a user-friendly software package, named KGGSeq (http://statgenpro.psychiatry.hku.hk/kggseq), will play a very useful role in exome sequencing-based discovery of human Mendelian disease genes.     

Comparison and evaluation of two exome capture kits and sequencing platforms for variant calling

Background

To promote the clinical application of next-generation sequencing, it is important to obtain accurate and consistent variants of target genomic regions at low cost. Ion Proton, the latest updated semiconductor-based sequencing instrument from Life Technologies, is designed to provide investigators with an inexpensive platform for human whole exome sequencing that achieves a rapid turnaround time. However, few studies have comprehensively compared and evaluated the accuracy of variant calling between Ion Proton and Illumina sequencing platforms such as HiSeq 2000, which is the most popular sequencing platform for the human genome. The Ion Proton sequencer combined with the Ion TargetSeq™ Exome Enrichment Kit together make up TargetSeq-Proton, whereas SureSelect-Hiseq is based on the Agilent SureSelect Human All Exon v4 Kit and the HiSeq 2000 sequencer.

Results

Here, we sequenced exonic DNA from four human blood samples using both TargetSeq-Proton and SureSelect-HiSeq. We then called variants in the exonic regions that overlapped between the two exome capture kits (33.6 Mb). The rates of shared variant loci called by two sequencing platforms were from 68.0 to 75.3 % in four samples, whereas the concordance of co-detected variant loci reached 99 %. Sanger sequencing validation revealed that the validated rate of concordant single nucleotide polymorphisms (SNPs) (91.5 %) was higher than the SNPs specific to TargetSeq-Proton (60.0 %) or specific to SureSelect-HiSeq (88.3 %). With regard to 1-bp small insertions and deletions (InDels), the Sanger sequencing validated rates of concordant variants (100.0 %) and SureSelect-HiSeq-specific (89.6 %) were higher than those of TargetSeq-Proton-specific (15.8 %).

Conclusions

In the sequencing of exonic regions, a combination of using of two sequencing strategies (SureSelect-HiSeq and TargetSeq-Proton) increased the variant calling specificity for concordant variant loci and the sensitivity for variant loci called by any one platform. However, for the sequencing of platform-specific variants, the accuracy of variant calling by HiSeq 2000 was higher than that of Ion Proton, specifically for the InDel detection. Moreover, the variant calling software also influences the detection of SNPs and, specifically, InDels in Ion Proton exome sequencing.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1796-6) contains supplementary material, which is available to authorized users.     

Mutations of 60 known causative genes in 157 families with retinitis pigmentosa based on exome sequencing

Retinitis pigmentosa (RP) is the most common and highly heterogeneous form of hereditary retinal degeneration. This study was to identify mutations in the 60 genes that were known to be associated with RP in 157 unrelated Chinese families with RP. Genomic DNA from probands was initially analyzed by whole exome sequencing. Sanger sequencing was used to confirm potential candidate variants affecting the encoded residues in the 60 genes, including heterozygous variants from genes that are related to autosomal dominant RP, homozygous or compound heterozygous variants from genes that are related to autosomal recessive RP, and hemizygous variants from genes that are related to X-linked RP. Synonymous and intronic variants were also examined to confirm whether they could affect splicing. A total of 244 candidate variants were detected by exome sequencing. Sanger sequencing confirmed 240 variants out of the 244 candidates. Informatics and segregation analyses suggested 110 potential pathogenic mutations in 28 out of the 60 genes involving 79 of the 157 (50 %) families, including 31 (39 %, 31/79) families with heterozygous mutations in autosomal dominant genes, 37 (47 %, 37/79) families with homozygous (9) or compound heterozygous (28) mutations in autosomal recessive genes, and 11 (14 %, 11/79) families with hemizygous mutations in X-linked genes. Of the 110 identified variants, 74 (67 %) were novel. The genetic defects in approximately half of the 157 studies families were detected by exome sequencing. A comprehensive analysis of the 60 known genes not only expanded the mutation spectrum and frequency of the 60 genes in Chinese patients with RP, but also provided an overview of the molecular etiology of RP in Chinese patients. The analysis of the known genes also supplied the foundation and clues for discovering novel causative RP genes.     

