Effects of genotype-by-sex interaction on quantitative trait linkage analysis

Towne, Bradford; Siervogel, Roger M.; Blangero, John

doi:10.1002/(sici)1098-2272(1997)14:6<1053::aid-gepi82>3.0.co;2-g

Cited by 60 publications

(42 citation statements)

References 13 publications

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“…Variation in heritability between the sexes can be investigated by fitting separate variance components for each sex (Towne et al 1993(Towne et al , 1997. A sex-specific model allowing the variance components to vary between the two sexes can be considered a special case of a bivariate trait model, in which no subject has observations for both traits, e.g.…”

Section: Discussionmentioning

confidence: 99%

Heritability of human hookworm infection in Papua New Guinea

et al. 2008

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S U M M A R YHookworms infect approximately 740 million humans worldwide and are an important cause of morbidity. The present study examines the role of additive genetic effects in determining the intensity of hookworm infection in humans, and whether these effects vary according to the sex of the host. Parasitological and epidemiological data for a population of 704 subjects in Papua New Guinea were used in variance components analysis. The ' narrow-sense ' heritability of hookworm infection was estimated as 0 . 15¡0 . 04 (P<0 . 001), and remained significant when controlling for shared environmental (household) effects. Allowing the variance components to vary between the sexes of the human host consistently revealed larger additive genetic effects in females than in males, reflected by heritabilities of 0 . 18 in females and 0 . 08 in males in a conservative model. Household effects were also higher in females than males, although the overall household effect was not significant. The results indicate that additive genetic effects are an important determinant of the intensity of human hookworm infection in this population. However, despite similar mean and variance of intensity in each sex, the factors responsible for generating variation in intensity differ markedly between males and females.

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Section: Discussionmentioning

confidence: 99%

Heritability of human hookworm infection in Papua New Guinea

et al. 2008

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“…Specifically, we modelled genotype-by-sex interactions, as previous work suggested strong sex effects. Indeed, modelling the true complexity of type 2 diabetes by incorporating genotype-by-sex effects may aid in the resolution of previously identified suggestive linkage peaks and improve QTL localisation by increasing the power to detect linkage [32].…”

Section: Discussionmentioning

confidence: 99%

Genotype-by-sex interaction in the aetiology of type 2 diabetes mellitus: support for sex-specific quantitative trait loci in Hypertension Genetic Epidemiology Network participants

Avery

Freedman

Kraja³

et al. 2006

Diabetologia

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Aims/hypothesis While there are sex-related differences in both the prevalence of type 2 diabetes mellitus and disease risk factors, there is only limited research on sexspecific influences on type 2 diabetes aetiology within the same study population. Thus, we assessed genotypeby-sex interaction using a liability threshold model in an attempt to localise sex-specific type 2 diabetes quantitative trait loci (QTLs). Subjects, materials and methods Hypertensive siblings and their offspring and/or parents in the Hypertension Genetic Epidemiology Network of the Family Blood Pressure Program were recruited from five field centres. The diabetic phenotype was adjusted for race, study centre, age and nonlinear age effects. In total, 567 diabetic individuals were identified in 385 families. Variance component linkage analyses in the combined sample and stratified by sex and race were performed (SOLAR program) using race-specific marker allele frequencies derived from a random sample of participants at each centre. Results We observed a QTL-specific genotype-by-sex interaction (p=0.009) on chromosome 17 at 31 cM, with females displaying a robust adjusted logarithm of odds (LOD) of 3.0 compared with 0.2 in males and 1.3 in the combined sample. Three additional regions demonstrating suggestive evidence for linkage were detected: chromosomes 2 and 5 in the female sample and chromosome 22 (adjusted LOD=1.9) in the combined sample.

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“…The greatest strength of the models (Blangero 1993) and on the variance level (Lange 1986;Itoh and Yamada 1990). These techniques apply both to continuous traits (Pletcher 1999;Pletcher and Geyer 1999;Jaffréic and Pletcher 2000;Pletcher and Jaffréic 2002;Purcell 2002;Purcell and Sham 2002;Meyer and Kirkpatrick 2005) and to categorical traits (Towne et al 1997;Viel et al 2005). It is also straightforward to model multiple QTL acting additively (Lange 2002).…”

Section: Discussionmentioning

confidence: 99%

Mixed Effects Models for Quantitative Trait Loci Mapping With Inbred Strains

et al. 2008

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Fixed effects models have dominated the statistical analysis of genetic crosses between inbred strains. In spite of their popularity, the traditional models ignore polygenic background and must be tailored to each specific cross. We reexamine the role of random effect models in gene mapping with inbred strains. The biggest difficulty in implementing random effect models is the lack of a coherent way of calculating trait covariances between relatives. The standard model for outbred populations is based on premises of genetic equilibrium that simply do not apply to crosses between inbred strains since every animal in a strain is genetically identical and completely homozygous. We fill this theoretical gap by introducing novel combinatorial entities called strain coefficients. With an appropriate theory, it is possible to reformulate QTL mapping and QTL association analysis as an application of mixed models involving both fixed and random effects. After developing this theory, our first example compares the mixed effects model to a standard fixed effects model using simulated advanced intercross line (AIL) data. Our second example deals with hormone data. Here multivariate traits and parameter identifiability questions arise. Our final example involves random mating among eight strains and vividly demonstrates the versatility of our models.

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Effects of genotype-by-sex interaction on quantitative trait linkage analysis

Cited by 60 publications

References 13 publications

Heritability of human hookworm infection in Papua New Guinea

Heritability of human hookworm infection in Papua New Guinea

Genotype-by-sex interaction in the aetiology of type 2 diabetes mellitus: support for sex-specific quantitative trait loci in Hypertension Genetic Epidemiology Network participants

Mixed Effects Models for Quantitative Trait Loci Mapping With Inbred Strains

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