{"title":"贡献图:RV系数的分解和图形显示，应用于阿尔茨海默病的遗传和脑成像生物标志物","authors":"JinCheol Choi, Donghuan Lu, Mirza Faisal Beg, Jinko Graham, Brad McNeney","doi":"10.1159/000501334","DOIUrl":null,"url":null,"abstract":"<p><strong>Background/aims: </strong>Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes memory loss and a decline in cognitive abilities. AD is the sixth leading cause of death in the USA, affecting an estimated 5 million Americans. To assess the association between multiple genetic variants and multiple measurements of structural changes in the brain, a recent study of AD used a multivariate measure of linear dependence, the RV coefficient. The authors decomposed the RV coefficient into contributions from individual variants and displayed these contributions graphically.</p><p><strong>Methods: </strong>We investigate the properties of such a \"contribution plot\" in terms of an underlying linear model, and discuss shrinkage estimation of the components of the plot when the correlation signal may be sparse.</p><p><strong>Results: </strong>The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).</p><p><strong>Conclusions: </strong>The contribution plot with shrinkage estimation can reveal truly associated explanatory variables.</p>","PeriodicalId":13226,"journal":{"name":"Human Heredity","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008771/pdf/","citationCount":"0","resultStr":"{\"title\":\"The Contribution Plot: Decomposition and Graphical Display of the RV Coefficient, with Application to Genetic and Brain Imaging Biomarkers of Alzheimer's Disease.\",\"authors\":\"JinCheol Choi, Donghuan Lu, Mirza Faisal Beg, Jinko Graham, Brad McNeney\",\"doi\":\"10.1159/000501334\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background/aims: </strong>Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes memory loss and a decline in cognitive abilities. AD is the sixth leading cause of death in the USA, affecting an estimated 5 million Americans. To assess the association between multiple genetic variants and multiple measurements of structural changes in the brain, a recent study of AD used a multivariate measure of linear dependence, the RV coefficient. The authors decomposed the RV coefficient into contributions from individual variants and displayed these contributions graphically.</p><p><strong>Methods: </strong>We investigate the properties of such a \\\"contribution plot\\\" in terms of an underlying linear model, and discuss shrinkage estimation of the components of the plot when the correlation signal may be sparse.</p><p><strong>Results: </strong>The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).</p><p><strong>Conclusions: </strong>The contribution plot with shrinkage estimation can reveal truly associated explanatory variables.</p>\",\"PeriodicalId\":13226,\"journal\":{\"name\":\"Human Heredity\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9008771/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human Heredity\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1159/000501334\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2019/8/20 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"GENETICS & HEREDITY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Heredity","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1159/000501334","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2019/8/20 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
The Contribution Plot: Decomposition and Graphical Display of the RV Coefficient, with Application to Genetic and Brain Imaging Biomarkers of Alzheimer's Disease.
Background/aims: Alzheimer's disease (AD) is a chronic neurodegenerative disease that causes memory loss and a decline in cognitive abilities. AD is the sixth leading cause of death in the USA, affecting an estimated 5 million Americans. To assess the association between multiple genetic variants and multiple measurements of structural changes in the brain, a recent study of AD used a multivariate measure of linear dependence, the RV coefficient. The authors decomposed the RV coefficient into contributions from individual variants and displayed these contributions graphically.
Methods: We investigate the properties of such a "contribution plot" in terms of an underlying linear model, and discuss shrinkage estimation of the components of the plot when the correlation signal may be sparse.
Results: The contribution plot is applied to simulated data and to genomic and brain imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
Conclusions: The contribution plot with shrinkage estimation can reveal truly associated explanatory variables.
期刊介绍：
Gathering original research reports and short communications from all over the world, ''Human Heredity'' is devoted to methodological and applied research on the genetics of human populations, association and linkage analysis, genetic mechanisms of disease, and new methods for statistical genetics, for example, analysis of rare variants and results from next generation sequencing. The value of this information to many branches of medicine is shown by the number of citations the journal receives in fields ranging from immunology and hematology to epidemiology and public health planning, and the fact that at least 50% of all ''Human Heredity'' papers are still cited more than 8 years after publication (according to ISI Journal Citation Reports). Special issues on methodological topics (such as ‘Consanguinity and Genomics’ in 2014; ‘Analyzing Rare Variants in Complex Diseases’ in 2012) or reviews of advances in particular fields (‘Genetic Diversity in European Populations: Evolutionary Evidence and Medical Implications’ in 2014; ‘Genes and the Environment in Obesity’ in 2013) are published every year. Renowned experts in the field are invited to contribute to these special issues.
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