Lysoplex: An efficient toolkit to detect DNA sequence variations in the autophagy-lysosomal pathway

The autophagy-lysosomal pathway (ALP) regulates cell homeostasis and plays a crucial role in human diseases, such as lysosomal storage disorders (LSDs) and common neurodegenerative diseases. Therefore, the identification of DNA sequence variations in genes involved in this pathway and their association with human diseases would have a significant impact on health. To this aim, we developed Lysoplex, a targeted next-generation sequencing (NGS) approach, which allowed us to obtain a uniform and accurate coding sequence coverage of a comprehensive set of 891 genes involved in lysosomal, endocytic, and autophagic pathways. Lysoplex was successfully validated on 14 different types of LSDs and then used to analyze 48 mutation-unknown patients with a clinical phenotype of neuronal ceroid lipofuscinosis (NCL), a genetically heterogeneous subtype of LSD. Lysoplex allowed us to identify pathogenic mutations in 67% of patients, most of whom had been unsuccessfully analyzed by several sequencing approaches. In addition, in 3 patients, we found potential disease-causing variants in novel NCL candidate genes. We then compared the variant detection power of Lysoplex with data derived from public whole exome sequencing (WES) efforts. On average, a 50% higher number of validated amino acid changes and truncating variations per gene were identified. Overall, we identified 61 truncating sequence variations and 488 missense variations with a high probability to cause loss of function in a total of 316 genes. Interestingly, some loss-of-function variations of genes involved in the ALP pathway were found in homozygosity in the normal population, suggesting that their role is not essential. Thus, Lysoplex provided a comprehensive catalog of sequence variants in ALP genes and allows the assessment of their relevance in cell biology as well as their contribution to human disease.     

Statistical guidance for experimental design and data analysis of mutation detection in rare monogenic mendelian diseases by exome sequencing

Recently, whole-genome sequencing, especially exome sequencing, has successfully led to the identification of causal mutations for rare monogenic Mendelian diseases. However, it is unclear whether this approach can be generalized and effectively applied to other Mendelian diseases with high locus heterogeneity. Moreover, the current exome sequencing approach has limitations such as false positive and false negative rates of mutation detection due to sequencing errors and other artifacts, but the impact of these limitations on experimental design has not been systematically analyzed. To address these questions, we present a statistical modeling framework to calculate the power, the probability of identifying truly disease-causing genes, under various inheritance models and experimental conditions, providing guidance for both proper experimental design and data analysis. Based on our model, we found that the exome sequencing approach is well-powered for mutation detection in recessive, but not dominant, Mendelian diseases with high locus heterogeneity. A disease gene responsible for as low as 5% of the disease population can be readily identified by sequencing just 200 unrelated patients. Based on these results, for identifying rare Mendelian disease genes, we propose that a viable approach is to combine, sequence, and analyze patients with the same disease together, leveraging the statistical framework presented in this work.     

Identification of sequence variants in genetic disease-causing genes using targeted next-generation sequencing

Background

Identification of gene variants plays an important role in research on and diagnosis of genetic diseases. A combination of enrichment of targeted genes and next-generation sequencing (targeted DNA-HiSeq) results in both high efficiency and low cost for targeted sequencing of genes of interest.

Methodology/Principal Findings

To identify mutations associated with genetic diseases, we designed an array-based gene chip to capture all of the exons of 193 genes involved in 103 genetic diseases. To evaluate this technology, we selected 7 samples from seven patients with six different genetic diseases resulting from six disease-causing genes and 100 samples from normal human adults as controls. The data obtained showed that on average, 99.14% of 3,382 exons with more than 30-fold coverage were successfully detected using Targeted DNA-HiSeq technology, and we found six known variants in four disease-causing genes and two novel mutations in two other disease-causing genes (the STS gene for XLI and the FBN1 gene for MFS) as well as one exon deletion mutation in the DMD gene. These results were confirmed in their entirety using either the Sanger sequencing method or real-time PCR.

