Browsing by Subject "Phenotype"

Now showing 1 - 10 of 54
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    3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies
    (Public Library of Science, 2021-05-13) Hoskens, Hanne; Liu, Dongjing; Naqvi, Sahin; Lee, Myoung Keun; Eller, Ryan J.; Indencleef, Karlijne; White, Julie D.; Li, Jiarui; Larmuseau, Maarten H. D.; Hens, Greet; Wysocka, Joanna; Walsh, Susan; Richmond, Stephen; Shriver, Mark D.; Shaffer, John R.; Peeters, Hilde; Weinberg, Seth M.; Claes, Peter; Biology, School of Science
    The analysis of contemporary genomic data typically operates on one-dimensional phenotypic measurements (e.g. standing height). Here we report on a data-driven, family-informed strategy to facial phenotyping that searches for biologically relevant traits and reduces multivariate 3D facial shape variability into amendable univariate measurements, while preserving its structurally complex nature. We performed a biometric identification of siblings in a sample of 424 children, defining 1,048 sib-shared facial traits. Subsequent quantification and analyses in an independent European cohort (n = 8,246) demonstrated significant heritability for a subset of traits (0.17-0.53) and highlighted 218 genome-wide significant loci (38 also study-wide) associated with facial variation shared by siblings. These loci showed preferential enrichment for active chromatin marks in cranial neural crest cells and embryonic craniofacial tissues and several regions harbor putative craniofacial genes, thereby enhancing our knowledge on the genetic architecture of normal-range facial variation.
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    A Cephalometric Study of Non-Cleft Parents of Children with Cleft Lip, Cleft Lip and Palate, and Cleft Palate : Evidence to Support a Different Etiology for Isolated Cleft Palate Versus Cleft Lip?
    (1993) Sadler, Charles A., Jr.; Ward, Richard E.; Bixler, David; Hathaway, Ronald R.; Shanks, James C.; Roberts, W. Eugene
    Cleft lip with or without cleft palate (CLI P) and isolated cleft palate (CP) have been shown to be separate epidemiologic and embryologic entities. Furthermore, it has been proposed that noncleft biologic parents of children with facial clefts may demonstrate craniofacial differences genetically predisposing them to pass on the cleft phenotype to their offspring. With these two hypotheses in mind, the objective of the present study was to determine if differences exist in the craniofacial morphology between parents of CL/P children and parents of CP children. Twenty-seven measurements were obtained from anterior-posterior (AP) cephalograms on 127 biologic parents of sporadic CL/P and CP children. Each measurement was compared with sex and age-matched normals, and Z-scores were determined. The mean Z-scores for each group were tested for significant differences from normal and from each other. In agreement with past literature, the craniofacial morphology of parents of cleft children was generally found to have greater facial widths and shorter facial heights. Differences between the findings of the present study and the past literature are discussed. Although CL/P and CP are thought to be separate entities, differences between the parents of CL/P and CP children were not statistically evident.
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    A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies
    (Springer Nature, 2022) 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 H. H.; 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; Biostatistics and Health Data Science, School of Medicine
    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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    Associations Between Dysmenorrhea Symptom-Based Phenotypes and Vaginal Microbiome: A Pilot Study
    (Wolters Kluwer, 2021) Chen, Chen X.; Carpenter, Janet S.; Gao, Xiang; Toh, Evelyn; Dong, Qunfeng; Nelson, David E.; Mitchell, Caroline; Fortenberry, J. Dennis
    BACKGROUND: Dysmenorrhea is highly prevalent; it places women at risk for other chronic pain conditions. There is a high degree of individual variability in menstrual pain severity, the number of painful sites, and co-occurring gastrointestinal symptoms. Distinct dysmenorrhea symptom-based phenotypes were previously identified, but the biological underpinnings of these phenotypes are less known. One underexplored contributor is the vaginal microbiome. The vaginal microbiota differs significantly among reproductive-age women and may modulate as well as amplify reproductive tract inflammation, which may contribute to dysmenorrhea symptoms. OBJECTIVES: The objective of this study was to examine associations between dysmenorrhea symptom-based phenotypes and vaginal microbiome compositions on- and off-menses. METHODS: We conducted a prospective, longitudinal, pilot study of 20 women (aged 15-24 years) grouped into three dysmenorrhea symptom-based phenotypes: "mild localized pain," "severe localized pain," and "severe multiple pain and gastrointestinal symptoms." Over one menstrual cycle, participants provided vaginal swabs when they were on- and off-menses. We assayed the vaginal microbiome using 16S rRNA gene sequencing. Permutational multivariate analysis of variance tests were used to compare microbiome compositions across phenotypes, with heat maps generated to visualize the relative abundance of bacterial taxa. RESULTS: The vaginal microbiome compositions (n = 40) were different across the three phenotypes. After separating the on-menses (n = 20) and off-menses (n = 20) specimens, the statistically significant difference was seen on-menses, but not off-menses. Compared to the "mild localized pain" phenotype, participants in the "multiple severe symptoms" phenotype had a lower lactobacilli level and a higher abundance of Prevotella, Atopobium, and Gardnerella when on-menses. We also observed trends of differences across phenotypes in vaginal microbiome change from off- to on-menses. DISCUSSION: The study provides proof-of-concept data to support larger studies on associations between dysmenorrhea symptom-based phenotypes and vaginal microbiome that might lead to new intervention targets and/or biomarkers for dysmenorrhea. This line of research has the potential to inform precision dysmenorrhea treatment that can improve women's quality of life.
