@article {87, title = {LOX Mutations Predispose to Thoracic Aortic Aneurysms and Dissections.}, journal = {Circ Res}, volume = {118}, year = {2016}, month = {2016 Mar 18}, pages = {928-34}, abstract = {

RATIONALE: Mutations in several genes have been identified that are responsible for 25\% of families with familial thoracic aortic aneurysms and dissections. However, the causative gene remains unknown in 75\% of families.

OBJECTIVES: To identify the causative mutation in families with autosomal dominant inheritance of thoracic aortic aneurysms and dissections.

METHODS AND RESULTS: Exome sequencing was used to identify the mutation responsible for a large family with thoracic aortic aneurysms and dissections. A heterozygous rare variant, c.839G>T (p.Ser280Arg), was identified in LOX, encoding a lysyl oxidase, that segregated with disease in the family. Sanger and exome sequencing was used to investigate mutations in LOX in an additional 410 probands from unrelated families. Additional LOX rare variants that segregated with disease in families were identified, including c.125G>A (p.Trp42*), c.604G>T (p.Gly202*), c.743C>T (p.Thr248Ile), c.800A>C (p.Gln267Pro), and c.1044T>A (p.Ser348Arg). The altered amino acids cause haploinsufficiency for LOX or are located at a highly conserved LOX catalytic domain, which is relatively invariant in the population. Expression of the LOX variants p.Ser280Arg and p.Ser348Arg resulted in significantly lower lysyl oxidase activity when compared with the wild-type protein. Individuals with LOX variants had fusiform enlargement of the root and ascending thoracic aorta, leading to ascending aortic dissections.

CONCLUSIONS: These data, along with previous studies showing that the deficiency of LOX in mice or inhibition of lysyl oxidases in turkeys and rats causes aortic dissections, support the conclusion that rare genetic variants in LOX predispose to thoracic aortic disease.

}, keywords = {Adult, Aged, Amino Acid Sequence, Aneurysm, Dissecting, Aortic Aneurysm, Thoracic, Female, Genetic Variation, Humans, Male, Middle Aged, Molecular Sequence Data, Mutation, Pedigree, Protein-Lysine 6-Oxidase}, issn = {1524-4571}, doi = {10.1161/CIRCRESAHA.115.307130}, author = {Guo, Dong-Chuan and Regalado, Ellen S and Gong, Limin and Duan, Xueyan and Santos-Cortez, Regie Lyn P and Arnaud, Pauline and Ren, Zhao and Cai, Bo and Hostetler, Ellen M and Moran, Rocio and Liang, David and Estrera, Anthony and Safi, Hazim J and Leal, Suzanne M and Bamshad, Michael J and Shendure, Jay and Nickerson, Deborah A and Jondeau, Guillaume and Boileau, Catherine and Milewicz, Dianna M} } @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 {219, title = {RNF213 rare variants in an ethnically diverse population with Moyamoya disease.}, journal = {Stroke}, volume = {45}, year = {2014}, month = {2014 Nov}, pages = {3200-7}, abstract = {

BACKGROUND AND PURPOSE: Moyamoya disease (MMD) is a rare, genetically heterogeneous cerebrovascular disease resulting from occlusion of the distal internal carotid arteries. A variant in the Ring Finger 213 gene (RNF213), altering arginine at position 4810 (p.R4810K), is associated with MMD in Asian populations. However, there are a lack of data on the role of RNF213 in patients with MMD of additional ethnicities and diasporic Asian populations. We investigate the contribution of RNF213 alterations to MMD in an ethnically diverse population based in the United States.

METHODS: We initially sequenced RNF213 exons 43, 44, and 45 (encoding the eponymous RING finger domain) and exon 60 (encoding p.R4810K) in 86 ethnically diverse patients with MMD. Comprehensive exome sequencing data from 24 additional patients with MMD was then analyzed to identify RNF213 variants globally. Segregation of variants with MMD and other vascular diseases was assessed in families.

