Blood Institute Exome Sequencing Project Research Articles

Protein Subdomain Enrichment of NUP155 Variants Identify a Novel Predicted Pathogenic Hotspot.

Functional variants in nuclear envelope genes are implicated as underlying causes of cardiopathology. To examine the potential association of single nucleotide variants of nucleoporin genes with cardiac disease, we employed a prognostic scoring approach to investigate variants of NUP155, a nucleoporin gene clinically linked with atrial fibrillation. Here we implemented bioinformatic profiling and predictive scoring, based on the gnomAD, National Heart Lung and Blood Institute-Exome Sequencing Project (NHLBI-ESP) Exome Variant Server, and dbNSFP databases to identify rare single nucleotide variants (SNVs) of NUP155 potentially associated with cardiopathology. This predictive scoring revealed 24 SNVs of NUP155 as potentially cardiopathogenic variants located primarily in the N-terminal crescent-shaped domain of NUP155. In addition, a predicted NUP155 R672G variant prioritized in our study was mapped to a region within the alpha helical stack of the crescent domain of NUP155. Bioinformatic analysis of inferred protein-protein interactions of NUP155 revealed over representation of top functions related to molecular transport, RNA trafficking, and RNA post-transcriptional modification. Topology analysis revealed prioritized hubs critical for maintaining network integrity and informational flow that included FN1, SIRT7, and CUL7 with nodal enrichment of RNA helicases in the topmost enriched subnetwork. Furthermore, integration of the top 5 subnetworks to capture network topology of an expanded framework revealed that FN1 maintained its hub status, with elevation of EED, CUL3, and EFTUD2. This is the first study to report novel discovery of a NUP155 subdomain hotspot that enriches for allelic variants of NUP155 predicted to be clinically damaging, and supports a role for RNA metabolism in cardiac disease and development.

A simple and accurate method to determine genomewide significance for association tests in sequencing studies.

Whole-exome sequencing (WES) and whole-genome sequencing (WGS) studies are underway to investigate the impact of genetic variants on complex diseases and traits. It is customary to perform single-variant association tests for common variants and region-based association tests for rare variants. The latter may target variants with similar or opposite effects, interrogate variants with different frequencies or different functional annotations, and examine a variety of regions. The large number of tests that are performed necessitates adjustment for multiple testing. The conventional Bonferroni correction is overly conservative as the test statistics are correlated. To address this challenge, we propose a simple and accurate method based on parametric bootstrap to assess genomewide significance. We show that the correlations of the test statistics are determined primarily by the genotypes, such that the same significance threshold can be used in different studies that share a common sequencing platform. We demonstrate the usefulness of the proposed method with WES data from the National Heart, Lung, and Blood Institute Exome Sequencing Project and WGS data from the 1000 Genomes Project. We recommend the p value of as the genomewide significance threshold for testing all common and low-frequency variants (MAFs 0.1%) in the human genome.

STEPS: an efficient prospective likelihood approach to genetic association analyses of secondary traits in extreme phenotype sequencing.

It has been well acknowledged that methods for secondary trait (ST) association analyses under a case-control design (ST$_{\text{CC}}$) should carefully consider the sampling process to avoid biased risk estimates. A similar situation also exists in the extreme phenotype sequencing (EPS) designs, which is to select subjects with extreme values of continuous primary phenotype for sequencing. EPS designs are commonly used in modern epidemiological and clinical studies such as the well-known National Heart, Lung, and Blood Institute Exome Sequencing Project. Although naïve generalized regression or ST$_{\text{CC}}$ method could be applied, their validity is questionable due to difference in statistical designs. Herein, we propose a general prospective likelihood framework to perform association testing for binary and continuous STs under EPS designs (STEPS), which can also incorporate covariates and interaction terms. We provide a computationally efficient and robust algorithm to obtain the maximum likelihood estimates. We also present two empirical mathematical formulas for power/sample size calculations to facilitate planning of binary/continuous STs association analyses under EPS designs. Extensive simulations and application to a genome-wide association study of benign ethnic neutropenia under an EPS design demonstrate the superiority of STEPS over all its alternatives above.