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    Characterizing Extreme Phenotypes for Pain Interference in Persons with Chronic Pain following Traumatic Brain Injury: A NIDILRR and VA TBI Model Systems Collaborative Project
    (Wolters Kluwer, 2024) Hoffman, Jeanne M.; Ketchum, Jessica M.; Agtarap, Stephanie; Dams-O’Connor, Kristen; Hammond, Flora M.; Martin, Aaron M.; Sevigny, Mitch; Walker, William C.; Harrison-Felix, Cynthia; Zafonte, Ross; Nakase-Richardson, Risa; Physical Medicine and Rehabilitation, School of Medicine
    Objective: To define and characterize extreme phenotypes based on pain interference for persons with chronic pain following traumatic brain injury (TBI). Setting: Eighteen Traumatic Brain Injury Model System (TBIMS) Centers. Participants: A total of 1762 TBIMS participants 1 to 30 years post-injury reporting chronic pain at their most recent follow-up interview. Primary measures: The Brief Pain Inventory (BPI) interference scale, sociodemographic, injury, functional outcome, pain, and treatment characteristics. Results: Participants were predominantly male (73%), White (75%), middle-aged (mean 46 years), and who were injured in motor vehicle accidents (53%) or falls (20%). Extreme phenotypes were identified based on upper and lower 25th percentiles to create low-interference ( n = 441) and high-interference ( n = 431) extreme phenotypes. Bivariate comparisons found several sociodemographic, injury, function, pain, and treatment differences between extreme phenotype groups, including significant differences ( P < .001) on all measures of concurrent function with those in the low-interference extreme phenotype experiencing better function than those in the high-interference extreme phenotype. Lasso regression combined with logistic regression identified multivariable predictors of low- versus high-interference extreme phenotypes. Reductions in the odds of low- versus high-interference phenotypes were significantly associated with higher pain intensity (odds ratio [OR] = 0.33), having neuropathic pain (OR = 0.40), migraine headache (OR = 0.41), leg/feet pain (OR = 0.34), or hip pain (OR = 0.46), and more pain catastrophizing (OR = 0.81). Conclusion: Results suggest that for those who experience current chronic pain, there is high variability in the experience and impact of pain. Future research is needed to better understand how pain experience impacts individuals with chronic pain and TBI given that pain characteristics were the primary distinguishing factors between phenotypes. The use of extreme phenotypes for pain interference may be useful to better stratify samples to determine efficacy of pain treatment for individuals with TBI.
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    Characterizing Extreme Phenotypes for Perceived Improvement from Treatment in Persons with Chronic Pain following Traumatic Brain Injury: A NIDILRR and VA TBI Model Systems Collaborative Project
    (Wolters Kluwer, 2024) Hoffman, Jeanne M.; Ketchum, Jessica M.; Agtarap, Stephanie; Dams-O’Connor, Kristen; Hammond, Flora M.; Martin, Aaron M.; Sevigny, Mitch; Walker, William C.; Harrison-Felix, Cynthia; Zafonte, Ross; Nakase-Richardson, Risa; Physical Medicine and Rehabilitation, School of Medicine
    Objective: To define and characterize extreme phenotypes based on perceived improvement in pain for persons with chronic pain following traumatic brain injury (TBI). Setting: Eighteen Traumatic Brain Injury Model System (TBIMS) Centers. Participants: A total of 1762 TBIMS participants 1 to 30 years post-injury reporting chronic pain at their most recent follow-up interview. Primary measures: The Patient's Global Impression of Change (PGIC) related to pain treatment. Sociodemographic, injury, functional outcome, pain, and pain treatment characteristics. Results: Participants were mostly male (73%), White (75%), middle-aged (mean 46 years), injured in motor vehicle accidents (53%), or falls (20%). Extreme phenotypes were created for an extreme improvement phenotype ( n = 512, 29.8%) defined as "moderately better" or above on the PGIC and an extreme no-change group ( n = 290, 16.9%) defined as no change or worse. Least absolute shrinkage and selection operator (LASSO) regression combined with logistic regression identified multivariable predictors of improvement versus no-change extreme phenotypes. Higher odds of extreme improvement phenotype were significantly associated with being female (odds ratio [OR] = 1.85), married versus single (OR = 2.02), better motor function (OR = 1.03), lower pain intensity (OR = 0.78), and less frequent pain, especially chest pain (OR = 0.36). Several pain treatments were associated with higher odds of being in the extreme improvement versus no-change phenotypes including pain medication (OR = 1.85), physical therapy (OR = 1.51), yoga (OR = 1.61), home exercise program (OR = 1.07), and massage (OR = 1.69). Conclusion: Investigation of extreme phenotypes based on perceived improvement with pain treatment highlights the ability to identify characteristics of individuals based on pain treatment responsiveness. A better understanding of the biopsychosocial characteristics of those who respond and do not respond to pain treatments received may help inform better surveillance, monitoring, and treatment. With further research, the identification of risk factors (such as pain intensity and frequency) for treatment response/nonresponse may provide indicators to prompt changes in care for individuals with chronic pain after TBI.
