Anim Biosci > Volume 32(10); 2019 > Article |
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Population1) | MNA±SD | NEA±SD | HE±SD | HO±SD | Rt±SD | FIS | NE(0.05) |
---|---|---|---|---|---|---|---|
LLS | 6.00±2.56 | 2.932±0.315 | 0.588±0.030 | 0.491±0.013 | 5.22±2.13 | 0.167 | 120.8 |
MGS | 5.33±1.95 | 2.847±0.189 | 0.598±0.033 | 0.573±0.013 | 4.7±1.56 | 0.043 | 74.1 |
YLS | 6.00±2.68 | 3.162±0.261 | 0.628±0.031 | 0.552±0.013 | 5.36±2.16 | 0.123 | 171.3 |
ZTS | 5.93±2.52 | 2.88±0.225 | 0.593±0.034 | 0.530±0.013 | 5.17±1.98 | 0.107 | 97.1 |
GZS | 5.63±2.27 | 2.77±0.227 | 0.582±0.032 | 0.546±0.013 | 4.91±1.88 | 0.062 | 113.6 |
LNS | 6.47±2.74 | 3.528±0.245 | 0.664±0.034 | 0.619±0.012 | 5.72±2.26 | 0.066 | 153.7 |
CDM | 5.93±2.41 | 2.877±0.2 | 0.606±0.028 | 0.595±0.012 | 5.11±1.79 | 0.018 | 80.4 |
LLY | 5.63±2.22 | 3.131±0.216 | 0.64±0.026 | 0.600±0.013 | 5.13±1.82 | 0.062 | 74.4 |
LZS | 5.47±2.26 | 2.793±0.219 | 0.583±0.034 | 0.539±0.012 | 4.71±1.77 | 0.075 | 75.9 |
MGR | 6.93±2.63 | 3.664±0.293 | 0.661±0.037 | 0.556±0.013 | 6.19±2.3 | 0.161 | 143.1 |
CDS | 7.90±3.08 | 4.102±0.291 | 0.709±0.029 | 0.653±0.011 | 6.87±2.47 | 0.080 | 124.1 |
XJS | 8.23±2.93 | 3.962±0.29 | 0.689±0.036 | 0.616±0.012 | 7.03±2.27 | 0.108 | 101.2 |
XZS | 7.37±3.10 | 3.625±0.239 | 0.685±0.027 | 0.612±0.012 | 6.61±2.49 | 0.108 | 96.1 |
ZWS | 6.87±2.69 | 3.903±0.303 | 0.694±0.035 | 0.622±0.018 | 6.8±2.66 | 0.106 | 569.7 |
SNB | 6.50±2.33 | 3.685±0.264 | 0.684±0.028 | 0.600±0.012 | 5.86±1.98 | 0.121 | 47.5 |
HWS | 7.37±2.28 | 4.294±0.312 | 0.729±0.025 | 0.588±0.013 | 6.75±1.96 | 0.195 | 113.2 |
JNQ | 7.30±2.51 | 4.369±0.299 | 0.737±0.024 | 0.646±0.012 | 6.56±2.12 | 0.124 | 98.3 |
YMH | 7.83±2.77 | 3.859±0.299 | 0.704±0.023 | 0.557±0.013 | 6.73±2.24 | 0.210 | 66.1 |
LBB | 7.67±2.51 | 3.822±0.234 | 0.707±0.025 | 0.628±0.013 | 6.72±2.07 | 0.113 | 76.7 |
THS | 6.47±2.34 | 3.344±0.237 | 0.657±0.027 | 0.615±0.011 | 5.59±1.95 | 0.022 | 134.7 |
CJB | 5.77±2.22 | 3.275±0.26 | 0.644±0.029 | 0.623±0.014 | 5.32±1.97 | 0.032 | 374.2 |
MTS | 5.37±2.09 | 3.014±0.224 | 0.625±0.025 | 0.569±0.012 | 4.78±1.82 | 0.091 | 69.4 |
YCB | 5.93±2.32 | 3.25±0.23 | 0.655±0.024 | 0.623±0.012 | 5.19±1.88 | 0.050 | 100.1 |
XDH | 5.37±2.24 | 2.912±0.225 | 0.608±0.030 | 0.549±0.013 | 4.74±1.81 | 0.097 | 96.8 |
FQS | 4.90±2.40 | 2.723±0.247 | 0.569±0.034 | 0.475±0.012 | 4.30±1.93 | 0.168 | 89 |
DYS | 4.40±2.25 | 2.424±0.217 | 0.507±0.039 | 0.434±0.013 | 3.89±1.91 | 0.146 | 32.7 |
Average | 6.33±2.47 | - | 0.644±0.030 | 0.577±0.013 | 5.61±2.04 | 0.167 | - |
n, sample size; MNA, mean number of alleles; SD, standard deviation; NEA, mean number of effective alleles; HE, expected heterozygosity; HO, observed heterozygosity; Rt, allelic richness; FIS, fixation index; NE(0.05), effective population size based on linkage disequilibrium (minor allele frequency 0.05).
