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1,300 results on '"Elston, Robert C"'

1. Patient Biochemistry and Treatment Need in Chronic Hepatitis B Virus Infection Across Three Continents: Retrospective Cross-Sectional Cohort Studies

Author

Gillespie, Iain A., Barnes, Eleanor, Wong, Ian C. K., Matthews, Philippa C., Cooke, Graham S., Tipple, Craig, Elston, Robert C., Liu, Yunhao, Smith, David A., Wang, Tingyan, Davies, Jim, Várnai, Kinga A., Freeman, Oliver, Man, Kenneth K. C., Lau, Wallis C. Y., Glampson, Ben, Meng, Xing, Morais, Eleonora, Liu, Sen, Mercuri, Luca, Boxall, Naomi, Jenner, Sarah, Kendrick, Stuart, Dong, Jane, and Theodore, Dickens

Published
2023
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2. Correction to: Patient Biochemistry and Treatment Need in Chronic Hepatitis B Virus Infection Across Three Continents: Retrospective Cross-Sectional Cohort Studies

Author

Gillespie, Iain A., Barnes, Eleanor, Wong, Ian C. K., Matthews, Philippa C., Cooke, Graham S., Tipple, Craig, Elston, Robert C., Liu, Yunhao, Smith, David A., Wang, Tingyan, Davies, Jim, Várnai, Kinga A., Freeman, Oliver, Man, Kenneth K. C., Lau, Wallis C. Y., Glampson, Ben, Meng, Xing, Morais, Eleonora, Liu, Sen, Mercuri, Luca, Boxall, Naomi, Jenner, Sarah, Kendrick, Stuart, Dong, Jane, and Theodore, Dickens

Published
2023
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3. Newton E. Morton (1929–2018)

Author

Sherman, Stephanie L, Rao, DC, Keats, Bronya J, Yee, Shirley, Spence, M Anne, Hassold, Terry J, Chakravarti, Aravinda, Elston, Robert C, Crolla, John A, Ennis, Sarah, and Risch, Neil

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Health Sciences, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Published
2018

4. Selecting SNPs informative for African, American Indian and European Ancestry: application to the Family Investigation of Nephropathy and Diabetes (FIND)

Author

Williams, Robert C, Elston, Robert C, Kumar, Pankaj, Knowler, William C, Abboud, Hanna E, Adler, Sharon, Bowden, Donald W, Divers, Jasmin, Freedman, Barry I, Igo, Robert P, Ipp, Eli, Iyengar, Sudha K, Kimmel, Paul L, Klag, Michael J, Kohn, Orly, Langefeld, Carl D, Leehey, David J, Nelson, Robert G, Nicholas, Susanne B, Pahl, Madeleine V, Parekh, Rulan S, Rotter, Jerome I, Schelling, Jeffrey R, Sedor, John R, Shah, Vallabh O, Smith, Michael W, Taylor, Kent D, Thameem, Farook, Thornley-Brown, Denyse, Winkler, Cheryl A, Guo, Xiuqing, Zager, Phillip, Hanson, Robert L, and the FIND Research Group

Subjects
Biological Sciences, Genetics, American Indian or Alaska Native, Diabetes, Health Disparities, Minority Health, Human Genome, Metabolic and endocrine, Black or African American, Algorithms, Chromosome Mapping, Diabetic Nephropathies, Genetic Markers, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Indians, North American, Likelihood Functions, Mexican Americans, Models, Genetic, Oligonucleotide Array Sequence Analysis, Polymorphism, Single Nucleotide, Principal Component Analysis, United States, White People, Individual genetic ancestry, Population structure, SNP, Diabetic nephropathy, FIND Research Group, Information and Computing Sciences, Medical and Health Sciences, Bioinformatics, Biological sciences, Biomedical and clinical sciences
Abstract

