@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} }