@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 {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} }