CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data

Independent inheritance of cognition and bipolar disorder in a family sample

Cognitive deficits in people with bipolar disorder (BD) may be the result of the illness or its treatment, but they could also reflect genetic risk factors shared between BD and cognition. We investigated this question using empirical genetic relationships within a sample of patients with BD and their unaffected relatives. Participants with bipolar I, II, or schizoaffective disorder ("narrow" BD, n = 69), related mood disorders ("broad" BD, n = 135), and their clinically unaffected relatives (n = 227) completed five cognitive tests. General cognitive function (g) was quantified via principal components analysis (PCA). Heritability and genetic correlations were estimated with SOLAR-Eclipse. Participants with "narrow" or "broad" diagnoses showed deficits in g, although affect recognition was unimpaired. Cognitive performance was significantly heritable (h2 = 0.322 for g, p < 0.005). Coheritability between psychopathology and g was small (0.0184 for narrow and 0.0327 for broad) and healthy relatives of those with BD were cognitively unimpaired. In this family sample, cognitive deficits were present in participants with BD but were not explained by substantial overlaps in genetic determinants of mood and cognition. These findings support the view that cognitive deficits in BD are largely the result of the illness or its treatment.

A genome-first approach to characterize DICER1 pathogenic variant prevalence, penetrance and cancer, thyroid, and other phenotypes in 2 population-scale cohorts

Purpose

Population-scale, exome-sequenced cohorts with linked electronic health records (EHR) permit genome-first exploration of phenotype. Phenotype and cancer risk are well-characterized in children with a pathogenic DICER1 (HGNC ID:17098) variant. Here, the prevalence, penetrance and phenotype of pathogenic germline DICER1 variants in adults was investigated in two population-scale cohorts.

Methods

Variant pathogenicity was classified using published DICER1 ClinGen criteria in the UK Biobank (469,787 exomes; unrelated: 437,663) and Geisinger (170,503 exomes; unrelated: 109,789) cohorts. In the UK Biobank cohort, cancer diagnoses in the EHR, cancer and death registry were queried. For the Geisinger cohort, the Geisinger Cancer Registry and EHR were queried.

Results

In the UK Biobank, there were 46 unique pathogenic DICER1 variants in 57 individuals (1:8,242;95%CI:1:6,362-1:10,677). In Geisinger, there were 16 unique pathogenic DICER1 variants (including one microdeletion) in 21 individuals (1:8,119;95%CI:1:5,310-1:12,412). Cohorts were well-powered to find larger effect sizes for common cancers. Cancers were not significantly enriched in DICER1 heterozygotes; however, there was a ~4-fold increased risk for thyroid disease in both cohorts. There were multiple ICD10 codes enriched >2-fold in both cohorts.

Conclusion

Estimates of pathogenic germline DICER1 prevalence, thyroid disease penetrance and cancer phenotype from genomically ascertained adults are determined in two large cohorts.

Single nucleotide polymorphisms conferring susceptibility to leukemia and oral mucositis: a multi-center pilot study of patients prior to conditioning therapy for hematopoietic cell transplant

PURPOSE

Leukemias have been associated with oral manifestations, reflecting susceptibility to cancer therapy-induced oral mucositis. We sought to identify SNPs associated with both leukemia and oral mucositis (OM).

METHODS

Whole exome sequencing was performed on leukemia and non-cancer blood disorder (ncBD) patients' saliva samples (N = 50) prior to conditioning therapy. WHO OM grading scores were determined: moderate to severe (OM2-4) vs. none to mild (OM0-1). Reads were processed using Trim Galorev0.6.7, Bowtie2v2.4.1, Samtoolsv1.10, Genome Analysis Toolkit (GATK)v4.2.6.1, and DeepVariantv1.4.0. We utilized the following pipelines: P1 analysis with PLINK2v3.7, SNP2GENEv1.4.1 and MAGMAv1.07b, and P2 [leukemia (N = 42) vs. ncBDs (N = 8)] and P3 [leukemia + OM2-4 (N = 18) vs. leukemia + OM0-1 (N = 24)] with Z-tests of genotypes and protein-protein interaction determination. GeneCardsSuitev5.14 was used to identify phenotypes (P1 and P2, leukemia; P3, oral mucositis) and average disease-causing likelihood and DGIdb for drug interactions. P1 and P2 genes were analyzed with CytoScape plugin BiNGOv3.0.3 to retrieve overrepresented Gene Ontology (GO) terms and Ensembl's VEP for SNP outcomes.

RESULTS

In P1, 457 candidate SNPs (28 genes) were identified and 21,604 SNPs (1016 genes) by MAGMAv1.07b. Eighteen genes were associated with "leukemia" per VarElectv5.14 analysis and predicted to be deleterious. In P2 and P3, 353 and 174 SNPs were significant, respectively. STRINGv12.0 returned 77 and 32 genes (C.L. = 0.7) for P2 and P3, respectively. VarElectv5.14 determined 60 genes from P2 associated with "leukemia" and 11 with "oral mucositis" from P3. Overrepresented GO terms included "cellular process," "signaling," "hemopoiesis," and "regulation of immune response."

CONCLUSIONS

We identified candidate SNPs possibly conferring susceptibility to develop leukemia and oral mucositis.

HMZDupFinder: a robust computational approach for detecting intragenic homozygous duplications from exome sequencing data

Homozygous duplications contribute to genetic disease by altering gene dosage or disrupting gene regulation and can be more deleterious to organismal biology than heterozygous duplications. Intragenic exonic duplications can result in loss-of-function (LoF) or gain-of-function (GoF) alleles that when homozygosed, i.e. brought to homozygous state at a locus by identity by descent or state, could potentially result in autosomal recessive (AR) rare disease traits. However, the detection and functional interpretation of homozygous duplications from exome sequencing data remains a challenge. We developed a framework algorithm, HMZDupFinder, that is designed to detect exonic homozygous duplications from exome sequencing (ES) data. The HMZDupFinder algorithm can efficiently process large datasets and accurately identifies small intragenic duplications, including those associated with rare disease traits. HMZDupFinder called 965 homozygous duplications with three or less exons from 8,707 ES with a recall rate of 70.9% and a precision of 16.1%. We experimentally confirmed 8/10 rare homozygous duplications. Pathogenicity assessment of these copy number variant alleles allowed clinical genomics contextualization for three homozygous duplications alleles, including two affecting known OMIM disease genes EDAR (MIM# 224900), TNNT1(MIM# 605355), and one variant in a novel candidate disease gene: PAAF1.

