Methodology of mapping quantitative trait loci for binary traits in a half-sib design using maximum likelihood
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作者:
Yin, ZJ
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机构:China Agr Univ, State Key Lab Agrobiotechnol, Beijing 100094, Peoples R China
Yin, ZJ
Zhang, Q
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机构:
China Agr Univ, State Key Lab Agrobiotechnol, Beijing 100094, Peoples R ChinaChina Agr Univ, State Key Lab Agrobiotechnol, Beijing 100094, Peoples R China
Zhang, Q
[1
]
Zhang, JG
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机构:China Agr Univ, State Key Lab Agrobiotechnol, Beijing 100094, Peoples R China
Zhang, JG
Ding, XD
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机构:China Agr Univ, State Key Lab Agrobiotechnol, Beijing 100094, Peoples R China
Ding, XD
Wang, CK
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机构:China Agr Univ, State Key Lab Agrobiotechnol, Beijing 100094, Peoples R China
Wang, CK
机构:
[1] China Agr Univ, State Key Lab Agrobiotechnol, Beijing 100094, Peoples R China
[2] China Agr Univ, Minist Agr China, Key Lab Anim Genet & Breeding, Beijing 100094, Peoples R China
discrete traits;
threshold models;
QTL mapping;
maximum likelihood;
D O I:
暂无
中图分类号:
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号:
0905 ;
摘要:
Maximum likelihood methodology was applied to analyze the efficiency and statistical power of interval mapping by using a threshold model. The factors that affect QTL detection efficiency (e.g. QTL effect, heritability and incidence of categories) were simulated in our study. Daughter design with multiple families was applied, and the size of segregating population is 500. The results showed that the threshold model has a great advantage in parameters estimation and power of QTL mapping, and has nice efficiency and accuracy for discrete traits. In addition, the accuracy and power of QTL mapping depended on the effect of putative quantitative trait loci, the value of heritability and incidence directly. With the increase of QTL effect, heritability and incidence of categories, the accuracy and power of QTL mapping improved correspondingly.
机构:
Univ Alabama Birmingham, Dept Biostat, Sect Stat Genet, Birmingham, AL 35294 USAUniv Alabama Birmingham, Dept Biostat, Sect Stat Genet, Birmingham, AL 35294 USA
Banerjee, Samprit
Yandell, Brian S.
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机构:
Univ Wisconsin, Dept Stat, Madison, WI 53706 USA
Univ Wisconsin, Dept Hort, Madison, WI 53706 USA
Univ Wisconsin, Dept Biostat, Madison, WI 53706 USA
Univ Wisconsin, Dept Med Informat, Madison, WI 53706 USAUniv Alabama Birmingham, Dept Biostat, Sect Stat Genet, Birmingham, AL 35294 USA
Yandell, Brian S.
Yi, Nengjun
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机构:
Univ Alabama Birmingham, Dept Biostat, Sect Stat Genet, Birmingham, AL 35294 USAUniv Alabama Birmingham, Dept Biostat, Sect Stat Genet, Birmingham, AL 35294 USA