Literature DB >> 16342185

Assessment and implications of linkage disequilibrium in genome-wide single-nucleotide polymorphism and microsatellite panels.

Ellen L Goode1, Gail P Jarvik.   

Abstract

Linkage disequilibrium (LD) between markers is more likely to exist in dense genome-wide single-nucleotide polymorphism (SNP) panels than in microsatellite panels. As part of Genetic Analysis Workshop 14 (GAW14), the extent of LD in the Illumina linkage panel III and the Affymetrix Genechip 10 K mapping array was assessed, using data from the Collaborative Study on the Genetics of Alcoholism (COGA). The impact of LD on linkage results was examined in COGA and simulated data, and characteristics of SNPs were assessed for their ability to detect population substructure and predict haplotypes. The authors of the papers summarized here observed greater LD in the Affymetrix than in the Illumina panel, possibly due to increased marker density in the Affymetrix panel, and found greater LD on chromosome X than on the autosomes. Simulation analyses suggest that intermarker LD can cause an upward bias in linkage statistics; however, the impact of LD on linkage analysis depends on the proportion of ungenotyped founders and the extent of LD. No large effect of LD on linkage peaks was observed in COGA analyses. In addition, the papers summarized here found that SNPs with high minor allele frequencies were the most informative compared with microsatellites for the detection of population substructure, and that SNPs in higher LD, and small numbers of SNPs, were the most reliable for haplotype prediction. As ease of genotyping continues to increase, study design and SNP selection for linkage and association studies (including genome-wide association studies) will be improved with consideration of LD in the particular populations studied.

Mesh:

Year:  2005        PMID: 16342185     DOI: 10.1002/gepi.20112

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  6 in total

1.  Examining the effect of linkage disequilibrium between markers on the Type I error rate and power of nonparametric multipoint linkage analysis of two-generation and multigenerational pedigrees in the presence of missing genotype data.

Authors:  Yoonhee Kim; Priya Duggal; Elizabeth M Gillanders; Ho Kim; Joan E Bailey-Wilson
Journal:  Genet Epidemiol       Date:  2008-01       Impact factor: 2.135

Review 2.  Finding genes underlying human disease.

Authors:  C M Stein; R C Elston
Journal:  Clin Genet       Date:  2009-02       Impact factor: 4.438

Review 3.  Benefits and limitations of genome-wide association studies.

Authors:  Vivian Tam; Nikunj Patel; Michelle Turcotte; Yohan Bossé; Guillaume Paré; David Meyre
Journal:  Nat Rev Genet       Date:  2019-08       Impact factor: 53.242

4.  Mapping a new spontaneous preterm birth susceptibility gene, IGF1R, using linkage, haplotype sharing, and association analysis.

Authors:  Ritva Haataja; Minna K Karjalainen; Aino Luukkonen; Kari Teramo; Hilkka Puttonen; Marja Ojaniemi; Teppo Varilo; Bimal P Chaudhari; Jevon Plunkett; Jeffrey C Murray; Steven A McCarroll; Leena Peltonen; Louis J Muglia; Aarno Palotie; Mikko Hallman
Journal:  PLoS Genet       Date:  2011-02-03       Impact factor: 5.917

5.  A potential novel spontaneous preterm birth gene, AR, identified by linkage and association analysis of X chromosomal markers.

Authors:  Minna K Karjalainen; Johanna M Huusko; Johanna Ulvila; Jenni Sotkasiira; Aino Luukkonen; Kari Teramo; Jevon Plunkett; Verneri Anttila; Aarno Palotie; Ritva Haataja; Louis J Muglia; Mikko Hallman
Journal:  PLoS One       Date:  2012-12-05       Impact factor: 3.240

6.  Genome-wide linkage in Utah autism pedigrees.

Authors:  K Allen-Brady; R Robison; D Cannon; T Varvil; M Villalobos; C Pingree; M F Leppert; J Miller; W M McMahon; H Coon
Journal:  Mol Psychiatry       Date:  2009-05-19       Impact factor: 15.992

  6 in total

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