labelSeg: segment annotation for tumor copy number alteration profiles

Somatic copy number alterations (SCNAs) are a predominant type of oncogenomic alterations that affect a large proportion of the genome in the majority of cancer samples. Current technologies allow high-throughput measurement of such copy number aberrations, generating results consisting of frequently large sets of SCNA segments. However, the automated annotation and integration of such data are particularly challenging because the measured signals reflect biased, relative copy number ratios. In this study, we introduce labelSeg, an algorithm designed for rapid and accurate annotation of CNA segments, with the aim of enhancing the interpretation of tumor SCNA profiles. Leveraging density-based clustering and exploiting the length-amplitude relationships of SCNA, our algorithm proficiently identifies distinct relative copy number states from individual segment profiles. Its compatibility with most CNA measurement platforms makes it suitable for large-scale integrative data analysis. We confirmed its performance on both simulated and sample-derived data from The Cancer Genome Atlas reference dataset, and we demonstrated its utility in integrating heterogeneous segment profiles from different data sources and measurement platforms. Our comparative and integrative analysis revealed common SCNA patterns in cancer and protein-coding genes with a strong correlation between SCNA and messenger RNA expression, promoting the investigation into the role of SCNA in cancer development.

Incorporating CNV analysis improves the yield of exome sequencing for rare monogenic disorders-an important consideration for resource-constrained settings

Exome sequencing (ES) is a recommended first-tier diagnostic test for many rare monogenic diseases. It allows for the detection of both single-nucleotide variants (SNVs) and copy number variants (CNVs) in coding exonic regions of the genome in a single test, and this dual analysis is a valuable approach, especially in limited resource settings. Single-nucleotide variants are well studied; however, the incorporation of copy number variant analysis tools into variant calling pipelines has not been implemented yet as a routine diagnostic test, and chromosomal microarray is still more widely used to detect copy number variants. Research shows that combined single and copy number variant analysis can lead to a diagnostic yield of up to 58%, increasing the yield with as much as 18% from the single-nucleotide variant only pipeline. Importantly, this is achieved with the consideration of computational costs only, without incurring any additional sequencing costs. This mini review provides an overview of copy number variant analysis from exome data and what the current recommendations are for this type of analysis. We also present an overview on rare monogenic disease research standard practices in resource-limited settings. We present evidence that integrating copy number variant detection tools into a standard exome sequencing analysis pipeline improves diagnostic yield and should be considered a significantly beneficial addition, with relatively low-cost implications. Routine implementation in underrepresented populations and limited resource settings will promote generation and sharing of CNV datasets and provide momentum to build core centers for this niche within genomic medicine.

Molecular characterization and clinical investigation of patients with heritable thoracic aortic aneurysm and dissection

OBJECTIVES

Thoracic aortic aneurysm and dissection has a genetic predisposition and a variety of clinical manifestations. This study aimed to investigate the clinical and molecular characterizations of patients with thoracic aortic aneurysm and dissection and further explore the relationship between the genotype and phenotype, as well as their postoperative outcomes.

METHODS

A total of 1095 individuals with thoracic aortic aneurysm and dissection admitted to our hospital between 2013 and 2022 were included. Next-generation sequencing and multiplex ligation-dependent probe amplification were performed, and mosaicism analysis was additionally implemented to identify the genetic causes.

RESULTS

A total of 376 causative variants were identified in 83.5% of patients with syndromic thoracic aortic aneurysm and dissection and 18.7% of patients with nonsyndromic thoracic aortic aneurysm and dissection, including 8 copy number variations and 2 mosaic variants. Patients in the "pathogenic" and "variant of uncertain significance" groups had younger ages of aortic events and higher aortic reintervention risks compared with genetically negative cases. In addition, patients with FBN1 haploinsufficiency variants had shorter reintervention-free survival than those with FBN1 dominant negative variants.

CONCLUSIONS

Our data expanded the genetic spectrum of heritable thoracic aortic aneurysm and dissection and indicated that copy number variations and mosaic variants contributed to a small proportion of the disease-causing alterations. Moreover, positive genetic results might have a possible predictive value for aortic event severity and postoperative risk stratification.

Idiopathic hypogonadotropic hypogonadism caused by compound heterozygosity for two novel mutations in the GNRH1 gene: a case report

BACKGROUND

Idiopathic hypogonadotropic hypogonadism (IHH) is a rare congenital or acquired genetic disorder caused by gonadotropin-releasing hormone (GnRH) deficiency. IHH patients are divided into two major groups, hyposmic or anosmic IHH (Kallmann syndrome) and normosmic IHH (nIHH), according to whether their sense of smell is intact. Here we report a case of novel compound heterozygous mutations in the GNRH1 gene in a 15-year-old male with nIHH.

CASE PRESENTATION

The patient presented typical clinical symptoms of delayed testicular development, with testosterone < 3.5 mmol/L and reduced gonadotropin (follicle-stimulating hormone, luteinizing hormone) levels. Two heterozygous variants of the GNRH1 gene were detected, nonsense variant 1: c.85G > T:p.G29* and variant 2: c.1A > G:p.M1V, which disrupted the start codon.

CONCLUSIONS

Two GNRH1 mutations responsible for nIHH are identified in this study. Our findings extend the mutational spectrum of GNRH1 by revealing novel causative mutations of nIHH.

GATK-gCNV enables the discovery of rare copy number variants from exome sequencing data

Copy number variants (CNVs) are major contributors to genetic diversity and disease. While standardized methods, such as the genome analysis toolkit (GATK), exist for detecting short variants, technical challenges have confounded uniform large-scale CNV analyses from whole-exome sequencing (WES) data. Given the profound impact of rare and de novo coding CNVs on genome organization and human disease, we developed GATK-gCNV, a flexible algorithm to discover rare CNVs from sequencing read-depth information, complete with open-source distribution via GATK. We benchmarked GATK-gCNV in 7,962 exomes from individuals in quartet families with matched genome sequencing and microarray data, finding up to 95% recall of rare coding CNVs at a resolution of more than two exons. We used GATK-gCNV to generate a reference catalog of rare coding CNVs in WES data from 197,306 individuals in the UK Biobank, and observed strong correlations between per-gene CNV rates and measures of mutational constraint, as well as rare CNV associations with multiple traits. In summary, GATK-gCNV is a tunable approach for sensitive and specific CNV discovery in WES data, with broad applications.

