博文
双列杂交试验之ASReml-R篇
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某林木有8个亲本，进行双列杂交试验，随机完全区组设计，下设2区组。目标性状为10年生胸径。
分析代码如下：
1 2 3 4 5 6 7 8 | #################### diallel mate design ######################## library(asreml) df<-asreml.read.table(file='dial.to.csv',header=T,sep=',') df.asr<-asreml(dbh~1,random=~ Block+Male+and(Female)+Fam+Female+Recipro, data= df, maxit=50) summary(df.asr)$varcomp #dial.to.bv<-coef(df.asr)$random #write.csv(dial.to.bv,file='dial.to.bv.csv') |
代码说明：dbh是胸径，block是区组，Male是父本，Female是母本，Fam是家系，Recipro是杂交组合代码。
运行结果如下：
1 2 3 4 5 6 7 8 | > summary(df.asr)$varcomp gamma component std.error z.ratio constraint Block!Block.var 0.00591629 0.01996032 0.1032074 0.1934001 Positive Male!Male.var 2.01274534 6.79057951 4.1035712 1.6547975 Positive Fam!Fam.var 0.62227576 2.09942755 1.7345587 1.2103525 Positive Female!Female.var 1.36716171 4.61251611 3.2528596 1.4179881 Positive Recipro!Recipro.var 1.23917287 4.18070869 1.8172940 2.3005131 Positive R!variance 1.00000000 3.37378971 0.6011222 5.6124861 Positive |
本例中，可以看出，虽然亲本一样，但作为父本和母本时，所得的方差分量不同，而且各自作为父本和母本时所得的育种值也不同，根据所得的父母本育种值，可知哪些亲本作为父本，哪些亲本作为母本，杂交结果会比较好。同时，杂交组合的育种值，可以分析特殊配合力，即哪些亲本相互组合可以获得更高的杂种优势。具体如下：
输出各亲本作为父本时的育种值：
1 2 3 4 5 6 7 8 9 10 | > coef(df.asr, pattern = 'Male') effect Male_G1 2.8419922 Male_G2 -0.8390384 Male_G3 4.4844745 Male_G4 -1.3457288 Male_G5 -1.2762229 Male_G6 0.1061856 Male_G7 -2.9641414 Male_G8 -1.0075208 |
输出各亲本作为母本时的育种值；
1 2 3 4 5 6 7 8 9 10 | > coef(df.asr, pattern = 'Female') effect Female_G1 0.1570306 Female_G2 -0.9412692 Female_G3 -2.4065105 Female_G4 3.4034672 Female_G5 1.1377920 Female_G6 1.2131451 Female_G7 -0.8119395 Female_G8 -1.7517159 |
最后，输出各杂交组合的育种值：
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 | > coef(df.asr, pattern = 'Recipro') effect Recipro_G1G1 0.55834654 Recipro_G1G2 -0.91213753 Recipro_G1G3 3.17754153 Recipro_G1G4 0.52682385 Recipro_G1G5 0.21302846 Recipro_G1G6 -2.71220293 Recipro_G1G7 0.67080105 Recipro_G1G8 0.08522780 Recipro_G2G1 -1.23155531 Recipro_G2G2 2.11181041 Recipro_G2G3 -2.16858281 Recipro_G2G4 0.63198062 Recipro_G2G5 -0.11236810 Recipro_G2G6 -0.42798672 Recipro_G2G7 1.64849755 Recipro_G2G8 -0.11519544 Recipro_G3G1 2.84726418 Recipro_G3G2 1.98873719 Recipro_G3G3 -0.08260142 Recipro_G3G4 1.16595510 Recipro_G3G5 -0.44464843 Recipro_G3G6 4.08785230 Recipro_G3G7 -2.02457838 Recipro_G3G8 -2.59567082 Recipro_G4G1 -0.79385737 Recipro_G4G2 -0.83241389 Recipro_G4G3 -0.71508543 Recipro_G4G4 0.46861624 Recipro_G4G5 -0.59370006 Recipro_G4G6 -0.55839345 Recipro_G4G7 0.75871240 Recipro_G4G8 -1.64737575 Recipro_G5G1 -0.90506868 Recipro_G5G2 -0.30541453 Recipro_G5G3 -0.98308996 Recipro_G5G4 -1.35300358 Recipro_G5G5 0.01032860 Recipro_G5G6 0.31743070 Recipro_G5G7 -0.45002689 Recipro_G5G8 1.85178415 Recipro_G6G1 1.81658791 Recipro_G6G2 -0.31796512 Recipro_G6G3 -2.16822503 Recipro_G6G4 0.58851704 Recipro_G6G5 -0.48389074 Recipro_G6G6 1.77341580 Recipro_G6G7 -1.62984070 Recipro_G6G8 -0.61283634 Recipro_G7G1 -0.87461037 Recipro_G7G2 -1.03814021 Recipro_G7G3 0.10908140 Recipro_G7G4 -1.03353833 Recipro_G7G5 1.22456136 Recipro_G7G6 -0.43644782 Recipro_G7G7 0.60097623 Recipro_G7G8 0.35910350 Recipro_G8G1 -1.27477188 Recipro_G8G2 -1.54765747 Recipro_G8G3 0.64966283 Recipro_G8G4 2.08960523 Recipro_G8G5 1.21800148 Recipro_G8G6 -0.94405411 Recipro_G8G7 -0.31049596 Recipro_G8G8 1.08718011 |
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