• Journal of Animal Science and Technology
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• v.55 no.2
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• pp.103-107
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• 2013

A crucial first step in the planning of any scientific experiment is to evaluate an appropriate sample size to permit sufficient statistical power to detect the desired effect. In this study, we investigated the optimal sample size of quantitative trait locus (QTL) linkage analysis for simple random sibship samples in pedigreed chicken populations, under the variance component framework implemented in the genetic power calculator program. Using the program, we could compute the statistical power required to achieve given sample sizes in variance component linkage analysis in random sibship data. For simplicity, an additive model was taken into account. Power calculations were performed to relate sample size to heritability attributable to a QTL. Under the various assumptions, comparative power curves indicated that the power to detect QTL with the variance component method is highly affected by a function of the effect size of QTL. Hence, more power can be achievable for QTL with a larger effect. In addition, a marked improvement in power could be obtained by increasing the sibship size. Thus, the use of chickens is advantageous regarding the sampling unit issue, since desirable sibship size can be easily obtained compared with other domestic species.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.329-340
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• 2009

For complex diseases such as diabetes, hypertension, it is believed that model-free methods might work better because they do not require a precise knowledge of the mode of inheritance controlling the disease trait. This is done by estimating the sharing probabilities that a pair shares zero, one, or two alleles identical by descent(IBD) and has some specific branches of test procedure, i.e., the mean test, the proportion test, and the minmax test. Among them, the minmax test is known to be more robust than others regardless of genetic mode of inheritance in current use. In this study, we compared the power of the methods which are based on minmax test and considering weighting schemes for sib-pairs to analyze sibship data. In simulation result, we found that the method based on Suarez' was more powerful than any others without respect to marker allele frequency, genetic mode of inheritance, sibship size. Also, The power of both Suarez- and Hodge-based methods was higher when marker allele frequency and sibship size were higher, and this result was remarkable in dominant mode of inheritance especially.

• The Korean Journal of Applied Statistics
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• v.23 no.5
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• pp.845-855
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• 2010

Extensively used case-control designs in medical studies can also be powerful and efficient for family association studies as long as an analysis method is developed for the evaluation of association between candidate genes and disease. Traditional Cochran-Armitage trend test is devised for independent subjects data, and to apply this trend test to the biologically related siblings one has to take into account the covariance among related family members in order to maintain the correct type I error rate. We propose a more powerful trend test by introducing weights that reflect the number of affected siblings in families for the evaluation of the association of genetic markers related to the disease. An application of our method to a sample family data, in addition to a small-scale simulation, is presented to compare the weighted and unweighted trend tests.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.81-94
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• 2008

Family-based tests such as the transmission and disequilibrium tests(TDT) have proved to be powerful tools in the search for disease genes. Unlike case-control studies, the tests are not affected by population admixture, which can lead to spurious association of multiple highly linked makers with disease-susceptible genes. Those tests have largely required knowledge of parental marker genotypes. However, parental data are often not available for late-onset diseases. In this article we propose sib-TDTs that overcome this problem by use of marker data from unaffected sib(s) instead of parents. To do this end, we fist defined a Mantel-Haenszel-type statistic for each haplotype and then proposed two tests based on this statistic. Simulation studies suggest that the proposed tests are robust to population admixture and are monotone increasing as a relative risk increases irrespective of mode of inheritance. We also illustrated the proposed tests with data adopted from Yonsei Cardiovascular Genome Center.