@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 {45, title = {FARVATX: Family-Based Rare Variant Association Test for X-Linked Genes.}, journal = {Genet Epidemiol}, volume = {40}, year = {2016}, month = {2016 Sep}, pages = {475-85}, abstract = {

Although the X chromosome has many genes that are functionally related to human diseases, the complicated biological properties of the X chromosome have prevented efficient genetic association analyses, and only a few significantly associated X-linked variants have been reported for complex traits. For instance, dosage compensation of X-linked genes is often achieved via the inactivation of one allele in each X-linked variant in females; however, some X-linked variants can escape this X chromosome inactivation. Efficient genetic analyses cannot be conducted without prior knowledge about the gene expression process of X-linked variants, and misspecified information can lead to power loss. In this report, we propose new statistical methods for rare X-linked variant genetic association analysis of dichotomous phenotypes with family-based samples. The proposed methods are computationally efficient and can complete X-linked analyses within a few hours. Simulation studies demonstrate the statistical efficiency of the proposed methods, which were then applied to rare-variant association analysis of the X chromosome in chronic obstructive pulmonary disease. Some promising significant X-linked genes were identified, illustrating the practical importance of the proposed methods.

}, keywords = {Alleles, Chromosomes, Human, X, Female, Genes, X-Linked, Genetic Variation, Humans, Models, Genetic, Pedigree, Phenotype, X Chromosome Inactivation}, issn = {1098-2272}, doi = {10.1002/gepi.21979}, author = {Choi, Sungkyoung and Lee, Sungyoung and Qiao, Dandi and Hardin, Megan and Cho, Michael H and Silverman, Edwin K and Park, Taesung and Won, Sungho} } @article {415, title = {Increasing Generality and Power of Rare-Variant Tests by Utilizing Extended Pedigrees.}, journal = {Am J Hum Genet}, volume = {99}, year = {2016}, month = {2016 Oct 06}, pages = {846-859}, abstract = {

Recently, multiple studies have performed whole-exome or whole-genome sequencing to identify groups of rare variants associated with complex traits and diseases. They have primarily utilized case-control study designs that often require thousands of individuals to reach acceptable statistical power. Family-based studies can be more powerful because a rare variant can be enriched in an extended pedigree and segregate with the phenotype. Although many methods have been proposed for using family data to discover rare variants involved in a disease, a majority of them focus on a specific pedigree structure and are designed to analyze either binary or continuously measured outcomes. In this article, we propose RareIBD, a general and powerful approach to identifying rare variants involved in disease susceptibility. Our method can be applied to large extended families of arbitrary structure, including pedigrees with only affected individuals. The method accommodates both binary and quantitative traits. A series of simulation experiments suggest that RareIBD is a powerful test that outperforms existing approaches. In addition, our method accounts for individuals in top generations, which are not usually genotyped in extended families. In contrast to available statistical tests, RareIBD generates accurate p values even when genetic data from these individuals are missing. We applied RareIBD, as well as other methods, to two extended family datasets generated by different genotyping technologies and representing different ethnicities. The analysis of real data confirmed that RareIBD is the only method that properly controls type I error.

}, keywords = {Datasets as Topic, Ethnic Groups, Family, Female, Genetic Predisposition to Disease, Genetic Variation, Genotype, Humans, Male, Models, Genetic, Pedigree, Phenotype, Research Design}, issn = {1537-6605}, doi = {10.1016/j.ajhg.2016.08.015}, author = {Sul, Jae Hoon and Cade, Brian E and Cho, Michael H and Qiao, Dandi and Silverman, Edwin K and Redline, Susan and Sunyaev, Shamil} }