A genotype-first approach identifies high incidence of NF1 pathogenic variants with distinct disease associations

Loss of function variants in the NF1 gene cause neurofibromatosis type 1 (NF1), a genetic disorder characterized by complete penetrance, prevalence of 1 in 3,000, characteristic physical exam findings, and a substantially increased risk for malignancy. However, our understanding of the disorder is entirely based on patients ascertained through phenotype-first approaches. Leveraging a genotype-first approach in two large patient cohorts, we demonstrate unexpectedly high prevalence (1 in 450-750) of NF1 pathogenic variants. Half were identified in individuals lacking clinical features of NF1, with many appearing to have post-zygotic mosaicism for the identified variant. Incidentally discovered variants were not associated with classic NF1 features but were associated with an increased incidence of malignancy compared to a control population. Our findings suggest that NF1 pathogenic variants are substantially more common than previously thought, often characterized by somatic mosaicism and reduced penetrance, and are important contributors to cancer risk in the general population.

Chromothripsis detection with multiple myeloma patients based on deep graph learning

MOTIVATION

Chromothripsis, associated with poor clinical outcomes, is prognostically vital in multiple myeloma. The catastrophic event is reported to be detectable prior to the progression of multiple myeloma. As a result, chromothripsis detection can contribute to risk estimation and early treatment guidelines for multiple myeloma patients. However, manual diagnosis remains the gold standard approach to detect chromothripsis events with the whole-genome sequencing technology to retrieve both copy number variation (CNV) and structural variation data. Meanwhile, CNV data are much easier to obtain than structural variation data. Hence, in order to reduce the reliance on human experts' efforts and structural variation data extraction, it is necessary to establish a reliable and accurate chromothripsis detection method based on CNV data.

RESULTS

To address those issues, we propose a method to detect chromothripsis solely based on CNV data. With the help of structure learning, the intrinsic relationship-directed acyclic graph of CNV features is inferred to derive a CNV embedding graph (i.e. CNV-DAG). Subsequently, a neural network based on Graph Transformer, local feature extraction, and non-linear feature interaction, is proposed with the embedding graph as the input to distinguish whether the chromothripsis event occurs. Ablation experiments, clustering, and feature importance analysis are also conducted to enable the proposed model to be explained by capturing mechanistic insights.

AVAILABILITY AND IMPLEMENTATION

The source code and data are freely available at https://github.com/luvyfdawnYu/CNV_chromothripsis.

Novel Intronic Mutations of the SLC12A3 Gene in Patients with Gitelman Syndrome

Aim

Mutations in the SLC12A3 gene have been reported to cause Gitelman syndrome (GS), characterized by hypokalemic metabolic alkalosis. The aim of this research is to investigate the genetic mutations and clinical features of patients with clinical suspicion of GS.

Methods

Six families were enrolled. The symptoms, clinical examination, laboratory results, genotypes, and effect of mutations on mRNA splicing were analyzed. Genomic DNA was screened for gene variations using whole exome sequence and Sanger sequencing. DNA sequences were compared with reference sequences.

Results

Genetic analysis revealed nine genetic variants of SLC12A3, including three novel heterozygous mutations (c.1096-2A>G, c.1862A>G, and c.2747+4del) and six previously characterized mutations (c.965-1_976delinsACCGAAAATTTT, c.506-1G>A, c.602-16G>A, c.533C >T, c.1456 G>A, and c.1108 G>C). Probands presented with the clinical syndrome of hypokalemia, increased plasma renin, hypocalciuria and hypokalemic alkalosis.

Conclusion

These clinical manifestations and genotypes were consistent with the diagnostic criteria of GS. The study described the phenotypes and genotypes of six pedigrees involving GS patients, demonstrating the importance of SLC12A3 gene screening for GS. This study expands the mutation spectrum of SLC12A3 gene in GS.

Integrating de novo and inherited variants in 42,607 autism cases identifies mutations in new moderate-risk genes

To capture the full spectrum of genetic risk for autism, we performed a two-stage analysis of rare de novo and inherited coding variants in 42,607 autism cases, including 35,130 new cases recruited online by SPARK. We identified 60 genes with exome-wide significance (P < 2.5 × 10^-6), including five new risk genes (NAV3, ITSN1, MARK2, SCAF1 and HNRNPUL2). The association of NAV3 with autism risk is primarily driven by rare inherited loss-of-function (LoF) variants, with an estimated relative risk of 4, consistent with moderate effect. Autistic individuals with LoF variants in the four moderate-risk genes (NAV3, ITSN1, SCAF1 and HNRNPUL2; n = 95) have less cognitive impairment than 129 autistic individuals with LoF variants in highly penetrant genes (CHD8, SCN2A, ADNP, FOXP1 and SHANK3) (59% vs 88%, P = 1.9 × 10^-6). Power calculations suggest that much larger numbers of autism cases are needed to identify additional moderate-risk genes.

Different Strategies for Counting the Depth of Coverage in Copy Number Variation Calling Tools

There are many copy number variation (CNV) detection tools based on the depth of coverage. A characteristic feature of all tools based on the depth of coverage is the first stage of data processing—counting the depth of coverage in the investigated sequencing regions. However, each tool implements this stage in a slightly different way. Herein, we used data from the 1000 Genomes Project to present the impact of another depth of coverage counting strategies on the results of the CNVs detection process. In the study, we used 7 CNV calling tools: CODEX, CANOES, exomeCopy, ExomeDepth, CLAMMS, CNVkit, and CNVind; from each of these applications, we separated the process of counting the depth of coverage into independent modules. Then, we counted the depth of coverage by mentioned modules, and finally, the obtained depth of coverage tables were used as the input data set to other CNV calling tools. The performed experiments showed that the best methods of counting the depth of coverage are the algorithms implemented in the CLAMMS and CNVkit applications. Both ways allow obtaining much better sets of detected CNVs compared to counting the depth of coverage implemented in other tools. What is more, some CNV detection tools are reasonably resistant to changing the input depth of coverage table. In this study, we proved that the exomeCopy application gives an approximately similar set of the resulting rare CNVs, regardless of the method of counting the depth of coverage table.