1) LLS, Longling yellow goat; MGS, Maguan poll goat; YLS, Yuling goat; ZTS, Zhaotong goat; GZS, Guizhou White goat; LNS, Liaoning Cashmere goat; CDM, Chengdu Brown goat; LLY, Longlin goat; LZS, Leizhou goat; MGR, Inner Mongolia Cashmere goat; CDS, Chaidamu goat; XJS, Xinjiang goat; XZS, Tibetan goat; ZWS, Zhongwei goat; SNB, Shannan White goat; HWS, Huanghuai goat; JNQ, Jining Gray goat; YMH, Yimeng Black goat; LBB, Lubei White goat; THS, Taihang goat; CJB, Yangtse River Delta White goat; MTS, Matou goat; YCB, Yichang White goat; XDH, Xiangdong Black goat; FQS, Fuqing goat; DYS, Daiyun goat.
Breeds1) | WEDs | Bootstrap | WLM | PCHe | PCWeitz | PCFst | PC5:1 |
---|---|---|---|---|---|---|---|
LLS | 0 | 0 | 0 | −0.047 | 2.81 | 0.349 | 2.333 |
MGS | 0.0243 | 0.0211 | 0 | −0.007 | 3.76 | 0.515 | 3.131 |
YLS | 0.0073 | 0.0143 | 0.0419 | −0.035 | 1.58 | 0.189 | 1.310 |
ZTS | 0 | 0 | 0 | −0.098 | 1.96 | 0.187 | 1.616 |
GZS | 0 | 0 | 0 | −0.161 | 2.67 | 0.232 | 2.197 |
LNS | 0.1361 | 0.1324 | 0.1264 | 0.258 | 3.71 | 0.736 | 3.133 |
CDM | 0.0048 | 0.0063 | 0.0242 | 0.121 | 6.71 | 1.034 | 5.610 |
LLY | 0 | 0 | 0 | −0.047 | 3.71 | 0.473 | 3.083 |
LZS | 0 | 0 | 0 | −0.148 | 6.57 | 0.783 | 5.448 |
MGR | 0.1622 | 0.1613 | 0.1071 | 0.307 | 7.00 | 1.235 | 5.882 |
CDS | 0.0229 | 0.0227 | 0.0964 | 0.173 | 2.81 | 0.538 | 2.370 |
XJS | 0 | 0 | 0 | 0.071 | 2.86 | 0.457 | 2.394 |
XZS | 0.0352 | 0.0255 | 0.0406 | 0.227 | 5.45 | 0.951 | 4.578 |
ZWS | 0.0877 | 0.0972 | 0.0781 | 0.074 | 2.79 | 0.450 | 2.336 |
SNB | 0 | 0 | 0 | 0.086 | 3.66 | 0.581 | 3.063 |
HWS | 0.2017 | 0.2027 | 0.0853 | 0.223 | 5.6 | 0.969 | 4.702 |
JNQ | 0.1488 | 0.1469 | 0.2621 | 0.363 | 2.21 | 0.619 | 1.902 |
YMH | 0 | 0 | 0 | 0.035 | 2.04 | 0.313 | 1.705 |
LBB | 0.1353 | 0.1296 | 0.1007 | 0.182 | 4.56 | 0.789 | 3.829 |
THS | 0 | 0 | 0.0181 | −0.014 | 1.94 | 0.256 | 1.614 |
CJB | 0 | 0 | 0 | −0.071 | 3.34 | 0.402 | 2.770 |
MTS | 0 | 0 | 0 | −0.195 | 3.49 | 0.316 | 2.875 |
YCB | 0 | 0 | 0 | −0.124 | 4.14 | 0.467 | 3.428 |
XDH | 0 | 0 | 0 | −0.218 | 2.52 | 0.161 | 2.063 |
FQS | 0.0337 | 0.04 | 0.0193 | −0.200 | 2.67 | 0.198 | 2.191 |
DYS | 0 | 0 | 0 | −0.207 | 3.49 | 0.305 | 2.873 |
Contribution made by each breed to total genetic diversity for 26 Chinese indigenous goat breeds based on methods.
