Detecting genes contributing to longevity using twin data
 Alexander Begun^{1}Email author
DOI: 10.1186/147973644273
© Henry Stewart Publications 2009
Received: 26 August 2009
Accepted: 26 August 2009
Published: 1 December 2009
Abstract
Searching for genes contributing to longevity is a typical task in association analysis. A number of methods can be used for finding this association  from the simplest method based on the technique of contingency tables to more complex algorithms involving demographic data, which allow us to estimate the genotypespecific hazard functions. The independence of individuals is the common assumption in all these methods. At the same time, data on related individuals such as twins are often used in genetic studies. This paper proposes an extension of the relative risk model to encompass twin data. We estimate the power and also discuss what happens if we treat the twin data using the univariate model.
Keywords
Heterogeneity longevity genes maximum likelihood method relative risk twin dataIntroduction
Most common diseases and traits have a complex structure, for which the phenotype is determined by interactions between genetic and environmental factors. As any individual genetic variant can have a relatively modest effect on a disease or trait, linkage analysis has less power than association analysis. Classical association studies in their simplest form compare the frequency of alleles or genotypes for candidate genes between cases and controls. These candidate genes are usually chosen on the basis of biological hypotheses or from previous linkage analyses.
To identify genes associated with longevity, information on genotype frequencies for two or more age groups is needed. A significant trend of genotype frequencies being associated with age can indicate a genelongevity association. In the basic 'gene frequency method', only the genotype frequencies in different age groups are compared [1–3] Some extensions of this method involve the use of demographic information about the population under study and allow the estimation of initial frequencies, relative risks and the age trajectories of mortality for candidate genes. These methods are known in the literature as the 'parametric method', the 'semiparametric method', the 'nonparametric method' and the 'relative risk method' [4]. The use of these methods, however, has two limitations. First, the initial gene frequencies in all cohorts represented in the study must be the same. Secondly, the mortalities for genotypes do not depend on the birth year of the cohort. In two recently published papers,[5, 6] the authors exclude the first limitation, assuming a time trend in the genetic frequencies of subsequent birth cohorts. In principle, the time and the cohort covariates influencing mortality can be incorporated into the models too. The flexible parameterisation in the extended relative risk model [6] also allows detection of the antagonistic pleiotropic effect.
The methods mentioned above have been developed for datasets consisting of independent individuals. In this paper, we propose a method for detecting longevity genes for the dataset consisting of twin pairs. This method retains all the advantages of the relative risk model for univariate data described previously [6].
Materials and methods
In accordance with (1), the logit of P_{ a } is a linear function of unknown parameters R, ν and δ with domain of definition R^{3}. This parameterisation includes the sudden change in the allele frequency by the value Rφ(x, x_{0}) in the cohort T  x_{0} and the slow linear cohort effect ν + δx of the allele frequency. Here, T stands for the year of data collection, x for the age, and t for the cohort. We assume that the value of x_{0} is known. The step function φ(x, x_{0}) is defined by the interval equations φ(x, x_{0}) = 1 for 0 ≤ x ≤ x_{0} and φ(x, x_{0}) = 0 for x >x_{0}.
with unknown a_{ g } ≥ 0, b_{ g } ≥ 0 and c_{ g } ≥ 0. Such parameterisation, where cumulative hazards H_{ g }(x) rather than survival functions S_{ g }(x) for different genotypes are parametrically related (eg ${S}_{g}\left(x\right)={S}_{0}{\left(x\right)}^{{b}_{g}}$, is more flexible and allows us to detect the antagonistic pleiotropic effect [6]. Without loss of generality, we can assume that a_{ AA } = b_{ AA } = 1.
(the maximum likelihood estimates [MLE]), where x_{ i } is the age of twin pair i at the moment of data collection, ${N}_{g}^{MZ}$ and ${N}_{g}^{DZ}$ are the observed numbers of MZ and DZ twin pairs in the genetic dataset (twin pairs with known genotypes and ages), respectively. To choose the optimal model, we can use the likelihood ratio test for nested models and either the Akaike information criterion (AIC) or the Bayesian information criterion (BIC) for nonnested models. Under the null hypothesis, we assume that a_{ aa } = a_{ Aa } = b_{ aa } = b_{ Aa } = 1 and c_{ aa } = c_{ Aa } = c_{ AA } = 0. Significant deviation from this hypothesis can indicate a genelongevity association.
Now, unknown parameters can be found through maximising the joint likelihood function Lik_{ g } × Lik_{ d }.
Results

The action of the dominant allele a on longevity can be characterised by parameters a_{ AA } = b_{ AA } = 1, c_{ aa } = c_{ AA } = 0, a_{ aa } = a_{ Aa } = 0.8, b_{ aa } = b_{ Aa } = 1.2;

The survival function for genotype AA has a form$\begin{array}{c}\stackrel{\u0303}{S}\left(x\right)={\left(1+{s}^{2}\stackrel{\u0303}{H}\left(x\right)\right)}^{1/{s}^{2}},\\ \stackrel{\u0303}{H}\left(x\right)=\stackrel{\u0303}{c}x+\mathit{\xe3}\left({e}^{\stackrel{\u0303}{b}x}1\right)/\stackrel{\u0303}{b}\end{array}$(11)

Individual frailty for twins are gammadistributed, with mean 1 and variance σ^{2} = 1. Frailty correlations ρ_{ MZ } and ρ_{ DZ } are equal to 0.5 and 0.25, respectively;

The HardyWeinberg equilibrium at the moment of conception holds. There is no genotype selection before birth;

