Statistical Genetics: Gene Mapping Through Linkage and AssociationBenjamin M. Neale, Manuel A. R. Ferreira, Sarah E. Medland, and Danielle Posthuma (Eds.). (2007). London: Taylor and Francis. ISBN: 978041541040

2008 ◽  
Vol 11 (1) ◽  
pp. 99-99
Author(s):  
John K. Hewitt
Keyword(s):  
Gene Mapping ◽  
Association Analysis ◽  
Complex Traits ◽  
Family Studies ◽  
Statistical Genetics ◽  
Psychiatric Genetics ◽  
Psychological Traits ◽  
Linkage And Association Analysis ◽  
World Class ◽  
Young Researchers

AbstractThis book was conceived at the 2005 methodology workshop in Boulder, Colorado. Its purpose was to provide background reading and a working text for students and researchers being introduced to the principles and practice of linkage and association analysis in the context of twin and family studies of complex traits, including behavioral and psychological traits. It is edited by four of the most interesting and dynamic young researchers in the field, and authored by them and a roster of 25 additional world class experts in statistical, behavioral, and psychiatric genetics.

scholarly journals Linkage and association analysis in pedigrees from different populations

BMC Genetics ◽  
2005 ◽  
Vol 6 (Suppl 1) ◽  
pp. S59 ◽  
Author(s):  
Joseph Beyene ◽  
Jun Yan ◽  
Celia MT Greenwood
Keyword(s):  
Association Analysis ◽  
Linkage And Association Analysis ◽  
Different Populations

Linkage and association analysis of hyperthyrotropinaemia in an Alpine population reveal two novel loci on chromosomes 3q28-29 and 6q26-27

2011 ◽  
Vol 48 (8) ◽  
pp. 549-556 ◽  
Author(s):  
C. B. Volpato ◽  
A. De Grandi ◽  
M. Gogele ◽  
D. Taliun ◽  
C. Fuchsberger ◽  
...  
Keyword(s):  
Association Analysis ◽  
Alpine Population ◽  
Linkage And Association Analysis

QTL analysis of Na+ and K+ concentrations in shoots and roots under NaCl stress based on linkage and association analysis in japonica rice

Euphytica ◽  
2014 ◽  
Vol 201 (1) ◽  
pp. 109-121 ◽  
Author(s):  
Hongliang Zheng ◽  
Hongwei Zhao ◽  
Hualong Liu ◽  
Jingguo Wang ◽  
Detang Zou
Keyword(s):  
Association Analysis ◽  
Qtl Analysis ◽  
Nacl Stress ◽  
Japonica Rice ◽  
Linkage And Association Analysis

scholarly journals Combined linkage and association analysis of classical Hodgkin lymphoma

Oncotarget ◽  
2018 ◽  
Vol 9 (29) ◽  
pp. 20377-20385 ◽  
Author(s):  
Alastair Lawrie ◽  
Shuo Han ◽  
Amit Sud ◽  
Fay Hosking ◽  
Timothee Cezard ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Association Analysis ◽  
Classical Hodgkin Lymphoma ◽  
Linkage And Association Analysis

Mapping and KASP marker development for wheat curl mite resistance in “TAM 112” wheat using linkage and association analysis

2018 ◽  
Vol 38 (10) ◽  
Author(s):  
Smit Dhakal ◽  
Chor-Tee Tan ◽  
Victoria Anderson ◽  
Hangjin Yu ◽  
Maria P. Fuentealba ◽  
...  
Keyword(s):  
Association Analysis ◽  
Marker Development ◽  
Wheat Curl Mite ◽  
Linkage And Association Analysis ◽  
Kasp Marker

Human Linkage and Association Analysis

2018 ◽  
pp. 70-83
Author(s):  
John P. Rice
Keyword(s):  
Linkage Analysis ◽  
Genome Wide Association Study ◽  
Case Control ◽  
Psychiatric Genetics ◽  
Case Control Studies ◽  
Complex Disorders ◽  
Monogenic Disorders ◽  
Genome Wide ◽  
Linkage And Association Analysis ◽  
Inflation Factor

The basic idea in linkage analysis is that a disease gene will segregate in a family with a close (linked) marker, and typing this marker will lead to its detection. The successes using this approach have been largely confined to Mendelian monogenic disorders or complex disorders with Mendelian subforms. During the last decade, psychiatric genetics abandoned linkage analysis and moved to case-control studies of association, with remarkable success in identifying susceptibility genes for mental disorders. In this chapter, we review the statistical underpinnings of linkage and association and discuss important issues such as population stratification, imputation, data cleaning, the genomic inflation factor, and QQ and Manhattan plots. The challenge for the next decade will be to understand the biology of these GWAS (genome-wide association study) hits.

scholarly journals An Accidental Genetic Epidemiologist

2020 ◽  
Vol 21 (1) ◽  
pp. 15-36
Author(s):  
Robert C. Elston
Keyword(s):  
Statistical Analysis ◽  
Association Analysis ◽  
Genetic Epidemiology ◽  
Early Life ◽  
Method Development ◽  
Paternity Testing ◽  
Linkage And Association Analysis ◽  
Computer Package ◽  
Heritability Estimates ◽  
Further Development

I briefly describe my early life and how, through a series of serendipitous events, I became a genetic epidemiologist. I discuss how the Elston–Stewart algorithm was discovered and its contribution to segregation, linkage, and association analysis. New linkage findings and paternity testing resulted from having a genotyping lab. The different meanings of interaction—statistical and biological—are clarified. The computer package S.A.G.E. (Statistical Analysis for Genetic Epidemiology), based on extensive method development over two decades, was conceived in 1986, flourished for 20 years, and is now freely available for use and further development. Finally, I describe methods to estimate and test hypotheses about familial correlations, and point out that the liability model often used to estimate disease heritability estimates the heritability of that liability, rather than of the disease itself, and so can be highly dependent on the assumed distribution of that liability.

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