Literature DB >> 11481587

The significance of not finding a gene.

M A Province1.   

Abstract

As more investigators conduct extensive whole-genome linkage scans for complex traits, interest is growing in meta-analysis as a way of integrating the weak or conflicting evidence from multiple studies. However, there is a bias in the most commonly used meta-analysis linkage technique (i.e., Fisher's [1925] method of combining of P values) when it is applied to many nonparametric (i.e., model free) linkage results. The bias arises in those methods (e.g., variance components, affected sib pair, extremely discordant sib pairs, etc.) that truncate all "negative evidence against linkage" into the single value of LOD = 0. If incorrectly handled, this bias can artificially inflate or deflate the combined meta-analysis linkage results for any given locus. This is an especially troublesome problem in the context of a genome scan, since LOD = 0 is expected to occur over half the unlinked genome. The bias can be overcome (nearly) completely by simply interpreting LOD = 0 as a P value of 1divided by 2ln(2) is approximately equal to .72 in Fisher's formula.

Mesh:

Year:  2001        PMID: 11481587      PMCID: PMC1235495          DOI: 10.1086/323316

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


  4 in total

1.  Meta-analysis by combining p-values: simulated linkage studies.

Authors:  R Guerra; C J Etzel; D R Goldstein; S R Sain
Journal:  Genet Epidemiol       Date:  1999       Impact factor: 2.135

2.  Meta-analysis of linkage data under worst-case conditions: a demonstration using the human OB region.

Authors:  D B Allison; M Heo
Journal:  Genetics       Date:  1998-02       Impact factor: 4.562

3.  Suggestive evidence for association of human chromosome 18q12-q21 and its orthologue on rat and mouse chromosome 18 with several autoimmune diseases.

Authors:  T R Merriman; H J Cordell; I A Eaves; P A Danoy; F Coraddu; R Barber; F Cucca; S Broadley; S Sawcer; A Compston; P Wordsworth; J Shatford; S Laval; J Jirholt; R Holmdahl; A N Theofilopoulos; D H Kono; J Tuomilehto; E Tuomilehto-Wolf; R Buzzetti; M G Marrosu; D E Undlien; K S Rønningen; C Ionesco-Tirgoviste; J P Shield; F Pociot; J Nerup; C O Jacob; C Polychronakos; S C Bain; J A Todd
Journal:  Diabetes       Date:  2001-01       Impact factor: 9.461

Review 4.  Schizophrenia and chromosome 6p.

Authors:  G Turecki; G A Rouleau; R Joober; J Mari; K Morgan
Journal:  Am J Med Genet       Date:  1997-04-18
  4 in total
  23 in total

1.  A combined analysis of genomewide linkage scans for body mass index from the National Heart, Lung, and Blood Institute Family Blood Pressure Program.

Authors:  Xiaodong Wu; Richard S Cooper; Ingrid Borecki; Craig Hanis; Molly Bray; Cora E Lewis; Xiaofeng Zhu; Donghui Kan; Amy Luke; David Curb
Journal:  Am J Hum Genet       Date:  2002-03-28       Impact factor: 11.025

2.  Reporting of linkage results.

Authors:  R C Elston
Journal:  Am J Hum Genet       Date:  2001-11       Impact factor: 11.025

3.  Meta-analysis of genetic-linkage analysis of quantitative-trait loci.

Authors:  Carol J Etzel; Rudy Guerra
Journal:  Am J Hum Genet       Date:  2002-05-28       Impact factor: 11.025

4.  Genome-wide meta-analysis for rheumatoid arthritis.

Authors:  Carol J Etzel; Wei V Chen; Neil Shepard; Damini Jawaheer; Francois Cornelis; Michael F Seldin; Peter K Gregersen; Christopher I Amos
Journal:  Hum Genet       Date:  2006-04-13       Impact factor: 4.132

5.  A subset-based approach improves power and interpretation for the combined analysis of genetic association studies of heterogeneous traits.

Authors:  Samsiddhi Bhattacharjee; Preetha Rajaraman; Kevin B Jacobs; William A Wheeler; Beatrice S Melin; Patricia Hartge; Meredith Yeager; Charles C Chung; Stephen J Chanock; Nilanjan Chatterjee
Journal:  Am J Hum Genet       Date:  2012-05-04       Impact factor: 11.025

6.  Systemic lupus erythematosus susceptibility loci defined by genome scan meta-analysis.

Authors:  Young Ho Lee; Swapan K Nath
Journal:  Hum Genet       Date:  2005-10-06       Impact factor: 4.132

7.  Meta-analyses of genome-wide linkage scans of anxiety-related phenotypes.

Authors:  Bradley T Webb; An-Yuan Guo; Brion S Maher; Zhongming Zhao; Edwin J van den Oord; Kenneth S Kendler; Brien P Riley; Nathan A Gillespie; Carol A Prescott; Christel M Middeldorp; Gonneke Willemsen; Eco Jc de Geus; Jouke-Jan Hottenga; Dorret I Boomsma; Eline P Slagboom; Naomi R Wray; Grant W Montgomery; Nicholas G Martin; Margie J Wright; Andrew C Heath; Pamela A Madden; Joel Gelernter; James A Knowles; Steven P Hamilton; Myrna M Weissman; Abby J Fyer; Patricia Huezo-Diaz; Peter McGuffin; Anne Farmer; Ian W Craig; Cathryn Lewis; Pak Sham; Raymond R Crowe; Jonathan Flint; John M Hettema
Journal:  Eur J Hum Genet       Date:  2012-04-04       Impact factor: 4.246

8.  Genome-wide association studies identified novel loci for non-high-density lipoprotein cholesterol and its postprandial lipemic response.

Authors:  Ping An; Robert J Straka; Toni I Pollin; Mary F Feitosa; Mary K Wojczynski; E Warwick Daw; Jeffrey R O'Connell; Quince Gibson; Kathleen A Ryan; Paul N Hopkins; Michael Y Tsai; Chao-Qiang Lai; Michael A Province; Jose M Ordovas; Alan R Shuldiner; Donna K Arnett; Ingrid B Borecki
Journal:  Hum Genet       Date:  2014-03-07       Impact factor: 4.132

9.  Linkage analysis incorporating gene-age interactions identifies seven novel lipid loci: the Family Blood Pressure Program.

Authors:  Jeannette Simino; Rezart Kume; Aldi T Kraja; Stephen T Turner; Craig L Hanis; Wayne Sheu; Ida Chen; Cashell Jaquish; Richard S Cooper; Aravinda Chakravarti; Thomas Quertermous; Eric Boerwinkle; Steven C Hunt; D C Rao
Journal:  Atherosclerosis       Date:  2014-04-26       Impact factor: 5.162

10.  A correlated meta-analysis strategy for data mining "OMIC" scans.

Authors:  Michael A Province; Ingrid B Borecki
Journal:  Pac Symp Biocomput       Date:  2013
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