Conclusions/Significance

Targeted DNA-HiSeq combines next-generation sequencing with the capture of sequences from a relevant subset of high-interest genes. This method was tested by capturing sequences from a DNA library through hybridization to oligonucleotide probes specific for genetic disorder-related genes and was found to show high selectivity, improve the detection of mutations, enabling the discovery of novel variants, and provide additional indel data. Thus, targeted DNA-HiSeq can be used to analyze the gene variant profiles of monogenic diseases with high sensitivity, fidelity, throughput and speed.     

Variant detection sensitivity and biases in whole genome and exome sequencing

Background

Less than two percent of the human genome is protein coding, yet that small fraction harbours the majority of known disease causing mutations. Despite rapidly falling whole genome sequencing (WGS) costs, much research and increasingly the clinical use of sequence data is likely to remain focused on the protein coding exome. We set out to quantify and understand how WGS compares with the targeted capture and sequencing of the exome (exome-seq), for the specific purpose of identifying single nucleotide polymorphisms (SNPs) in exome targeted regions.

Results

We have compared polymorphism detection sensitivity and systematic biases using a set of tissue samples that have been subject to both deep exome and whole genome sequencing. The scoring of detection sensitivity was based on sequence down sampling and reference to a set of gold-standard SNP calls for each sample. Despite evidence of incremental improvements in exome capture technology over time, whole genome sequencing has greater uniformity of sequence read coverage and reduced biases in the detection of non-reference alleles than exome-seq. Exome-seq achieves 95% SNP detection sensitivity at a mean on-target depth of 40 reads, whereas WGS only requires a mean of 14 reads. Known disease causing mutations are not biased towards easy or hard to sequence areas of the genome for either exome-seq or WGS.

Conclusions

From an economic perspective, WGS is at parity with exome-seq for variant detection in the targeted coding regions. WGS offers benefits in uniformity of read coverage and more balanced allele ratio calls, both of which can in most cases be offset by deeper exome-seq, with the caveat that some exome-seq targets will never achieve sufficient mapped read depth for variant detection due to technical difficulties or probe failures. As WGS is intrinsically richer data that can provide insight into polymorphisms outside coding regions and reveal genomic rearrangements, it is likely to progressively replace exome-seq for many applications.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-247) contains supplementary material, which is available to authorized users.     

Large-scale detection of rare variants via pooled multiplexed next-generation sequencing: towards next-generation Ecotilling

Common variants, such as those identified by genome-wide association scans, explain only a small proportion of trait variation. Growing evidence suggests that rare functional variants, which are usually missed by genome-wide association scans, play an important role in determining the phenotype. We used pooled multiplexed next-generation sequencing and a customized analysis workflow to detect mutations in five candidate genes for lignin biosynthesis in 768 pooled Populus nigra accessions. We identified a total of 36 non-synonymous single nucleotide polymorphisms, one of which causes a premature stop codon. The most common variant was estimated to be present in 672 of the 1536 tested chromosomes, while the rarest was estimated to occur only once in 1536 chromosomes. Comparison with individual Sanger sequencing in a selected sub-sample confirmed that variants are identified with high sensitivity and specificity, and that the variant frequency was estimated accurately. This proposed method for identification of rare polymorphisms allows accurate detection of variation in many individuals, and is cost-effective compared to individual sequencing.     

Mutations in glucokinase and other genes detected in neonatal and type 1B diabetes patient using whole exome sequencing may lead to disease-causing changes in protein activity

Monogenic diabetes is caused by mutations that reduce β-cell function. While Sanger sequencing is the standard method used to detect mutated genes. Next-generation sequencing techniques, such as whole exome sequencing (WES), can be used to find multiple gene mutations in one assay. We used WES to detect genetic mutations in both permanent neonatal (PND) and type 1B diabetes (T1BD).A total of five PND and nine T1BD patients were enrolled in this study. WES variants were assessed using VarioWatch, excluding those identified previously. Sanger sequencing was used to confirm the mutations, and their pathogenicity was established via the literature or bioinformatic/functional analysis. The PND and T1BD patients were diagnosed at 0.1–0.5 and 0.8–2.7?years of age, respectively. Diabetic ketoacidosis was present at diagnosis in 60% of PND patients and 44.4% of T1BD patients. We found five novel mutations in five different genes. Notably, patient 602 had a novel homozygous missense mutation c.1295C?>?A (T432?K) in the glucokinase (GCK) gene. Compared to the wild-type recombinant protein, the mutant protein had significantly lower enzymatic activity (2.5%, p?=?0.0002) and V_max (1.23?±?0.019 vs. 0.33?±?0.016, respectively; p?=?0.005). WES is a robust technique that can be used to unravel the etiologies of genetically heterogeneous forms of diabetes. Homozygous inactivating mutations of the GCK gene may have a significant role in PND pathogenesis.     