Abstract 2591: A guide to filtering TARGET Complete Genomics germline variants

Abstract With the public release of TARGET’s pediatric cancer data, researchers are eager to investigate and integrate variants from both tumor and germline sequencing data, but caution should be used when interpreting TARGET variant calls from Complete Genomics data, as they unlike the Illumina based calls for which most of our databases and tools are customized. Here, we use germline samples from 55 neuroblastoma cases with both whole genome Complete Genomics variants and whole exome Illumina variants from GATK, Pindel, Platypus, and bam2mpg to investigate differences between germline variant calls from Complete Genomics and Illumina platforms. Our study of ~160,000 single nucleotide polymorphisms (SNPS) and ~19,000 insertions or deletions (indels) reveals that while SNP sets largely agree between Complete Genomics and Illumina, Complete Genomics data have many indels not present in Illumina calls, making it difficult to screen Complete Genomics indels against popular Illumina based variant databases like those provided by the Exome Aggregation Consortium and the National Heart, Lung, and Blood Institute Exome Sequencing Project. Further probing of Complete Genomics indels uncovered inconsistent indel formatting resulting a loss of recurrent variants, and miscalculated read depths in earlier versions of the Complete Genomics platform. To solve these issues and make Complete Genomics variant calls comparable to Illumina calls, we introduce a Snakemake pipeline that reformats Complete Genomics variant files and creates variant features for use in decision tree or random forest classifiers trained to recognize Complete Genomics variants that will be present in Illumina variant call sets. We determine that the most important features for locating Complete Genomics variants likely to be found in Illumina germline variant call sets are the presence of reads from both directions, average variant allele fraction across the cohort, a variant call in the matched tumor, the variant fraction of reads, and read depth. Using ten fold cross validation with our decision tree, we obtain >95% recall and >98% precision for SNPs, and >90% recall and >85% precision for indels. We advocate the use of our pipeline and filtering scheme for making Complete Genomics variant calls comparable to Illumina based calls. Citation Format: James P. Evans, Rajesh Patidar, Zalman Vaksman, Sivasish Sindiri, Douglas R. Stewart, Javed Khan, Jun S. Wei, Sharon J. Diskin. A guide to filtering TARGET Complete Genomics germline variants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2591. doi:10.1158/1538-7445.AM2017-2591

Monogenic and polygenic determinants of sarcoma risk: an international genetic study

Sarcomas are rare, phenotypically heterogeneous cancers that disproportionately affect the young. Outside rare syndromes, the nature, extent, and clinical significance of their genetic origins are not known. We aimed to investigate the genetic basis for bone and soft-tissue sarcoma seen in routine clinical practice. In this genetic study, we included 1162 patients with sarcoma from four cohorts (the International Sarcoma Kindred Study [ISKS], 966 probands; Project GENESIS, 48 probands; Asan Bio-Resource Center, 138 probands; and kConFab, ten probands), who were older than 15 years at the time of consent and had a histologically confirmed diagnosis of sarcoma, recruited from specialist sarcoma clinics without regard to family history. Detailed clinical, pathological, and pedigree information was collected, and cancer diagnoses in probands and relatives were independently verified. Targeted exon sequencing using blood (n=1114) or saliva (n=48) samples was done on 72 genes (selected due to associations with increased cancer risk) and rare variants were stratified into classes approximating the International Agency for Research on Cancer (IARC) clinical classification for genetic variation. We did a case-control rare variant burden analysis using 6545 Caucasian controls included from three cohorts (ISKS, 235 controls; LifePool, 2010 controls; and National Heart, Lung, and Blood Institute Exome Sequencing Project [ESP], 4300 controls). The median age at cancer diagnosis in 1162 sarcoma probands was 46 years (IQR 29-58), 170 (15%) of 1162 probands had multiple primary cancers, and 155 (17%) of 911 families with informative pedigrees fitted recognisable cancer syndromes. Using a case-control rare variant burden analysis, 638 (55%) of 1162 sarcoma probands bore an excess of pathogenic germline variants (combined odds ratio [OR] 1·43, 95% CI 1·24-1·64, p<0·0001), with 227 known or expected pathogenic variants occurring in 217 individuals. All classes of pathogenic variants (known, expected, or predicted) were associated with earlier age of cancer onset. In addition to TP53, ATM, ATR, and BRCA2, an unexpected excess of functionally pathogenic variants was seen in ERCC2. Probands were more likely than controls to have multiple pathogenic variants compared with the combined control cohort group and the LifePool control cohort (OR 2·22, 95% CI 1·57-3·14, p=1·2 × 10(-6)) and the cumulative burden of multiple variants correlated with earlier age at cancer diagnosis (Mantel-Cox log-rank test for trend, p=0·0032). 66 of 1162 probands carried notifiable variants following expert clinical review (those recognised to be clinically significant to health and about which patients should be advised), whereas 293 (25%) probands carried variants with potential therapeutic significance. About half of patients with sarcoma have putatively pathogenic monogenic and polygenic variation in known and novel cancer genes, with implications for risk management and treatment. Rainbows for Kate Foundation, Johanna Sewell Research Foundation, Australian National Health and Medical Research Council, Cancer Australia, Sarcoma UK, National Cancer Institute, Liddy Shriver Sarcoma Initiative.

