Types Of Relative Pairs Research Articles

ODP089 Familial correlation and heritability of muscle strength in Korean adults (KNHANES 2014-2019)

Abstract Objective The onset and progression of sarcopenia are highly variable between individuals due to genetic and environmental factors. However, studies that have measured muscle strength directly in large numbers of matched parent-adult offspring pairs are limited. In this study, we investigated familial correlation and heritability of handgrip strength (HGS) in Korean adults. Methods This was a family-based cohort study from the Korea National Health and Nutrition Examination Survey (2014-2019) including 5,004 Koreans aged ≥19 years from 1,527 families. HGS was measured using a digital grip strength dynamometer. Familial correlations for HGS were calculated in different types of relative pairs. Variance component methods were used to obtain heritability estimates. Results Mean HGS of parents were 39.3 ± 7.5 kg in fathers and 24.4 ± 5. 0 kg in mothers and those of offspring were 41.8 ± 8. 0 kg in sons and 24. 0 ± 4.8 kg in daughters. Correlation coefficient estimates between parent-offspring pairs, sibling pairs, and spouse pairs for HGS were significant as 0. 07, 0.10, and 0.23 (p <0. 001, p = 0. 041, and p <0. 001), respectively. The total variance of the phenotype under study was explained by additive genetic (15.4%), common shared environmental (11. 0%), and unique environmental (73.6%) influences. The risk of low HGS conferred by parents’ low HGS were found to be significant for individuals whose parents had low HGS (OR 3.10, 95%CI 1.14-8.45). Conclusion Muscle strength showed a familial correlation in Korean adults. A significant heritable tendency of muscle strength from parents to their adult offspring and common shared environmental influence can explain this correlation. Presentation: No date and time listed

Heritabilities of Intraocular Pressure in the Population of Korea

Intraocular pressure (IOP) is the principal modifiable risk factor for the progression of primary open-angle glaucoma. Studies that have measured the IOP directly in large numbers of matched parent-offspring pairs are limited in Asian populations. To compare IOPs in parents and their offspring in Korea. Family-based cohort study examining data from the Korean National Health and Nutrition Examination Survey conducted between 2008 and 2009. Data were obtained from 9700 participants from Korea. Familial correlations for IOP were calculated in different types of relative pairs. Variance component methods were used to obtain heritability estimates. The individuals were stratified into 2 groups (those with and those without an IOP of ≥ 19 mm Hg; ie, high IOP group and nonhigh IOP group) based on the mean plus 2 SD IOP value of the entire study population. We evaluated the impact of parents' high IOP on offspring's high IOP. The relationship between parental systemic disease and high IOP in their offspring was also investigated. The mean (SD) IOPs in the right and left eyes were 13.90 (2.74) and 13.89 (2.74) mm Hg, respectively. Correlation coefficient estimates between parent-offspring pairs, sibling pairs, and spouse pairs for IOP were significant as 0.19, 0.31, and 0.29 (P < .001, P = .001, and P < .001, respectively). The total variance of the phenotype under study was explained by 2 sources of variation, additive genetic (36% [95% CI, 32%-40%]) and unique environment (64% [95% CI, 60%-68%]). The risks of high IOP conferred by parents' high IOP were found to be significant for participants whose parents had high IOP (odds ratio, 9.76 [95% CI, 2.16-44.12]). In this study, high IOP was not associated with parental diabetes mellitus, hypertension, obesity, or metabolic syndrome. Intraocular pressure showed a significant heritable tendency from parents to their offspring with a heritability estimate of 0.36 in Asian populations. The risk of high IOP was significantly increased in participants whose parents had high IOP. This has potential implications for the screening of family members of patients with ocular hypertension and glaucoma.

Effective sample size: Quick estimation of the effect of related samples in genetic case–control association analyses

Affected relatives are essential for pedigree linkage analysis, however, they cause a violation of the independent sample assumption in case–control association studies. To avoid the correlation between samples, a common practice is to take only one affected sample per pedigree in association analysis. Although several methods exist in handling correlated samples, they are still not widely used in part because these are not easily implemented, or because they are not widely known. We advocate the effective sample size method as a simple and accessible approach for case–control association analysis with correlated samples. This method modifies the chi-square test statistic, p-value, and 95% confidence interval of the odds-ratio by replacing the apparent number of allele or genotype counts with the effective ones in the standard formula, without the need for specialized computer programs. We present a simple formula for calculating effective sample size for many types of relative pairs and relative sets. For allele frequency estimation, the effective sample size method captures the variance inflation exactly. For genotype frequency, simulations showed that effective sample size provides a satisfactory approximation. A gene which is previously identified as a type 1 diabetes susceptibility locus, the interferon-induced helicase gene ( IFIH1), is shown to be significantly associated with rheumatoid arthritis when the effective sample size method is applied. This significant association is not established if only one affected sib per pedigree were used in the association analysis. Relationship between the effective sample size method and other methods – the generalized estimation equation, variance of eigenvalues for correlation matrices, and genomic controls – are discussed.