BackgroundThe presence of population structure in a sample may confound the search for important genetic loci associated with disease. Our four samples in the Family Investigation of Nephropathy and Diabetes (FIND), European Americans, Mexican Americans, African Americans, and American Indians are part of a genome- wide association study in which population structure might be particularly important. We therefore decided to study in detail one component of this, individual genetic ancestry (IGA). From SNPs present on the Affymetrix 6.0 Human SNP array, we identified 3 sets of ancestry informative markers (AIMs), each maximized for the information in one the three contrasts among ancestral populations: Europeans (HAPMAP, CEU), Africans (HAPMAP, YRI and LWK), and Native Americans (full heritage Pima Indians). We estimate IGA and present an algorithm for their standard errors, compare IGA to principal components, emphasize the importance of balancing information in the ancestry informative markers (AIMs), and test the association of IGA with diabetic nephropathy in the combined sample.ResultsA fixed parental allele maximum likelihood algorithm was applied to the FIND to estimate IGA in four samples: 869 American Indians; 1385 African Americans; 1451 Mexican Americans; and 826 European Americans. When the information in the AIMs is unbalanced, the estimates are incorrect with large error. Individual genetic admixture is highly correlated with principle components for capturing population structure. It takes ~700 SNPs to reduce the average standard error of individual admixture below 0.01. When the samples are combined, the resulting population structure creates associations between IGA and diabetic nephropathy.ConclusionsThe identified set of AIMs, which include American Indian parental allele frequencies, may be particularly useful for estimating genetic admixture in populations from the Americas. Failure to balance information in maximum likelihood, poly-ancestry models creates biased estimates of individual admixture with large error. This also occurs when estimating IGA using the Bayesian clustering method as implemented in the program STRUCTURE. Odds ratios for the associations of IGA with disease are consistent with what is known about the incidence and prevalence of diabetic nephropathy in these populations.

Published
2016

5. A weighted U statistic for association analysis considering genetic heterogeneity

Author

Wei, Changshuai, Elston, Robert C., and Lu, Qing

Subjects
Statistics - Methodology
Abstract

Converging evidence suggests that common complex diseases with the same or similar clinical manifestations could have different underlying genetic etiologies. While current research interests have shifted toward uncovering rare variants and structural variations predisposing to human diseases, the impact of heterogeneity in genetic studies of complex diseases has been largely overlooked. Most of the existing statistical methods assume the disease under investigation has a homogeneous genetic effect and could, therefore, have low power if the disease undergoes heterogeneous pathophysiological and etiological processes. In this paper, we propose a heterogeneity weighted U (HWU) method for association analyses considering genetic heterogeneity. HWU can be applied to various types of phenotypes (e.g., binary and continuous) and is computationally effcient for high- dimensional genetic data. Through simulations, we showed the advantage of HWU when the underlying genetic etiology of a disease was heterogeneous, as well as the robustness of HWU against different model assumptions (e.g., phenotype distributions). Using HWU, we conducted a genome-wide analysis of nicotine dependence from the Study of Addiction: Genetics and Environments (SAGE) dataset. The genome-wide analysis of nearly one million genetic markers took 7 hours, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence.

Published
2015
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7. Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND).

Author

Iyengar, Sudha K, Sedor, John R, Freedman, Barry I, Kao, WH Linda, Kretzler, Matthias, Keller, Benjamin J, Abboud, Hanna E, Adler, Sharon G, Best, Lyle G, Bowden, Donald W, Burlock, Allison, Chen, Yii-Der Ida, Cole, Shelley A, Comeau, Mary E, Curtis, Jeffrey M, Divers, Jasmin, Drechsler, Christiane, Duggirala, Ravi, Elston, Robert C, Guo, Xiuqing, Huang, Huateng, Hoffmann, Michael Marcus, Howard, Barbara V, Ipp, Eli, Kimmel, Paul L, Klag, Michael J, Knowler, William C, Kohn, Orly F, Leak, Tennille S, Leehey, David J, Li, Man, Malhotra, Alka, März, Winfried, Nair, Viji, Nelson, Robert G, Nicholas, Susanne B, O'Brien, Stephen J, Pahl, Madeleine V, Parekh, Rulan S, Pezzolesi, Marcus G, Rasooly, Rebekah S, Rotimi, Charles N, Rotter, Jerome I, Schelling, Jeffrey R, Seldin, Michael F, Shah, Vallabh O, Smiles, Adam M, Smith, Michael W, Taylor, Kent D, Thameem, Farook, Thornley-Brown, Denyse P, Truitt, Barbara J, Wanner, Christoph, Weil, E Jennifer, Winkler, Cheryl A, Zager, Philip G, Igo, Robert P, Hanson, Robert L, Langefeld, Carl D, and Family Investigation of Nephropathy and Diabetes (FIND)

