2018
DOI: 10.1093/bioinformatics/bty541
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Comparative assessment of different familial aggregation methods in the context of large and unstructured pedigrees

Abstract: Supplementary data are available at Bioinformatics online.

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Cited by 3 publications
(2 citation statements)
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“…Analogously, 10,399 unaffected participants served as controls, and after removal of 807 singletons and 23 opt-outs from analysis in the control group, we arrived at 9569 controls and 103 cases, connected by 9898 un-phenotyped individuals. As discussed previously [ 21 ], in the context of large pedigrees, FamAgg’s Kinship Sum test represents the best method to detect familial aggregation as this test can quantitatively identify individuals with a significantly high number of affected kin pedigree members. Briefly, for a given case, this test takes the sum of kinship coefficients over all affected relatives and computes an empirical p -value derived from a background distribution resulting from 1,000,000 rounds of randomly assigning the 103 cases in the same set of pedigrees.…”
Section: Methodsmentioning
confidence: 99%
“…Analogously, 10,399 unaffected participants served as controls, and after removal of 807 singletons and 23 opt-outs from analysis in the control group, we arrived at 9569 controls and 103 cases, connected by 9898 un-phenotyped individuals. As discussed previously [ 21 ], in the context of large pedigrees, FamAgg’s Kinship Sum test represents the best method to detect familial aggregation as this test can quantitatively identify individuals with a significantly high number of affected kin pedigree members. Briefly, for a given case, this test takes the sum of kinship coefficients over all affected relatives and computes an empirical p -value derived from a background distribution resulting from 1,000,000 rounds of randomly assigning the 103 cases in the same set of pedigrees.…”
Section: Methodsmentioning
confidence: 99%
“…Index test in this work). For an in-depth assessment of these tests, refer to [ 25 ]. Each test returns an empirical p -value to observe an outcome at least as extreme as the one found in this study.…”
Section: Methodsmentioning
confidence: 99%