Literature DB >> 16149880

Statistical tools for linkage analysis and genetic association studies.

Paola Forabosco1, Mario Falchi, Marcella Devoto.   

Abstract

Genetic mapping by linkage analysis has been an invaluable tool in the positional strategy to identify the molecular basis of many rare Mendelian disorders. With the attention of the scientific and medical community shifting towards the analysis of more common, complex traits, it has become necessary to develop new approaches that take into account the complexity of the genetic basis of these disorders and their possible interaction with other, nongenetic factors. Linkage disequilibrium studies are now becoming increasingly popular thanks to the advent of genotyping platforms that allow genome-wide searching for association between hundreds of thousands of random polymorphisms and disease phenotypes in large samples of unrelated individuals. Moreover, the definition of the disease phenotype itself is being reconsidered to include quantitative traits that may better define the underlying biologic mechanisms for many pathologic conditions. This article will review classic and new approaches to genetic mapping by linkage and association analysis and discuss the directions this field is likely to take in the near future.

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Year:  2005        PMID: 16149880     DOI: 10.1586/14737159.5.5.781

Source DB:  PubMed          Journal:  Expert Rev Mol Diagn        ISSN: 1473-7159            Impact factor:   5.225


  7 in total

Review 1.  Linkage analysis in the next-generation sequencing era.

Authors:  Joan E Bailey-Wilson; Alexander F Wilson
Journal:  Hum Hered       Date:  2011-12-23       Impact factor: 0.444

2.  Genomewide rapid association using mixed model and regression: a fast and simple method for genomewide pedigree-based quantitative trait loci association analysis.

Authors:  Yurii S Aulchenko; Dirk-Jan de Koning; Chris Haley
Journal:  Genetics       Date:  2007-07-29       Impact factor: 4.562

3.  Mapping the road to resilience: novel math for the study of frailty.

Authors:  Luigi Ferrucci; Francesco Giallauria; David Schlessinger
Journal:  Mech Ageing Dev       Date:  2008-09-25       Impact factor: 5.432

4.  Does probabilistic modelling of linkage disequilibrium evolution improve the accuracy of QTL location in animal pedigree?

Authors:  Christine Cierco-Ayrolles; Sébastien Dejean; Andrés Legarra; Hélène Gilbert; Tom Druet; Florence Ytournel; Delphine Estivals; Naïma Oumouhou; Brigitte Mangin
Journal:  Genet Sel Evol       Date:  2010-10-22       Impact factor: 4.297

5.  Biomedical Data Commons (BMDC) prioritizes B-lymphocyte non-coding genetic variants in Type 1 Diabetes.

Authors:  Samantha N Piekos; Sadhana Gaddam; Pranav Bhardwaj; Prashanth Radhakrishnan; Ramanathan V Guha; Anthony E Oro
Journal:  PLoS Comput Biol       Date:  2021-09-20       Impact factor: 4.475

6.  Teaching molecular genetics: chapter 4-positional cloning of genetic disorders.

Authors:  Aldamaria Puliti; Gianluca Caridi; Roberto Ravazzolo; Gian Marco Ghiggeri
Journal:  Pediatr Nephrol       Date:  2007-07-28       Impact factor: 3.714

7.  Genetic Analysis of QTL for Resistance to Maize Lethal Necrosis in Multiple Mapping Populations.

Authors:  Luka A O Awata; Yoseph Beyene; Manje Gowda; Suresh L M; McDonald B Jumbo; Pangirayi Tongoona; Eric Danquah; Beatrice E Ifie; Philip W Marchelo-Dragga; Michael Olsen; Veronica Ogugo; Stephen Mugo; Boddupalli M Prasanna
Journal:  Genes (Basel)       Date:  2019-12-26       Impact factor: 4.096

  7 in total

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