. 2022 Nov 10;31(22):3873-3885.

doi: 10.1093/hmg/ddac117.

Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data

Wonji Kim¹, Julian Hecker¹, R Graham Barr², Eric Boerwinkle^{3

4}, Brian Cade⁵, Adolfo Correa⁶, Josée Dupuis⁷, Sina A Gharib^{8

9}, Leslie Lange¹⁰, Stephanie J London¹¹, Alanna C Morrison¹², George T O'Connor¹³, Elizabeth C Oelsner², Bruce M Psaty^{8

14}, Ramachandran S Vasan^{15

16}, Susan Redline¹⁷, Stephen S Rich¹⁸, Jerome I Rotter¹⁹, Bing Yu³, Christoph Lange^{1

20}, Ani Manichaikul²¹, Jin J Zhou²², Tamar Sofer²³, Edwin K Silverman¹, Dandi Qiao¹, Michael H Cho¹; NHLBI Trans-Omics in Precision Medicine (TOPMed) Consortium and TOPMed Lung Working Group

Affiliations

¹ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
² Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA.
³ Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
⁴ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.
⁵ Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA.
⁶ Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.
⁷ Department of Biostatistics, Boston University of Public Health, Boston, MA 02118, USA.
⁸ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA.
⁹ Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA 98109, USA.
¹⁰ Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA.
¹¹ Epidemiology Branch, National Institute of Environmental Health Sciences, Department of Health and Human Services, National Institutes of Health, Research Triangle Park, NC 27709, USA.
¹² Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
¹³ Pulmonary Center, Boston University School of Medicine, Boston, MA 02118, USA.
¹⁴ Departments of Epidemiology and Health Services, University of Washington, Seattle, WA 98101, USA.
¹⁵ Lung and Blood Institute Framingham Heart Study, Boston University and National Heart, Framingham, MA 01702, USA.
¹⁶ Department of Preventive Medicine and Epidemiology, School of Medicine and Public Health, Boston University, Boston, MA 02118, USA.
¹⁷ Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
¹⁸ Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
¹⁹ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA.
²⁰ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
²¹ Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.
²² Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85721, USA.
²³ Division of Sleep and Circadian Disorder, Brigham and Women's Hospital, Boston, MA 02115, USA.

PMID: 35766891
PMCID: PMC9652112
DOI: 10.1093/hmg/ddac117

Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data

Wonji Kim et al. Hum Mol Genet. 2022.

. 2022 Nov 10;31(22):3873-3885.

doi: 10.1093/hmg/ddac117.

Authors

Affiliations

¹ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
² Departments of Medicine and Epidemiology, Columbia University Medical Center, New York, NY 10032, USA.
³ Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
⁴ Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.
⁵ Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA 02115, USA.
⁶ Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA.
⁷ Department of Biostatistics, Boston University of Public Health, Boston, MA 02118, USA.
⁸ Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA 98101, USA.
⁹ Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA 98109, USA.
¹⁰ Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA.
¹¹ Epidemiology Branch, National Institute of Environmental Health Sciences, Department of Health and Human Services, National Institutes of Health, Research Triangle Park, NC 27709, USA.
¹² Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
¹³ Pulmonary Center, Boston University School of Medicine, Boston, MA 02118, USA.
¹⁴ Departments of Epidemiology and Health Services, University of Washington, Seattle, WA 98101, USA.
¹⁵ Lung and Blood Institute Framingham Heart Study, Boston University and National Heart, Framingham, MA 01702, USA.
¹⁶ Department of Preventive Medicine and Epidemiology, School of Medicine and Public Health, Boston University, Boston, MA 02118, USA.
¹⁷ Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.
¹⁸ Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
¹⁹ The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA.
²⁰ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
²¹ Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA.
²² Department of Epidemiology and Biostatistics, University of Arizona, Tucson, AZ 85721, USA.
²³ Division of Sleep and Circadian Disorder, Brigham and Women's Hospital, Boston, MA 02115, USA.

PMID: 35766891
PMCID: PMC9652112
DOI: 10.1093/hmg/ddac117

Abstract

Rationale: Genetic variation has a substantial contribution to chronic obstructive pulmonary disease (COPD) and lung function measurements. Heritability estimates using genome-wide genotyping data can be biased if analyses do not appropriately account for the nonuniform distribution of genetic effects across the allele frequency and linkage disequilibrium (LD) spectrum. In addition, the contribution of rare variants has been unclear.

Objectives: We sought to assess the heritability of COPD and lung function using whole-genome sequence data from the Trans-Omics for Precision Medicine program.

Methods: Using the genome-based restricted maximum likelihood method, we partitioned the genome into bins based on minor allele frequency and LD scores and estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio in 11 051 European ancestry and 5853 African-American participants.

Measurements and main results: In European ancestry participants, the estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio were 35.5%, 55.6% and 32.5%, of which 18.8%, 19.7%, 17.8% were from common variants, and 16.6%, 35.8%, and 14.6% were from rare variants. These estimates had wide confidence intervals, with common variants and some sets of rare variants showing a statistically significant contribution (P-value < 0.05). In African-Americans, common variant heritability was similar to European ancestry participants, but lower sample size precluded calculation of rare variant heritability.

Conclusions: Our study provides updated and unbiased estimates of heritability for COPD and lung function, and suggests an important contribution of rare variants. Larger studies of more diverse ancestry will improve accuracy of these estimates.

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Figures

**Figure 1**
Heritability estimates stratified in 16 bins (eight MAF bins and two LD bins) in European ancestry. The bars display SEs.

**Figure 2**
Quality control for the SNVs and participants. Multiple standard quality controls were performed for the raw data to exclude outlier SNVs and participants.

**Figure 3**
Workflow of statistical analysis for the heritability estimation of COPD-related phenotypes. Overview of the statistical analysis to estimate heritability of the phenotypes is illustrated.

**Figure 4**
MAF distribution of variants in WGS data. Values indicate MAF and the proportion of variants in the MAF groups (%).

See this image and copyright information in PMC

References

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Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data

Affiliations

Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data

Authors

Affiliations

Abstract

Figures

References

Publication types

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources

Medical

Research Materials