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6 results on '"Stanaway I.B."'

3. Response to Li and Hopper.

Author

Thomas M., Sakoda L.C., Hoffmeister M., Rosenthal E.A., Lee J.K., van Duijnhoven F.J.B., Platz E.A., Wu A.H., Dampier C.H., de la Chapelle A., Wolk A., Joshi A.D., Burnett-Hartman A., Gsur A., Lindblom A., Castells A., Win A.K., Namjou B., Van Guelpen B., Tangen C.M., He Q., Li C.I., Schafmayer C., Joshu C.E., Ulrich C.M., Bishop D.T., Buchanan D.D., Schaid D., Drew D.A., Muller D.C., Duggan D., Crosslin D.R., Albanes D., Giovannucci E.L., Larson E., Qu F., Mentch F., Giles G.G., Hakonarson H., Hampel H., Stanaway I.B., Figueiredo J.C., Huyghe J.R., Minnier J., Chang-Claude J., Hampe J., Harley J.B., Visvanathan K., Curtis K.R., Offit K., Li L., Le Marchand L., Vodickova L., Gunter M.J., Jenkins M.A., Slattery M.L., Lemire M., Woods M.O., Song M., Murphy N., Lindor N.M., Dikilitas O., Pharoah P.D.P., Campbell P.T., Newcomb P.A., Milne R.L., MacInnis R.J., Castellvi-Bel S., Ogino S., Berndt S.I., Bezieau S., Thibodeau S.N., Gallinger S.J., Zaidi S.H., Harrison T.A., Keku T.O., Hudson T.J., Vymetalkova V., Moreno V., Martin V., Arndt V., Wei W.-Q., Chung W., Su Y.-R., Hayes R.B., White E., Vodicka P., Casey G., Gruber S.B., Schoen R.E., Chan A.T., Potter J.D., Brenner H., Jarvik G.P., Corley D.A., Peters U., Hsu L., Thomas M., Sakoda L.C., Hoffmeister M., Rosenthal E.A., Lee J.K., van Duijnhoven F.J.B., Platz E.A., Wu A.H., Dampier C.H., de la Chapelle A., Wolk A., Joshi A.D., Burnett-Hartman A., Gsur A., Lindblom A., Castells A., Win A.K., Namjou B., Van Guelpen B., Tangen C.M., He Q., Li C.I., Schafmayer C., Joshu C.E., Ulrich C.M., Bishop D.T., Buchanan D.D., Schaid D., Drew D.A., Muller D.C., Duggan D., Crosslin D.R., Albanes D., Giovannucci E.L., Larson E., Qu F., Mentch F., Giles G.G., Hakonarson H., Hampel H., Stanaway I.B., Figueiredo J.C., Huyghe J.R., Minnier J., Chang-Claude J., Hampe J., Harley J.B., Visvanathan K., Curtis K.R., Offit K., Li L., Le Marchand L., Vodickova L., Gunter M.J., Jenkins M.A., Slattery M.L., Lemire M., Woods M.O., Song M., Murphy N., Lindor N.M., Dikilitas O., Pharoah P.D.P., Campbell P.T., Newcomb P.A., Milne R.L., MacInnis R.J., Castellvi-Bel S., Ogino S., Berndt S.I., Bezieau S., Thibodeau S.N., Gallinger S.J., Zaidi S.H., Harrison T.A., Keku T.O., Hudson T.J., Vymetalkova V., Moreno V., Martin V., Arndt V., Wei W.-Q., Chung W., Su Y.-R., Hayes R.B., White E., Vodicka P., Casey G., Gruber S.B., Schoen R.E., Chan A.T., Potter J.D., Brenner H., Jarvik G.P., Corley D.A., Peters U., and Hsu L.

Published
2021

4. Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk.

