• Asian-Australasian Journal of Animal Sciences
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• v.19 no.12
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• pp.1696-1701
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• 2006

A random regression model was applied for the first time for the analysis of test day records and to study the genetic persistency of first lactation milk yield of Indian Murrah buffaloes. Wilmink's Function was chosen to describe the shape of lactation curves. Heritabilities of test day milk yield varied from 0.33 to 0.58 in different test days. The highest heritability was found in the initial test day ($5^{th}$ day) milk yield. Genetic correlations among test day milk yields were higher in the initial test day milk yield and decreased when the test day interval was increased. The magnitude of genetic correlations between test day and 305 day milk yield varied from 0.25 to 0.99. The genetic persistencies of first lactation milk yield were estimated based on daily breeding values using two methods. $P_1$ is the genetic persistency estimated as a summation of the deviation of estimated daily breeding value on days to attain peak yield from each day after days to attain peak yield to different lactation days. $P_2$ is the genetic persistency estimated as the additional genetic yield (gained or lost) from days to attain peak yield to estimated breeding value on different lactation days relative to an average buffalo having the same yield on days to attain peak yield. The mean genetic persistency on 90, 120, 180, 240, 278 and 305 days in milk was estimated as -4.23, -21.67, -101.67, -229.57, -330.06 and -388.64, respectively by $P_1$, whereas by $P_2$ on same days in milk were estimated as -3.96 (-0.32 kg), -23.94 (-0.87 kg), -112.81 (-1.96 kg), -245.83 (-2.81 kg), -350.04 (-3.28 kg) and -407.58 (-3.40 kg) respectively. Higher magnitude of rank correlations indicated that the ranking of buffaloes based on their genetic persistency in both methods were similar for evaluation of genetic persistency of buffaloes. Based on the estimated range of genetic persistency three types of genetic persistency were identified. Genetic correlations among genetic persistency in different days in milk and between genetic persistencies on the same day in milk were very high. The genetic correlations between genetic persistency for different days in milk and estimated breeding value for 305 DIM was increased from 90 DIM to 180 DIM, and highest around 240 DIM which indicates a minimum of 240 days as an optimum first lactation length might be required for genetic evaluation of Indian Murrah buffaloes.

• Journal of Genetic Medicine
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• v.8 no.2
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• pp.100-104
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• 2011

Genetic testing has been generalized for the diagnosis of diseases and is an important method of research with advances in the life sciences. In particular, we should give better attention to the genetic test for a fetus. Because the fetus has no autonomy, ethical and social issues can arise. Therefore, appropriate genetic counseling is needed for parents to be informed with the characteristics, natural progress, and possible treatment of a genetic disease, prior to the prenatal genetic test. Physicians should also inform parents how a particular genetic risk factor relates with the likelihood of a disease, in order to assist the parents in making the best decision. Furthermore, the current law for prenatal genetic testing should be approached rationally.

• Journal of Advanced Marine Engineering and Technology
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• v.22 no.5
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• pp.687-692
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• 1998

Test pattern generation is one of most difficult problems encountered in automating the design of logic circuits. The goal is to obtain the highest fault coverage with the minimum number of test patterns for a given circuit and fault set. although there have been many deterministic algorithms and heuristics the problem is still highly complex and time-consuming. Therefore new approach-es are needed to augment the existing techniques. This paper considers the problem of test pattern improvement for combinational circuits as a restricted subproblem of the test pattern generation. The problem is to maximize the fault coverage with a fixed number of test patterns for a given cir-cuit and fault set. We propose a new approach by use of a genetic algorithm. In this approach the genetic algorithm evolves test patterns to improve their fault coverage. A fault simulation is used to compute the fault coverage of the test patterns Experimental results show that the genetic algorithm based approach can achieve higher fault coverages than traditional techniques for most combinational circuits. Another advantage of the approach is that the genetic algorithm needs no detailed knowledge of faulty circuits under test.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.6
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• pp.775-781
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• 2016

A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM) considering different order of Legendre polynomial for the additive genetic effect (4th order) and the permanent environmental effect (5th order). Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11) to 0.99 (TD-4 and TD-5). The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields.

• Journal of Genetic Medicine
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• v.4 no.1
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• pp.1-5
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• 2007

Unprecedented amount of genetic information being generated from the result of Human Genome Project (HGP) and advances in genetic research is already forcing changes in the paradigm of health and disease. The ultimate goal of genetic medicine is to use genetic information and technology to develop new ways of treatment or even prevention of the disease on an individual level for 'personalized medicine'. Genetics is play ing an increasingly important role in the diagnosis, monitoring and management of common multifactorial diseases in addition to rare single-gene disorders. While wide range of genetic testing have provided benefits to patients and family, uncertainties surrounding test interpretation, the current lack of available medical options for the diseases, and risks for discrimination and social stigmatization may remain to be resolved. However an increasing number of genetic tests are becoming commercially available, including direct to consumer genetic testing, yet public is often unaw are of their clinical and social implications. The personal nature of information generated by a genetic test, its power to affect major life decisions and family members, and its potential misuse raise important ethical considerations. Therefore appropriate genetic counseling is needed for patient to be informed with the benefits, limitations and risks of genetic tests, prior to informed consent for the tests. Physician also should be familiar with the legal and ethical issues involved in genetic testing to tell patients how w ell a particular genetic risk factor relates with likelihood of disease, and be able to provide appropriate genetic counseling. Genetic counseling become a mandatory requirement as global standard for many genetic testing such as prenatal diagnosis, presymtomatic DNA diagnostic tests and cancer susceptibility gene test for familial cancer syndrome. In oder to meet the challenge of genetic medicine of 21 century in korean health care system, professional education program and certification board for medical genetics specialist including non-MD genetic counselors should be addressed by medical society and regulatory policy of national health insurance reimbursement for genetic counseling to be in place to promote the implementation of clinical genetic service including genetic counseling for proper genetic testing.