Program Update and Novel Use of the DESPAIR Program to Design a Genome-Wide Linkage Study Using Relative Pairs

DESPAIR (DESign for PAIRs) is a computer program useful for designing a two-stage linkage study using relative pairs for a dichotomous phenotype. It determines the optimal two-stage study design – i.e., for specified power and significance level, how many pairs of relatives should be studied, how many equally spaced markers should be used initially, and what criterion should be used to specify the markers around which further searching should be done at a second stage. The program will calculate either the number of relative pairs required for a given number of first-stage markers or the number of markers required for a given number of relative pairs. We highlight the use of the latest version of DESPAIR to decide to what extent additional fine mapping in a candidate region of interest can lead to an increase in power in a previous linkage study sample. We also discuss new features of the program, such as the mean difference test for affected and discordant relative pairs and estimation of full sibling pair equivalents to design a study when several types of relative pairs are available. DESPAIR is part of the S.A.G.E. program package and is freely available for use online.

Testing Genetic Linkage with Relative Pairs and Covariates by Quasi-Likelihood Score Statistics

Background/Aims: Genetic linkage analysis of common diseases is complicated by the heterogeneity of genetic and environmental factors that increase disease risk, and possibly interactions among them. Most linkage methods that account for covariates are restricted to sib pairs, with the exception of the conditional logistic regression model [<citeref rid="ref001">1</citeref>] implemented in LODPAL in the S.A.G.E. software [<citeref rid="ref002">2</citeref>]. Although this model can be applied to arbitrary pedigrees, at times it can be difficult to maximize the likelihood due to model constraints, and it does not account for the dependence among the different types of relative pairs in a pedigree. Methods: To overcome these limitations, we developed a new approach based on score statistics for quasi- likelihoods, implemented as weighted least squares. Our methods can be used to test three different hypotheses: (1) a test for linkage without covariates; (2) a test for linkage with covariates, and (3) a test for effects of covariates on identity by descent sharing (i.e., heterogeneity). Furthermore, our methods are robust because they account for the dependence among different relative pairs within a pedigree. Results and Conclusion: Although application of our methods to a prostate cancer linkage study did not find any critical covariates in our data, the results illustrate the utility and interpretation of our methods, and suggest, nonetheless, that our methods will be useful for a broad range of genetic linkage heterogeneity analyses.

The Linkage Information Content Value of Polymorphism Genetic Markers in Model-Free Linkage Analysis

Guo and Elston [Hum Hered 1999;49:112–118] developed a linkage information content (LIC) value to measure the informativeness of a marker for identity-by-descent (IBD) sharing status of relative pairs. LIC values were derived for five types of relative pairs: full sib, half sib, grandparent-grandchild, first cousin and avuncular. In this paper, we give corrected LIC values for full sib, grandparent-grandchild, first cousin and avuncular pairs, and indicate the availability of a computer program to calculate them.

Haseman and Elston revisited.

Haseman and Elston (H-E) [1972] proposed a method to detect quantitative trait loci by linkage to a marker. The squared sib-pair trait difference is regressed on the proportion of marker alleles the pair is estimated to share identical by descent: a significantly negative regression coefficient suggests linkage. It has been shown that a maximum likelihood method that directly models the sib-pair covariance has more power. This increase in power can also be obtained using the H-E regression procedure by changing the dependent variable from the squared difference to the mean-corrected product of the sibs' trait values. Multiple sibs in a sibship can be accommodated by allowing for the correlations between pairs of products in a generalized least squares procedure. Multiple trait loci, including epistatic interactions, involve only multiple linear regression. Multivariate traits can use the method of Amos et al. [1990] to find the linear function of the traits that maximizes the evidence for linkage, which now leads more simply to a test of significance. Multiple markers can be the basis of a multipoint analysis. Results of simulation studies for a continuous trait are presented that investigate Type I error and power. A similar general scheme can be used to study affected sib pairs, testing whether their identity by descent sharing probabilities are greater than would be expected in the absence of linkage, and to study other types of relative pairs.

Power of a score test for quantitative trait linkage analysis of relative pairs.