Progress in Detecting Genetic Alterations and Their Association with Human Disease

The completion of the Human Genome Project provided a reference sequence to which researchers could compare sequences from individual patients in the hope of identifying disease-causing mutations. However, this still necessitated candidate gene testing or a very limited screen of multiple genes using Sanger sequencing. With the advent of high-throughput Sanger sequencing, it became possible to screen hundreds of patients for alterations in hundreds of genes. This process was time consuming and limited to a few locations/institutions that had the space to house tens of sequencing equipment. The development of next generation sequencing revolutionized the process. It is now feasible to sequence the entire exome of multiple individuals in about 10 days. However, this meant that a massive amount of data needed to be filtered to identify the relevant alteration. This is presently the rate-limiting step in providing a convincing association between a genetic alteration and a human disorder.     

Evaluating somatic tumor mutation detection without matched normal samples

Background

Observations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual’s inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a “normal” sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual.

Results

We evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43–60%) but lesser precision (20–21%) to current matched tumor/normal approaches (recall 43–73%, precision 30–82%) when compared to a “gold-standard” tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis.

Conclusions

A detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications.

     

Secondary variants in individuals undergoing exome sequencing: screening of 572 individuals identifies high-penetrance mutations in cancer-susceptibility genes

Genome- and exome-sequencing costs are continuing to fall, and many individuals are undergoing these assessments as research participants and patients. The issue of secondary (so-called incidental) findings in exome analysis is controversial, and data are needed on methods of detection and their frequency. We piloted secondary variant detection by analyzing exomes for mutations in cancer-susceptibility syndromes in subjects ascertained for atherosclerosis phenotypes. We performed exome sequencing on 572 ClinSeq participants, and in 37 genes, we interpreted variants that cause high-penetrance cancer syndromes by using an algorithm that filtered results on the basis of mutation type, quality, and frequency and that filtered mutation-database entries on the basis of defined categories of causation. We identified 454 sequence variants that differed from the human reference. Exclusions were made on the basis of sequence quality (26 variants) and high frequency in the cohort (77 variants) or dbSNP (17 variants), leaving 334 variants of potential clinical importance. These were further filtered on the basis of curation of literature reports. Seven participants, four of whom were of Ashkenazi Jewish descent and three of whom did not meet family-history-based referral criteria, had deleterious BRCA1 or BRCA2 mutations. One participant had a deleterious SDHC mutation, which causes paragangliomas. Exome sequencing, coupled with multidisciplinary interpretation, detected clinically important mutations in cancer-susceptibility genes; four of such mutations were in individuals without a significant family history of disease. We conclude that secondary variants of high clinical importance will be detected at an appreciable frequency in exomes, and we suggest that priority be given to the development of more efficient modes of interpretation with trials in larger patient groups.     

Exome Sequencing Identifies a Novel Gene,WNK1, for Susceptibility to Pelvic Organ Prolapse (POP)

Pelvic organ prolapse (POP) is a common gynecological disorder; however, the genetic components remain largely unidentified. Exome sequencing has been widely used to identify pathogenic gene mutations of several diseases because of its high chromosomal coverage and accuracy. In this study, we performed whole exome sequencing (WES), for the first time, on 8 peripheral blood DNA samples from representative POP cases. After filtering the sequencing data from the dbSNP database (build 138) and the 1000 Genomes Project, 2 missense variants in WNK1, c.2668G > A (p.G890R) and c.6761C> T (p.P2254L), were identified and further validated via Sanger sequencing. In validation stage, the c.2668G > A (p.G890R) variant and 8 additional variants were detected in 11 out of 161 POP patients. All these variants were absent in 231 healthy controls. Functional experiments showed that fibroblasts from the utero-sacral ligaments of POP with WNK1 mutations exhibited loose and irregular alignment compared with fibroblasts from healthy controls. In sum, our study identified a novel gene, WNK1, for POP susceptibility, expanded the causal mutation spectrums of POP, and provided evidence for the genetic diagnosis and medical management of POP in the future.     