Subjects
Family Investigation of Nephropathy and Diabetes, Humans, Diabetic Nephropathies, Diabetes Mellitus, Type 2, Genetic Predisposition to Disease, RNA-Binding Proteins, African Americans, Indians, North American, European Continental Ancestry Group, Hispanic Americans, United States, Genome-Wide Association Study, Diabetes, Genetics, Human Genome, Clinical Research, Kidney Disease, American Indian or Alaska Native, Prevention, 2.1 Biological and endogenous factors, Metabolic and endocrine, Renal and urogenital, Developmental Biology
Abstract

Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD.

Published
2015

8. A Genome-Wide Search for Linkage of Estimated Glomerular Filtration Rate (eGFR) in the Family Investigation of Nephropathy and Diabetes (FIND)

Author

Thameem, Farook, Igo, Robert P, Freedman, Barry I, Langefeld, Carl, Hanson, Robert L, Schelling, Jeffrey R, Elston, Robert C, Duggirala, Ravindranath, Nicholas, Susanne B, Goddard, Katrina A. B, Divers, Jasmin, Guo, Xiuqing, Ipp, Eli, Kimmel, Paul L, Meoni, Lucy A, Shah, Vallabh O, Smith, Michael W, Winkler, Cheryl A, Zager, Philip G, Knowler, William C, Nelson, Robert G, Pahl, Madeline V, Parekh, Rulan S, Kao, W. H. Linda, Rasooly, Rebekah S, Adler, Sharon G, Abboud, Hanna E, Iyengar, Sudha K, Sedor, John R, and Cai, Tao

Subjects
Chronic Kidney-Disease, Renal-Function, Mexican-Americans, Genetic-Variants, Serum Creatinine, Identifies 6, Association, Loci, Albuminuria, Traits
Published
2013

9. Genomewide linkage scan for diabetic renal failure and albuminuria: the FIND study.

Author

Igo, Robert P, Jr, Iyengar, Sudha K, Nicholas, Susanne B, Goddard, Katrina A B, Langefeld, Carl D, Hanson, Robert L, Duggirala, Ravindranath, Divers, Jasmin, Abboud, Hanna, Adler, Sharon G, Arar, Nedal H, Horvath, Amanda, Elston, Robert C, Bowden, Donald W, Guo, Xiuqing, Ipp, Eli, Kao, W H Linda, Kimmel, Paul L, Knowler, William C, Meoni, Lucy A, Molineros, Julio, Nelson, Robert G, Pahl, Madeline V, Parekh, Rulan S, Rasooly, Rebekah S, Schelling, Jeffrey R, Shah, Vallabh O, Smith, Michael W, Winkler, Cheryl A, Zager, Philip G, Sedor, John R, and Freedman, Barry I

Subjects
Aged, Albuminuria: genetics, metabolism, Chromosome Mapping, Diabetic Nephropathies: genetics, metabolism, Ethnic Groups, Female, Genetic Linkage, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Male, Middle Aged, Pedigree, Polymorphism, Single Nucleotide, Renal Insufficiency: genetics, metabolism, Risk, Time Factors
Abstract

