@article {835, title = {Inherited causes of clonal haematopoiesis in 97,691 whole genomes.}, journal = {Nature}, volume = {586}, year = {2020}, month = {2020 10}, pages = {763-768}, abstract = {

Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer and coronary heart disease-this phenomenon is~termed clonal haematopoiesis of indeterminate potential (CHIP). Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP~driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.

}, keywords = {Adult, Africa, African Continental Ancestry Group, Aged, Aged, 80 and over, alpha Karyopherins, Cell Self Renewal, Clonal Hematopoiesis, DNA-Binding Proteins, Female, Genetic Predisposition to Disease, Genome, Human, Germ-Line Mutation, Hematopoietic Stem Cells, Humans, Intracellular Signaling Peptides and Proteins, Male, Middle Aged, National Heart, Lung, and Blood Institute (U.S.), Phenotype, Precision Medicine, Proto-Oncogene Proteins, Tripartite Motif Proteins, United States, Whole Genome Sequencing}, issn = {1476-4687}, doi = {10.1038/s41586-020-2819-2}, author = {Bick, Alexander G and Weinstock, Joshua S and Nandakumar, Satish K and Fulco, Charles P and Bao, Erik L and Zekavat, Seyedeh M and Szeto, Mindy D and Liao, Xiaotian and Leventhal, Matthew J and Nasser, Joseph and Chang, Kyle and Laurie, Cecelia and Burugula, Bala Bharathi and Gibson, Christopher J and Lin, Amy E and Taub, Margaret A and Aguet, Francois and Ardlie, Kristin and Mitchell, Braxton D and Barnes, Kathleen C and Moscati, Arden and Fornage, Myriam and Redline, Susan and Psaty, Bruce M and Silverman, Edwin K and Weiss, Scott T and Palmer, Nicholette D and Vasan, Ramachandran S and Burchard, Esteban G and Kardia, Sharon L R and He, Jiang and Kaplan, Robert C and Smith, Nicholas L and Arnett, Donna K and Schwartz, David A and Correa, Adolfo and de Andrade, Mariza and Guo, Xiuqing and Konkle, Barbara A and Custer, Brian and Peralta, Juan M and Gui, Hongsheng and Meyers, Deborah A and McGarvey, Stephen T and Chen, Ida Yii-Der and Shoemaker, M Benjamin and Peyser, Patricia A and Broome, Jai G and Gogarten, Stephanie M and Wang, Fei Fei and Wong, Quenna and Montasser, May E and Daya, Michelle and Kenny, Eimear E and North, Kari E and Launer, Lenore J and Cade, Brian E and Bis, Joshua C and Cho, Michael H and Lasky-Su, Jessica and Bowden, Donald W and Cupples, L Adrienne and Mak, Angel C Y and Becker, Lewis C and Smith, Jennifer A and Kelly, Tanika N and Aslibekyan, Stella and Heckbert, Susan R and Tiwari, Hemant K and Yang, Ivana V and Heit, John A and Lubitz, Steven A and Johnsen, Jill M and Curran, Joanne E and Wenzel, Sally E and Weeks, Daniel E and Rao, Dabeeru C and Darbar, Dawood and Moon, Jee-Young and Tracy, Russell P and Buth, Erin J and Rafaels, Nicholas and Loos, Ruth J F and Durda, Peter and Liu, Yongmei and Hou, Lifang and Lee, Jiwon and Kachroo, Priyadarshini and Freedman, Barry I and Levy, Daniel and Bielak, Lawrence F and Hixson, James E and Floyd, James S and Whitsel, Eric A and Ellinor, Patrick T and Irvin, Marguerite R and Fingerlin, Tasha E and Raffield, Laura M and Armasu, Sebastian M and Wheeler, Marsha M and Sabino, Ester C and Blangero, John and Williams, L Keoki and Levy, Bruce D and Sheu, Wayne Huey-Herng and Roden, Dan M and Boerwinkle, Eric and Manson, JoAnn E and Mathias, Rasika A and Desai, Pinkal and Taylor, Kent D and Johnson, Andrew D and Auer, Paul L and Kooperberg, Charles and Laurie, Cathy C and Blackwell, Thomas W and Smith, Albert V and Zhao, Hongyu and Lange, Ethan and Lange, Leslie and Rich, Stephen S and Rotter, Jerome I and Wilson, James G and Scheet, Paul and Kitzman, Jacob O and Lander, Eric S and Engreitz, Jesse M and Ebert, Benjamin L and Reiner, Alexander P and Jaiswal, Siddhartha and Abecasis, Gon{\c c}alo and Sankaran, Vijay G and Kathiresan, Sekar and Natarajan, Pradeep} } @article {668, title = {metaFARVAT: An Efficient Tool for Meta-Analysis of Family-Based, Case-Control, and Population-Based Rare Variant Association Studies.}, journal = {Front Genet}, volume = {10}, year = {2019}, month = {2019}, pages = {572}, abstract = {

Family-based designs have been shown to be powerful in detecting the significant rare variants associated with human diseases. However, very few significant results have been found owing to relatively small sample sizes and the fact that statistical analyses often suffer from high false-negative error rates. These limitations can be avoided by combining results from multiple studies via meta-analysis. However, statistical methods for meta-analysis with rare variants are limited for family-based samples. In this report, we propose a tool for the meta-analysis of family-based rare variant associations, metaFARVAT. metaFARVAT is based on a quasi-likelihood score for each variant. These scores are combined to generate burden test, variable-threshold test, sequence kernel association test (SKAT), and optimal SKAT statistics. The proposed method tests homogeneous and heterogeneous effects of variants among different studies and can be applied to both quantitative and dichotomous phenotypes. Simulation results demonstrated the robustness and efficiency of the proposed method in different scenarios. By applying metaFARVAT to data from a family-based study and a case-control study, we identified a few promising candidate genes, including , which is associated with chronic obstructive pulmonary disease.