The slow continuous component of the cohort effect has parameters ν = 2 and δ = 0.005. This corresponds to the frequency P_{ a } ≈ 0.182 for individuals born in year T (the year of data collection) and decreases in the frequency by 0.4 per cent per year. The sudden jump of P_{ a } with parameter R = 0.5 occurred in the cohort T50;

The birth dates of all twin pairs from the longevity dataset are uniformly distributed over the cohort interval [T110, T100]. The ages of the twins from the genetic dataset at the moment of data collection are uniformly distributed over the age interval [0105] years.
Nearly one in every 100 deliveries is a twin birth, and the DZ/MZ ratio is approximately equal to 2. From this, it follows that in the stationary population consisting of 300,000 individuals with crude birth and death rates q_{0} equal to 15 per 1,000, the life expectancy at birth e_{0} is equal to 1,000/15 ≈ 66.7 years and we will find approximately (1/300) × (300,000 × q_{0}e_{0}) = 1,000 MZ and (2/300) × (300,000 × q_{0}e_{0}) = 2,000 DZ twin pairs. We will also find 150 MZ and 300 DZ newborn twin pairs over the tenyear cohort interval. Since the influence of a decrease in child mortality before the age of 1113 years on the univariate survival and, therefore, selection is relatively small, we have not included this effect in the simulated data. In general, chosen simulation parameters produce a bivariate lifespan distribution which is similar to the true one.
Parameter estimates (sample means) and their standard deviations (in brackets) for 1,000 simulations, calculated using the bivariate (univariate) model applied to the joint bivariate genetic and longevity data* (***) or to the bivariate genetic data **(****)
True  Est.*  Est.**  Est.***  Est.****  

a _{ aa }  0.800  0.775 (0.219)  0.693 (0.431)  0.605 (0.736)  0.614 (0.517) 
b _{ aa }  1.200  1.198 (0.039)  1.196 (0.062)  1.261 (0.070)  1.252 (0.065) 
ν  2.000  2.009 (0.178)  2.016 (0.180)  1.996 (0.183)  1.996 (0.182) 
10^{3}·δ  5.000  5.066 (2.642)  5.121 (2.660)  4.762 (2.876)  4.934 (2.736) 
R  0.500  0.509 (0.126)  0.514 (0.129)  0.505 (0.131)  0.501 (0.130) 
σ  1.000  1.096 (0.520)  1.368 (0.998)  1.654 (1.008)  1.538 (1.060) 
ρ _{ MZ }  0.500  0.558 (0.245)  0.539 (0.392)     
ρ _{ DZ }  0.250  0.293 (0.212)  0.358 (0.393)     
Power    0.833  0.628  0.874  0.719 
Discussion
The maximum likelihood method yields correct estimates if the model is correctly specified. In this case, the MLE of unknown parameters under certain regularity conditions are asymptotically unbiased, normal and efficient. If we treat the bivariate data in the same way as the univariate data, and the marginal model is correctly specified, then the robust HubertWhite 'sandwich' estimator of the covariance matrix of parameter estimates yields an asymptotically consistent covariance matrix [8–10]. As we see in Table 1, there is an increase in statistical power when using the more robust univariate model compared with the bivariate model. Nevertheless, the estimates of parameters a_{ aa } and b_{ aa } for the relative risk of the longevity genotype and the estimate of σ for the standard deviation of frailty are closer to their true values if we use the bivariate model. Including the information on longevity in the dataset, however, can substantially improve statistical estimates, increase the power and decrease the variance. It seems that implementation of the approach based on the more robust univariate model, compared with the bivariate model, is preferable for the sample sizes used in this study. Based on the correlation estimates in the MZ and DZ twins, we are able to estimate the contribution of the candidate gene to the heritability [11]. Under the null hypothesis (no heritability), we put ρ_{ MZ } = ρ_{ DZ }. The effect of antagonistic pleiotropy is clearly seen in Figure 1. The presence of allele a in an individual's genotype guarantees the lower hazard of mortality only up to the age of approximately 76 years. The hazard of individuals with genotype AA is then lower than that of individuals with allele a in the genotype. Similar to the univariate model, the bivariate model effectively identifies not only the slow cohort trend of P_{ g }, including the antagonistic pleiotropic effect, but also the sudden change in this parameter. As expected, the frequencies of allele a and of the genotypes containing allele a increase continuously in the age intervals [050] and [5080], fall abruptly at the age of 50 and decrease continuously after the age of 80 (see Figure 2). Univariate and bivariate (for twins) genotype frequencies at the longevity locus at the moment of conception depend on the genotype frequencies in the parental population and the transmission probabilities. In the model we have used, two assumptions were made relating to the longevity locus. First, that the HardyWeinberg equilibrium holds for the parental population. Secondly, that the segregation ratio does not deviate from 0.5 [12]. In principle, we can dispense with both of these assumptions and include them as null hypotheses in the study. Significant deviation from the null hypotheses can be tested using the likelihood ratio test. Rejection of the hypothesis about the HardyWeinberg equilibrium can indicate possible genotype selection during the gestation period. Significant deviation from Mendelian transmission can mean, for example, that longevity is not governed by the alleles at a single locus. Population admixture and stratification can lead to linkage disequilibrium between longevity and marker loci. In such situations, the study may reveal evidence for ('spurious') association with the marker, even if it is unlinked to the longevity locus. If the subpopulation factors influencing the allele frequencies in the marker and longevity loci are identified (eg ethnicity, geographical origin, etc), they can be included in the study. Another solution for this problem is to partition the association effects into between and withinfamily components [13, 14]. It was shown that admixture impacts the betweenfamily component estimate, and that the withinfamily component estimate is independent of any 'spurious' effects when samples from a number of population strata are combined.
Authors’ Affiliations
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