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    Clinical and molecular implications of RGS2 promoter genetic variation in severe asthma
    (Elsevier, 2022) Cardet, Juan Carlos; Kim, Donghwa; Bleecker, Eugene R.; Casale, Thomas B.; Israel, Elliot; Mauger, David; Meyers, Deborah A.; Ampleford, Elizabeth; Hawkins, Gregory A.; Tu, Yaping; Liggett, Stephen B.; Ortega, Victor E.; SARP-3 investigators; Pediatrics, School of Medicine
    Background: Regulator of G protein signaling (RGS) 2 terminates bronchoconstrictive Gαq signaling; murine RGS2 knockout demonstrate airway hyperresponsiveness. While RGS2 promoter variants rs2746071 and rs2746072 associate with a clinical mild asthma phenotype, their impact on human airway smooth muscle (HASM) contractility and asthma severity outcomes is unknown. Objective: We sought to determine whether reductions in RGS2 expression seen with these 2 RGS2 promoter variants augment HASM contractility and associate with an asthma severity phenotype. Methods: We transfected HASM with a range of RGS2-specific small interfering RNA (siRNA) concentrations and determined RGS2 protein expression by Western blot analysis and intracellular calcium flux induced by histamine (a Gαq-coupled H1 receptor bronchoconstrictive agonist). We conducted regression-based genotype association analyses of RGS2 variants from 611 patients from the National Heart, Lung, and Blood Institute Severe Asthma Research Program 3. Results: RGS2-specific siRNA caused dose-dependent increases in histamine-stimulated bronchoconstrictive intracellular calcium signaling (2-way ANOVA, P < .0001) with a concomitant decrease in RGS2 protein expression. RGS2-specific siRNA did not affect Gαq-independent ionomycin-induced intracellular calcium signaling (P = .42). The minor allele frequency of rs2746071 and rs2746072 was 0.46 and 0.28 among African American/non-Hispanic Black patients and was 0.28 and 0.27 among non-Hispanic White patients, among whom these single nucleotide polymorphisms were in stronger linkage disequilibrium (r2 = 0.97). Among non-Hispanic White patients, risk allele homozygotes for rs2746072 and rs2746071 each had nearly 2-fold greater asthma exacerbation rates relative to alternative genotypes with wild-type alleles (Padditive = 2.86 × 10-5/Precessive = 5.22 × 10-6 and Padditive = 3.46 × 10-6/Precessive = 6.74 × 10-7, respectively) at baseline, which was confirmed by prospective longitudinal exacerbation data. Conclusion: RGS2 promoter variation associates with a molecular and clinical phenotype characterized by enhanced bronchoconstrictive stimulation in vitro and higher asthma exacerbations rates in non-Hispanic White patients.
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    Craniofacial Morphology in familial cases of cleft lip/palate: phenotypic heterogeneity and genetic predisposition in unaffected family members
    (1993) Litz, Stephanie M.; Bixler, David; Fleener, Donald E.; Hennon, David Kent, 1933-; Sadove, A. Michael; Ward, Richard E.; Avery, David R.