Diabetic nephropathy (DN) is a leading cause of mortality and morbidity in patients with type 1 and type 2 diabetes. The multicenter FIND consortium aims to identify genes for DN and its associated quantitative traits, e.g. the urine albumin:creatinine ratio (ACR). Herein, the results of whole-genome linkage analysis and a sparse association scan for ACR and a dichotomous DN phenotype are reported in diabetic individuals.A genomewide scan comprising more than 5,500 autosomal single nucleotide polymorphism markers (average spacing of 0.6 cM) was performed on 1,235 nuclear and extended pedigrees (3,972 diabetic participants) ascertained for DN from African-American (AA), American-Indian (AI), European-American (EA) and Mexican-American (MA) populations.Strong evidence for linkage to DN was detected on chromosome 6p (p = 8.0 × 10(-5), LOD = 3.09) in EA families as well as suggestive evidence for linkage to chromosome 7p in AI families. Regions on chromosomes 3p in AA, 7q in EA, 16q in AA and 22q in MA displayed suggestive evidence of linkage for urine ACR. The linkage peak on chromosome 22q overlaps the MYH9/APOL1 gene region, previously implicated in AA diabetic and nondiabetic nephropathies.These results strengthen the evidence for previously identified genomic regions and implicate several novel loci potentially involved in the pathogenesis of DN.

Published
2011

10. Gene-gene interaction in maternal and perinatal research.

Author

Sinsheimer, Janet S, Elston, Robert C, and Fu, Wenjiang J

Subjects
Fetus, Humans, Heart Defects, Congenital, DNA, Epistasis, Genetic, Pregnancy, Genotype, Phenotype, Female, Congenital Abnormalities, Histocompatibility, Maternal-Fetal, Genome-Wide Association Study, Heart Defects, Congenital, Epistasis, Genetic, Histocompatibility, Maternal-Fetal, Biological Sciences, Information and Computing Sciences, Technology
Published
2010

16. A Genome-Wide Search for Chromosomal Loci Linked to Mental Health Wellness in Relatives at High Risk for Bipolar Affective Disorder among the Old Order Amish

Author

Ginns, Edward I., St. Jean, Pamela, Philibert, Robert A., Galdzicka, Marzena, Damschroder-Williams, Patricia, Thiel, Bonnie, Long, Robert T., Ingraham, Loring J., Dalwaldi, Harnisha, Murray, Melissa A., Ehlert, Melissa, Paul, Sharon, Remortel, Brian G., Patel, Ashima P., Shaio, Cary, Lau, Elaine, Dymarskaia, Inna, Martin, Brian M., Stubblefield, Barbara, Falls, Kathleen M., Carulli, John P., Keith, Tim P., Lacy, Lucy G., Allen, Cleona R., Hostetter, Abram M., Elston, Robert C., Schork, Nicholas J., Egeland, Janice A., and Paul, Steven M.

Published
1998

17. Supplementary Figure S1 from Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm

Author

Sun, Xiangqing, primary, Elston, Robert C., primary, Barnholtz-Sloan, Jill S., primary, Falk, Gary W., primary, Grady, William M., primary, Faulx, Ashley, primary, Mittal, Sumeet K., primary, Canto, Marcia, primary, Shaheen, Nicholas J., primary, Wang, Jean S., primary, Iyer, Prasad G., primary, Abrams, Julian A., primary, Tian, Ye D., primary, Willis, Joseph E., primary, Guda, Kishore, primary, Markowitz, Sanford D., primary, Chandar, Apoorva, primary, Warfe, James M., primary, Brock, Wendy, primary, and Chak, Amitabh, primary

Published
2023
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19. Data from Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm

Author

Sun, Xiangqing, primary, Elston, Robert C., primary, Barnholtz-Sloan, Jill S., primary, Falk, Gary W., primary, Grady, William M., primary, Faulx, Ashley, primary, Mittal, Sumeet K., primary, Canto, Marcia, primary, Shaheen, Nicholas J., primary, Wang, Jean S., primary, Iyer, Prasad G., primary, Abrams, Julian A., primary, Tian, Ye D., primary, Willis, Joseph E., primary, Guda, Kishore, primary, Markowitz, Sanford D., primary, Chandar, Apoorva, primary, Warfe, James M., primary, Brock, Wendy, primary, and Chak, Amitabh, primary

Published
2023
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24. Supplementary Methods and Materials. Supplementary Tables S1-S4 from Predicting Barrett's Esophagus in Families: An Esophagus Translational Research Network (BETRNet) Model Fitting Clinical Data to a Familial Paradigm