Rare variant associations with waist-to-hip ratio in European-American and African-American women from the NHLBI-Exome Sequencing Project.

Waist-to-hip ratio (WHR), a relative comparison of waist and hip circumferences, is an easily accessible measurement of body fat distribution, in particular central abdominal fat. A high WHR indicates more intra-abdominal fat deposition and is an established risk factor for cardiovascular disease and type 2 diabetes. Recent genome-wide association studies have identified numerous common genetic loci influencing WHR, but the contributions of rare variants have not been previously reported. We investigated rare variant associations with WHR in 1510 European-American and 1186 African-American women from the National Heart, Lung, and Blood Institute-Exome Sequencing Project. Association analysis was performed on the gene level using several rare variant association methods. The strongest association was observed for rare variants in IKBKB (P=4.0 × 10(-8)) in European-Americans, where rare variants in this gene are predicted to decrease WHRs. The activation of the IKBKB gene is involved in inflammatory processes and insulin resistance, which may affect normal food intake and body weight and shape. Meanwhile, aggregation of rare variants in COBLL1, previously found to harbor common variants associated with WHR and fasting insulin, were nominally associated (P=2.23 × 10(-4)) with higher WHR in European-Americans. However, these significant results are not shared between African-Americans and European-Americans that may be due to differences in the allelic architecture of the two populations and the small sample sizes. Our study indicates that the combined effect of rare variants contribute to the inter-individual variation in fat distribution through the regulation of insulin response.

Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling

High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of Type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and uses all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project. Supplementary materials for this article are available online.

αIIbβ3 Variants Defined By Next Generation Sequencing: Implications for Predicting Variants Likely to Cause Glanzmann Thrombasthenia and Alloimmune Disorders

