Do the MN and Jk systems influence environmental variability in serum lipid levels?

Martin, N.G.; Rowell, David M.; Whitfield, John B.

doi:10.1111/j.1399-0004.1983.tb00061.x

Cited by 21 publications

(17 citation statements)

References 14 publications

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“…their phenotypic discordance) provides a direct measure of the within-pair variance, and can therefore be used to investigate environmental sensitivity (Martin et al 1983). For some behavioral traits in human populations, there is evidence of heterogeneity of variance.…”

Section: Introductionmentioning

confidence: 99%

Statistical Power to Detect Genetic Loci Affecting Environmental Sensitivity

Visscher

Posthuma

2010

Behav Genet

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There is evidence in different species of genetic control of environmental variation, independent of scale effects. The statistical power to detect genetic control of environmental or phenotypic variability for a quantitative trait was investigated analytically using a monozygotic (MZ) twin difference design and a design using unrelated individuals. The model assumed multiplicative or additive effects of alleles on trait variance at a bi-allelic locus and an additive (regression) model for statistical analysis. If genetic control acts on phenotypic variance then the design using unrelated individuals is more efficient but 10,000s of observations are needed to detect loci explaining at most 3.5% of the variance of the variance at genome-wide significance. If genetic control acts purely on environmental variation then an MZ twin difference design is more efficient when the MZ trait correlation is larger than *0.3. For a locus that explains a given proportion of the variation in variance, twice the number of observations is needed for detection when compared to a locus that explains the same proportion of variation in phenotypes.

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Section: Introductionmentioning

confidence: 99%

Statistical Power to Detect Genetic Loci Affecting Environmental Sensitivity

Visscher

Posthuma

2010

Behav Genet

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“…Whereas typical candidate gene-phenotype association studies examine differences in phenotypic means or disease risk by genotype or allele status, candidate gene association with measures of trait variability, that is, 'variability genes' (Berg et al, 1989), can be viewed as testing gene-environment interaction (Berg et al, 1989;Martin, 2000;Martin et al, 1983). A primary strategy is to make use of MZ twins given that within-pair differences would reflect nonshared environmental influences.…”

Section: Gene-environment Interactionmentioning

confidence: 99%

“…In the present study on gene-environment interaction we consider APOE as well as other putative AD candidates to consider whether they influence variability in nonpathological memory change. Using an MZ pair differences approaches (e.g., Berg et al, 1989;Martin, 2000;Martin et al, 1983) we ask the following questions:…”

Section: The Current Studymentioning

confidence: 99%

“…After finding evidence of heterogeneity, we proceeded with tests of genotype-environment interaction for all gene candidates, where we compared within-pair differences for those pairs who carried a putative risk allele versus those who did not carry the risk allele (e.g., APOE e4 carriers versus noncarriers; see Martin et al, 1983). The results of the variance ratio test will suggest whether particular genotypes may be more sensitive to as yet anonymous nonshared environmental factors, resulting in greater environmental variability, or those that may be relatively impervious to environmental factors and show smaller environmental variability.…”

Section: Variance Ratio Test: G × × Ementioning

confidence: 99%

“…This was done for untransformed EB estimates as well as for rank-normalized EB estimates. Second, to test for specific candidate gene-environment interactions we conducted variance ratio tests (Martin et al, 1983) for each gene candidate where significant heterogeneity was found for the EB estimates. Lastly, we examined the association of absolute pair differences in EB estimates of cognitive change (linear or quadratic) to absolute pair differences in social and stress measures in a series of regression analyses.…”

Section: Analysesmentioning

confidence: 99%

See 2 more Smart Citations

Genotype–Environment Interactions: Cognitive Aging and Social Factors

Reynolds¹,

Gatz

Berg

et al. 2007

Twin Res Hum Genet

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The possibility of genotype–environment interaction for memory performance and change was examined in 150 monozygotic (MZ) twin pairs from the Swedish Adoption Twin Study of Aging (SATSA). We used an MZ twin pair difference approach to examine the possibility that genotype was associated with intrapair variability and thus suggestive of genotype–nonshared environment interactions. Multiple ‘variability genes’ were found for longitudinal change in a semantic memory task including candidates coding for apolipoprotein E (APOE) and estrogen receptor alpha (ESR1) as well as serotonin candidates (HTR2A and 5HTT). One candidate also related to variability in change in episodic memory (5HTT). Of the significant associations observed, generally results indicated that MZ pairs who carry putative risk alleles were less variable than noncarriers, suggesting that noncarriers may be more sensitive to environmental contexts. We sought to ‘contextualize’ the possible nonshared environmental influences for found gene–environment (G × E) effects by considering intrapair differences in measured social and stress factors, including social support, life events and depressive symptoms. Results suggested that nonshared environmental influences associated with depressive symptoms may moderate the G × E relationship observed for ESR1 and APOE and longitudinal semantic memory change whereby noncarriers of putative risk alleles may be relatively more sensitive to depressionevoking environmental contexts than carriers of the risk allele. Thus, the contexts that facilitate or reduce depressive symptoms may affect semantic memory resiliency dependent on genotype. Further work ought to consider larger sample sizes as well as consider additional social and contextual factors.

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Gene–Environment Interactions

Boomsma

Martin

2002

Biological Psychiatry

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Do the MN and Jk systems influence environmental variability in serum lipid levels?

Cited by 21 publications

References 14 publications

Statistical Power to Detect Genetic Loci Affecting Environmental Sensitivity

Statistical Power to Detect Genetic Loci Affecting Environmental Sensitivity

Genotype–Environment Interactions: Cognitive Aging and Social Factors

Gene–Environment Interactions

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