Author

Sun, Xiangqing, primary, Elston, Robert C., primary, Barnholtz-Sloan, Jill S., primary, Falk, Gary W., primary, Grady, William M., primary, Faulx, Ashley, primary, Mittal, Sumeet K., primary, Canto, Marcia, primary, Shaheen, Nicholas J., primary, Wang, Jean S., primary, Iyer, Prasad G., primary, Abrams, Julian A., primary, Tian, Ye D., primary, Willis, Joseph E., primary, Guda, Kishore, primary, Markowitz, Sanford D., primary, Chandar, Apoorva, primary, Warfe, James M., primary, Brock, Wendy, primary, and Chak, Amitabh, primary

Published
2023
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26. Supplementary Figure from Genetic Linkage of Prostate Cancer Risk to the Chromosome 3 Region Bearing FHIT

Author

Larson, Garry P., primary, Ding, Yan, primary, Cheng, Li S-C., primary, Lundberg, Cathryn, primary, Gagalang, Virgil, primary, Rivas, Guillermo, primary, Geller, Louis, primary, Weitzel, Jeffrey, primary, MacDonald, Deborah, primary, Archambeau, John, primary, Slater, Jerry, primary, Neuberg, Donna, primary, Daly, Mary B., primary, Angel, Irene, primary, Benson, Al B., primary, Smith, Kimberly, primary, Kirkwood, John M., primary, O'Dwyer, Peter J., primary, Raskay, Barbara, primary, Sutphen, Rebecca, primary, Drew, Rosalind, primary, Stewart, James A., primary, Werndli, Jae, primary, Johnson, David, primary, Ruckdeschel, John C., primary, Elston, Robert C., primary, and Krontiris, Theodore G., primary

Published
2023
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27. Analysis of Family Data

Author

Zheng, Gang, Yang, Yaning, Zhu, Xiaofeng, Elston, Robert C., Zheng, Gang, Yang, Yaning, Zhu, Xiaofeng, and Elston, Robert C.

Published
2012
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29. Population Structure

Author

Zheng, Gang, Yang, Yaning, Zhu, Xiaofeng, Elston, Robert C., Zheng, Gang, Yang, Yaning, Zhu, Xiaofeng, and Elston, Robert C.

Published
2012
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34. Gene-Gene Interactions

Author

Zheng, Gang, Yang, Yaning, Zhu, Xiaofeng, Elston, Robert C., Zheng, Gang, Yang, Yaning, Zhu, Xiaofeng, and Elston, Robert C.

Published
2012
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35. Robust Procedures

Author

Zheng, Gang, Yang, Yaning, Zhu, Xiaofeng, Elston, Robert C., Zheng, Gang, Yang, Yaning, Zhu, Xiaofeng, and Elston, Robert C.

Published
2012
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42. Novel recurrently mutated genes in African American colon cancers

Author

Guda, Kishore, Veigl, Martina L., Varadan, Vinay, Nosrati, Arman, Ravi, Lakshmeswari, Lutterbaugh, James, Beard, Lydia, Willson, James K. V., Sedwick, W. David, Wang, Zhenghe John, Molyneaux, Neil, Miron, Alexander, Adams, Mark D., Elston, Robert C., Markowitz, Sanford D., and Willis, Joseph E.

Published
2015

50. Natural selection on EPAS1 (HIF2α) associated with low hemoglobin concentration in Tibetan highlanders

Author

Beall, Cynthia M., Cavalleri, Gianpiero L., Deng, Libin, Elston, Robert C., Gao, Yang, Knight, Jo, Li, Chaohua, Li, Jiang Chuan, Liang, Yu, McCormack, Mark, Montgomery, Hugh E., Pan, Hao, Robbins, Peter A., Shianna, Kevin V., Tam, Siu Cheung, Tsering, Ngodrop, Veeramah, Krishna R., Wang, Wei, Wangdui, Puchung, Weale, Michael E., Xu, Yaomin, Xu, Zhe, Yang, Ling, Zaman, M. Justin, Zeng, Changqing, Zhang, Li, Zhang, Xianglong, Zhaxi, Pingcuo, Zheng, Yong Tang, and Ellison, Peter T.

Published
2010

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