}, issn = {1664-8021}, doi = {10.3389/fgene.2019.00572}, author = {Wang, Longfei and Lee, Sungyoung and Qiao, Dandi and Cho, Michael H and Silverman, Edwin K and Lange, Christoph and Won, Sungho} } @article {522, title = {Whole exome sequencing analysis in severe chronic obstructive pulmonary disease.}, journal = {Hum Mol Genet}, volume = {27}, year = {2018}, month = {2018 11 01}, pages = {3801-3812}, abstract = {

Chronic obstructive pulmonary disease (COPD), one of the leading causes of death worldwide, is substantially influenced by genetic factors. Alpha-1 antitrypsin deficiency demonstrates that rare coding variants of large effect can influence COPD susceptibility. To identify additional rare coding variants in patients with severe COPD, we conducted whole exome sequencing analysis in 2543 subjects from two family-based studies (Boston Early-Onset COPD Study and International COPD Genetics Network) and one case-control study (COPDGene). Applying a gene-based segregation test in the family-based data, we identified significant segregation of rare loss of function variants in TBC1D10A and RFPL1 (P-value~<~2x10-6), but were unable to find similar variants in the case-control study. In single-variant, gene-based and pathway association analyses, we were unable to find significant findings that replicated or were significant in meta-analysis. However, we found that the top results in the two datasets were in proximity to each other in the protein-protein interaction network (P-value~=~0.014), suggesting enrichment of these results for similar biological processes. A network of these association results and their neighbors was significantly enriched in the transforming growth factor beta-receptor binding and cilia-related pathways. Finally, in a more detailed examination of candidate genes, we identified individuals with putative high-risk variants, including patients harboring homozygous mutations in genes associated with cutis laxa and Niemann-Pick Disease Type C. Our results likely reflect heterogeneity of genetic risk for COPD along with limitations of statistical power and functional annotation, and highlight the potential of network analysis to gain insight into genetic association studies.

}, keywords = {Adolescent, Adult, Aged, Case-Control Studies, DNA Mutational Analysis, Female, Genetic Association Studies, Genetic Predisposition to Disease, Humans, Male, Middle Aged, Mutation, Polymorphism, Single Nucleotide, Pulmonary Disease, Chronic Obstructive, Whole Exome Sequencing, Young Adult}, issn = {1460-2083}, doi = {10.1093/hmg/ddy269}, author = {Qiao, Dandi and Ameli, Asher and Prokopenko, Dmitry and Chen, Han and Kho, Alvin T and Parker, Margaret M and Morrow, Jarrett and Hobbs, Brian D and Liu, Yanhong and Beaty, Terri H and Crapo, James D and Barnes, Kathleen C and Nickerson, Deborah A and Bamshad, Michael and Hersh, Craig P and Lomas, David A and Agusti, Alvar and Make, Barry J and Calverley, Peter M A and Donner, Claudio F and Wouters, Emiel F and Vestbo, J{\o}rgen and Par{\'e}, Peter D and Levy, Robert D and Rennard, Stephen I and Tal-Singer, Ruth and Spitz, Margaret R and Sharma, Amitabh and Ruczinski, Ingo and Lange, Christoph and Silverman, Edwin K and Cho, Michael H} } @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 {98, title = {Exome Sequencing Analysis in Severe, Early-Onset Chronic Obstructive Pulmonary Disease.}, journal = {Am J Respir Crit Care Med}, volume = {193}, year = {2016}, month = {2016 06 15}, pages = {1353-63}, abstract = {

RATIONALE: Genomic regions identified by genome-wide association studies explain only a small fraction of heritability for chronic obstructive pulmonary disease (COPD). Alpha-1 antitrypsin deficiency shows that rare coding variants of large effect also influence COPD susceptibility. We hypothesized that exome sequencing in families identified through a proband with severe, early-onset COPD would identify additional rare genetic determinants of large effect.

OBJECTIVES: To identify rare genetic determinants of severe COPD.

METHODS: We applied filtering approaches to identify potential causal variants for COPD in whole exomes from 347 subjects in 49 extended pedigrees from the Boston Early-Onset COPD Study. We assessed the power of this approach under different levels of genetic heterogeneity using simulations. We tested genes identified in these families using gene-based association tests in exomes of 204 cases with severe COPD and 195 resistant smokers from the COPDGene study. In addition, we examined previously described loci associated with COPD using these datasets.

MEASUREMENTS AND MAIN RESULTS: We identified 69 genes with predicted deleterious nonsynonymous, stop, or splice variants that segregated with severe COPD in at least two pedigrees. Four genes (DNAH8, ALCAM, RARS, and GBF1) also demonstrated an increase in rare nonsynonymous, stop, and/or splice mutations in cases compared with resistant smokers from the COPDGene study; however, these results were not statistically significant. We demonstrate the limitations of the power of this approach under genetic heterogeneity through simulation.

CONCLUSIONS: Rare deleterious coding variants may increase risk for COPD, but multiple genes likely contribute to COPD susceptibility.

}, keywords = {Adult, alpha 1-Antitrypsin Deficiency, Boston, Exome, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Middle Aged, Pulmonary Disease, Chronic Obstructive, Severity of Illness Index}, issn = {1535-4970}, doi = {10.1164/rccm.201506-1223OC}, author = {Qiao, Dandi and Lange, Christoph and Beaty, Terri H and Crapo, James D and Barnes, Kathleen C and Bamshad, Michael and Hersh, Craig P and Morrow, Jarrett and Pinto-Plata, Victor M and Marchetti, Nathaniel and Bueno, Raphael and Celli, Bartolome R and Criner, Gerald J 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} } @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} }