The score test of Dudoit and Speed [(2000) Biostatistics 1:1-26] to detect linkage between a trait locus and a marker locus, using identity by descent data on sib pairs, is extended to other types of relative pairs (grandparent/grandchild, avuncular, and half-sib relationships). The test is based on the likelihood of the recombination fraction theta between trait and marker loci, conditional on phenotypes of the relatives. We present results of simulation studies characterizing power and robustness properties of this linkage score test, and compare the power of the score test to that of the classical and modified Haseman-Elston tests. The score test has considerable power, particularly under sampling schemes where selection is on double probands. Use of a generic additive model [Goldstein et al., submitted] with allele frequency p = 0.2, heritability H = 0.3, and a moderate residual correlation of rho = 0.2 resulted in a very good overall performance across a wide range of trait-generating models.

Likelihood-based models for genetic linkage analysis using affected sib pairs.

Risch [Am J Hum Genet 1990; 46:229-253] recently developed a likelihood method for testing genetic linkage using affected sib pairs. This paper proposes a reparameterization of the Risch likelihood in terms of genetic variance components divided by the affected-sib-pair ascertainment probability. The new parameters allow for ease of interpretation, particularly for multilocus models. A single-degree-of-freedom test of the total disease genetic effect at a marker locus can be constructed that has power comparable to the likelihood ratio 'triangle' test. Extension of the models to other types of relative pairs is also discussed. The models may be used in genome scanning and in studying single or multilocus models of inheritance.

Robust multipoint linkage analysis: an extension of the Haseman-Elston method.

The use of multiple markers, rather than a single marker, can increase the likelihood of detecting linkage to a locus underlying a quantitative trait. In this paper, the Haseman-Elston sibpair method is extended to include information from multiple markers. The result is a linear regression of the squared pair trait difference on the jointly estimated proportions of genes shared identical by descent at the two closest flanking marker loci. The results strengthen the theoretical motivation for the interval mapping technique proposed by Fulker and Cardon [1994: Am J Hum Genet 54:1092-1103] and extend the method to include information from multiple markers, account for a trait dominance component, and examine the region just outside that defined by the markers. Simulations show that modest increases in power and substantial decreases in bias of parameter estimates are obtained when identity-by-descent probabilities are jointly estimated. The regression relationship is also developed for other types of relative pairs.

Some empirical properties of an all‐relative‐pairs linkage test

Olson and Wijsman [Genet Epidemiol 10:87-102, 1993] recently proposed a single test of linkage which combines information from different types of relative pairs in a pedigree. Relative-pair-type-specific regression parameters that relate the squared pair trait difference to the estimated number of marker genes shared identical by descent between the pair are estimated using generalized estimating equation methodology, then combined to give a single linkage test statistic. Questions remain concerning the small sample and robustness properties of this test statistic; these questions are addressed in the present paper using simulation. The test is substantially anti-conservative for samples with fewer than about ten families and are approximately valid for samples larger than about 15 families. In addition, the test appears robust in the presence of trait genotype by environment interaction, trait family-specific errors, a second major trait locus, and trait dominance. Surprisingly, the sibpair test was more powerful than the all-relative-pairs test for dominant traits with high heritability. Finally, adjusting for the presence of a marker known to be linked to one trait locus only marginally improves the power for detecting a second trait locus.

Linkage between quantitative trait and marker loci: Methods using all relative pairs

Relative-pair methods for detection of linkage between a quantitative trait and a marker locus have been proposed by a number of authors [e.g., Haseman and Elston, Behav Genet 3-19, 1972; Amos and Elston, Genet Epidemiol 349-360, 1989]. However, development of tests of significance that combine information from different types of relative pairs has been hampered by the presence of correlations between relative pairs from the same pedigree. In this paper, the methodology of generalized estimating equations is used to provide an estimate of the robust covariance matrix of the estimates of the set of relative-pair-type-specific regression parameters. Using this matrix, an asymptotically most powerful test of linkage which optimally combines the information contained in the different types of relative pairs is constructed. This test requires optimal weights that depend on unknown values of heritability and recombination fraction to be chosen a priori. However, simulations show that, in the regions of recombination fraction and heritability of practical interest, the power of the test does not depend strongly on the assumptions made when choosing the optimal weights; as a result, weights that depend only on the number of each type of relative pair and the variability of the marker identity-by-descent probabilities work well in practice. In addition, an approximation to the regression model leads to a simple approach to testing linkage in which only a single regression parameter is estimated from data containing different types of relative pairs. The resulting test is slightly less powerful than the test described above, but its computational simplicity and lack of dependence on a priori weighting schemes suggest potential usefulness in large linkage studies.