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백색 Leghorn 종 산란계 계통에 있어 경제형질에 대한 유전적 변화 추세의 추정
박영일,이수찬,서강석,김태훈,정기홍,조용민,이일주,김상용 한국축산학회 1999 한국축산학회지 Vol.41 No.3
Genetic trends on six economic traits were estimated in a line of White Leghorn layers selected over a period of 11 generations from 1976 to 1989 on the basis of the data from 7,602 cocks and 24,392 hens. The average selection differential per generation over the period was -2.59 days for age at first egg, I1.9 eggs for egg production to 43 weeks, 5.70 eggs for egg production from 44 to 57 weeks, 0.73 g for egg weight at 30 weeks. 0.5 i g for egg weight at 40 weeks, and 0.55 g for egg weight at 60 weeks. The animal model was used for the prediction of the breeding value for each trait. The estimated average changes of breeding value per generation over the generations 0-11 were -0.27 day for age at first egg, 1.38 egg for egg production to 43 weeks, 0.52 egg for egg production from 44 to 57 weeks, 0.28 g for egg weight at 30 weeks, 0.26 g for egg weight at 40 weeks, and 0.22 g for egg weight at 60 weeks. In view of the results obtained in this study, a substantial amount of genetic improvement in the six traits appeared to have been achieved in the line by selection for higher egg production, heavier egg weights and for lower age at first egg.
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최진성,이일주,조규호,서강석,이정규 한국동물자원과학회 2004 한국축산학회지 Vol.46 No.2
This study was conducted to estimate genetic parameter of Duroc, Landrace and Yorkshire breeds based on the on-farm performance tested records of 57,316 pigs under the supervision of Korean Animal Improvement Association from 1992 to 1999. Genetic parameters were estimated with a multiple trait animal model by using DF-REML. The result obtained in this study was summarized as follow; The estimated heritabilities of Duroc, Landrace and Yorkshire were 0.46~0.65 for the average backfat thickness, 0.28~0.31 for loin depth, 0.50~0.60 for percent lean, 0.45~0.55 for the average daily gain, 0.38~0.50 for age at 90㎏, respectively. Phenotypic correlation of average backfat thickness with loin depth, percent lean, average daily gain and age at 90㎏ for the three breeds were -0.12~-0.01, -0.81~-0.76, 0.34~0.46, and -0.41~-0.33, respectively. Phenotypic correlation of loin depth with percent lean, average daily gain and age at 90㎏ were 0.12~0.23, 0.03~0.21, and -0.17~-0.03, respectively. Phenotypic correlation of percent lean with average daily gain and age at 90㎏ were -0.37~-0.26 and 0.26~0.35, respectively. Phenotypic correlation of average daily gain with age at 90㎏ was -0.97~-0.95. The estimated genetic correlation coefficients fo average backfat thickness with loin depth, percent lean, average daily gain and age at 90㎏ estimated for the three breeds were -0.17~0.03, -0.79~-0.69, 0.24~0.45 and -0.41~-0.19, respectively. The estimated genetic correlation coefficients of loin depth with percent lean, average daily gain and age at 90㎏ were 0.11~0.19, 0.23 and -0.30~-0.20, respectively. The estimated correlation coefficients of percent lean with average daily gain and age at 90㎏ were -0.36~-0.13 and 0.10~0.34, respectively. The estimated genetic correlation coefficients of average daily gain with age at 90㎏ was -0.96~-0.95.
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돼지의 선발에 있어서 능력검정 방법에 따른 육종가 및 유전적 개량량의 변화
조규호,김시동,김명직,이일주,전광주 한국동물자원과학회 2001 한국축산학회지 Vol.43 No.6
본 연구에서는 이유시체중(㎏), 90㎏ 도달일령을 대상형질로 하여 폐쇄돈군에서 이유시 체중을 기초로 5가지의 선발방법에 따라 선발된 선발축의 산육 능력을 검정하여 검정이 완료된 개체들에 대해 유전능력 평가를 실시한후 검정대상 선발두수의 크기에 따른 유전적 개량량을 비교하여 이상적인 검정방법을 찾고자 실시하였다. 시뮬레이션 결과 90㎏ 도달일령에 대한 육종가 추정치의 변화는 Method Ⅴ그룹에서 -36.2일의 단축효과를 보였으며 세대당 유전적 개량량 또한 Method Ⅴ그룹에서 -7.5일로 가장 높은 개량을 보였다. 따라서 개량적인 측면에 있어서는 전수검정을 실시하는 것이 가장 좋을 것으로 사료되지만 농장의 검정능력이나 전수검정을 실시할 때 드는 초과비용에 대한 경제적인 분석은 더 연구되어져야 될 것으로 사료된다. This study was carried out to find the ideal testing method by comparing predicted breeding values, and genetic gain by the size of testing group. Tested traits were weaning weight and days to 90㎏. Five methods for selecting testing group were adopted: Method Ⅰ (MⅠ ; 30% selected based on litter weight at weaning for female, and 30% selected based on individual weight at weaning for mail), Method Ⅱ (MⅡ ; 30% selected based on individual weight at weaning for female and male, respectively), Method Ⅲ (MⅢ ; 50% selected based on individual weight at weaning for female and male, respectively), Method Ⅳ (MⅣ ; 70% selected based on individual weight at weaning for female and male, respectively), Method Ⅴ (MⅤ ; all piglets were tested). Genetic ability was evaluated by multiple trait animal model using MTDFREML package. Based on the predicted breeding values for days to 90㎏, the next generation was selected up to 5th generation. The above procedure were repeated and simulated by 4 times. The results obtained in the present study are summarized as follows; Phenotypic mean and predicted breeding values mean of days to 90㎏ was 114.3, -27.8, 113.7, -27.0, 107.5, -32.6, 105.7, -34.4, 103.7 and -36.2 days for MⅠ, MⅡ, MⅢ, MⅣ and MⅤ, respectively. Phenotypic and predicted breeding values variance of days to 90㎏ was 20.9, 4.1, 19.0, 3.4, 15.3, 1.8, 13.8, 1.7, 13.3 and 1.2 days for MⅠ, MⅡ, MⅢ, MⅣ and MⅤ, respectively. Genetic gain of days to 90㎏ was -6.2, -6.0, -7.1, -7.3 and -7.5 days for MⅠ, MⅡ, MⅢ, MⅣ and MⅤ, respectively. In all case, Method Ⅴ group, all piglets be tested, is the most efficient for improving the target economic traits.
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젖소의 체세포 지수와 생산형질에 대한 환경효과와 유전모수의 추정
김시동,박영일,최유림,한광진,이일주,정종현,김낙환 한국동물자원과학회 2001 한국축산학회지 Vol.43 No.4
Effects of environmental factors and genetic parameters for somatic cell score(SCS) and production traits were estimated based on 4,722 records of Hostein cows at 113 test farms measured from January, 1994 to June, 1999. Calving year, calving season, cooperative and farm had significant effects on the five traits studied, SCS, milk, fat, protein and SNF(solid not fat) production, except for the farm effect on protein production. The SCS was significantly higher in the cows calving in summer than in other seasons. Milk production was highest in the cows calving in winter and lowest in summer. Heritabilities estimated by multiple trait animal model were 0.03, 0.28, 0.28, 0.30 and 0.29, respectively for SCS, milk, fat, protein and SNF production. Genetic correlations of SCS with milk, fat, protein and SNF production estimated were 0.18, 0.21, 0.25 and 0.22, respectively. Genetic correlations among fat, protein and SNF production were positive and high. Phenotypic correlations of SCS with milk, fat, protein and SNF production estimated were very low.
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