@article {632, title = {Sequence variants in nine different genes underlying rare skin disorders in 10 consanguineous families.}, journal = {Int J Dermatol}, volume = {56}, year = {2017}, month = {2017 Dec}, pages = {1406-1413}, abstract = {

BACKGROUND: Genodermatoses represent genetic anomalies of skin tissues including hair follicles, sebaceous glands, eccrine glands, nails, and teeth. Ten consanguineous families segregating various genodermatosis phenotypes were investigated in the present study.

METHODS: Homozygosity mapping, exome, and Sanger sequencing were employed to search for the disease-causing variants in the 10 families.

RESULTS: Exome sequencing identified seven homozygous sequence variants in different families, including: c.27delT in FERMT1; c.836delA in ABHD5; c.2453C>T in ERCC5; c.5314C>T in COL7A1; c.1630C>T in ALOXE3; c.502C>T in PPOX; and c.10G>T in ALDH3A2. Sanger sequencing revealed three homozygous variants: c.1718~+~2A>G in FERMT1; c.10459A>T in FLG; and c.92delT in the KRT14 genes as the underlying genetic cause of skin phenotypes.

CONCLUSION: This study supports the use of exome sequencing as a powerful, efficient tool for identifying genes that underlie rare monogenic skin disorders.

}, keywords = {1-Acylglycerol-3-Phosphate O-Acyltransferase, Aldehyde Oxidoreductases, Blister, Collagen Type VII, Consanguinity, DNA Mutational Analysis, DNA-Binding Proteins, Endonucleases, Epidermolysis Bullosa, Epidermolysis Bullosa Dystrophica, Epidermolysis Bullosa Simplex, Exome, Female, Flavoproteins, Homozygote, Humans, Ichthyosiform Erythroderma, Congenital, Ichthyosis Vulgaris, Ichthyosis, Lamellar, INDEL Mutation, Intermediate Filament Proteins, Keratin-14, Lipid Metabolism, Inborn Errors, Lipoxygenase, Male, Membrane Proteins, Mitochondrial Proteins, Muscular Diseases, Neoplasm Proteins, Nuclear Proteins, Pedigree, Periodontal Diseases, Phenotype, Photosensitivity Disorders, Porphyria, Variegate, Protoporphyrinogen Oxidase, Rare Diseases, Sjogren-Larsson Syndrome, Skin Diseases, Genetic, Transcription Factors, Xeroderma Pigmentosum}, issn = {1365-4632}, doi = {10.1111/ijd.13778}, author = {Shah, Khadim and Mehmood, Sabba and Jan, Abid and Abbe, Izoduwa and Hussain Ali, Raja and Khan, Anwar and Chishti, Muhammad S and Lee, Kwanghyuk and Ahmad, Farooq and Ansar, Muhammad and Shahzad, Shaheen and Nickerson, Deborah A and Bamshad, Michael J and Coucke, Paul J and Santos-Cortez, Regie L P and Spritz, Richard A and Leal, Suzanne M and Ahmad, Wasim} } @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} }