@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 {145, title = {Generation of sequence-based data for pedigree-segregating Mendelian or Complex traits.}, journal = {Bioinformatics}, volume = {31}, year = {2015}, month = {2015 Nov 15}, pages = {3706-8}, abstract = {

MOTIVATION: There is great interest in analyzing next generation sequence data that has been generated for pedigrees. However, unlike for population-based data there are only a limited number of rare variant methods to analyze pedigree data. One limitation is the ability to evaluate type I and II errors for family-based methods, due to lack of software that can simulate realistic sequence data for pedigrees.

SUMMARY: We developed RarePedSim (Rare-variant Pedigree-based Simulator), a program to simulate region/gene-level genotype and phenotype data for complex and Mendelian traits for any given pedigree structure. Using a genetic model, sequence variant data can be generated either conditionally or unconditionally on pedigree members{\textquoteright} qualitative or quantitative phenotypes. Additionally, qualitative or quantitative traits can be generated conditional on variant data. Sequence data can either be simulated using realistic population demographic models or obtained from sequence-based studies. Variant sites can be annotated with positions, allele frequencies and functionality. For rare variants, RarePedSim is the only program that can efficiently generate both genotypes and phenotypes, regardless of pedigree structure. Data generated by RarePedSim are in standard Linkage file (.ped) and Variant Call (.vcf) formats, ready to be used for a variety of purposes, including evaluation of type I error and power, for association methods including mixed models and linkage analysis methods.

AVAILABILITY AND IMPLEMENTATION: bioinformatics.org/simped/rare

CONTACT: sleal@bcm.edu.

}, keywords = {Base Sequence, Chromosome Segregation, Humans, Pedigree, Quantitative Trait, Heritable, Sequence Analysis, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btv412}, author = {Li, Biao and Wang, Gao T and Leal, Suzanne M} } @article {152, title = {Rare A2ML1 variants confer susceptibility to otitis media.}, journal = {Nat Genet}, volume = {47}, year = {2015}, month = {2015 Aug}, pages = {917-20}, abstract = {

A duplication variant within the middle ear-specific gene A2ML1 cosegregates with otitis media in an indigenous Filipino pedigree (LOD score = 7.5 at reduced penetrance) and lies within a founder haplotype that is also shared by 3 otitis-prone European-American and Hispanic-American children but is absent in non-otitis-prone children and >62,000 next-generation sequences. We identified seven additional A2ML1 variants in six otitis-prone children. Collectively, our studies support a role for A2ML1 in the pathophysiology of otitis media.

}, keywords = {alpha-Macroglobulins, Animals, Base Sequence, Child, Cochlea, Exome, Family Health, Female, Gene Duplication, Gene Frequency, Genetic Predisposition to Disease, Genotype, Haplotypes, Humans, Male, Mice, Inbred C57BL, Models, Molecular, Otitis Media, Pedigree, Principal Component Analysis, Protein Conformation, Sequence Analysis, DNA}, issn = {1546-1718}, doi = {10.1038/ng.3347}, author = {Santos-Cortez, Regie Lyn P and Chiong, Charlotte M and Reyes-Quintos, Ma Rina T and Tantoco, Ma Leah C and Wang, Xin and Acharya, Anushree and Abbe, Izoduwa and Giese, Arnaud P and Smith, Joshua D and Allen, E Kaitlynn and Li, Biao and Cutiongco-de la Paz, Eva Maria and Garcia, Marieflor Cristy and Llanes, Erasmo Gonzalo D V and Labra, Patrick John and Gloria-Cruz, Teresa Luisa I and Chan, Abner L and Wang, Gao T and Daly, Kathleen A and Shendure, Jay and Bamshad, Michael J and Nickerson, Deborah A and Patel, Janak A and Riazuddin, Saima and Sale, Michele M and Chonmaitree, Tasnee and Ahmed, Zubair M and Abes, Generoso T and Leal, Suzanne 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 {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} }