Strategy and model building in the fourth dimension: a null model for genotype × age interaction as a Gaussian stationary stochastic process

Diego, Vincent P.; Almasy, Laura; Dyer, Thomas D.; Soler, Júlia Maria Pavan; Blangero, John

doi:10.1186/1471-2156-4-s1-s34

Cited by 26 publications

(44 citation statements)

References 16 publications

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“…It can be shown that in the absence of a genotype-environment interaction (null hypothesis): (1) the genetic variance (σ G 2 ) will be homogenous across the levels of environmental exposure; and (2) the same quantitative trait measured in participants living in different levels of environmental exposure (e.g. active vs inactive or different ages) will have a genetic correlation (ρ G ) of 1.0 [5,25,27]. Hence, the presence of genotype-environment interactions is determined by testing two null hypotheses, which for the sake of simplicity will be referred to as class 1 and class 2 interactions from here on.…”

Section: Statistical Analysesmentioning

confidence: 99%

The heritable basis of gene–environment interactions in cardiometabolic traits

et al. 2016

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Aims/hypothesis Little is known about the heritable basis of gene-environment interactions in humans. We therefore screened multiple cardiometabolic traits to assess the probability that they are influenced by genotype-environment interactions. Methods Fourteen established environmental risk exposures and 11 cardiometabolic traits were analysed in the VIKING study, a cohort of 16,430 Swedish adults from 1682 extended pedigrees with available detailed genealogical, phenotypic and demographic information, using a maximum likelihood variance decomposition method in Sequential Oligogenic Linkage Analysis Routines software.Results All cardiometabolic traits had statistically significant heritability estimates, with narrow-sense heritabilities (h 2 ) ranging from 24% to 47%. Genotype-environment interactions were detected for age and sex (for the majority of traits), physical activity (for triacylglycerols, 2 h glucose and diastolic BP), smoking (for weight), alcohol intake (for weight, BMI and 2 h glucose) and diet pattern (for weight, BMI, glycaemic traits and systolic BP). Genotype-age interactions for weight and systolic BP, genotype-sex interactions for BMI and triacylglycerols and genotype-alcohol intake interactions for weight remained significant after multiple test correction. Conclusions/interpretation Age, sex and alcohol intake are likely to be major modifiers of genetic effects for a range of cardiometabolic traits. This information may prove valuable for studies that seek to identify specific loci that modify the effects of lifestyle in cardiometabolic disease.

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Section: Statistical Analysesmentioning

confidence: 99%

The heritable basis of gene–environment interactions in cardiometabolic traits

et al. 2016

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“…A low-degree polynomial may be adequate to model the change in variance over time but inadequate for approximating the covariance structure or vice versa. Alternative models that fit separate functions for the change in variance and the change in correlation or covariance have been proposed for polygenic effects (character process models, e.g., Pletcher and Geyer 1999;Diego et al 2003).…”

Section: Discussionmentioning

confidence: 99%

Quantitative Trait Locus Analysis of Longitudinal Quantitative Trait Data in Complex Pedigrees

et al. 2005

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There is currently considerable interest in genetic analysis of quantitative traits such as blood pressure and body mass index. Despite the fact that these traits change throughout life they are commonly analyzed only at a single time point. The genetic basis of such traits can be better understood by collecting and effectively analyzing longitudinal data. Analyses of these data are complicated by the need to incorporate information from complex pedigree structures and genetic markers. We propose conducting longitudinal quantitative trait locus (QTL) analyses on such data sets by using a flexible random regression estimation technique. The relationship between genetic effects at different ages is efficiently modeled using covariance functions (CFs). Using simulated data we show that the change in genetic effects over time can be well characterized using CFs and that including parameters to model the change in effect with age can provide substantial increases in power to detect QTL compared with repeated measure or univariate techniques. The asymptotic distributions of the methods used are investigated and methods for overcoming the practical difficulties in fitting CFs are discussed. The CF-based techniques should allow efficient multivariate analyses of many data sets in human and natural population genetics.

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“…Individuals in this cohort were randomly selected from the community, with the constraints that they are of MexicanAmerican ancestry, are part of a large family, and live within the San Antonio region (see ref. 50 for recruitment details). No other inclusion or exclusion criteria were imposed.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, the discrete two-environment model is extended to reflect pairwise differences in environment between subjects, thereby sampling the full range of subject ages in the cross-sectional sample (21,50,51). The hypothesis in the extended model is that the phenotypic covariance between individuals is a function of interaction between their similarity in age and their genetic similarity.…”

Section: Methodsmentioning

confidence: 99%

Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging

Glahn

Kent

Sprooten

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Identification of genes associated with brain aging should markedly improve our understanding of the biological processes that govern normal age-related decline. However, challenges to identifying genes that facilitate successful brain aging are considerable, including a lack of established phenotypes and difficulties in modeling the effects of aging per se, rather than genes that influence the underlying trait. In a large cohort of randomly selected pedigrees (n = 1,129 subjects), we documented profound aging effects from young adulthood to old age (18-83 y) on neurocognitive ability and diffusion-based white-matter measures. Despite significant phenotypic correlation between white-matter integrity and tests of processing speed, working memory, declarative memory, and intelligence, no evidence for pleiotropy between these classes of phenotypes was observed. Applying an advanced quantitative gene-by-environment interaction analysis where age is treated as an environmental factor, we demonstrate a heritable basis for neurocognitive deterioration as a function of age. Furthermore, by decomposing gene-by-aging (G × A) interactions, we infer that different genes influence some neurocognitive traits as a function of age, whereas other neurocognitive traits are influenced by the same genes, but to differential levels, from young adulthood to old age. In contrast, increasing white-matter incoherence with age appears to be nongenetic. These results clearly demonstrate that traits sensitive to the genetic influences on brain aging can be identified, a critical first step in delineating the biological mechanisms of successful aging.neurocognition | diffusion tensor imaging | fractional anisotropy | genetic correlation | gene x environment interaction P opulation projections suggest for the first time in human history there will be more individuals over the age of 65 than below the age of 14 by 2050 (1). This milestone reflects the dramatic increase of the average lifespan of people worldwide, rather than a reduction in the total number of children being born. Indeed, 25% of the US population is expected to be over the age of 60 midway through this century (1). The implications of our aging population are substantial, because aging is associated with decreased mental and physical ability coupled with increased health care utilization. Thus, there is considerable interest in delineating the biological mechanisms that influence age-related changes to facilitate successful aging (2), defined as avoidance of disease or disability, maintaining good physical and cognitive function, and engagement in social and productive activities. Because the brain appears to play a pivotal role in aging biology (3), one promising strategy is to define measures of brain structure and function that index concomitant aging outcomes (4). The observation that many measures of brain aging are heritable and can be localized to specific genomic regions (6) indicates that genetic factors play a crucial role in the brain's ability to either prosper or ...

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Strategy and model building in the fourth dimension: a null model for genotype × age interaction as a Gaussian stationary stochastic process

Abstract: Background: Using univariate and multivariate variance components linkage analysis methods, we studied possible genotype × age interaction in cardiovascular phenotypes related to the aging process from the Framingham Heart Study.

Cited by 26 publications

References 16 publications

The heritable basis of gene–environment interactions in cardiometabolic traits

The heritable basis of gene–environment interactions in cardiometabolic traits

Quantitative Trait Locus Analysis of Longitudinal Quantitative Trait Data in Complex Pedigrees

Genetic basis of neurocognitive decline and reduced white-matter integrity in normal human brain aging

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