Rare loss of function variants in the hepatokine gene INHBE protect from abdominal obesity

Identifying genetic variants associated with lower waist-to-hip ratio can reveal new therapeutic targets for abdominal obesity. We use exome sequences from 362,679 individuals to identify genes associated with waist-to-hip ratio adjusted for BMI (WHRadjBMI), a surrogate for abdominal fat that is causally linked to type 2 diabetes and coronary heart disease. Predicted loss of function (pLOF) variants in INHBE associate with lower WHRadjBMI and this association replicates in data from AMP-T2D-GENES. INHBE encodes a secreted protein, the hepatokine activin E. In vitro characterization of the most common INHBE pLOF variant in our study, indicates an in-frame deletion resulting in a 90% reduction in secreted protein levels. We detect associations with lower WHRadjBMI for variants in ACVR1C, encoding an activin receptor, further highlighting the involvement of activins in regulating fat distribution. These findings highlight activin E as a potential therapeutic target for abdominal obesity, a phenotype linked to cardiometabolic disease.

Algorithmic improvements for discovery of germline copy number variants in next-generation sequencing data

Background

Copy number variants (CNVs) play a significant role in human heredity and disease. However, sensitive and specific characterization of germline CNVs from NGS data has remained challenging, particularly for hybridization-capture data in which read counts are the primary source of copy number information.

Results

We describe two algorithmic adaptations that improve CNV detection accuracy in a Hidden Markov Model (HMM) context. First, we present a method for computing target- and copy number-specific emission distributions. Second, we demonstrate that the Pointwise Maximum a posteriori (PMAP) HMM decoding procedure yields improved sensitivity for small CNV calls compared to the more common Viterbi HMM decoder. We develop a prototype implementation, called Cobalt, and compare it to other CNV detection tools using sets of simulated and previously detected CNVs with sizes spanning a single exon to a full chromosome.

Conclusions

In both the simulation and previously detected CNV studies Cobalt shows similar sensitivity but significantly fewer false positive detections compared to other callers. Overall sensitivity is 80–90% for deletion CNVs spanning 1–4 targets and 90–100% for larger deletion events, while sensitivity is somewhat lower for small duplication CNVs.

Reduced reproductive success is associated with selective constraint on human genes

Genome-wide sequencing of human populations has revealed substantial variation among genes in the intensity of purifying selection acting on damaging genetic variants¹. Although genes under the strongest selective constraint are highly enriched for associations with Mendelian disorders, most of these genes are not associated with disease and therefore the nature of the selection acting on them is not known². Here we show that genetic variants that damage these genes are associated with markedly reduced reproductive success, primarily owing to increased childlessness, with a stronger effect in males than in females. We present evidence that increased childlessness is probably mediated by genetically associated cognitive and behavioural traits, which may mean that male carriers are less likely to find reproductive partners. This reduction in reproductive success may account for 20% of purifying selection against heterozygous variants that ablate protein-coding genes. Although this genetic association may only account for a very minor fraction of the overall likelihood of being childless (less than 1%), especially when compared to more influential sociodemographic factors, it may influence how genes evolve over time.

Benchmarking germline CNV calling tools from exome sequencing data

Whole-exome sequencing is an attractive alternative to microarray analysis because of the low cost and potential ability to detect copy number variations (CNV) of various sizes (from 1-2 exons to several Mb). Previous comparison of the most popular CNV calling tools showed a high portion of false-positive calls. Moreover, due to a lack of a gold standard CNV set, the results are limited and incomparable. Here, we aimed to perform a comprehensive analysis of tools capable of germline CNV calling available at the moment using a single CNV standard and reference sample set. Compiling variants from previous studies with Bayesian estimation approach, we constructed an internal standard for NA12878 sample (pilot National Institute of Standards and Technology Reference Material) including 110,050 CNV or non-CNV exons. The standard was used to evaluate the performance of 16 germline CNV calling tools on the NA12878 sample and 10 correlated exomes as a reference set with respect to length distribution, concordance, and efficiency. Each algorithm had a certain range of detected lengths and showed low concordance with other tools. Most tools are focused on detection of a limited number of CNVs one to seven exons long with a false-positive rate below 50%. EXCAVATOR2, exomeCopy, and FishingCNV focused on detection of a wide range of variations but showed low precision. Upon unified comparison, the tools were not equivalent. The analysis performed allows choosing algorithms or ensembles of algorithms most suitable for a specific goal, e.g. population studies or medical genetics.

Non-coding region variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms

Clinical genetic testing of protein-coding regions identifies a likely causative variant in only around half of developmental disorder (DD) cases. The contribution of regulatory variation in non-coding regions to rare disease, including DD, remains very poorly understood. We screened 9,858 probands from the Deciphering Developmental Disorders (DDD) study for de novo mutations in the 5' untranslated regions (5' UTRs) of genes within which variants have previously been shown to cause DD through a dominant haploinsufficient mechanism. We identified four single-nucleotide variants and two copy-number variants upstream of MEF2C in a total of ten individual probands. We developed multiple bespoke and orthogonal experimental approaches to demonstrate that these variants cause DD through three distinct loss-of-function mechanisms, disrupting transcription, translation, and/or protein function. These non-coding region variants represent 23% of likely diagnoses identified in MEF2C in the DDD cohort, but these would all be missed in standard clinical genetics approaches. Nonetheless, these variants are readily detectable in exome sequence data, with 30.7% of 5' UTR bases across all genes well covered in the DDD dataset. Our analyses show that non-coding variants upstream of genes within which coding variants are known to cause DD are an important cause of severe disease and demonstrate that analyzing 5' UTRs can increase diagnostic yield. We also show how non-coding variants can help inform both the disease-causing mechanism underlying protein-coding variants and dosage tolerance of the gene.

