@article {204, title = {Challenges and solutions for gene identification in the presence of familial locus heterogeneity.}, journal = {Eur J Hum Genet}, volume = {23}, year = {2015}, month = {2015 Sep}, pages = {1207-15}, abstract = {

Next-generation sequencing (NGS) of exomes and genomes has accelerated the identification of genes involved in Mendelian phenotypes. However, many NGS studies fall short of identifying causal variants, with estimates for success rates as low as 25\% for uncovering the pathological variant underlying disease etiology. An important reason for such failures is familial locus heterogeneity, where within a single pedigree causal variants in two or more genes underlie Mendelian trait etiology. As examples of intra- and inter-sibship familial locus heterogeneity, we present 10 consanguineous Pakistani families segregating hearing impairment due to homozygous variants in two different hearing impairment genes and a European-American pedigree in which hearing impairment is caused by four variants in three different genes. We have identified 41 additional pedigrees with syndromic and nonsyndromic hearing impairment for which a single previously reported hearing impairment gene has been identified but only segregates with the phenotype in a subset of affected pedigree members. We estimate that locus heterogeneity occurs in 15.3\% (95\% confidence interval: 11.9\%, 19.9\%) of the families in our collection. We demonstrate novel approaches to apply linkage analysis and homozygosity mapping (for autosomal recessive consanguineous pedigrees), which can be used to detect locus heterogeneity using either NGS or SNP array data. Results from linkage analysis and homozygosity mapping can also be used to group sibships or individuals most likely to be segregating the same causal variants and thereby increase the success rate of gene identification.

}, keywords = {Asian Continental Ancestry Group, Calcium-Binding Proteins, Chromosome Mapping, Connexin 26, Connexins, Consanguinity, European Continental Ancestry Group, Female, Genes, Recessive, Genetic Heterogeneity, Genetic Linkage, Genetic Loci, Genetic Predisposition to Disease, Genome, Human, Genotype, Hearing Loss, Hepatocyte Growth Factor, High-Throughput Nucleotide Sequencing, Homozygote, Humans, Male, Membrane Transport Proteins, Mutation, Pedigree, Phenotype, Sulfate Transporters}, issn = {1476-5438}, doi = {10.1038/ejhg.2014.266}, author = {Rehman, Atteeq U and Santos-Cortez, Regie Lyn P and Drummond, Meghan C and Shahzad, Mohsin and Lee, Kwanghyuk and Morell, Robert J and Ansar, Muhammad and Jan, Abid and Wang, Xin and Aziz, Abdul and Riazuddin, Saima and Smith, Joshua D and Wang, Gao T and Ahmed, Zubair M and Gul, Khitab and Shearer, A Eliot and Smith, Richard J H and Shendure, Jay and Bamshad, Michael J and Nickerson, Deborah A and Hinnant, John and Khan, Shaheen N and Fisher, Rachel A and Ahmad, Wasim and Friderici, Karen H and Riazuddin, Sheikh and Friedman, Thomas B and Wilch, Ellen S and Leal, Suzanne M} } @article {178, title = {Collapsed haplotype pattern method for linkage analysis of next-generation sequence data.}, journal = {Eur J Hum Genet}, volume = {23}, year = {2015}, month = {2015 Dec}, pages = {1739-43}, abstract = {

Recent advances in next-generation sequencing (NGS) make it possible to directly sequence genomes and exomes of individuals with Mendelian diseases and screen sequence data for causal variants. With the reduction in cost of NGS, DNA samples from entire families can be sequenced and linkage analysis can be performed directly using NGS data. Inspired by {\textquoteright}burden{\textquoteright} tests, which are used for complex trait rare variant association studies, we developed the collapsed haplotype pattern (CHP) method for linkage analysis. Using data from several deafness genes we demonstrate that the CHP method is substantially more powerful than analyzing individual variants. Unlike applying NGS data filtering approaches, the CHP method provides statistical evidence of a gene{\textquoteright}s involvement in disease etiology and is also less likely to exclude causal variants in the presence of phenocopies and/or reduced penetrance. The CHP method was implemented in the SEQLinkage software package, which can perform linkage analysis on NGS data or can generate data compatible with many linkage analysis programs, reviving them for use in NGS era.

}, keywords = {Chromosome Mapping, Genetic Linkage, Haplotypes, Hearing Loss, High-Throughput Nucleotide Sequencing, Humans, Sequence Analysis, DNA, Software}, issn = {1476-5438}, doi = {10.1038/ejhg.2015.64}, author = {Wang, Gao T and Zhang, Di and Li, Biao and Dai, Hang 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 {294, title = {Recurrent gain-of-function mutation in PRKG1 causes thoracic aortic aneurysms and acute aortic dissections.}, journal = {Am J Hum Genet}, volume = {93}, year = {2013}, month = {2013 Aug 08}, pages = {398-404}, abstract = {

Gene mutations that lead to decreased contraction of vascular smooth-muscle cells (SMCs) can cause inherited thoracic aortic aneurysms and dissections. Exome sequencing of distant relatives affected by thoracic aortic disease and subsequent Sanger sequencing of additional probands with familial thoracic aortic disease identified the same rare variant, PRKG1 c.530G>A (p.Arg177Gln), in four families. This mutation segregated with aortic disease in these families with a combined two-point LOD score of 7.88. The majority of affected individuals presented with acute aortic dissections (63\%) at relatively young ages (mean 31 years, range 17-51 years). PRKG1 encodes type I cGMP-dependent protein kinase (PKG-1), which is activated upon binding of cGMP and controls SMC relaxation. Although the p.Arg177Gln alteration disrupts binding to the high-affinity cGMP binding site within the regulatory domain, the altered PKG-1 is constitutively active even in the absence of cGMP. The increased PKG-1 activity leads to decreased phosphorylation of the myosin regulatory light chain in fibroblasts and is predicted to cause decreased contraction of vascular SMCs. Thus, identification of a gain-of-function mutation in PRKG1 as a cause of thoracic aortic disease provides further evidence that proper SMC contractile function is critical for maintaining the integrity of the thoracic aorta throughout a lifetime.

}, keywords = {Acute Disease, Adolescent, Adult, Amino Acid Sequence, Aneurysm, Dissecting, Aorta, Thoracic, Aortic Aneurysm, Thoracic, Cyclic GMP, Cyclic GMP-Dependent Protein Kinase Type I, Exome, Female, Fibroblasts, High-Throughput Nucleotide Sequencing, Humans, Male, Middle Aged, Molecular Sequence Data, Muscle Contraction, Muscle, Smooth, Vascular, Mutation, Myosin Light Chains, Pedigree}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2013.06.019}, author = {Guo, Dong-Chuan and Regalado, Ellen and Casteel, Darren E and Santos-Cortez, Regie L and Gong, Limin and Kim, Jeong Joo and Dyack, Sarah and Horne, S Gabrielle and Chang, Guijuan and Jondeau, Guillaume and Boileau, Catherine and Coselli, Joseph S and Li, Zhenyu and Leal, Suzanne M and Shendure, Jay and Rieder, Mark J and Bamshad, Michael J and Nickerson, Deborah A and Kim, Choel and Milewicz, Dianna 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 {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} }