Author

Huyghe J.R., Thomas M., Sakoda L.C., Hoffmeister M., Rosenthal E.A., Lee J.K., van Duijnhoven F.J.B., Platz E.A., Wu A.H., Dampier C.H., de la Chapelle A., Wolk A., Joshi A.D., Burnett-Hartman A., Gsur A., Lindblom A., Castells A., Win A.K., Namjou B., Van Guelpen B., Tangen C.M., He Q., Li C.I., Schafmayer C., Joshu C.E., Ulrich C.M., Bishop D.T., Buchanan D.D., Schaid D., Drew D.A., Muller D.C., Duggan D., Crosslin D.R., Albanes D., Giovannucci E.L., Larson E., Qu F., Mentch F., Giles G.G., Hakonarson H., Hampel H., Stanaway I.B., Figueiredo J.C., Minnier J., Chang-Claude J., Hampe J., Harley J.B., Visvanathan K., Curtis K.R., Offit K., Li L., Le Marchand L., Vodickova L., Gunter M.J., Jenkins M.A., Slattery M.L., Lemire M., Woods M.O., Song M., Murphy N., Lindor N.M., Dikilitas O., Pharoah P.D.P., Campbell P.T., Newcomb P.A., Milne R.L., MacInnis R.J., Castellvi-Bel S., Ogino S., Berndt S.I., Bezieau S., Thibodeau S.N., Gallinger S.J., Zaidi S.H., Harrison T.A., Keku T.O., Hudson T.J., Vymetalkova V., Moreno V., Martin V., Arndt V., Wei W.-Q., Chung W., Su Y.-R., Hayes R.B., White E., Vodicka P., Casey G., Gruber S.B., Schoen R.E., Chan A.T., Potter J.D., Brenner H., Jarvik G.P., Corley D.A., Peters U., Hsu L., Huyghe J.R., Thomas M., Sakoda L.C., Hoffmeister M., Rosenthal E.A., Lee J.K., van Duijnhoven F.J.B., Platz E.A., Wu A.H., Dampier C.H., de la Chapelle A., Wolk A., Joshi A.D., Burnett-Hartman A., Gsur A., Lindblom A., Castells A., Win A.K., Namjou B., Van Guelpen B., Tangen C.M., He Q., Li C.I., Schafmayer C., Joshu C.E., Ulrich C.M., Bishop D.T., Buchanan D.D., Schaid D., Drew D.A., Muller D.C., Duggan D., Crosslin D.R., Albanes D., Giovannucci E.L., Larson E., Qu F., Mentch F., Giles G.G., Hakonarson H., Hampel H., Stanaway I.B., Figueiredo J.C., Minnier J., Chang-Claude J., Hampe J., Harley J.B., Visvanathan K., Curtis K.R., Offit K., Li L., Le Marchand L., Vodickova L., Gunter M.J., Jenkins M.A., Slattery M.L., Lemire M., Woods M.O., Song M., Murphy N., Lindor N.M., Dikilitas O., Pharoah P.D.P., Campbell P.T., Newcomb P.A., Milne R.L., MacInnis R.J., Castellvi-Bel S., Ogino S., Berndt S.I., Bezieau S., Thibodeau S.N., Gallinger S.J., Zaidi S.H., Harrison T.A., Keku T.O., Hudson T.J., Vymetalkova V., Moreno V., Martin V., Arndt V., Wei W.-Q., Chung W., Su Y.-R., Hayes R.B., White E., Vodicka P., Casey G., Gruber S.B., Schoen R.E., Chan A.T., Potter J.D., Brenner H., Jarvik G.P., Corley D.A., Peters U., and Hsu L.

