@article {404, title = {Gene-based segregation method for identifying rare variants in family-based sequencing studies.}, journal = {Genet Epidemiol}, volume = {41}, year = {2017}, month = {2017 05}, pages = {309-319}, abstract = {

Whole-exome sequencing using family data has identified rare coding variants in Mendelian diseases or complex diseases with Mendelian subtypes, using filters based on variant novelty, functionality, and segregation with the phenotype within families. However, formal statistical approaches are limited. We propose a gene-based segregation test (GESE) that quantifies the uncertainty of the filtering approach. It is constructed using the probability of segregation events under the null hypothesis of Mendelian transmission. This test takes into account different degrees of relatedness in families, the number of functional rare variants in the gene, and their minor allele frequencies in the corresponding population. In addition, a weighted version of this test allows incorporating additional subject phenotypes to improve statistical power. We show via simulations that the GESE and weighted GESE tests maintain appropriate type I error rate, and have greater power than several commonly used region-based methods. We apply our method to whole-exome sequencing data from 49 extended pedigrees with severe, early-onset chronic obstructive pulmonary disease (COPD) in the Boston Early-Onset COPD study (BEOCOPD) and identify several promising candidate genes. Our proposed methods show great potential for identifying rare coding variants of large effect and high penetrance for family-based sequencing data. The proposed tests are implemented in an R package that is available on CRAN (https://cran.r-project.org/web/packages/GESE/).

}, keywords = {Age of Onset, Boston, Computer Simulation, Databases, Genetic, Family, Genetic Variation, Genome, Human, Humans, Models, Genetic, Penetrance, Pulmonary Disease, Chronic Obstructive, Reference Standards, Sequence Analysis, DNA}, issn = {1098-2272}, doi = {10.1002/gepi.22037}, author = {Qiao, Dandi and Lange, Christoph and Laird, Nan M and Won, Sungho and Hersh, Craig P and Morrow, Jarrett and Hobbs, Brian D and Lutz, Sharon M and Ruczinski, Ingo and Beaty, Terri H and Silverman, Edwin K and Cho, Michael H} } @article {47, title = {Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.}, journal = {Genet Epidemiol}, volume = {40}, year = {2016}, month = {2016 Sep}, pages = {502-11}, abstract = {

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows.

}, keywords = {Computer Simulation, Genetic Association Studies, Genetic Variation, Humans, Likelihood Functions, Models, Genetic, Phenotype}, issn = {1098-2272}, doi = {10.1002/gepi.21985}, author = {Wang, Longfei and Lee, Sungyoung and Gim, Jungsoo and Qiao, Dandi and Cho, Michael and Elston, Robert C and Silverman, Edwin K and Won, Sungho} } @article {210, title = {PRIMUS: rapid reconstruction of pedigrees from genome-wide estimates of identity by descent.}, journal = {Am J Hum Genet}, volume = {95}, year = {2014}, month = {2014 Nov 06}, pages = {553-64}, abstract = {

Understanding and correctly utilizing relatedness among samples is essential for genetic analysis; however, managing sample records and pedigrees can often be error prone and incomplete. Data sets ascertained by random sampling often harbor cryptic relatedness that can be leveraged in genetic analyses for maximizing power. We have developed a method that uses genome-wide estimates of~pairwise identity by descent to identify families and quickly reconstruct and score all possible pedigrees that fit the genetic data by using up to third-degree relatives, and we have included it in the software package PRIMUS (Pedigree Reconstruction and Identification of the~Maximally Unrelated Set). Here, we validate its performance on simulated, clinical, and HapMap pedigrees. Among these samples, we demonstrate that PRIMUS can verify reported pedigree structures and identify cryptic relationships. Finally, we show that PRIMUS reconstructed pedigrees, all of which were previously unknown, for 203 families from a cohort collected in Starr County, TX (1,890 samples).

}, keywords = {Base Sequence, Computer Simulation, Exome, Gene Frequency, Genetics, Population, Humans, Molecular Sequence Data, Pedigree, Pulmonary Disease, Chronic Obstructive, Sequence Analysis, DNA, Software, Texas}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2014.10.005}, author = {Staples, Jeffrey and Qiao, Dandi and Cho, Michael H and Silverman, Edwin K and Nickerson, Deborah A and Below, Jennifer E} }