@article {512, title = {Confirmation of the Role of DHX38 in the Etiology of Early-Onset Retinitis Pigmentosa.}, journal = {Invest Ophthalmol Vis Sci}, volume = {59}, year = {2018}, month = {2018 09 04}, pages = {4552-4557}, abstract = {

Purpose: Retinitis pigmentosa (RP) is a genetically heterogeneous trait with autosomal-recessive (ar) inheritance underlying 50\% of genetic disease cases. Sixty-one arRP genes have been identified, and recently, DHX38 has been reported as a potential candidate gene for arRP with only a single family reported with a variant of unknown significance. We identified a missense variant in DHX38 that co-segregates with the arRP phenotype in two Pakistani families confirming the involvement of DHX38 in the etiology of early-onset RP.

Methods: Exome sequencing was performed using two DNA samples from affected members of Pakistani families (MA88 and MA157) with early onset arRP. Sanger sequencing of DNA samples from all family members confirmed the segregation of candidate variant within both families.

Results: A novel missense DHX38 variant c.971G>A; p.(Arg324Gln) was identified which segregates with the arRP phenotype and yielded a logarithm of the odds (LOD) score of 5.0 and 4.3 for families MA88 and MA157, respectively. This variant is predicted to be conserved and deleterious by several bioinformatics tools.

Conclusions: We identified a second deleterious DHX38 variant that segregates with arRP in two families, providing additional evidence that DHX38 is involved in RP etiology. DHX38 encodes for pre-mRNA splicing factor PRP16, which is important in catalyzing pre-mRNA splicing.

}, keywords = {Adolescent, Adult, Cataract, Computational Biology, DEAD-box RNA Helicases, Female, Genes, Recessive, Genetic Association Studies, Genetic Linkage, Humans, Male, Mutation, Missense, Nucleotide Mapping, Ophthalmoscopy, Pedigree, Retinitis Pigmentosa, RNA Splicing Factors, Sequence Analysis, DNA, Whole Exome Sequencing, Young Adult}, issn = {1552-5783}, doi = {10.1167/iovs.18-23849}, author = {Latif, Zahid and Chakchouk, Imen and Schrauwen, Isabelle and Lee, Kwanghyuk and Santos-Cortez, Regie Lyn P and Abbe, Izoduwa and Acharya, Anushree and Jarral, Afeefa and Ali, Imran and Ullah, Ehsan and Khan, Muhammad Nasim and Ali, Ghazanfar and Tahir, Tufail Hussain and Bamshad, Michael J and Nickerson, Deborah A and Ahmad, Wasim and Ansar, Muhammad and Leal, Suzanne M} } @article {86, title = {FOXE3 mutations predispose to thoracic aortic aneurysms and dissections.}, journal = {J Clin Invest}, volume = {126}, year = {2016}, month = {2016 Mar 01}, pages = {948-61}, abstract = {

The ascending thoracic aorta is designed to withstand biomechanical forces from pulsatile blood. Thoracic aortic aneurysms and acute aortic dissections (TAADs) occur as a result of genetically triggered defects in aortic structure and a dysfunctional response to these forces. Here, we describe mutations in the forkhead transcription factor FOXE3 that predispose mutation-bearing individuals to TAAD. We performed exome sequencing of a large family with multiple members with TAADs and identified a rare variant in FOXE3 with an altered amino acid in the DNA-binding domain (p.Asp153His) that segregated with disease in this family. Additional pathogenic FOXE3 variants were identified in unrelated TAAD families. In mice, Foxe3 deficiency reduced smooth muscle cell (SMC) density and impaired SMC differentiation in the ascending aorta. Foxe3 expression was induced in aortic SMCs after transverse aortic constriction, and Foxe3 deficiency increased SMC apoptosis and ascending aortic rupture with increased aortic pressure. These phenotypes were rescued by inhibiting p53 activity, either by administration of a p53 inhibitor (pifithrin-α), or by crossing Foxe3-/- mice with p53-/- mice. Our data demonstrate that FOXE3 mutations lead to a reduced number of aortic SMCs during development and increased SMC apoptosis in the ascending aorta in response to increased biomechanical forces, thus defining an additional molecular pathway that leads to familial thoracic aortic disease.

