Matches in SemOpenAlex for { <https://semopenalex.org/work/W1656656141> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W1656656141 endingPage "17" @default.
- W1656656141 startingPage "1" @default.
- W1656656141 abstract "형제 쌍(sibpair)의 연속형 형질(continuous traits) 자료를 이용한 유전연관성 검정 법(linkage test)으로서 Haseman과 Elston (1972)의 최소제곱(ordinary least square, OLS) 회귀분석법이 주로 사용된다. 비모수적 방법으로서 제시된 Kruglyak과 Lander (1995)의 검정통계량은 Haseman과 Elston (1972)의 방법에 대응되는 방법처럼 보이지만 실제로는 매우 다르다. 본 논문에서는 Kruglyak와 Lander (1995)의 검정통계량과 Haseman과 Elston (1972)의 검정통계량의 관계를 설명하고 모의실험으로 두 검정통계량의 검정력을 비교한다. 유전연관성에 사용되는 형제 자료의 특징은 한정된 설명변수의 값에 매우 많은 자료가 반복(replicated)되었다는 점이며, 이러한 반복 자료에 더욱 적절한 가중 회귀분석법을 제안한다. 가중 회귀분석법의 효율성을 정규분포 또는 정규분포가 아닌 연속형 형질 모의실험 자료로 알아본 결과 형제 쌍 자료의 유전연관성 검정에서 가중 회귀분석법이 다른 검정법들보다도 검정력이 높음을 확인하였다. The ordinary least squares regression method of Haseman and Elston(1972) is most widely used in genetic linkage studies for continuous traits of sib pairs. Kruglyak and Lander(1995) suggested a statistic which appears to be a nonparametric counterpart to the Haseman and Elston(1972)'s regression method, but in fact these two methods are quite different. In this paper the relationships between these two methods are described and will be compared by simulation studies. One of the characteristics of the sib-pair linkage study is that the explanatory variable has only three different values and thus dependent variable is heavily replicated in each value of the explanatory variable. We propose a weighted least squares regression method which is more appropriate to this situation and the efficiency of the weighted regression in genetic linkage study was explored with normal and non-normal simulated continuous traits data. Simulation studies demonstrated that the weighted regression is more powerful than other tests." @default.
- W1656656141 created "2016-06-24" @default.
- W1656656141 creator A5062093949 @default.
- W1656656141 creator A5080150656 @default.
- W1656656141 date "2008-02-29" @default.
- W1656656141 modified "2023-10-18" @default.
- W1656656141 title "Comparisons of Kruglyak and Lander's Nonparametric Linkage Test and Weighted Regression Incorporating Replications" @default.
- W1656656141 cites W1964511379 @default.
- W1656656141 cites W1972978214 @default.
- W1656656141 cites W1977104259 @default.
- W1656656141 cites W1977856930 @default.
- W1656656141 cites W1979540356 @default.
- W1656656141 cites W1983401532 @default.
- W1656656141 cites W2002306732 @default.
- W1656656141 cites W2014158063 @default.
- W1656656141 cites W2021167568 @default.
- W1656656141 cites W2022575489 @default.
- W1656656141 cites W2025481335 @default.
- W1656656141 cites W2053438786 @default.
- W1656656141 cites W2065091130 @default.
- W1656656141 cites W2074235278 @default.
- W1656656141 cites W2076057077 @default.
- W1656656141 cites W2077252204 @default.
- W1656656141 cites W2085500012 @default.
- W1656656141 cites W2089111071 @default.
- W1656656141 cites W2091467053 @default.
- W1656656141 cites W2118113396 @default.
- W1656656141 cites W2154450522 @default.
- W1656656141 cites W2315095963 @default.
- W1656656141 cites W2317327750 @default.
- W1656656141 cites W2801490189 @default.
- W1656656141 cites W3021444882 @default.
- W1656656141 cites W3123186704 @default.
- W1656656141 cites W1970367627 @default.
- W1656656141 doi "https://doi.org/10.5351/kjas.2008.21.1.001" @default.
- W1656656141 hasPublicationYear "2008" @default.
- W1656656141 type Work @default.
- W1656656141 sameAs 1656656141 @default.
- W1656656141 citedByCount "1" @default.
- W1656656141 countsByYear W16566561412013 @default.
- W1656656141 crossrefType "journal-article" @default.
- W1656656141 hasAuthorship W1656656141A5062093949 @default.
- W1656656141 hasAuthorship W1656656141A5080150656 @default.
- W1656656141 hasBestOaLocation W16566561411 @default.
- W1656656141 hasConcept C104317684 @default.
- W1656656141 hasConcept C105795698 @default.
- W1656656141 hasConcept C149782125 @default.
- W1656656141 hasConcept C152877465 @default.
- W1656656141 hasConcept C31266012 @default.
- W1656656141 hasConcept C33923547 @default.
- W1656656141 hasConcept C48921125 @default.
- W1656656141 hasConcept C54355233 @default.
- W1656656141 hasConcept C74127309 @default.
- W1656656141 hasConcept C83546350 @default.
- W1656656141 hasConcept C86803240 @default.
- W1656656141 hasConcept C99656134 @default.
- W1656656141 hasConceptScore W1656656141C104317684 @default.
- W1656656141 hasConceptScore W1656656141C105795698 @default.
- W1656656141 hasConceptScore W1656656141C149782125 @default.
- W1656656141 hasConceptScore W1656656141C152877465 @default.
- W1656656141 hasConceptScore W1656656141C31266012 @default.
- W1656656141 hasConceptScore W1656656141C33923547 @default.
- W1656656141 hasConceptScore W1656656141C48921125 @default.
- W1656656141 hasConceptScore W1656656141C54355233 @default.
- W1656656141 hasConceptScore W1656656141C74127309 @default.
- W1656656141 hasConceptScore W1656656141C83546350 @default.
- W1656656141 hasConceptScore W1656656141C86803240 @default.
- W1656656141 hasConceptScore W1656656141C99656134 @default.
- W1656656141 hasIssue "1" @default.
- W1656656141 hasLocation W16566561411 @default.
- W1656656141 hasOpenAccess W1656656141 @default.
- W1656656141 hasPrimaryLocation W16566561411 @default.
- W1656656141 hasRelatedWork W2017000144 @default.
- W1656656141 hasRelatedWork W2018697919 @default.
- W1656656141 hasRelatedWork W2020471417 @default.
- W1656656141 hasRelatedWork W2023475031 @default.
- W1656656141 hasRelatedWork W2060912888 @default.
- W1656656141 hasRelatedWork W2325374573 @default.
- W1656656141 hasRelatedWork W3196594648 @default.
- W1656656141 hasRelatedWork W4240670533 @default.
- W1656656141 hasRelatedWork W4249094282 @default.
- W1656656141 hasRelatedWork W288566741 @default.
- W1656656141 hasVolume "21" @default.
- W1656656141 isParatext "false" @default.
- W1656656141 isRetracted "false" @default.
- W1656656141 magId "1656656141" @default.
- W1656656141 workType "article" @default.