# Statistical Methods to Integrate Rich Functional and Phenotypic Data in Whole Genome Sequencing Analyses > **NIH NIH K99** · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · 2021 · $162,864 ## Abstract This proposal is a five-year program to support Dr. Sheila Gaynor's career development in her transition from a postdoctoral fellow to an independent investigator in statistical genetics and genomics, with expertise in whole genome sequencing (WGS) studies of pulmonary traits and lung diseases. Dr. Gaynor has training in compu- tational biology and biostatistics, and is currently a postdoctoral fellow in the Department of Biostatistics at the Harvard T. H. Chan School of Public Health. The proposed aims develop and strengthen Dr. Gaynor's expertise in statistical genetics, pulmonary disease, lung biology, and computing. In this program, she will develop scal- able statistical and computational methods to (1) investigate the role of rare variants in influencing lung function and conferring lung disease risk with an emphasis on leveraging incomplete phenotypes and (2) fine-map lung function- and disease-associated variants to specify likely biologically causal variants. Lung diseases such as chronic pulmonary obstructive disorder (COPD) are leading causes of morbidity and comorbidity. They have a notable genetic component, but a limited number of associated variants have been iden- tified and their potential causal or functional roles are not well understood. Current large-scale whole genome sequencing efforts, including the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program, allow for the in- vestigation of complex disease genetics in unprecedented ways across heart, lung, blood, and sleep phenotypes. Aim 1 of this proposal is to develop a statistical and computational framework for testing rare variant associations with one or more phenotypes, where phenotypic data is limited but functional data is available. Aim 2 of this proposal is to develop computationally efficient statistical fine-mapping methods to identify likely causal variants using functional data, such as tissue-specific and cell-type specific features. Dr. Gaynor will apply these methods to data from the TOPMed Program to study pulmonary function and COPD. Aim 3 is to develop software for the efficient and open-souce implementation of the statistical methods in Aims 1 and 2. In the K99 phase, Dr. Gaynor will be mentored in statistical genetics by Dr. Xihong Lin, Professor of Bio- statistics and of Statistics, with a focus on statistical methods for powerful inference, mixed models, and missing data. She will be co-mentored by Dr. Edwin Silverman, Division Chief at Brigham and Women's Hospital, in pul- monology, genetic epidemiology, and collaborative WGS efforts. The training in this phase will include structured mentorship, collaborative research in the TOPMed Program, coursework in pulmonology and computer science, scientific seminars and conferences, and training in grant writing, communication, and leadership skills to support career and professional development. With the skills acquired in the K99 phase, Dr. Gaynor will transition to a new institution during the R00 phase to com... ## Key facts - **NIH application ID:** 10125292 - **Project number:** 1K99HL151877-01A1 - **Recipient organization:** HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH - **Principal Investigator:** Sheila Gaynor - **Activity code:** K99 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $162,864 - **Award type:** 1 - **Project period:** 2021-08-01 → 2021-10-15 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10125292 ## Citation > US National Institutes of Health, RePORTER application 10125292, Statistical Methods to Integrate Rich Functional and Phenotypic Data in Whole Genome Sequencing Analyses (1K99HL151877-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10125292. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*