}, keywords = {Adult, Aneurysm, Dissecting, Animals, Aorta, Aortic Aneurysm, Thoracic, Apoptosis, Cells, Cultured, Cyclin-Dependent Kinase Inhibitor p21, Female, Forkhead Transcription Factors, Gene Expression, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Male, Mice, Inbred C57BL, Mice, Knockout, Middle Aged, Muscle, Smooth, Vascular, Mutation, Missense, Myocytes, Smooth Muscle, Pedigree, Tumor Suppressor Protein p53, Vascular Remodeling, Zebrafish}, issn = {1558-8238}, doi = {10.1172/JCI83778}, author = {Kuang, Shao-Qing and Medina-Martinez, Olga and Guo, Dong-Chuan and Gong, Limin and Regalado, Ellen S and Reynolds, Corey L and Boileau, Catherine and Jondeau, Guillaume and Prakash, Siddharth K and Kwartler, Callie S and Zhu, Lawrence Yang and Peters, Andrew M and Duan, Xue-Yan and Bamshad, Michael J and Shendure, Jay and Nickerson, Debbie A and Santos-Cortez, Regie L and Dong, Xiurong and Leal, Suzanne M and Majesky, Mark W and Swindell, Eric C and Jamrich, Milan and Milewicz, Dianna M} } @article {173, title = {COPA mutations impair ER-Golgi transport and cause hereditary autoimmune-mediated lung disease and arthritis.}, journal = {Nat Genet}, volume = {47}, year = {2015}, month = {2015 Jun}, pages = {654-60}, abstract = {

Unbiased genetic studies have uncovered surprising molecular mechanisms in human cellular immunity and autoimmunity. We performed whole-exome sequencing and targeted sequencing in five families with an apparent mendelian syndrome of autoimmunity characterized by high-titer autoantibodies, inflammatory arthritis and interstitial lung disease. We identified four unique deleterious variants in the COPA gene (encoding coatomer subunit α) affecting the same functional domain. Hypothesizing that mutant COPA leads to defective intracellular transport via coat protein complex I (COPI), we show that COPA variants impair binding to proteins targeted for retrograde Golgi-to-ER transport. Additionally, expression of mutant COPA results in ER stress and the upregulation of cytokines priming for a T helper type 17 (TH17) response. Patient-derived CD4(+) T cells also demonstrate significant skewing toward a TH17 phenotype that is implicated in autoimmunity. Our findings uncover an unexpected molecular link between a vesicular transport protein and a syndrome of autoimmunity manifested by lung and joint disease.

}, keywords = {Amino Acid Sequence, Arthritis, Autoimmune Diseases, Child, Preschool, Coatomer Protein, Endoplasmic Reticulum, Endoplasmic Reticulum Stress, Female, Genetic Association Studies, Genetic Predisposition to Disease, Golgi Apparatus, HEK293 Cells, Humans, Infant, Lod Score, Lung Diseases, Interstitial, Male, Molecular Sequence Data, Pedigree, Protein Transport}, issn = {1546-1718}, doi = {10.1038/ng.3279}, author = {Watkin, Levi B and Jessen, Birthe and Wiszniewski, Wojciech and Vece, Timothy J and Jan, Max and Sha, Youbao and Thamsen, Maike and Santos-Cortez, Regie L P and Lee, Kwanghyuk and Gambin, Tomasz and Forbes, Lisa R and Law, Christopher S and Stray-Pedersen, Asbj{\o}rg and Cheng, Mickie H and Mace, Emily M and Anderson, Mark S and Liu, Dongfang and Tang, Ling Fung and Nicholas, Sarah K and Nahmod, Karen and Makedonas, George and Canter, Debra L and Kwok, Pui-Yan and Hicks, John and Jones, Kirk D and Penney, Samantha and Jhangiani, Shalini N and Rosenblum, Michael D and Dell, Sharon D and Waterfield, Michael R and Papa, Feroz R and Muzny, Donna M and Zaitlen, Noah and Leal, Suzanne M and Gonzaga-Jauregui, Claudia and Boerwinkle, Eric and Eissa, N Tony and Gibbs, Richard A and Lupski, James R and Orange, Jordan S and Shum, Anthony K} } @article {250, title = {Power analysis and sample size estimation for sequence-based association studies.}, journal = {Bioinformatics}, volume = {30}, year = {2014}, month = {2014 Aug 15}, pages = {2377-8}, abstract = {

MOTIVATION: Statistical methods have been developed to test for complex trait rare variant (RV) associations, in which variants are aggregated across a region, which is typically a gene. Power analysis and sample size estimation for sequence-based RV association studies are challenging because of the necessity to realistically model the underlying allelic architecture of complex diseases within a suitable analytical framework to assess the performance of a variety of RV association methods in an unbiased manner.

