Browsing by Author "Gaynor, Sheila M"

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    ItemOpen Access
    A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.
    (Nature methods, 2022-12) Li, Zilin; Li, Xihao; Zhou, Hufeng; Gaynor, Sheila M; Selvaraj, Margaret Sunitha; Arapoglou, Theodore; Quick, Corbin; Liu, Yaowu; Chen, Han; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Auer, Paul L; Bielak, Lawrence F; Bis, Joshua C; Blackwell, Thomas W; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Conomos, Matthew P; Correa, Adolfo; Cupples, L Adrienne; Curran, Joanne E; de Vries, Paul S; Duggirala, Ravindranath; Franceschini, Nora; Freedman, Barry I; Göring, Harald HH; Guo, Xiuqing; Kalyani, Rita R; Kooperberg, Charles; Kral, Brian G; Lange, Leslie A; Lin, Bridget M; Manichaikul, Ani; Manning, Alisa K; Martin, Lisa W; Mathias, Rasika A; Meigs, James B; Mitchell, Braxton D; Montasser, May E; Morrison, Alanna C; Naseri, Take; O'Connell, Jeffrey R; Palmer, Nicholette D; Peyser, Patricia A; Psaty, Bruce M; Raffield, Laura M; Redline, Susan; Reiner, Alexander P; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M; Rich, Stephen S; Smith, Jennifer A; Taylor, Kent D; Taub, Margaret A; Vasan, Ramachandran S; Weeks, Daniel E; Wilson, James G; Yanek, Lisa R; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium; TOPMed Lipids Working Group; Rotter, Jerome I; Willer, Cristen J; Natarajan, Pradeep; Peloso, Gina M; Lin, Xihong
    Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.
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    ItemOpen Access
    Phenotypic profile clustering pragmatically identifies diagnostically and mechanistically informative subgroups of chronic pain patients.
    (Pain, 2021-05) Gaynor, Sheila M; Bortsov, Andrey; Bair, Eric; Fillingim, Roger B; Greenspan, Joel D; Ohrbach, Richard; Diatchenko, Luda; Nackley, Andrea; Tchivileva, Inna E; Whitehead, William; Alonso, Aurelio A; Buchheit, Thomas E; Boortz-Marx, Richard L; Liedtke, Wolfgang; Park, Jongbae J; Maixner, William; Smith, Shad B

    Abstract

    Traditional classification and prognostic approaches for chronic pain conditions focus primarily on anatomically based clinical characteristics not based on underlying biopsychosocial factors contributing to perception of clinical pain and future pain trajectories. Using a supervised clustering approach in a cohort of temporomandibular disorder cases and controls from the Orofacial Pain: Prospective Evaluation and Risk Assessment study, we recently developed and validated a rapid algorithm (ROPA) to pragmatically classify chronic pain patients into 3 groups that differed in clinical pain report, biopsychosocial profiles, functional limitations, and comorbid conditions. The present aim was to examine the generalizability of this clustering procedure in 2 additional cohorts: a cohort of patients with chronic overlapping pain conditions (Complex Persistent Pain Conditions study) and a real-world clinical population of patients seeking treatment at duke innovative pain therapies. In each cohort, we applied a ROPA for cluster prediction, which requires only 4 input variables: pressure pain threshold and anxiety, depression, and somatization scales. In both complex persistent pain condition and duke innovative pain therapies, we distinguished 3 clusters, including one with more severe clinical characteristics and psychological distress. We observed strong concordance with observed cluster solutions, indicating the ROPA method allows for reliable subtyping of clinical populations with minimal patient burden. The ROPA clustering algorithm represents a rapid and valid stratification tool independent of anatomic diagnosis. ROPA holds promise in classifying patients based on pathophysiological mechanisms rather than structural or anatomical diagnoses. As such, this method of classifying patients will facilitate personalized pain medicine for patients with chronic pain.
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    ItemOpen Access
    Powerful, scalable and resource-efficient meta-analysis of rare variant associations in large whole genome sequencing studies.
    (Nature genetics, 2023-01) Li, Xihao; Quick, Corbin; Zhou, Hufeng; Gaynor, Sheila M; Liu, Yaowu; Chen, Han; Selvaraj, Margaret Sunitha; Sun, Ryan; Dey, Rounak; Arnett, Donna K; Bielak, Lawrence F; Bis, Joshua C; Blangero, John; Boerwinkle, Eric; Bowden, Donald W; Brody, Jennifer A; Cade, Brian E; Correa, Adolfo; Cupples, L Adrienne; Curran, Joanne E; de Vries, Paul S; Duggirala, Ravindranath; Freedman, Barry I; Göring, Harald HH; Guo, Xiuqing; Haessler, Jeffrey; Kalyani, Rita R; Kooperberg, Charles; Kral, Brian G; Lange, Leslie A; Manichaikul, Ani; Martin, Lisa W; McGarvey, Stephen T; Mitchell, Braxton D; Montasser, May E; Morrison, Alanna C; Naseri, Take; O'Connell, Jeffrey R; Palmer, Nicholette D; Peyser, Patricia A; Psaty, Bruce M; Raffield, Laura M; Redline, Susan; Reiner, Alexander P; Reupena, Muagututi'a Sefuiva; Rice, Kenneth M; Rich, Stephen S; Sitlani, Colleen M; Smith, Jennifer A; Taylor, Kent D; Vasan, Ramachandran S; Willer, Cristen J; Wilson, James G; Yanek, Lisa R; Zhao, Wei; NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group; Rotter, Jerome I; Natarajan, Pradeep; Peloso, Gina M; Li, Zilin; Lin, Xihong
    Meta-analysis of whole genome sequencing/whole exome sequencing (WGS/WES) studies provides an attractive solution to the problem of collecting large sample sizes for discovering rare variants associated with complex phenotypes. Existing rare variant meta-analysis approaches are not scalable to biobank-scale WGS data. Here we present MetaSTAAR, a powerful and resource-efficient rare variant meta-analysis framework for large-scale WGS/WES studies. MetaSTAAR accounts for relatedness and population structure, can analyze both quantitative and dichotomous traits and boosts the power of rare variant tests by incorporating multiple variant functional annotations. Through meta-analysis of four lipid traits in 30,138 ancestrally diverse samples from 14 studies of the Trans Omics for Precision Medicine (TOPMed) Program, we show that MetaSTAAR performs rare variant meta-analysis at scale and produces results comparable to using pooled data. Additionally, we identified several conditionally significant rare variant associations with lipid traits. We further demonstrate that MetaSTAAR is scalable to biobank-scale cohorts through meta-analysis of TOPMed WGS data and UK Biobank WES data of ~200,000 samples.