# Authors contributed equally to this work.~ Currently at Genomics England Ltd, London, United KingdomNext generation sequencing is transforming our understanding of human genetic variation and is becoming a routine part of human genetic analysis. The identification of millions of new variants, which are mainly rare and assessing their implications for human health presents new challenges to researchers and clinicians. We have analyzed missense variants in the ITGB2A and ITGB3 genes obtained from whole exome and whole genome sequencing (WES & WGS) data from 5 databases: The Human Genome Mutation Database, the 1000 Genomes project, the UK10K Whole Exome Sequencing project, the UK10K Whole Genome Sequencing project, and The National Heart, Lung and Blood Institute Exome Sequencing Project. Together, these encompass variants of the platelet αIIbβ3 integrin receptor from ~32,000 alleles derived from 16,108 individuals. We identified 111 missense variants that have previously been associated with Glanzmann thrombasthenia (GT), 20 variants associated with alloimmune thrombocytopenia, and 5 variants associated with aniso/macrothrombocytopenia. None of the GT variants were found in the last four databases, indicating that they have minor allele frequencies (MAF) less than ~0.01%, attesting to both their rarity and the likelihood that they entered the population within the last ~2,500 years.We also identified 114 novel missense variants in ITGB2A affecting ~11% of the amino acids and 68 novel missense variants in ITGB3 affecting ~9% of the amino acids. 96% of the novel variants had MAF <0.1%, indicating their rarity. Based on sequence conservation, MAF, and/or location of the substituted residue on a complete model of αIIbβ3 that suggested a possible effect on protein folding, we selected three novel variants (αIIb P943A and P176H, and β3 C547G) that affect amino acids previously associated with GT for expression in HEK 293 cells. Both αIIb P176H and β3 C547G severely affected αIIbβ3 expression, whereas αIIb P943A had only a partial effect on expression and no effect on DTT-induced fibrinogen binding. We were not surprised that the latter variant did not have a severe effect on expression or function because it has an MAF (0.46%) that is much higher than the MAFs of the other GT-causing variants.To estimate the percentage of the 114 novel identified variants that are likely to be deleterious we used 3 different algorithms, CADD, Polyphen 2-HDVI, and SIFT. The algorithms showed moderate concordance in their rankings of the likelihood that a variant is deleterious. To compare their predictive powers, we performed receiver operating characteristic (ROC) analysis based on their ability to discriminate confirmed GT missense variants (positive controls) from alloantigens (negative controls); the area under the curve (AUC) values were 0.91, 0.88, and 0.90, respectively. At cutoff values that achieved greater than 95% sensitivity for each algorithm: 1) the specificity values were 75%, 65%, and 60%, and 2) the percentages of novel αIIb+β3 missense variants predicted to be deleterious were 43%, 56%, and 58%. Polyphen 2-HDVI and SIFT identified αIIb P176H and β3 C547G as highly likely to be deleterious and αIIb P943A as much less likely to be deleterious, whereas CADD did not differentiate them in the same way. We conclude that ~1.1% of individuals in the populations studied carry at least one missense variant in αIIb or β3 and that 0.6% carry a variant that might be deleterious and therefore may result in a hemorrhagic GT-like phenotype. The rarity of almost all of the novel missense variants identified indicates that they entered the population recently. Despite having detailed knowledge of the structure and function of αIIbβ3, it is difficult to predict with certainty the impact of any single missense variant. This will pose serious challenges as more individuals undergo WES and WGS; we anticipate that linkage to health record data, as will happen for the UK 100,000 Genomes project, will aid clinical interpretation. Finally, “hypomorphic” gene variants that produce only a partial decrease in expression, such as αIIb P943A, may contribute to the wide variation in αIIbβ3 surface expression observed in the healthy population. DisclosuresNo relevant conflicts of interest to declare.

Pathogenic Variants for Mendelian and Complex Traits in Exomes of 6,517 European and African Americans: Implications for the Return of Incidental Results

Exome sequencing (ES) is rapidly being deployed for use in clinical settings despite limited empirical data about the number and types of incidental results (with potential clinical utility) that could be offered for return to an individual. We analyzed deidentified ES data from 6,517 participants (2,204 African Americans and 4,313 European Americans) from the National Heart, Lung, and Blood Institute Exome Sequencing Project. We characterized the frequencies of pathogenic alleles in genes underlying Mendelian conditions commonly assessed by newborn-screening (NBS, n = 39) programs, genes associated with age-related macular degeneration (ARMD, n = 17), and genes known to influence drug response (PGx, n = 14). From these 70 genes, we identified 10,789 variants and curated them by manual review of OMIM, HGMD, locus-specific databases, or primary literature to a total of 399 validated pathogenic variants. The mean number of risk alleles per individual was 15.3. Every individual had at least five known PGx alleles, 99% of individuals had at least one ARMD risk allele, and 45% of individuals were carriers for at least one pathogenic NBS allele. The carrier burden for severe recessive childhood disorders was 0.57. Our results demonstrate that risk alleles of potential clinical utility for both Mendelian and complex traits are detectable in every individual. These findings highlight the necessity of developing guidelines and policies that consider the return of results to all individuals and underscore the need to develop innovative approaches and tools that enable individuals to exercise their choice about the return of incidental results.