Abstract

Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might be

Published
2020

5. A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies

Author

Joo, Y.Y. (Yoonjung Yoonie), Actkins, K. (Ky'Era), Pacheco, J.A. (Jennifer A.), Basile, A.O. (Anna O.), Carroll, R. (Robert), Crosslin, D.R. (David), Day, F.R. (Felix), Denny, J.C. (Joshua C.), Edwards, D.R.V. (Digna R. Velez), Hakonarson, H. (Hakon), Harley, J.B. (John B.), Hebbring, S.J. (Scott J.), Ho, K. (Kevin), Jarvik, G.P. (Gail), Jones, M.R. (Michelle), Karaderi, T. (Tugce), Mentch, F.D. (Frank D.), Meun, C. (Cindy), Namjou, B. (Bahram), Pendergrass, S.A. (Sarah), Ritchie, M.D. (Marylyn D.), Stanaway, I.B. (Ian B.), Urbanek, M. (Margrit), Walunas, T.L. (Theresa L.), Smith, M. (Maureen), Chisholm, R.L. (Rex L.), Kho, M.M.L. (Marcia), Davis, L. (Lea), Geoffrey Hayes, M. (M.), Joo, Y.Y. (Yoonjung Yoonie), Actkins, K. (Ky'Era), Pacheco, J.A. (Jennifer A.), Basile, A.O. (Anna O.), Carroll, R. (Robert), Crosslin, D.R. (David), Day, F.R. (Felix), Denny, J.C. (Joshua C.), Edwards, D.R.V. (Digna R. Velez), Hakonarson, H. (Hakon), Harley, J.B. (John B.), Hebbring, S.J. (Scott J.), Ho, K. (Kevin), Jarvik, G.P. (Gail), Jones, M.R. (Michelle), Karaderi, T. (Tugce), Mentch, F.D. (Frank D.), Meun, C. (Cindy), Namjou, B. (Bahram), Pendergrass, S.A. (Sarah), Ritchie, M.D. (Marylyn D.), Stanaway, I.B. (Ian B.), Urbanek, M. (Margrit), Walunas, T.L. (Theresa L.), Smith, M. (Maureen), Chisholm, R.L. (Rex L.), Kho, M.M.L. (Marcia), Davis, L. (Lea), and Geoffrey Hayes, M. (M.)

Abstract

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated tobe unidentified in clinical practice. Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-widecomorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventivetreatment.Design, Patients, and Methods: Leveraging the electronic health records (EHRs) of 124 852individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores(PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). Weevaluated its predictive capability across different ancestries and perform a PRS-based phenomewide association study (PheWAS) to assess the phenomic expression of the heightened risk ofPCOS.Results: The integrated polygenic prediction improved the average performance (pseudo-R2)for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null modelacross European, African, and multi-ancestry participants respectively. The subsequent PRSpowered PheWAS identified a high level of shared biology between PCOS and a range ofmetabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity","type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension",and "sleep apnea" reaching phenome-wide significance.Conclusions: Our study has expanded the methodological utility of PRS in patient stratificationand risk prediction, especially in a multifactorial condition like PCOS, across different geneticorigins. By utilizing the individual genome-phenome data available from the EHR, our approachalso demonstrates that polygenic prediction by PRS can provide valuable opportunities todiscover the pleiotropic phenomic network associated with PCOS pathogenesis.Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA,European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records andGenomi

Published
2020
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6. Identification of Rare Variants from Exome Sequence in a Large Pedigree with Autism.

Author

Marchani, E.E., Chapman, N.H., Cheung, C.Y.K., Ankenman, K., Stanaway, I.B., Coon, H.H., Nickerson, D., Bernier, R., Brkanac, Z., and Wijsman, E.M.

Abstract

We carried out analyses with the goal of identifying rare variants in exome sequence data that contribute to disease risk for a complex trait. We analyzed a large, 47-member, multigenerational pedigree with 11 cases of autism spectrum disorder, using genotypes from 3 technologies representing increasing resolution: a multiallelic linkage marker panel, a dense diallelic marker panel, and variants from exome sequencing. Genome-scan marker genotypes were available on most subjects, and exome sequence data was available on 5 subjects. We used genome-scan linkage analysis to identify and prioritize the chromosome 22 region of interest, and to select subjects for exome sequencing. Inheritance vectors (IVs) generated by Markov chain Monte Carlo analysis of multilocus marker data were the foundation of most analyses. Genotype imputation used IVs to determine which sequence variants reside on the haplotype that co-segregates with the autism diagnosis. Together with a rare-allele frequency filter, we identified only one rare variant on the risk haplotype, illustrating the potential of this approach to prioritize variants. The associated gene, MYH9, is biologically unlikely, and we speculate that for this complex trait, the key variants may lie outside the exome. Copyright © 2013 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Published
2013
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