Literature DB >> 11078478

Ascertainment adjustment: where does it take us?

P R Burton1, L J Palmer, K Jacobs, K J Keen, J M Olson, R C Elston.   

Abstract

It is commonly assumed that the parameter estimates of a statistical genetics model that has been adjusted for ascertainment will estimate parameters in the general population from which the ascertained subpopulation was originally drawn. We show that this is true only in certain restricted circumstances. More generally, ascertainment-adjusted parameter estimates reflect parameters in the ascertained subpopulation. In many situations, this shift in perspective is immaterial: the parameters of interest are the same in the ascertained sample and in the population from which it was drawn, and it is therefore irrelevant to which population inferences are presumed to apply. In other circumstances, however, this is not so. This has important implications, particularly for studies investigating the etiology of complex diseases.

Mesh:

Year:  2000        PMID: 11078478      PMCID: PMC1287927          DOI: 10.1086/316899

Source DB:  PubMed          Journal:  Am J Hum Genet        ISSN: 0002-9297            Impact factor:   11.025


  11 in total

1.  Genetic variance components analysis for binary phenotypes using generalized linear mixed models (GLMMs) and Gibbs sampling.

Authors:  P R Burton; K J Tiller; L C Gurrin; W O Cookson; A W Musk; L J Palmer
Journal:  Genet Epidemiol       Date:  1999       Impact factor: 2.135

2.  Conditioning on subsets of the data: applications to ascertainment and other genetic problems.

Authors:  S E Hodge
Journal:  Am J Hum Genet       Date:  1988-10       Impact factor: 11.025

3.  A resolution of the ascertainment sampling problem. I. Theory.

Authors:  W J Ewens; N C Shute
Journal:  Theor Popul Biol       Date:  1986-12       Impact factor: 1.570

4.  Sampling considerations in the gathering and analysis of pedigree data.

Authors:  R C Elston; E Sobel
Journal:  Am J Hum Genet       Date:  1979-01       Impact factor: 11.025

5.  Ascertainment in the sequential sampling of pedigrees.

Authors:  C Cannings; E A Thompson
Journal:  Clin Genet       Date:  1977-10       Impact factor: 4.438

6.  A simple method of estimating the segregation ratio under complete ascertainment.

Authors:  C C Li; N Mantel
Journal:  Am J Hum Genet       Date:  1968-01       Impact factor: 11.025

7.  'Twixt cup and lip: how intractable is the ascertainment problem?

Authors:  R C Elston
Journal:  Am J Hum Genet       Date:  1995-01       Impact factor: 11.025

8.  Helping doctors to draw appropriate inferences from the analysis of medical studies.

Authors:  P R Burton
Journal:  Stat Med       Date:  1994-09-15       Impact factor: 2.373

9.  The heterogeneity problem. I: Separating genetic from environmental forms of the same disease.

Authors:  D A Greenberg; S E Hodge
Journal:  Am J Med Genet       Date:  1985-06

10.  Complex segregation analysis with pointers.

Authors:  J M Lalouel; N E Morton
Journal:  Hum Hered       Date:  1981       Impact factor: 0.444

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  12 in total

1.  Ascertainment-adjusted parameter estimates revisited.

Authors:  Michael P Epstein; Xihong Lin; Michael Boehnke
Journal:  Am J Hum Genet       Date:  2002-03-05       Impact factor: 11.025

2.  Response to Epstein et al.

Authors:  Paul R Burton; Lyle J Palmer; Kevin J Keen; Jane M Olson; Robert C Elston
Journal:  Am J Hum Genet       Date:  2002-08       Impact factor: 11.025

3.  Ascertainment-adjusted maximum likelihood estimation for the additive genetic gamma frailty model.

Authors:  Wanlong Sun; Hongzhe Li
Journal:  Lifetime Data Anal       Date:  2004-09       Impact factor: 1.588

4.  Ascertainment adjustment in genetic studies of ordinal traits.

Authors:  Rui Feng; Heping Zhang
Journal:  Hum Genet       Date:  2006-03-10       Impact factor: 4.132

5.  Familial recurrence risk with varying amount of family history.

Authors:  Daniel J Schaid; Shannon K McDonnell; Stephen N Thibodeau
Journal:  Genet Epidemiol       Date:  2019-02-11       Impact factor: 2.135

6.  A Variance-Component Framework for Pedigree Analysis of Continuous and Categorical Outcomes.

Authors:  Michael P Epstein; Jessica E Hunter; Emily G Allen; Stephanie L Sherman; Xihong Lin; Michael Boehnke
Journal:  Stat Biosci       Date:  2009-11

7.  Genome-wide association studies, field synopses, and the development of the knowledge base on genetic variation and human diseases.

Authors:  Muin J Khoury; Lars Bertram; Paolo Boffetta; Adam S Butterworth; Stephen J Chanock; Siobhan M Dolan; Isabel Fortier; Montserrat Garcia-Closas; Marta Gwinn; Julian P T Higgins; A Cecile J W Janssens; James Ostell; Ryan P Owen; Roberta A Pagon; Timothy R Rebbeck; Nathaniel Rothman; Jonine L Bernstein; Paul R Burton; Harry Campbell; Anand Chockalingam; Helena Furberg; Julian Little; Thomas R O'Brien; Daniela Seminara; Paolo Vineis; Deborah M Winn; Wei Yu; John P A Ioannidis
Journal:  Am J Epidemiol       Date:  2009-06-04       Impact factor: 4.897

8.  Behavioral and Molecular Genetics of Reading-Related AM and FM Detection Thresholds.

Authors:  Matthew Bruni; Judy F Flax; Steven Buyske; Amber D Shindhelm; Caroline Witton; Linda M Brzustowicz; Christopher W Bartlett
Journal:  Behav Genet       Date:  2016-11-09       Impact factor: 2.805

9.  Fitting ACE structural equation models to case-control family data.

Authors:  K N Javaras; J I Hudson; N M Laird
Journal:  Genet Epidemiol       Date:  2010-04       Impact factor: 2.135

10.  strum: an R package for structural modeling of latent variables for general pedigrees.

Authors:  Yeunjoo E Song; Catherine M Stein; Nathan J Morris
Journal:  BMC Genet       Date:  2015-04-09       Impact factor: 2.797

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