RESULTS: RNF213 p.R4810K was identified in 56\% (9/16) of patients with MMD of Asian descent and not in 94 patients of non-Asian descent. 3.6\% (4/110) of patients had variants in the exons encoding the RING finger domain. Seven additional variants were identified in 29\% (7/24) of patients with MMD who underwent exome sequencing. Segregation analysis supported an association with MMD for 2 variants and a lack of association with disease for 1 variant.

CONCLUSIONS: These results confirm that alterations in RNF213 predispose patients of diverse ethnicities to MMD, and that the p.R4810K variant predisposes individuals of Asian descent in the United States to MMD.

}, keywords = {Adenosine Triphosphatases, Adolescent, Adult, Child, Cohort Studies, Ethnic Groups, Female, Genetic Variation, Humans, Male, Moyamoya Disease, Population Surveillance, Ubiquitin-Protein Ligases, Young Adult}, issn = {1524-4628}, doi = {10.1161/STROKEAHA.114.006244}, author = {Cecchi, Alana C and Guo, Dongchuan and Ren, Zhao and Flynn, Kelly and Santos-Cortez, Regie Lyn P and Leal, Suzanne M and Wang, Gao T and Regalado, Ellen S and Steinberg, Gary K and Shendure, Jay and Bamshad, Michael J and Grotta, James C and Nickerson, Deborah A and Pannu, Hariyadarshi and Milewicz, Dianna 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 {280, title = {Effect of catechol-o-methyltransferase-gene (COMT) variants on experimental and acute postoperative pain in 1,000 women undergoing surgery for breast cancer.}, journal = {Anesthesiology}, volume = {119}, year = {2013}, month = {2013 Dec}, pages = {1422-33}, abstract = {

BACKGROUND: Catechol-O-methyltransferase (COMT) metabolizes catecholamines in different tissues. Polymorphisms in COMT gene can attenuate COMT activity and increase sensitivity to pain. Human studies exploring the effect of COMT polymorphisms on pain sensitivity have mostly included small, heterogeneous samples and have ignored several important single nucleotide polymorphisms (SNPs). This study examines the effect of COMT polymorphisms on experimental and postoperative pain phenotypes in a large ethnically homogeneous female patient cohort.

METHODS: Intensity of cold (+2-4{\textdegree}C) and heat (+48{\textdegree}C) pain and tolerance to cold pain were assessed in 1,000 patients scheduled for breast cancer surgery. Acute postoperative pain and oxycodone requirements were recorded. Twenty-two COMT SNPs were genotyped and their association with six pain phenotypes analyzed with linear regression.

RESULTS: There was no association between any of the tested pain phenotypes and SNP rs4680. The strongest association signals were seen between rs165774 and heat pain intensity as well as rs887200 and cold pain intensity. In both cases, minor allele carriers reported less pain. Neither of these results remained significant after strict multiple testing corrections. When analyzed further, the effect of rs887200 was, however, shown to be significant and consistent throughout the cold pressure test. No evidence of association between the SNPs and postoperative oxycodone consumption was found.

CONCLUSIONS: SNPs rs887200 and rs165774 located in the untranslated regions of the gene had the strongest effects on pain sensitivity. Their effect on pain is described here for the first time. These results should be confirmed in further studies and the potential functional mechanisms of the variants studied.

}, keywords = {Acute Pain, Adult, Aged, Analgesics, Opioid, Anesthesia, Breast Neoplasms, Catechol O-Methyltransferase, Cold Temperature, Databases, Genetic, Female, Follow-Up Studies, Genetic Variation, Genome-Wide Association Study, Genotype, Haplotypes, Hot Temperature, Humans, Middle Aged, Oxycodone, Pain Measurement, Pain, Postoperative, Polymorphism, Single Nucleotide}, issn = {1528-1175}, doi = {10.1097/ALN.0000000000000013}, author = {Kambur, Oleg and Kaunisto, Mari A and Tikkanen, Emmi and Leal, Suzanne M and Ripatti, Samuli and Kalso, Eija A} } @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 {330, title = {Next generation analytic tools for large scale genetic epidemiology studies of complex diseases.}, journal = {Genet Epidemiol}, volume = {36}, year = {2012}, month = {2012 Jan}, pages = {22-35}, abstract = {