SUMMARY: We developed SEQPower, a software package to perform statistical power analysis for sequence-based association data under a variety of genetic variant and disease phenotype models. It aids epidemiologists in determining the best study design, sample size and statistical tests for sequence-based association studies. It also provides biostatisticians with a platform to fairly compare RV association methods and to validate and assess novel association tests.

AVAILABILITY AND IMPLEMENTATION: The SEQPower program, source code, multi-platform executables, documentation, list of association tests, examples and tutorials are available at http://bioinformatics.org/spower.

}, keywords = {Alleles, Data Interpretation, Statistical, Disease, Genetic Association Studies, Genetic Variation, Humans, Phenotype, Sample Size, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btu296}, author = {Wang, Gao T and Li, Biao and Santos-Cortez, Regie P Lyn and Peng, Bo and Leal, Suzanne M} } @article {279, title = {Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data.}, journal = {Am J Hum Genet}, volume = {94}, year = {2014}, month = {2014 Jan 02}, pages = {33-46}, abstract = {

Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits.

}, keywords = {Alleles, Autistic Disorder, Computer Simulation, Exome, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Haplotypes, Humans, Linkage Disequilibrium, Models, Genetic, Phenotype, Sequence Analysis, DNA}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2013.11.021}, author = {He, Zongxiao and O{\textquoteright}Roak, Brian J and Smith, Joshua D and Wang, Gao and Hooker, Stanley and Santos-Cortez, Regie Lyn P and Li, Biao and Kan, Mengyuan and Krumm, Nik and Nickerson, Deborah A and Shendure, Jay and Eichler, Evan E and Leal, Suzanne M} } @article {249, title = {Variant association tools for quality control and analysis of large-scale sequence and genotyping array data.}, journal = {Am J Hum Genet}, volume = {94}, year = {2014}, month = {2014 May 01}, pages = {770-83}, abstract = {

Currently there is great interest in detecting associations between complex traits and rare variants. In this report, we describe Variant Association Tools (VAT) and the VAT pipeline, which implements best practices for rare-variant association studies. Highlights of VAT include variant-site and call-level quality control (QC), summary statistics, phenotype- and genotype-based sample selection, variant annotation, selection of variants for association analysis, and a collection of rare-variant association methods for analyzing qualitative and quantitative traits. The association testing framework for VAT is regression based, which readily allows for flexible construction of association models with multiple covariates and weighting themes based on allele frequencies or predicted functionality. Additionally, pathway analyses, conditional analyses, and analyses of gene-gene and gene-environment interactions can be performed. VAT is capable of rapidly scanning through data by using multi-process computation, adaptive permutation, and simultaneously conducting association analysis via multiple methods. Results are available in text or graphic file formats and additionally can be output to relational databases for further annotation and filtering. An interface to R language also facilitates user implementation of novel association methods. The VAT{\textquoteright}s data QC and association-analysis pipeline can be applied to sequence, imputed, and genotyping array, e.g., "exome chip," data, providing a reliable and reproducible computational environment in which to analyze small- to large-scale studies with data from the latest genotyping and sequencing technologies. Application of the VAT pipeline is demonstrated through analysis of data from the 1000 Genomes project.

}, keywords = {Genetic Association Studies, Genetic Variation, Genotyping Techniques, High-Throughput Nucleotide Sequencing, Humans, Multifactorial Inheritance, Quality Control, Software}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2014.04.004}, author = {Wang, Gao T and Peng, Bo and Leal, Suzanne M} } @article {643, title = {A fast and noise-resilient approach to detect rare-variant associations with deep sequencing data for complex disorders.}, journal = {Genet Epidemiol}, volume = {36}, year = {2012}, month = {2012 Nov}, pages = {675-85}, abstract = {