SinoDuplex: An Improved Duplex Sequencing Approach to Detect Low-frequency Variants in Plasma cfDNA Samples

Accurate detection of low frequency mutations from plasma cell-free DNA in blood using targeted next generation sequencing technology has shown promising benefits in clinical settings. Duplex sequencing technology is the most commonly used approach in liquid biopsies. Unique molecular identifiers are attached to each double-stranded DNA template, followed by production of low-error consensus sequences to detect low frequency variants. However, high sequencing costs have hindered application of this approach in clinical practice. Here, we have developed an improved duplex sequencing approach called Sino Duplex, which utilizes a pool of adapters containing pre-defined barcode sequences to generate far fewer barcode combinations than with random sequences, and implemented a novel computational analysis algorithm to generate duplex consensus sequences more precisely. Sino Duplex increased the output of duplex sequencing technology, making it more cost-effective. We evaluated our approach using reference standard samples and cell-free DNA samples from lung cancer patients. Our results showed that Sino Duplex has high sensitivity and specificity in detecting very low allele frequency mutations. The source code for Sino Duplex is freely available at https://github.com/Sin Oncology/sinoduplex.     

Accurate detection of subclonal single nucleotide variants in whole genome amplified and pooled cancer samples using HaloPlex target enrichment

Background

Target enrichment and resequencing is a widely used approach for identification of cancer genes and genetic variants associated with diseases. Although cost effective compared to whole genome sequencing, analysis of many samples constitutes a significant cost, which could be reduced by pooling samples before capture. Another limitation to the number of cancer samples that can be analyzed is often the amount of available tumor DNA. We evaluated the performance of whole genome amplified DNA and the power to detect subclonal somatic single nucleotide variants in non-indexed pools of cancer samples using the HaloPlex technology for target enrichment and next generation sequencing.

Results

We captured a set of 1528 putative somatic single nucleotide variants and germline SNPs, which were identified by whole genome sequencing, with the HaloPlex technology and sequenced to a depth of 792–1752. We found that the allele fractions of the analyzed variants are well preserved during whole genome amplification and that capture specificity or variant calling is not affected. We detected a large majority of the known single nucleotide variants present uniquely in one sample with allele fractions as low as 0.1 in non-indexed pools of up to ten samples. We also identified and experimentally validated six novel variants in the samples included in the pools.

Conclusion

Our work demonstrates that whole genome amplified DNA can be used for target enrichment equally well as genomic DNA and that accurate variant detection is possible in non-indexed pools of cancer samples. These findings show that analysis of a large number of samples is feasible at low cost, even when only small amounts of DNA is available, and thereby significantly increases the chances of indentifying recurrent mutations in cancer samples.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-14-856) contains supplementary material, which is available to authorized users.     

Mutation Spectrum of Six Genes in Chinese Phenylketonuria Patients Obtained through Next-Generation Sequencing

Background

The identification of gene variants plays an important role in the diagnosis of genetic diseases.

Methodology/Principal Findings

To develop a rapid method for the diagnosis of phenylketonuria (PKU) and tetrahydrobiopterin (BH4) deficiency, we designed a multiplex, PCR-based primer panel to amplify all the exons and flanking regions (50 bp average) of six PKU-associated genes (PAH, PTS, GCH1, QDPR, PCBD1 and GFRP). The Ion Torrent Personal Genome Machine (PGM) System was used to detect mutations in all the exons of these six genes. We tested 93 DNA samples from blood specimens from 35 patients and their parents (32 families) and 26 healthy adults. Using strict bioinformatic criteria, this sequencing data provided, on average, 99.14% coverage of the 39 exons at more than 70-fold mean depth of coverage. We found 23 previously documented variants in the PAH gene and six novel mutations in the PAH and PTS genes. A detailed analysis of the mutation spectrum of these patients is described in this study.