Predicting Discovery Rates of Genomic Features

Successful sequencing experiments require judicious sample selection. However, this selection must often be performed on the basis of limited preliminary data. Predicting the statistical properties of the final sample based on preliminary data can be challenging, because numerous uncertain model assumptions may be involved. Here, we ask whether we can predict "omics" variation across many samples by sequencing only a fraction of them. In the infinite-genome limit, we find that a pilot study sequencing 5% of a population is sufficient to predict the number of genetic variants in the entire population within 6% of the correct value, using an estimator agnostic to demography, selection, or population structure. To reach similar accuracy in a finite genome with millions of polymorphisms, the pilot study would require ∼15% of the population. We present computationally efficient jackknife and linear programming methods that exhibit substantially less bias than the state of the art when applied to simulated data and subsampled 1000 Genomes Project data. Extrapolating based on the National Heart, Lung, and Blood Institute Exome Sequencing Project data, we predict that 7.2% of sites in the capture region would be variable in a sample of 50,000 African Americans and 8.8% in a European sample of equal size. Finally, we show how the linear programming method can also predict discovery rates of various genomic features, such as the number of transcription factor binding sites across different cell types.

Rare coding variation in paraoxonase-1 is associated with ischemic stroke in the NHLBI Exome Sequencing Project

HDL-associated paraoxonase-1 (PON1) is an enzyme whose activity is associated with cerebrovascular disease. Common PON1 genetic variants have not been consistently associated with cerebrovascular disease. Rare coding variation that likely alters PON1 enzyme function may be more strongly associated with stroke. The National Heart, Lung, and Blood Institute Exome Sequencing Project sequenced the coding regions (exomes) of the genome for heart, lung, and blood-related phenotypes (including ischemic stroke). In this sample of 4,204 unrelated participants, 496 had verified, noncardioembolic ischemic stroke. After filtering, 28 nonsynonymous PON1 variants were identified. Analysis with the sequence kernel association test, adjusted for covariates, identified significant associations between PON1 variants and ischemic stroke (P = 3.01 × 10(-3)). Stratified analyses demonstrated a stronger association of PON1 variants with ischemic stroke in African ancestry (AA) participants (P = 5.03 × 10(-3)). Ethnic differences in the association between PON1 variants with stroke could be due to the effects of PON1Val109Ile (overall P = 7.88 × 10(-3); AA P = 6.52 × 10(-4)), found at higher frequency in AA participants (1.16% vs. 0.02%) and whose protein is less stable than the common allele. In summary, rare genetic variation in PON1 was associated with ischemic stroke, with stronger associations identified in those of AA. Increased focus on PON1 enzyme function and its role in cerebrovascular disease is warranted.

Meta-Analysis of Sequencing Studies With Heterogeneous Genetic Associations

Recent advances in sequencing technologies have made it possible to explore the influence of rare variants on complex diseases and traits. Meta-analysis is essential to this exploration because large sample sizes are required to detect rare variants. Several methods are available to conduct meta-analysis for rare variants under fixed-effects models, which assume that the genetic effects are the same across all studies. In practice, genetic associations are likely to be heterogeneous among studies because of differences in population composition, environmental factors, phenotype and genotype measurements, or analysis method. We propose random-effects models which allow the genetic effects to vary among studies and develop the corresponding meta-analysis methods for gene-level association tests. Our methods take score statistics, rather than individual participant data, as input and thus can accommodate any study designs and any phenotypes. We produce the random-effects versions of all commonly used gene-level association tests, including burden, variable threshold, and variance-component tests. We demonstrate through extensive simulation studies that our random-effects tests are substantially more powerful than the fixed-effects tests in the presence of moderate and high between-study heterogeneity and achieve similar power to the latter when the heterogeneity is low. The usefulness of the proposed methods is further illustrated with data from National Heart, Lung, and Blood Institute Exome Sequencing Project (NHLBI ESP). The relevant software is freely available.

Arrhythmogenic Right Ventricular Cardiomyopathy: Growing Evidence for Complex Inheritance