Detection of homozygous and hemizygous complete or partial exon deletions by whole-exome sequencing

The detection of copy number variations (CNVs) in whole-exome sequencing (WES) data is important, as CNVs may underlie a number of human genetic disorders. The recently developed HMZDelFinder algorithm can detect rare homozygous and hemizygous (HMZ) deletions in WES data more effectively than other widely used tools. Here, we present HMZDelFinder_opt, an approach that outperforms HMZDelFinder for the detection of HMZ deletions, including partial exon deletions in particular, in WES data from laboratory patient collections that were generated over time in different experimental conditions. We show that using an optimized reference control set of WES data, based on a PCA-derived Euclidean distance for coverage, strongly improves the detection of HMZ complete exon deletions both in real patients carrying validated disease-causing deletions and in simulated data. Furthermore, we develop a sliding window approach enabling HMZDelFinder_opt to identify HMZ partial deletions of exons that are undiscovered by HMZDelFinder. HMZDelFinder_opt is a timely and powerful approach for detecting HMZ deletions, particularly partial exon deletions, in WES data from inherently heterogeneous laboratory patient collections.

Copy Number Variant Detection with Low-Coverage Whole-Genome Sequencing Represents a Viable Alternative to the Conventional Array-CGH

Copy number variations (CNVs) represent a type of structural variant involving alterations in the number of copies of specific regions of DNA that can either be deleted or duplicated. CNVs contribute substantially to normal population variability, however, abnormal CNVs cause numerous genetic disorders. At present, several methods for CNV detection are applied, ranging from the conventional cytogenetic analysis, through microarray-based methods (aCGH), to next-generation sequencing (NGS). In this paper, we present GenomeScreen, an NGS-based CNV detection method for low-coverage, whole-genome sequencing. We determined the theoretical limits of its accuracy and obtained confirmation in an extensive in silico study and in real patient samples with known genotypes. In theory, at least 6 M uniquely mapped reads are required to detect a CNV with the length of 100 kilobases (kb) or more with high confidence (Z-score > 7). In practice, the in silico analysis required at least 8 M to obtain >99% accuracy (for 100 kb deviations). We compared GenomeScreen with one of the currently used aCGH methods in diagnostic laboratories, which has mean resolution of 200 kb. GenomeScreen and aCGH both detected 59 deviations, while GenomeScreen furthermore detected 134 other (usually) smaller variations. When compared to aCGH, overall performance of the proposed GenemoScreen tool is comparable or superior in terms of accuracy, turn-around time, and cost-effectiveness, thus providing reasonable benefits, particularly in a prenatal diagnosis setting.

Bayesian copy number detection and association in large-scale studies

BACKGROUND

Germline copy number variants (CNVs) increase risk for many diseases, yet detection of CNVs and quantifying their contribution to disease risk in large-scale studies is challenging due to biological and technical sources of heterogeneity that vary across the genome within and between samples.

METHODS

We developed an approach called CNPBayes to identify latent batch effects in genome-wide association studies involving copy number, to provide probabilistic estimates of integer copy number across the estimated batches, and to fully integrate the copy number uncertainty in the association model for disease.

RESULTS

Applying a hidden Markov model (HMM) to identify CNVs in a large multi-site Pancreatic Cancer Case Control study (PanC4) of 7598 participants, we found CNV inference was highly sensitive to technical noise that varied appreciably among participants. Applying CNPBayes to this dataset, we found that the major sources of technical variation were linked to sample processing by the centralized laboratory and not the individual study sites. Modeling the latent batch effects at each CNV region hierarchically, we developed probabilistic estimates of copy number that were directly incorporated in a Bayesian regression model for pancreatic cancer risk. Candidate associations aided by this approach include deletions of 8q24 near regulatory elements of the tumor oncogene MYC and of Tumor Suppressor Candidate 3 (TUSC3).

CONCLUSIONS

Laboratory effects may not account for the major sources of technical variation in genome-wide association studies. This study provides a robust Bayesian inferential framework for identifying latent batch effects, estimating copy number, and evaluating the role of copy number in heritable diseases.

A highly sensitive and specific workflow for detecting rare copy-number variants from exome sequencing data

Background

Exome sequencing (ES) is a first-tier diagnostic test for many suspected Mendelian disorders. While it is routine to detect small sequence variants, it is not a standard practice in clinical settings to detect germline copy-number variants (CNVs) from ES data due to several reasons relating to performance. In this work, we comprehensively characterized one of the most sensitive ES-based CNV tools, ExomeDepth, against SNP array, a standard of care test in clinical settings to detect genome-wide CNVs.

Methods

We propose a modified ExomeDepth workflow by excluding exons with low mappability prior to variant calling to drastically reduce the false positives originating from the repetitive regions of the genome, and an iterative variant calling framework to assess the reproducibility. We used a cohort of 307 individuals with clinical ES data and clinical SNP array to estimate the sensitivity and false discovery rate of the CNV detection using exome sequencing. Further, we performed targeted testing of the STRC gene in 1972 individuals. To reduce the number of variants for downstream analysis, we performed a large-scale iterative variant calling process with random control cohorts to assess the reproducibility of the CNVs.

Results

The modified workflow presented in this paper reduced the number of total variants identified by one third while retaining a higher sensitivity of 97% and resulted in an improved false discovery rate of 11.4% compared to the default ExomeDepth pipeline. The exclusion of exons with low mappability removes 4.5% of the exons, including a subset of exons (0.6%) in disease-associated genes which are intractable by short-read next-generation sequencing (NGS). Results from the reproducibility analysis showed that the clinically reported variants were reproducible 100% of the time and that the modified workflow can be used to rank variants from high to low confidence. Targeted testing of 30 CNVs identified in STRC, a challenging gene to ascertain by NGS, showed a 100% validation rate.

Conclusions

In summary, we introduced a modification to the default ExomeDepth workflow to reduce the false positives originating from the repetitive regions of the genome, created a large-scale iterative variant calling framework for reproducibility, and provided recommendations for implementation in clinical settings.

Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology

Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use.

Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders

Rare genetic disorders (RGDs) often exhibit significant clinical variability among affected individuals, a disease characteristic termed variable expressivity. Recently, the aggregate effect of common variation, quantified as polygenic scores (PGSs), has emerged as an effective tool for predictions of disease risk and trait variation in the general population. Here, we measure the effect of PGSs on 11 RGDs including four sex-chromosome aneuploidies (47,XXX; 47,XXY; 47,XYY; 45,X) that affect height; two copy-number variant (CNV) disorders (16p11.2 deletions and duplications) and a Mendelian disease (melanocortin 4 receptor deficiency (MC4R)) that affect BMI; and two Mendelian diseases affecting cholesterol: familial hypercholesterolemia (FH; LDLR and APOB) and familial hypobetalipoproteinemia (FHBL; PCSK9 and APOB). Our results demonstrate that common, polygenic factors of relevant complex traits frequently contribute to variable expressivity of RGDs and that PGSs may be a useful metric for predicting clinical severity in affected individuals and for risk stratification.

DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark

Background

XHMM is a widely used tool for copy-number variant (CNV) discovery from whole exome sequencing data but can require hours to days to run for large cohorts. A more scalable implementation would reduce the need for specialized computational resources and enable increased exploration of the configuration parameter space to obtain the best possible results.

Results

DECA is a horizontally scalable implementation of the XHMM algorithm using the ADAM framework and Apache Spark that incorporates novel algorithmic optimizations to eliminate unneeded computation. DECA parallelizes XHMM on both multi-core shared memory computers and large shared-nothing Spark clusters. We performed CNV discovery from the read-depth matrix in 2535 exomes in 9.3 min on a 16-core workstation (35.3× speedup vs. XHMM), 12.7 min using 10 executor cores on a Spark cluster (18.8× speedup vs. XHMM), and 9.8 min using 32 executor cores on Amazon AWS’ Elastic MapReduce. We performed CNV discovery from the original BAM files in 292 min using 640 executor cores on a Spark cluster.

Conclusions

We describe DECA’s performance, our algorithmic and implementation enhancements to XHMM to obtain that performance, and our lessons learned porting a complex genome analysis application to ADAM and Spark. ADAM and Apache Spark are a performant and productive platform for implementing large-scale genome analyses, but efficiently utilizing large clusters can require algorithmic optimizations and careful attention to Spark’s configuration parameters.

GSTM1 Copy Number Is Not Associated With Risk of Kidney Failure in a Large Cohort

Deletion of glutathione S-transferase µ1 (GSTM1) is common in populations and has been asserted to associate with chronic kidney disease progression in some research studies. The association needs to be validated. We estimated GSTM1 copy number using whole exome sequencing data in the DiscovEHR cohort. Kidney failure was defined as requiring dialysis or receiving kidney transplant using data from the electronic health record and linkage to the United States Renal Data System, or the most recent eGFR < 15 ml/min/1.73 m². In a cohort of 46,983 unrelated participants, 28.8% of blacks and 52.1% of whites had 0 copies of GSTM1. Over a mean of 9.2 years follow-up, 645 kidney failure events were observed in 46,187 white participants, and 28 in 796 black participants. No significant association was observed between GSTM1 copy number and kidney failure in Cox regression adjusting for age, sex, BMI, smoking status, genetic principal components, or comorbid conditions (hypertension, diabetes, heart failure, coronary artery disease, and stroke), whether using a genotypic, dominant, or recessive model. In sensitivity analyses, GSTM1 copy number was not associated with kidney failure in participants that were 45 years or older at baseline, had baseline eGFR < 60 ml/min/1.73 m², or with baseline year between 1996 and 2002. In conclusion, we found no association between GSTM1 copy number and kidney failure in a large cohort study.

Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes

Autism spectrum disorder (ASD) is a genetically heterogeneous condition, caused by a combination of rare de novo and inherited variants as well as common variants in at least several hundred genes. However, significantly larger sample sizes are needed to identify the complete set of genetic risk factors. We conducted a pilot study for SPARK (SPARKForAutism.org) of 457 families with ASD, all consented online. Whole exome sequencing (WES) and genotyping data were generated for each family using DNA from saliva. We identified variants in genes and loci that are clinically recognized causes or significant contributors to ASD in 10.4% of families without previous genetic findings. In addition, we identified variants that are possibly associated with ASD in an additional 3.4% of families. A meta-analysis using the TADA framework at a false discovery rate (FDR) of 0.1 provides statistical support for 26 ASD risk genes. While most of these genes are already known ASD risk genes, BRSK2 has the strongest statistical support and reaches genome-wide significance as a risk gene for ASD (p-value = 2.3e-06). Future studies leveraging the thousands of individuals with ASD who have enrolled in SPARK are likely to further clarify the genetic risk factors associated with ASD as well as allow accelerate ASD research that incorporates genetic etiology.

A machine-learning approach for accurate detection of copy number variants from exome sequencing

Copy number variants (CNVs) are a major cause of several genetic disorders, making their detection an essential component of genetic analysis pipelines. Current methods for detecting CNVs from exome-sequencing data are limited by high false-positive rates and low concordance because of inherent biases of individual algorithms. To overcome these issues, calls generated by two or more algorithms are often intersected using Venn diagram approaches to identify "high-confidence" CNVs. However, this approach is inadequate, because it misses potentially true calls that do not have consensus from multiple callers. Here, we present CN-Learn, a machine-learning framework that integrates calls from multiple CNV detection algorithms and learns to accurately identify true CNVs using caller-specific and genomic features from a small subset of validated CNVs. Using CNVs predicted by four exome-based CNV callers (CANOES, CODEX, XHMM, and CLAMMS) from 503 samples, we demonstrate that CN-Learn identifies true CNVs at higher precision (∼90%) and recall (∼85%) rates while maintaining robust performance even when trained with minimal data (∼30 samples). CN-Learn recovers twice as many CNVs compared to individual callers or Venn diagram-based approaches, with features such as exome capture probe count, caller concordance, and GC content providing the most discriminatory power. In fact, ∼58% of all true CNVs recovered by CN-Learn were either singletons or calls that lacked support from at least one caller. Our study underscores the limitations of current approaches for CNV identification and provides an effective method that yields high-quality CNVs for application in clinical diagnostics.

Comparison of kNN and k-means optimization methods of reference set selection for improved CNV callers performance

BACKGROUND

There are over 25 tools dedicated for the detection of Copy Number Variants (CNVs) using Whole Exome Sequencing (WES) data based on read depth analysis. The tools reported consist of several steps, including: (i) calculation of read depth for each sequencing target, (ii) normalization, (iii) segmentation and (iv) actual CNV calling. The essential aspect of the entire process is the normalization stage, in which systematic errors and biases are removed and the reference sample set is used to increase the signal-to-noise ratio. Although some CNV calling tools use dedicated algorithms to obtain the optimal reference sample set, most of the advanced CNV callers do not include this feature. To our knowledge, this work is the first attempt to assess the impact of reference sample set selection on CNV detection performance.

