@article {525, title = {MendelProb: probability and sample size calculations for Mendelian studies of exome and whole genome sequence data.}, journal = {Bioinformatics}, volume = {35}, year = {2019}, month = {2019 02 01}, pages = {529-531}, abstract = {

Motivation: For the design of genetic studies, it is necessary to perform power calculations. Although for Mendelian traits the power of detecting linkage for pedigree(s) can be determined, it is also of great interest to determine the probability of identifying multiple pedigrees or unrelated cases with variants in the same gene. For many diseases, due to extreme locus heterogeneity this probability can be small. If only one family is observed segregating a variant classified as likely pathogenic or of unknown significance, the gene cannot be implicated in disease etiology. The probability of identifying several disease families or cases is dependent on the gene-specific disease prevalence and the sample size. The observation of multiple disease families or cases with variants in the same gene as well as evidence of pathogenicity from other sources, e.g. in silico prediction, expression and functional studies, can aid in implicating a gene in disease etiology. MendelProb can determine the probability of detecting a minimum number of families or cases with variants in the same gene. It can also calculate the probability of detecting genes with variants in different data types, e.g. identifying a variant in at least one family that can establish linkage and more the two additional families regardless of their size. Additionally, for a specified probability MendelProb can determine the number of probands which need to be screened to detect a minimum number of individuals with variants within the same gene.

Results: A single Mendelian disease family is not sufficient to implicate a gene in disease etiology. It is necessary to observe multiple families or cases with potentially pathogenic variants in the same gene. MendelProb, an R library, was developed to determine the probability of observing multiple families and cases with variants within a gene and to also establish the numbers of probands to screen to detect multiple observations of variants within a gene.

Availability and implementation: https://github.com/statgenetics/mendelprob.

}, keywords = {Exome, Genetic Linkage, Genomics, Humans, Pedigree, Probability, Sample Size, Software}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bty542}, author = {He, Zongxiao and Wang, Lu and DeWan, Andrew T and Leal, Suzanne M} } @article {279, title = {Rare-variant extensions of the transmission disequilibrium test: application to autism exome sequence data.}, journal = {Am J Hum Genet}, volume = {94}, year = {2014}, month = {2014 Jan 02}, pages = {33-46}, abstract = {

Many population-based rare-variant (RV) association tests, which aggregate variants across a region, have been developed to analyze sequence data. A drawback of analyzing population-based data is that it is difficult to adequately control for population substructure and admixture, and spurious associations can occur. For RVs, this problem can be substantial, because the spectrum of rare variation can differ greatly between populations. A solution is to analyze parent-child trio data, by using the transmission disequilibrium test (TDT), which is robust to population substructure and admixture. We extended the TDT to test for RV associations using four commonly used methods. We demonstrate that for all RV-TDT methods, using proper analysis strategies, type I error is well-controlled even when there are high levels of population substructure or admixture. For trio data, unlike for population-based data, RV allele-counting association methods will lead to inflated type I errors. However type I errors can be properly controlled by obtaining p values empirically through haplotype permutation. The power of the RV-TDT methods was evaluated and compared to the analysis of case-control data with a number of genetic and disease models. The RV-TDT was also used to analyze exome data from 199 Simons Simplex Collection autism trios and an association was observed with variants in ABCA7. Given the problem of adequately controlling for population substructure and admixture in RV association studies and the growing number of sequence-based trio studies, the RV-TDT is extremely beneficial to elucidate the involvement of RVs in the etiology of complex traits.

}, keywords = {Alleles, Autistic Disorder, Computer Simulation, Exome, Gene Frequency, Genetic Association Studies, Genetic Predisposition to Disease, Genetic Variation, Haplotypes, Humans, Linkage Disequilibrium, Models, Genetic, Phenotype, Sequence Analysis, DNA}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2013.11.021}, author = {He, Zongxiao and O{\textquoteright}Roak, Brian J and Smith, Joshua D and Wang, Gao and Hooker, Stanley and Santos-Cortez, Regie Lyn P and Li, Biao and Kan, Mengyuan and Krumm, Nik and Nickerson, Deborah A and Shendure, Jay and Eichler, Evan E and Leal, Suzanne M} }