@article {299, title = {Mutations in KARS, encoding lysyl-tRNA synthetase, cause autosomal-recessive nonsyndromic hearing impairment DFNB89.}, journal = {Am J Hum Genet}, volume = {93}, year = {2013}, month = {2013 Jul 11}, pages = {132-40}, abstract = {

Previously, DFNB89, a locus associated with autosomal-recessive nonsyndromic hearing impairment (ARNSHI), was mapped to chromosomal region 16q21-q23.2 in three unrelated, consanguineous Pakistani families. Through whole-exome sequencing of a hearing-impaired individual from each family, missense mutations were identified at highly conserved residues of lysyl-tRNA synthetase (KARS): the c.1129G>A (p.Asp377Asn) variant was found in one family, and the c.517T>C (p.Tyr173His) variant was found in the other two families. Both variants were predicted to be damaging by multiple bioinformatics tools. The two variants both segregated with the nonsyndromic-hearing-impairment phenotype within the three families, and neither mutation was identified in ethnically matched controls or within variant databases. Individuals homozygous for KARS mutations had symmetric, severe hearing impairment across all frequencies but did not show evidence of auditory or limb neuropathy. It has been demonstrated that KARS is expressed in hair cells of zebrafish, chickens, and mice. Moreover, KARS has strong localization to the spiral ligament region of the cochlea, as well as to Deiters{\textquoteright} cells, the sulcus epithelium, the basilar membrane, and the surface of the spiral limbus. It is hypothesized that KARS variants affect aminoacylation in inner-ear cells by interfering with binding activity to tRNA or p38 and with tetramer formation. The identification of rare KARS variants in ARNSHI-affected families defines a gene that is associated with ARNSHI.

}, keywords = {Animals, Case-Control Studies, Chickens, Cochlea, Computational Biology, Consanguinity, Female, Genetic Linkage, Hair Cells, Auditory, Inner, Haplotypes, Hearing Loss, Homozygote, Humans, Lysine-tRNA Ligase, Male, Mice, Mutation, Missense, Pedigree, Transfer RNA Aminoacylation, Zebrafish}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2013.05.018}, author = {Santos-Cortez, Regie Lyn P and Lee, Kwanghyuk and Azeem, Zahid and Antonellis, Patrick J and Pollock, Lana M and Khan, Saadullah and Andrade-Elizondo, Paula B and Chiu, Ilene and Adams, Mark D and Basit, Sulman and Smith, Joshua D and Nickerson, Deborah A and McDermott, Brian M and Ahmad, Wasim 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} }