The identification of the genetic underpinnings of the rare Mendelian cardiac diseases associated with sudden cardiac death (SCD) in the young has led to an improvement in the management of patients with some of these disorders.1 For example, genetic testing in the long-QT syndrome (LQTS) provides an important means of confirming the clinical diagnosis and allows for cascade familial screening in affected families. Moreover, in this disorder, numerous genotype–phenotype relationships have been uncovered, such as the relation between the affected gene and arrhythmogenic triggers, and genotype-specific response to pharmacotherapy. In the LQTS, genetic testing, which identifies a (putative) mutation in ≈70% of probands, has joined traditional risk factors, such as sex and the extent of QTc-interval prolongation, as an independent prognostic risk factor.1 This contrasts sharply with other disorders associated with SCD in the young, 1 of which is arrhythmogenic right ventricular cardiomyopathy (ARVC), where the role of genotype as a prognostic factor is unclear.1 Article see p 533 ARVC2 is a progressive disease of the myocardium, which leads to cardiomyopathic changes involving pathological fatty infiltration and cardiomyocyte loss and SCD from ventricular arrhythmias at a young age. Although it has classically been considered to affect the right ventricle (hence the name), the increasing awareness of the disease has led to the recognition of forms that present left ventricular or biventricular involvement.2 Genetic studies have identified a number of genes for the disorder; most of these encode components of the cardiac desmosome ( DSC2 , DSG2 , DSP , JUP , and PKP2 ), although other genes have also been implicated (eg, TMEM43 and PLN ). About 50% to 70% of probands harbor a causal or possibly causal mutation in a desmosomal gene.2 However, although gene identification has provided important molecular insight for initiation of mechanistic studies …

Actionable, Pathogenic Incidental Findings in 1,000 Participants’ Exomes

The incorporation of genomics into medicine is stimulating interest on the return of incidental findings (IFs) from exome and genomesequencing. However, no large-scale study has yet estimated the number of expected actionable findings per individual; therefore, we classified actionable pathogenic single-nucleotide variants in 500 European- and 500 African-descent participants randomly selected from the National Heart, Lung, and Blood Institute Exome Sequencing Project. The 1,000 individuals were screened for variants in 114 genes selected by an expert panel for their association with medically actionable genetic conditions possibly undiagnosed in adults. Among the 1,000 participants, 585 instances of 239 unique variants were identified as disease causing in the Human Gene Mutation Database (HGMD). The primary literature supporting the variants' pathogenicity was reviewed. Of the identified IFs, only 16 unique autosomal-dominant variants in 17 individuals were assessed to be pathogenic or likely pathogenic, and one participant had two pathogenic variants for an autosomal-recessive disease. Furthermore, one pathogenic and four likely pathogenic variants not listed as disease causing in HGMD were identified. These data can provide an estimate of the frequency (∼3.4% for European descent and ∼1.2% for African descent) of the high-penetrance actionable pathogenic or likely pathogenic variants in adults. The 23 participants with pathogenic or likely pathogenic variants were disproportionately of European (17) versus African (6) descent. The process of classifying these variants underscores the need for a more comprehensive and diverse centralized resource to provide curated information on pathogenicity for clinical use to minimize health disparities in genomic medicine.

Quantitative trait analysis in sequencing studies under trait-dependent sampling

It is not economically feasible to sequence all study subjects in a large cohort. A cost-effective strategy is to sequence only the subjects with the extreme values of a quantitative trait. In the National Heart, Lung, and Blood Institute Exome Sequencing Project, subjects with the highest or lowest values of body mass index, LDL, or blood pressure were selected for whole-exome sequencing. Failure to account for such trait-dependent sampling can cause severe inflation of type I error and substantial loss of power in quantitative trait analysis, especially when combining results from multiple studies with different selection criteria. We present valid and efficient statistical methods for association analysis of sequencing data under trait-dependent sampling. We pay special attention to gene-based analysis of rare variants. Our methods can be used to perform quantitative trait analysis not only for the trait that is used to select subjects for sequencing but for any other traits that are measured. For a particular trait of interest, our approach properly combines the association results from all studies with measurements of that trait. This meta-analysis is substantially more powerful than the analysis of any single study. By contrast, meta-analysis of standard linear regression results (ignoring trait-dependent sampling) can be less powerful than the analysis of a single study. The advantages of the proposed methods are demonstrated through simulation studies and the National Heart, Lung, and Blood Institute Exome Sequencing Project data. The methods are applicable to other types of genetic association studies and nongenetic studies.