Conclusions/Significance

These results were confirmed by Sanger sequencing. In conclusion, benchtop next-generation sequencing technology can be used to detect mutations in monogenic diseases and can detect both point mutations and indels with high sensitivity, fidelity and throughput at a lower cost than conventional methods in clinical applications.     

Whole genome sequencing and its applications in medical genetics

Fundamental improvement was made for genome sequencing since the next-generation sequencing (NGS) came out in the 2000s. The newer technologies make use of the power of massively-parallel short-read DNA sequencing, genome alignment and assembly methods to digitally and rapidly search the genomes on a revolutionary scale, which enable large-scale whole genome sequencing (WGS) accessible and practical for researchers. Nowadays, whole genome sequencing is more and more prevalent in detecting the genetics of diseases, studying causative relations with cancers, making genome-level comparative analysis, reconstruction of human population history, and giving clinical implications and instructions. In this review, we first give a typical pipeline of whole genome sequencing, including the lab template preparation, sequencing, genome assembling and quality control, variants calling and annotations. We compare the difference between whole genome and whole exome sequencing (WES), and explore a wide range of applications of whole genome sequencing for both mendelian diseases and complex diseases in medical genetics. We highlight the impact of whole genome sequencing in cancer studies, regulatory variant analysis, predictive medicine and precision medicine, as well as discuss the challenges of the whole genome sequencing.      

Next generation molecular diagnosis of mitochondrial disorders

Mitochondrial disorders are by far the most genetically heterogeneous group of diseases, involving two genomes, the 16.6 kb mitochondrial genome and ~ 1500 genes encoded in the nuclear genome. For maternally inherited mitochondrial DNA disorders, a complete molecular diagnosis requires several different methods for the detection and quantification of mtDNA point mutations and large deletions. For mitochondrial disorders caused by autosomal recessive, dominant, and X-linked nuclear genes, the diagnosis has relied on clinical, biochemical, and molecular studies to point to a group of candidate genes followed by stepwise Sanger sequencing of the candidate genes one-by-one. The development of Next Generation Sequencing (NGS) has revolutionized the diagnostic approach. Using massively parallel sequencing (MPS) analysis of the entire mitochondrial genome, mtDNA point mutations and deletions can be detected and quantified in one single step. The NGS approach also allows simultaneous analyses of a group of genes or the whole exome, thus, the mutations in causative gene(s) can be identified in one-step. New approaches make genetic analyses much faster and more efficient. Huge amounts of sequencing data produced by the new technologies brought new challenges to bioinformatics, analytical pipelines, and interpretation of numerous novel variants. This article reviews the clinical utility of next generation sequencing for the molecular diagnoses of complex dual genome mitochondrial disorders.     

Whole Exome Sequencing of Distant Relatives in Multiplex Families Implicates Rare Variants in Candidate Genes for Oral Clefts

A dozen genes/regions have been confirmed as genetic risk factors for oral clefts in human association and linkage studies, and animal models argue even more genes may be involved. Genomic sequencing studies should identify specific causal variants and may reveal additional genes as influencing risk to oral clefts, which have a complex and heterogeneous etiology. We conducted a whole exome sequencing (WES) study to search for potentially causal variants using affected relatives drawn from multiplex cleft families. Two or three affected second, third, and higher degree relatives from 55 multiplex families were sequenced. We examined rare single nucleotide variants (SNVs) shared by affected relatives in 348 recognized candidate genes. Exact probabilities that affected relatives would share these rare variants were calculated, given pedigree structures, and corrected for the number of variants tested. Five novel and potentially damaging SNVs shared by affected distant relatives were found and confirmed by Sanger sequencing. One damaging SNV in CDH1, shared by three affected second cousins from a single family, attained statistical significance (P = 0.02 after correcting for multiple tests). Family-based designs such as the one used in this WES study offer important advantages for identifying genes likely to be causing complex and heterogeneous disorders.