METHODS

We used WES data from the 1000 Genomes project to evaluate the impact of various methods of reference sample set selection on CNV calling performance of three chosen state-of-the-art tools: CODEX, CNVkit and exomeCopy. Two naive solutions (all samples as reference set and random selection) as well as two clustering methods (k-means and k nearest neighbours (kNN) with a variable number of clusters or group sizes) have been evaluated to discover the best performing sample selection method.

RESULTS AND CONCLUSIONS

The performed experiments have shown that the appropriate selection of the reference sample set may greatly improve the CNV detection rate. In particular, we found that smart reduction of reference sample size may significantly increase the algorithms' precision while having negligible negative effect on sensitivity. We observed that a complete CNV calling process with the k-means algorithm as the selection method has significantly better time complexity than kNN-based solution.

Distal chromosome 16p11.2 duplications containing SH2B1 in patients with scoliosis

INTRODUCTION

Adolescent idiopathic scoliosis (AIS) is a common musculoskeletal disorder with strong evidence for a genetic contribution. CNVs play an important role in congenital scoliosis, but their role in idiopathic scoliosis has been largely unexplored.

METHODS

Exome sequence data from 1197 AIS cases and 1664 in-house controls was analysed using coverage data to identify rare CNVs. CNV calls were filtered to include only highly confident CNVs with >10 average reads per region and mean log-ratio of coverage consistent with single-copy duplication or deletion. The frequency of 55 common recurrent CNVs was determined and correlated with clinical characteristics.

RESULTS

Distal chromosome 16p11.2 microduplications containing the gene SH2B1 were found in 0.7% of AIS cases (8/1197). We replicated this finding in two additional AIS cohorts (8/1097 and 2/433), resulting in 0.7% (18/2727) of all AIS cases harbouring a chromosome 16p11.2 microduplication, compared with 0.06% of local controls (1/1664) and 0.04% of published controls (8/19584) (p=2.28×10^-11, OR=16.15). Furthermore, examination of electronic health records of 92 455 patients from the Geisinger health system showed scoliosis in 30% (20/66) patients with chromosome 16p11.2 microduplications containing SH2B1 compared with 7.6% (10/132) of controls (p=5.6×10^-4, OR=3.9).

CONCLUSIONS

Recurrent distal chromosome 16p11.2 duplications explain nearly 1% of AIS. Distal chromosome 16p11.2 duplications may contribute to scoliosis pathogenesis by directly impairing growth or by altering expression of nearby genes, such as TBX6. Individuals with distal chromosome 16p11.2 microduplications should be screened for scoliosis to facilitate early treatment.

Detection of de novo copy number deletions from targeted sequencing of trios

MOTIVATION

De novo copy number deletions have been implicated in many diseases, but there is no formal method to date that identifies de novo deletions in parent-offspring trios from capture-based sequencing platforms.

RESULTS

We developed Minimum Distance for Targeted Sequencing (MDTS) to fill this void. MDTS has similar sensitivity (recall), but a much lower false positive rate compared to less specific CNV callers, resulting in a much higher positive predictive value (precision). MDTS also exhibited much better scalability.

AVAILABILITY AND IMPLEMENTATION

MDTS is freely available as open source software from the Bioconductor repository.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing

High-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, as a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs.

Spinal muscular atrophy within Amish and Mennonite populations: Ancestral haplotypes and natural history

We correlate chromosome 5 haplotypes and SMN2 copy number with disease expression in 42 Mennonite and 14 Amish patients with spinal muscular atrophy (SMA). A single haplotype (A1) with 1 copy of SMN2 segregated among all Amish patients. SMN1 deletions segregated on four different Mennonite haplotypes that carried 1 (M1a, M1b, M1c) or 2 (M2) copies of SMN2. DNA microsatellite and microarray data revealed structural similarities among A1, M1a, M1b, and M2. Clinical data were parsed according to both SMN1 genotype and SMN2 copy number (2 copies, n = 44; 3 copies, n = 9; or 4 copies, n = 3). No infant with 2 copies of SMN2 sat unassisted. In contrast, all 9 Mennonites with the M1a/M2 genotype (3 copies of SMN2) sat during infancy at a median age of 7 months, and 5 (56%) walked and dressed independently at median ages of 18 and 36 months, respectively. All are alive at a median age of 11 (range 2–31) years without ventilatory support. Among 13 Amish and 26 Mennonite patients with 2 copies of SMN2 who did not receive feeding or ventilatory support, A1/A1 as compared to M1a/M1a genotype was associated with earlier clinical onset (p = 0.0040) and shorter lifespan (median survival 3.9 versus 5.7 months, p = 0.0314). These phenotypic differences were not explained by variation in SMN1 deletion size or SMN2 coding sequence, which were conserved across haplotypes. Distinctive features of SMA within Plain communities provide a population-specific framework to study variations of disease expression and the impact of disease-modifying therapies administered early in life.

Exome Sequencing in Children With Pulmonary Arterial Hypertension Demonstrates Differences Compared With Adults

BACKGROUND

Pulmonary arterial hypertension (PAH) is a rare disease characterized by pulmonary arteriole remodeling, elevated arterial pressure and resistance, and subsequent heart failure. Compared with adult-onset disease, pediatric-onset PAH is more heterogeneous and often associated with worse prognosis. Although BMPR2 mutations underlie ≈70% of adult familial PAH (FPAH) cases, the genetic basis of PAH in children is less understood.

METHODS

We performed genetic analysis of 155 pediatric- and 257 adult-onset PAH patients, including both FPAH and sporadic, idiopathic PAH (IPAH). After screening for 2 common PAH risk genes, mutation-negative FPAH and all IPAH cases were evaluated by exome sequencing.

