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Comparison of missing data approaches in linkage analysis
BACKGROUND: Observational cohort studies have been little used in linkage analyses due to their general lack of large, disease-specific pedigrees. Nevertheless, the longitudinal nature of such studies makes them potentially valuable for assessing the linkage between genotypes and temporal trends in...
Autores principales: | , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2003
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866480/ https://www.ncbi.nlm.nih.gov/pubmed/14975112 http://dx.doi.org/10.1186/1471-2156-4-S1-S44 |
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author | Xing, Chao Schumacher, Fredrick R Conti, David V Witte, John S |
author_facet | Xing, Chao Schumacher, Fredrick R Conti, David V Witte, John S |
author_sort | Xing, Chao |
collection | PubMed |
description | BACKGROUND: Observational cohort studies have been little used in linkage analyses due to their general lack of large, disease-specific pedigrees. Nevertheless, the longitudinal nature of such studies makes them potentially valuable for assessing the linkage between genotypes and temporal trends in phenotypes. The repeated phenotype measures in cohort studies (i.e., across time), however, can have extensive missing information. Existing methods for handling missing data in observational studies may decrease efficiency, introduce biases, and give spurious results. The impact of such methods when undertaking linkage analysis of cohort studies is unclear. Therefore, we compare here six methods of imputing missing repeated phenotypes on results from genome-wide linkage analyses of four quantitative traits from the Framingham Heart Study cohort. RESULTS: We found that simply deleting observations with missing values gave many more nominally statistically significant linkages than the other five approaches. Among the latter, those with similar underlying methodology (i.e., imputation- versus model-based) gave the most consistent results, although some discrepancies remained. CONCLUSION: Different methods for addressing missing values in linkage analyses of cohort studies can give substantially diverse results, and must be carefully considered to protect against biases and spurious findings. |
format | Text |
id | pubmed-1866480 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2003 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-18664802007-05-11 Comparison of missing data approaches in linkage analysis Xing, Chao Schumacher, Fredrick R Conti, David V Witte, John S BMC Genet Proceedings BACKGROUND: Observational cohort studies have been little used in linkage analyses due to their general lack of large, disease-specific pedigrees. Nevertheless, the longitudinal nature of such studies makes them potentially valuable for assessing the linkage between genotypes and temporal trends in phenotypes. The repeated phenotype measures in cohort studies (i.e., across time), however, can have extensive missing information. Existing methods for handling missing data in observational studies may decrease efficiency, introduce biases, and give spurious results. The impact of such methods when undertaking linkage analysis of cohort studies is unclear. Therefore, we compare here six methods of imputing missing repeated phenotypes on results from genome-wide linkage analyses of four quantitative traits from the Framingham Heart Study cohort. RESULTS: We found that simply deleting observations with missing values gave many more nominally statistically significant linkages than the other five approaches. Among the latter, those with similar underlying methodology (i.e., imputation- versus model-based) gave the most consistent results, although some discrepancies remained. CONCLUSION: Different methods for addressing missing values in linkage analyses of cohort studies can give substantially diverse results, and must be carefully considered to protect against biases and spurious findings. BioMed Central 2003-12-31 /pmc/articles/PMC1866480/ /pubmed/14975112 http://dx.doi.org/10.1186/1471-2156-4-S1-S44 Text en Copyright © 2003 Xing et al; licensee BioMed Central Ltd http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Proceedings Xing, Chao Schumacher, Fredrick R Conti, David V Witte, John S Comparison of missing data approaches in linkage analysis |
title | Comparison of missing data approaches in linkage analysis |
title_full | Comparison of missing data approaches in linkage analysis |
title_fullStr | Comparison of missing data approaches in linkage analysis |
title_full_unstemmed | Comparison of missing data approaches in linkage analysis |
title_short | Comparison of missing data approaches in linkage analysis |
title_sort | comparison of missing data approaches in linkage analysis |
topic | Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1866480/ https://www.ncbi.nlm.nih.gov/pubmed/14975112 http://dx.doi.org/10.1186/1471-2156-4-S1-S44 |
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