Next generation sequencing technology has enabled the paradigm shift in genetic association studies from the common disease/common variant to common disease/rare-variant hypothesis. Analyzing individual rare variants is known to be underpowered; therefore association methods have been developed that aggregate variants across a genetic region, which for exome sequencing is usually a gene. The foreseeable widespread use of whole genome sequencing poses new challenges in statistical analysis. It calls for new rare-variant association methods that are statistically powerful, robust against high levels of noise due to inclusion of noncausal variants, and yet computationally efficient. We propose a simple and powerful statistic that combines the disease-associated P-values of individual variants using a weight that is the inverse of the expected standard deviation of the allele frequencies under the null. This approach, dubbed as Sigma-P method, is extremely robust to the inclusion of a high proportion of noncausal variants and is also powerful when both detrimental and protective variants are present within a genetic region. The performance of the Sigma-P method was tested using simulated data based on realistic population demographic and disease models and its power was compared to several previously published methods. The results demonstrate that this method generally outperforms other rare-variant association methods over a wide range of models. Additionally, sequence data on the ANGPTL family of genes from the Dallas Heart Study were tested for associations with nine metabolic traits and both known and novel putative associations were uncovered using the Sigma-P method.

}, keywords = {Angiopoietin-like 4 Protein, Angiopoietin-like Proteins, Angiopoietins, Case-Control Studies, Data Interpretation, Statistical, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, High-Throughput Nucleotide Sequencing, Humans, Metabolism, Sequence Analysis, DNA, Texas, Triglycerides}, issn = {1098-2272}, doi = {10.1002/gepi.21662}, author = {Cheung, Yee Him and Wang, Gao and Leal, Suzanne M and Wang, Shuang} } @article {326, title = {SEQCHIP: a powerful method to integrate sequence and genotype data for the detection of rare variant associations.}, journal = {Bioinformatics}, volume = {28}, year = {2012}, month = {2012 Jul 01}, pages = {1745-51}, abstract = {

MOTIVATION: Next-generation sequencing greatly increases the capacity to detect rare-variant complex-trait associations. However, it is still expensive to sequence a large number of samples and therefore often small datasets are used. Given cost constraints, a potentially more powerful two-step strategy is to sequence a subset of the sample to discover variants, and genotype the identified variants in the remaining sample. If only cases are sequenced, directly combining sequence and genotype data will lead to inflated type-I errors in rare-variant association analysis. Although several methods have been developed to correct for the bias, they are either underpowered or theoretically invalid. We proposed a new method SEQCHIP to integrate genotype and sequence data, which can be used with most existing rare-variant tests.

RESULTS: It is demonstrated using both simulated and real datasets that the SEQCHIP method has controlled type-I errors, and is substantially more powerful than all other currently available methods.

AVAILABILITY: SEQCHIP is implemented in an R-Package and is available at http://linkage.rockefeller.edu/suzanne/seqchip/Seqchip.html.

}, keywords = {Adenoma, Case-Control Studies, Colorectal Neoplasms, Genetic Association Studies, Genetic Variation, Genotype, Humans, Phenotype, Sequence Analysis, DNA, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts263}, author = {Liu, Dajiang J and Leal, Suzanne M} } @article {321, title = {SimRare: a program to generate and analyze sequence-based data for association studies of quantitative and qualitative traits.}, journal = {Bioinformatics}, volume = {28}, year = {2012}, month = {2012 Oct 15}, pages = {2703-4}, abstract = {

MOTIVATION: Currently, there is great interest in detecting complex trait rare variant associations using next-generation sequence data. On a monthly basis, new rare variant association methods are published. It is difficult to evaluate these methods because there is no standard to generate data and often comparisons are biased. In order to fairly compare rare variant association methods, it is necessary to generate data using realistic population demographic and phenotypic models.

RESULT: SimRare is an interactive program that integrates generation of rare variant genotype/phenotype data and evaluation of association methods using a unified platform. Variant data are generated for gene regions using forward-time simulation that incorporates realistic population demographic and evolutionary scenarios. Phenotype data can be obtained for both case-control and quantitative traits. SimRare has a user-friendly interface that allows for easy entry of genetic and phenotypic parameters. Novel rare variant association methods implemented in R can also be imported into SimRare, to evaluate their performance and compare results, e.g. power and Type I error, with other currently available methods both numerically and graphically.

}, keywords = {Genetic Association Studies, Genetic Variation, Humans, Phenotype, Quantitative Trait, Heritable, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts499}, author = {Li, Biao and Wang, Gao and Leal, Suzanne M} }