Testing for Rare Variant Associations in the Presence of Missing Data

For studies of genetically complex diseases, many association methods have been developed to analyze rare variants. When variant calls are missing, naïve implementation of rare variant association (RVA) methods may lead to inflated type I error rates as well as a reduction in power. To overcome these problems, we developed extensions for four commonly used RVA tests. Data from the National Heart Lung and Blood Institute-Exome Sequencing Project were used to demonstrate that missing variant calls can lead to increased false-positive rates and that the extended RVA methods control type I error without reducing power. We suggest a combined strategy of data filtering based on variant and sample level missing genotypes along with implementation of these extended RVA tests.

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Using Reference Databases of Genetic Variation to Evaluate the Potential Pathogenicity of Candidate Disease Variants

The potential pathogenicity of genetic variants identified in disease-based resequencing studies is often overlooked where variants have previously been reported in dbSNP, the 1000 genomes project, or the National Heart, Lung and Blood Institute Exome Sequencing Project (ESP). In this work, we estimate that collectively, these databases capture ∼52% of mutations (dbSNP 50.4%; 1000 genomes 4.8%; and ESP 10.2%) reported as disease causing within phenotype-based locus-specific databases (LSDBs). To investigate whether these mutations may simply represent benign population variants, we evaluated whether the carrier frequencies associated with mutations implicated in amyotrophic lateral sclerosis were higher than what could be accounted for by high-penetrance disease models. In doing so, we have questioned the veracity of 51 mutations, but also demonstrated that each of the three databases included credible disease variants. Our results demonstrate the benefits of using databases such as dbSNP, the 1000 genomes project, and the ESP to evaluate the pathogenicity of putative disease variants, and suggest that many disease mutations reported across LSDBs may not actually be pathogenic. However, they also demonstrate that even in the context of rare Mendelian disorders, the potential pathogenicity of variants reported by these databases should not be overlooked without proper evaluation.

RTTN Mutations Link Primary Cilia Function to Organization of the Human Cerebral Cortex

Polymicrogyria is a malformation of the developing cerebral cortex caused by abnormal organization and characterized by many small gyri and fusion of the outer molecular layer. We have identified autosomal-recessive mutations in RTTN, encoding Rotatin, in individuals with bilateral diffuse polymicrogyria from two separate families. Rotatin determines early embryonic axial rotation, as well as anteroposterior and dorsoventral patterning in the mouse. Human Rotatin has recently been identified as a centrosome-associated protein. The Drosophila melanogaster homolog of Rotatin, Ana3, is needed for structural integrity of centrioles and basal bodies and maintenance of sensory neurons. We show that Rotatin colocalizes with the basal bodies at the primary cilium. Cultured fibroblasts from affected individuals have structural abnormalities of the cilia and exhibit downregulation of BMP4, WNT5A, and WNT2B, which are key regulators of cortical patterning and are expressed at the cortical hem, the cortex-organizing center that gives rise to Cajal-Retzius (CR) neurons. Interestingly, we have shown that in mouse embryos, Rotatin colocalizes with CR neurons at the subpial marginal zone. Knockdown experiments in human fibroblasts and neural stem cells confirm a role for RTTN in cilia structure and function. RTTN mutations therefore link aberrant ciliary function to abnormal development and organization of the cortex in human individuals.

Optimal Unified Approach for Rare-Variant Association Testing with Application to Small-Sample Case-Control Whole-Exome Sequencing Studies

We propose in this paper a unified approach for testing the association between rare variants and phenotypes in sequencing association studies. This approach maximizes power by adaptively using the data to optimally combine the burden test and the nonburden sequence kernel association test (SKAT). Burden tests are more powerful when most variants in a region are causal and the effects are in the same direction, whereas SKAT is more powerful when a large fraction of the variants in a region are noncausal or the effects of causal variants are in different directions. The proposed unified test maintains the power in both scenarios. We show that the unified test corresponds to the optimal test in an extended family of SKAT tests, which we refer to as SKAT-O. The second goal of this paper is to develop a small-sample adjustment procedure for the proposed methods for the correction of conservative type I error rates of SKAT family tests when the trait of interest is dichotomous and the sample size is small. Both small-sample-adjusted SKAT and the optimal unified test (SKAT-O) are computationally efficient and can easily be applied to genome-wide sequencing association studies. We evaluate the finite sample performance of the proposed methods using extensive simulation studies and illustrate their application using the acute-lung-injury exome-sequencing data of the National Heart, Lung, and Blood Institute Exome Sequencing Project.