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RISS 인기검색어
- 검색키워드
- 저자 : Hiroko Taniai (검색결과 2 건)
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Heritability of Cerebral Palsy Using Structural Equation Modeling
Makoto Tomita,Hiroko Taniai,문승호,Takeshi Nishiyama,Satoshi Sumi 한국자료분석학회 2009 Journal of the Korean Data Analysis Society Vol.11 No.4
Cerebral palsy(CP) is an umbrella term encompassing a group of non- progressive, non-contagious conditions that cause physical disability in human development, however it is unknown etiology with evidence for genetic influences. The current study combined 10 monozygotic twins, 17 dizygotic twins and 1 monozygotic triplets in order to improve statistical power. At least one proband in each pair was diagnosed as having cerebral palsy. An investigation of genetic structure underlying cerebral palsy was performed. We use binary responses for this study. Among all possible values of the parameters, we found the maximum-likelihood estimate(MLE), the value that maximizes the log-likelihood for the model using statistical package "Mx". A best fitting model of influences on cerebral palsy, incorporating additive genetic (A), shared environmental (C) and non-shared environmental influences (E), was generated using this model framework. From the AE or CE model which was the best model by Akaike's information criterion(AIC), however we got a result of the estimated heritability was almost zero for cerebral palsy.
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Makoto Tomita,Takeshi Nishiyama,Hiroko Taniai,Taishi Miyachi,Satoshi Sumi,문승호 한국자료분석학회 2009 Journal of the Korean Data Analysis Society Vol.11 No.3
Autism is a severe developmental disorder of unknown etiology but with evidence for genetic influences. The current study combined 47 twins in order to improve statistical power. At least one proband in each pair was diagnosed as having autism spectrum disorder(ASD), using the DSM-IV category of pervasive developmental disorder. An investigation of genetic structure underlying autistic traits was performed. We use bivariate coefficient for this study. Among all possible values of the parameters, we found the maximum-likelihood estimate(MLE), the value that maximizes the log-likelihood for the model. A best fitting model of influences on autistic traits, incorporating additive genetic (A), shared environmental (C) and non-shared environmental influences (E), was generated using this model framework. We got the AE model as the best model by Akaike's information criterion(AIC), then the estimated heritability was 0.74 for restricted and repetitive behaviors and interests(RRBI), whereas for social impairments(SI) it was 0.96. They are very high values of heritability, respectively.
KCI
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