RESULTS

We observed similar frequencies of rare, deleterious BMPR2 mutations in pediatric- and adult-onset patients: ≈55% in FPAH and 10% in IPAH patients in both age groups. However, there was significant enrichment of TBX4 mutations in pediatric- compared with adult-onset patients (IPAH: 10/130 pediatric versus 0/178 adult-onset), and TBX4 carriers had younger mean age-of-onset compared with BMPR2 carriers. Mutations in other known PAH risk genes were infrequent in both age groups. Notably, among pediatric IPAH patients without mutations in known risk genes, exome sequencing revealed a 2-fold enrichment of de novo likely gene-damaging and predicted deleterious missense variants.

CONCLUSIONS

Mutations in known PAH risk genes accounted for ≈70% to 80% of FPAH in both age groups, 21% of pediatric-onset IPAH, and 11% of adult-onset IPAH. Rare, predicted deleterious variants in TBX4 are enriched in pediatric patients and de novo variants in novel genes may explain ≈19% of pediatric-onset IPAH cases.

WISExome: a within-sample comparison approach to detect copy number variations in whole exome sequencing data

In clinical genetics, detection of single nucleotide polymorphisms (SNVs) as well as copy number variations (CNVs) is essential for patient genotyping. Obtaining both CNV and SNV information from WES data would significantly simplify clinical workflow. Unfortunately, the sequence reads obtained with WES vary between samples, complicating accurate CNV detection with WES. To avoid being dependent on other samples, we developed a within-sample comparison approach (WISExome). For every (WES) target region on the genome, we identified a set of reference target regions elsewhere on the genome with similar read frequency behavior. For a new sample, aberrations are detected by comparing the read frequency of a target region with the distribution of read frequencies in the reference set. WISExome correctly identifies known pathogenic CNVs (range 4 Kb–5.2 Mb). Moreover, WISExome prioritizes pathogenic CNVs by sorting them on quality and annotations of overlapping genes in OMIM. When comparing WISExome to four existing CNV detection tools, we found that CoNIFER detects much fewer CNVs and XHMM breaks calls made by other tools into smaller calls (fragmentation). CODEX and CLAMMS seem to perform more similar to WISExome. CODEX finds all known pathogenic CNVs, but detects much more calls than all other methods. CLAMMS and WISExome agree the most. CLAMMS does, however, miss one of the known CNVs and shows slightly more fragmentation. Taken together, WISExome is a promising tool for genome diagnostics laboratories as the workflow can be solely based on WES data.

Homozygous and hemizygous CNV detection from exome sequencing data in a Mendelian disease cohort

We developed an algorithm, HMZDelFinder, that uses whole exome sequencing (WES) data to identify rare and intragenic homozygous and hemizygous (HMZ) deletions that may represent complete loss-of-function of the indicated gene. HMZDelFinder was applied to 4866 samples in the Baylor–Hopkins Center for Mendelian Genomics (BHCMG) cohort and detected 773 HMZ deletion calls (567 homozygous or 206 hemizygous) with an estimated sensitivity of 86.5% (82% for single-exonic and 88% for multi-exonic calls) and precision of 78% (53% single-exonic and 96% for multi-exonic calls). Out of 773 HMZDelFinder-detected deletion calls, 82 were subjected to array comparative genomic hybridization (aCGH) and/or breakpoint PCR and 64 were confirmed. These include 18 single-exon deletions out of which 8 were exclusively detected by HMZDelFinder and not by any of seven other CNV detection tools examined. Further investigation of the 64 validated deletion calls revealed at least 15 pathogenic HMZ deletions. Of those, 7 accounted for 17–50% of pathogenic CNVs in different disease cohorts where 7.1–11% of the molecular diagnosis solved rate was attributed to CNVs. In summary, we present an algorithm to detect rare, intragenic, single-exon deletion CNVs using WES data; this tool can be useful for disease gene discovery efforts and clinical WES analyses.

Identifying and mitigating batch effects in whole genome sequencing data

BACKGROUND

Large sample sets of whole genome sequencing with deep coverage are being generated, however assembling datasets from different sources inevitably introduces batch effects. These batch effects are not well understood and can be due to changes in the sequencing protocol or bioinformatics tools used to process the data. No systematic algorithms or heuristics exist to detect and filter batch effects or remove associations impacted by batch effects in whole genome sequencing data.

RESULTS

We describe key quality metrics, provide a freely available software package to compute them, and demonstrate that identification of batch effects is aided by principal components analysis of these metrics. To mitigate batch effects, we developed new site-specific filters that identified and removed variants that falsely associated with the phenotype due to batch effect. These include filtering based on: a haplotype based genotype correction, a differential genotype quality test, and removing sites with missing genotype rate greater than 30% after setting genotypes with quality scores less than 20 to missing. This method removed 96.1% of unconfirmed genome-wide significant SNP associations and 97.6% of unconfirmed genome-wide significant indel associations. We performed analyses to demonstrate that: 1) These filters impacted variants known to be disease associated as 2 out of 16 confirmed associations in an AMD candidate SNP analysis were filtered, representing a reduction in power of 12.5%, 2) In the absence of batch effects, these filters removed only a small proportion of variants across the genome (type I error rate of 3%), and 3) in an independent dataset, the method removed 90.2% of unconfirmed genome-wide SNP associations and 89.8% of unconfirmed genome-wide indel associations.

CONCLUSIONS

Researchers currently do not have effective tools to identify and mitigate batch effects in whole genome sequencing data. We developed and validated methods and filters to address this deficiency.

Whole exome association of rare deletions in multiplex oral cleft families

By sequencing the exomes of distantly related individuals in multiplex families, rare mutational and structural changes to coding DNA can be characterized and their relationship to disease risk can be assessed. Recently, several rare single nucleotide variants (SNVs) were associated with an increased risk of nonsyndromic oral cleft, highlighting the importance of rare sequence variants in oral clefts and illustrating the strength of family-based study designs. However, the extent to which rare deletions in coding regions of the genome occur and contribute to risk of nonsyndromic clefts is not well understood. To identify putative structural variants underlying risk, we developed a pipeline for rare hemizygous deletions in families from whole exome sequencing and statistical inference based on rare variant sharing. Among 56 multiplex families with 115 individuals, we identified 53 regions with one or more rare hemizygous deletions. We found 45 of the 53 regions contained rare deletions occurring in only one family member. Members of the same family shared a rare deletion in only eight regions. We also devised a scalable global test for enrichment of shared rare deletions.