    This study investigated familial cases of cleft lip with or without cleft palate to determine whether the unaffected members of each family can be identified as gene carriers for the cleft trait. This research presumes that such carriers will have henotypic features identifiable by cephalometric analysis that are associated with an increased risk to cleft offspring. Using population genetics methodology, a pedigree analysis was made for each family member was assigned to one of four groups: (1) obligate normal, (2) affected, (3) carrier, and (4) unknown. LA and PA cephalographs were taken on each subject and a clinical oral-facial examination carried out on participating family members. Various anatomic landmarks located on the LA and PA films were digitized and from them, a total of 28 linear measurements were made. To eliminate the effect of sex and differential age responses, Z scores were calculated. Through univariate analysis, only one variable, NCR-MO, was shown to be significantly different between the two groups. This variable difference by itself is not adequate to differentiate those in the normal group from the carrier group. Even though only one variable was significant, other differences in the variables between these groups become obvious when the group variables were plotted as Z scores. Since Z scores are pure values with no limits (2--the number of standard deviations in a given variable differs from normal). Thereby, age-related growth differences were minimized. Further information is gained when these Z scores are plotted as pattern profiles, Figures 5-7. These profiles of mean Z scores for each variable pointed out areas of the face in which the differences were so great that specific anatomic areas appeared to be associated with one of the four groups. For example, gene carriers demonstrated specific alterations in facial height that might conceivably be used to discriminate that group from the other three groups. The family normals and carriers were then analyzed by using a stepwise multivariate analysis. By this approach, a discriminant function was generated consisting of six variables (three each from the lateral and frontal headplates), which proved to be significant in distinguishing an individual's phenotype. These variables define facial height, width and depth. The specific findings included a decrease in mid-facial height and depth along with an increased lower facial height and width in the gene carrier population as compared to the normals. The function then was used to predict group membership of the same two groups. Comparing this analytical prediction to that of the grouping system that resulted from the pedigree analysis, all but one individual was classified correctly in both the normal and carrier population. A discriminant score was also determined for the unknown population of family members which were defined as non-cleft blood relatives of cleft probands. Thus, they were a mixture of two types--those unaffected who carried a genetic liability for producing a cleft child and those unaffected who did not. A prediction of their placement into either the normal or carrier group was made with the discriminate function. One-third were classed in the normal group and two-thirds as gene carriers. The results of this study confirm that the phenotype of these unaffected family members designated as obligate gene carriers differs significantly from that of the family normals. This information is not only quite useful for genetic counselling but gives both a better understanding or the genetic control of clefting and can lead to molecular research to identify the specific gene in question.
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    Defining Suicidal Thought and Behavior Phenotypes for Genetic Studies
    (medRxiv, 2024-07-29) Monson, Eric T.; Colbert, Sarah M. C.; Andreassen, Ole A.; Ayinde, Olatunde O.; Bejan, Cosmin A.; Ceja, Zuriel; Coon, Hilary; DiBlasi, Emily; Izotova, Anastasia; Kaufman, Erin A.; Koromina, Maria; Myung, Woojae; Nurnberger, John I., Jr.; Serretti, Alessandro; Smoller, Jordan W.; Stein, Murray B.; Zai, Clement C.; Suicide Working Group of the Psychiatric Genomics Consortium; Aslan, Mihaela; Barr, Peter B.; Bigdeli, Tim B.; Harvey, Philip D.; Kimbrel, Nathan A.; Patel, Pujan R.; Cooperative Studies Program (CSP) #572; Ruderfer, Douglas; Docherty, Anna R.; Mullins, Niamh; Mann, J. John; Psychiatry, School of Medicine
    Background: Standardized definitions of suicidality phenotypes, including suicidal ideation (SI), attempt (SA), and death (SD) are a critical step towards improving understanding and comparison of results in suicide research. The complexity of suicidality contributes to heterogeneity in phenotype definitions, impeding evaluation of clinical and genetic risk factors across studies and efforts to combine samples within consortia. Here, we present expert and data-supported recommendations for defining suicidality and control phenotypes to facilitate merging current/legacy samples with definition variability and aid future sample creation. Methods: A subgroup of clinician researchers and experts from the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) reviewed existing PGC definitions for SI, SA, SD, and control groups and generated preliminary consensus guidelines for instrument-derived and international classification of disease (ICD) data. ICD lists were validated in two independent datasets (N = 9,151 and 12,394). Results: Recommendations are provided for evaluated instruments for SA and SI, emphasizing selection of lifetime measures phenotype-specific wording. Recommendations are also provided for defining SI and SD from ICD data. As the SA ICD definition is complex, SA code list recommendations were validated against instrument results with sensitivity (range = 15.4% to 80.6%), specificity (range = 67.6% to 97.4%), and positive predictive values (range = 0.59-0.93) reported. Conclusions: Best-practice guidelines are presented for the use of existing information to define SI/SA/SD in consortia research. These proposed definitions are expected to facilitate more homogeneous data aggregation for genetic and multisite studies. Future research should involve refinement, improved generalizability, and validation in diverse populations.
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    Dendritic Cell Therapy in Transplantation, Phenotype Governs Destination and Function
    (Lippincott, Williams & Wilkins, 2018-10) Samy, Kannan P.; Brennan, Todd V.; Surgery, School of Medicine