Channing Genetics and Genetic Epidemiology Research

Genetics is the study of heredity and inherited characteristics and diseases. Genetic epidemiology studies the role of genetic factors in disease risk within and across populations.

Genetic factors have some influence on most human traits and diseases. Finding and understanding these genetic factors may lead to the discovery of new disease pathways, better prediction of risk or progression of disease or response to therapy, and novel therapies.

Investigators at the Channing Division of Network Medicine (CDNM) have made major contributions to genetics and genetic epidemiology, including:

• Discovery of genetic risk factors for respiratory diseases and traits in large, multi-cohort genome-wide association studies (GWAS)
• Whole exome and whole genome sequencing analyses
• Development of statistical genetics methods
• Integrative genomics, including molecular quantitative trait loci (QTL) studies of multiple “omics” data types (see Metabolomics, Transcriptomics)
• Studies of the functional impact of observed genetic associations with diseases and traits (see Functional genomics)
• Genetic studies of drug response (see Pharmacogenomics)

Current Research

We work in multidisciplinary teams, including disease experts, genetic epidemiologists, and statistical geneticists, to generate, quality control, and test genotyping and sequencing data for association in deeply phenotyped cohorts.

We apply a variety of computational tools for additional analyses, including fine mapping, shared heritability, identifying functional variants, and integrative genomics.

Achievements

Research at Channing identified genetics as a risk factor for COPD, identified some of the first genetic loci for asthma and COPD, and have since participated in or led the largest efforts in both diseases; developed family-based association tests (FBAT, PBAT) and family-based rare variant association (GESE); and performed some of the first integrative genomics studies in respiratory disease.

Recent research highlights of CDNM work in this area:

• New Genetic Markers for COPD Discovered by BWH Researchers

Genome-wide association studies

CDNM investigators have leadership roles and collaborate in many large GWAS consortia, including identification of:

• COPD susceptibility loci using GWAS, as leaders in the International COPD Genetics Consortium (ICGC) (Genetic loci associated with chronic obstructive pulmonary disease overlap with loci for lung function and pulmonary fibrosis)
• Asthma susceptibility loci, as part of the Trans-National Asthma Genetic Consortium (TAGC) (Multiancestry association study identifies new asthma risk loci that colocalize with immune-cell enhancer marks)
• Associations with other respiratory phenotypes, such as CT imaging, bronchodilator response, and biomarkers
• Associations with cancer and other chronic diseases in studies such as the Nurses’ Health and Physicians’ Health Studies

Next-generation sequencing studies

We have been at the forefront of sequence data analysis, studying:

• Exome sequencing in COPD (case-control and family-based), as part of the NHLBI Exome Sequencing Project and Life After Linkage effort
• Whole genome sequencing in asthma and COPD, as part the Trans-Omics in& Precision Medicine (TOPMed) (Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma)

Bioinformatics and integrative genomics

Beyond association analyses, studies include:

• Disease subtyping and relationship to genetic variants
• Fine mapping and functional follow-up (see Functional genomics)
• Use of genotyping data to perform disease associations with imputed gene expression (Integrative genomics identifies new genes associated with severe COPD and emphysema)
• Identification of expression quantitative trait loci (QTL) in immune and respiratory cells (see Transcriptomics), methylation QTL, proteomics, and metabolomics (see Metabolomics)
• Studies of drug response in asthma and COPD (see Pharmacogenomics) (Whole-Genome Sequencing of Pharmacogenetic Drug Response in Racially Diverse Children with Asthma)

Statistical methods development

With collaborators at the Harvard T.H. Chan School of Public Health, we work on development of new methods, such as:

• Rare variant population stratification
• Family-based analyses, including studies of rare variants