Over the past several years, genome-wide association studies (GWAS) have succeeded in identifying hundreds of genetic markers associated with common diseases. However, most of these markers confer relatively small increments of risk and explain only a small proportion of familial clustering. To identify obstacles to future progress in genetic epidemiology research and provide recommendations to NIH for overcoming these barriers, the National Cancer Institute sponsored a workshop entitled "Next Generation Analytic Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases" on September 15-16, 2010. The goal of the workshop was to facilitate discussions on (1) statistical strategies and methods to efficiently identify genetic and environmental factors contributing to the risk of complex disease; and (2) how to develop, apply, and evaluate these strategies for the design, analysis, and interpretation of large-scale complex disease association studies in order to guide NIH in setting the future agenda in this area of research. The workshop was organized as a series of short presentations covering scientific (gene-gene and gene-environment interaction, complex phenotypes, and rare variants and next generation sequencing) and methodological (simulation modeling and computational resources and data management) topic areas. Specific needs to advance the field were identified during each session and are summarized.

}, keywords = {Data Mining, Gene-Environment Interaction, Genetic Variation, Genome-Wide Association Study, Humans, Molecular Epidemiology, National Institutes of Health (U.S.), Neoplasms, Phenotype, United States}, issn = {1098-2272}, doi = {10.1002/gepi.20652}, author = {Mechanic, Leah E and Chen, Huann-Sheng and Amos, Christopher I and Chatterjee, Nilanjan and Cox, Nancy J and Divi, Rao L and Fan, Ruzong and Harris, Emily L and Jacobs, Kevin and Kraft, Peter and Leal, Suzanne M and McAllister, Kimberly and Moore, Jason H and Paltoo, Dina N and Province, Michael A and Ramos, Erin M and Ritchie, Marylyn D and Roeder, Kathryn and Schaid, Daniel J and Stephens, Matthew and Thomas, Duncan C and Weinberg, Clarice R and Witte, John S and Zhang, Shunpu and Z{\"o}llner, Sebastian and Feuer, Eric J and Gillanders, Elizabeth M} } @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} } @article {316, title = {A unified method for detecting secondary trait associations with rare variants: application to sequence data.}, journal = {PLoS Genet}, volume = {8}, year = {2012}, month = {2012}, pages = {e1003075}, abstract = {

Next-generation sequencing has made possible the detection of rare variant (RV) associations with quantitative traits (QT). Due to high sequencing cost, many studies can only sequence a modest number of selected samples with extreme QT. Therefore association testing in individual studies can be underpowered. Besides the primary trait, many clinically important secondary traits are often measured. It is highly beneficial if multiple studies can be jointly analyzed for detecting associations with commonly measured traits. However, analyzing secondary traits in selected samples can be biased if sample ascertainment is not properly modeled. Some methods exist for analyzing secondary traits in selected samples, where some burden tests can be implemented. However p-values can only be evaluated analytically via asymptotic approximations, which may not be accurate. Additionally, potentially more powerful sequence kernel association tests, variable selection-based methods, and burden tests that require permutations cannot be incorporated. To overcome these limitations, we developed a unified method for analyzing secondary trait associations with RVs (STAR) in selected samples, incorporating all RV tests. Statistical significance can be evaluated either through permutations or analytically. STAR makes it possible to apply more powerful RV tests to analyze secondary trait associations. It also enables jointly analyzing multiple cohorts ascertained under different study designs, which greatly boosts power. The performance of STAR and commonly used RV association tests were comprehensively evaluated using simulation studies. STAR was also implemented to analyze a dataset from the SardiNIA project where samples with extreme low-density lipoprotein levels were sequenced. A significant association between LDLR and systolic blood pressure was identified, which is supported by pharmacogenetic studies. In summary, for sequencing studies, STAR is an important tool for detecting secondary-trait RV associations.

}, keywords = {Blood Pressure, Computer Simulation, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, High-Throughput Nucleotide Sequencing, Humans, Italy, Lipoproteins, LDL, Models, Genetic, Phenotype, Quantitative Trait Loci, Software}, issn = {1553-7404}, doi = {10.1371/journal.pgen.1003075}, author = {Liu, Dajiang J and Leal, Suzanne M} }