ArticleHuman heredity2011
Unifying ideas for non-parametric linkage analysis.
Article in Human heredity, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectivesNon-parametric linkage analysis (NPL) exploits marker allele sharing among affected relatives to map genes influencing complex traits. Computational barriers force approximate analysis on large pedigrees and the adoption of a questionable perfect data assumption (PDA) in assigning p values. To improve NPL significance testing on large pedigrees, we examine the adverse consequences of missing data and PDA. We also introduce a novel statistic, Q-NPL, appropriate for NPL analysis of quantitative traits.
methodsUsing simulated and real data sets with qualitative traits, we compare NPL analysis results for four testing procedures and various degrees of missing data. The simulated data sets vary from all nuclear families, to all large pedigrees, to a mix of pedigrees of different sizes. We implemented the Kong and Cox linear adjustment of p values in the software packages Mendel and SimWalk. We perform similar analysis with Q-NPL on quantitative traits of various heritabilities.
resultsThe Kong and Cox extension for significance testing is robust to realistic missing data patterns, greatly improves p values in approximate analyses, and works equally well for qualitative and quantitative traits and small and large pedigrees. The Q-NPL statistic is robust to missing data and shows good power to detect linkage for quantitative traits with a wide spectrum of heritabilities.
conclusionsThe Kong and Cox extension should be a standard tool for calculating NPL p values. It allows the combination of exact and estimated analyses into a single significance score. Q-NPL should be a standard statistic for NPL analysis of quantitative traits. The new statistics are implemented in Mendel and SimWalk.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.