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Relative risks and effective number of meioses: A unified approach for general genetic models and phenotypes

Kurbasic, Azra (författare)
Lund University,Lunds universitet,Matematisk statistik,Matematikcentrum,Institutioner vid LTH,Lunds Tekniska Högskola,Mathematical Statistics,Centre for Mathematical Sciences,Departments at LTH,Faculty of Engineering, LTH
Hossjer, O (författare)
 (creator_code:org_t)
Wiley, 2006
2006
Engelska.
Ingår i: Annals of Human Genetics. - : Wiley. - 1469-1809 .- 0003-4800. ; 70:6, s. 907-922
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • Many common diseases are known to have genetic components, but since they are non-Mendelian, i.e. a large number of genetic factors affect the phenotype, these components are difficult to localize. These traits are often called complex and analysis of siblings is a valuable tool for mapping them. It has been shown that the power of the affected relative pairs method to detect linkage of a disease susceptibility locus depends on the locus contribution to increased risk of relatives compared with population prevalence Risch, 1990a,b). In this paper we generalize calculation of relative risk to arbitrary phenotypes and genetic models, but also show that the relative risk can be split into the relative risk at the main locus and the relative risk due to interaction between the main locus and loci at other chromosomes. We demonstrate how the main locus contribution to the relative risk is related to probabilities of allele sharing identical by descent at the main locus, as well as power to detect linkage. To this end we use the effective number of meioses, introduced by Hossjer (2005a) as a convenient tool. Relative risks and effective number of meioses are computed for several genetic models with binary or quantitative phenotypes, with or without polygenic effects.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Nyckelord

linkage analysis
complex diseases
relative risk
effective number of
meioses

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Kurbasic, Azra
Hossjer, O
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NATURVETENSKAP
NATURVETENSKAP
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Lunds universitet

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