• Genomics & Informatics
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• v.14 no.4
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• pp.187-195
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• 2016

In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's $T^2$ test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.

• Asian-Australasian Journal of Animal Sciences
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• v.26 no.9
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• pp.1229-1236
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• 2013

India possesses a total buffalo population of 105 million out of which 26.1% inhabit Uttar Pradesh. The buffalo of Uttar Pradesh are described as nondescript or local buffaloes. Currently, there is no report about the genetic diversity, phylogenetic relationship and matrilineal genetic structure of these buffaloes. To determine the origin and genetic diversity of UP buffaloes, we sequenced and analysed the mitochondrial DNA D-loop sequences in 259 samples from entire Uttar Pradesh. One hundred nine haplotypes were identified in UP buffaloes that were defined by 96 polymorphic sites. We implemented neutrality tests to assess signatures of recent historical demographic events like Tajima's D test and Fu's Fs test. The phylogenetic studies revealed that there was no geographic differentiation and UP buffaloes had a single maternal lineage while buffaloes of Eastern UP were distinctive from rest of the UP buffaloes.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.912-922
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• 2007

Evaluation of distance teaming systems needs a method that maintains a consistent level of difficulty for each test. In this paper, we propose a new algorithm for test sheet generation based on genetic algorithm. Unlike the existing methods that difficulty of each test item is assigned by tutors, in the proposed method, that can be adjusted by the result of the previous tests and the average difficulty of test sheet can be consistently reserved. We propose the new genetic operators to prevent duplications of test items in a test sheet and apply the adjusted difficulty of each test item. The result of simulation shows that difficulty of the test sheet generated by proposed method can be more regular than the random method and the simulated annealing method.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.9
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• pp.1226-1234
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• 2015

The development of effective genetic evaluations and selection of sires requires accurate estimates of genetic parameters for all economically important traits in the breeding goal. The main objective of this study was to assess the relative performance of the traditional lactation average model (LAM) against the random regression test-day model (RRM) in the estimation of genetic parameters and prediction of breeding values for Holstein Friesian herds in Ethiopia. The data used consisted of 6,500 test-day (TD) records from 800 first-lactation Holstein Friesian cows that calved between 1997 and 2013. Co-variance components were estimated using the average information restricted maximum likelihood method under single trait animal model. The estimate of heritability for first-lactation milk yield was 0.30 from LAM whilst estimates from the RRM model ranged from 0.17 to 0.29 for the different stages of lactation. Genetic correlations between different TDs in first-lactation Holstein Friesian ranged from 0.37 to 0.99. The observed genetic correlation was less than unity between milk yields at different TDs, which indicated that the assumption of LAM may not be optimal for accurate evaluation of the genetic merit of animals. A close look at estimated breeding values from both models showed that RRM had higher standard deviation compared to LAM indicating that the TD model makes efficient utilization of TD information. Correlations of breeding values between models ranged from 0.90 to 0.96 for different group of sires and cows and marked re-rankings were observed in top sires and cows in moving from the traditional LAM to RRM evaluations.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1387-1399
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• 2020

Objective: The objective of this study was to estimate the genetic parameters and trends for milk, fat, and protein yields in the first three lactations of Thai dairy cattle using a 3-trait,-3-lactation random regression test-day model. Methods: Data included 168,996, 63,388, and 27,145 test-day records from the first, second, and third lactations, respectively. Records were from 19,068 cows calving from 1993 to 2013 in 124 herds. (Co) variance components were estimated by Bayesian methods. Gibbs sampling was used to obtain posterior distributions. The model included herd-year-month of testing, breed group-season of calving-month in tested milk group, linear and quadratic age at calving as fixed effects, and random regression coefficients for additive genetic and permanent environmental effects, which were defined as modified constant, linear, quadratic, cubic and quartic Legendre coefficients. Results: Average daily heritabilities ranged from 0.36 to 0.48 for milk, 0.33 to 0.44 for fat and 0.37 to 0.48 for protein yields; they were higher in the third lactation for all traits. Heritabilities of test-day milk and protein yields for selected days in milk were higher in the middle than at the beginning or end of lactation, whereas those for test-day fat yields were high at the beginning and end of lactation. Genetics correlations (305-d yield) among production yields within lactations (0.44 to 0.69) were higher than those across lactations (0.36 to 0.68). The largest genetic correlation was observed between the first and second lactation. The genetic trends of 305-d milk, fat and protein yields were 230 to 250, 25 to 29, and 30 to 35 kg per year, respectively. Conclusion: A random regression model seems to be a flexible and reliable procedure for the genetic evaluation of production yields. It can be used to perform breeding value estimation for national genetic evaluation in the Thai dairy cattle population.