MEK, marker-estimated kinships; WEDs, which vary based on weighted equal drift similarity; Bootstrap, WEDS with bootstrap procedure; WLM, weighted log-linear model; PCweitz, Weitzman approach; PCHe, proportion of expected heterozygosity; PCFst, aggregate methods based on Fst; and PC5:1, the Piyasation and Kinghorn formula. Values representing high contributions to genetic diversity are shown in boldface.
1) LLS, Longling yellow goat; MGS, Maguan poll goat; YLS, Yuling goat; ZTS, Zhaotong goat; GZS, Guizhou White goat; LNS, Liaoning Cashmere goat; CDM, Chengdu Brown goat; LLY, Longlin goat; LZS, Leizhou goat; MGR, Inner Mongolia Cashmere goat; CDS, Chaidamu goat; XJS, Xinjiang goat; XZS, Tibetan goat; ZWS, Zhongwei goat; SNB, Shannan White goat; HWS, Huanghuai goat; JNQ, Jining Gray goat; YMH, Yimeng Black goat; LBB, Lubei White goat; THS, Taihang goat; CJB, Yangtse River Delta White goat; MTS, Matou goat; YCB, Yichang White goat; XDH, Xiangdong Black goat; FQS, Fuqing goat; DYS, Daiyun goat.
Breed2) | f ii | DNei | Contribution to f | Contribution to D | GDT|i | Loss/gain (%) | PC1 (%) | PC2 (%) |
---|---|---|---|---|---|---|---|---|
LLS | 0.4165 | 0.1232 | 0.0108 | 0.0272 | 0.7408 | 0 | 3.673 | 3.673 |
MGS | 0.4078 | 0.1218 | 0.0102 | 0.0264 | 0.7406 | 0 | 3.565 | 3.700 |
YLS | 0.3781 | 0.1053 | 0.0091 | 0.0253 | 0.7408 | 0 | 3.417 | 3.767 |
ZTS | 0.4130 | 0.1166 | 0.0105 | 0.0260 | 0.7412 | 0.1 | 3.511 | 3.646 |
GZS | 0.4236 | 0.1156 | 0.0110 | 0.0256 | 0.7417 | 0.2 | 3.457 | 3.592 |
LNS | 0.3439 | 0.1123 | 0.0093 | 0.0330 | 0.7386 | −0.3 | 4.456 | 3.997 |
CDM | 0.4001 | 0.1279 | 0.0111 | 0.0313 | 0.7396 | −0.1 | 4.227 | 3.781 |
LLY | 0.3678 | 0.0990 | 0.0099 | 0.0282 | 0.7409 | 0 | 3.808 | 3.794 |
LZS | 0.4233 | 0.1177 | 0.0121 | 0.0288 | 0.7416 | 0.1 | 3.889 | 3.605 |
MGR | 0.3442 | 0.1222 | 0.0074 | 0.0276 | 0.7382 | −0.3 | 3.727 | 4.037 |
CDS | 0.2967 | 0.0829 | 0.0086 | 0.0343 | 0.7392 | −0.2 | 4.632 | 4.078 |
XJS | 0.3159 | 0.0841 | 0.0094 | 0.0335 | 0.7400 | −0.1 | 4.524 | 3.983 |
XZS | 0.3217 | 0.1014 | 0.0081 | 0.0306 | 0.7388 | −0.2 | 4.132 | 4.051 |
ZWS | 0.3217 | 0.0961 | 0.0040 | 0.0143 | 0.7400 | −0.1 | 1.931 | 4.010 |
SNB | 0.3218 | 0.0884 | 0.0090 | 0.0318 | 0.7399 | −0.1 | 4.294 | 3.983 |
HWS | 0.2780 | 0.0808 | 0.0067 | 0.0297 | 0.7389 | −0.2 | 4.011 | 4.172 |
JNQ | 0.2692 | 0.0840 | 0.0073 | 0.0356 | 0.7378 | −0.4 | 4.808 | 4.240 |
YMH | 0.3038 | 0.0752 | 0.0083 | 0.0303 | 0.7403 | 0 | 4.092 | 4.010 |
LBB | 0.3011 | 0.0889 | 0.0071 | 0.0280 | 0.7392 | −0.2 | 3.781 | 4.091 |
THS | 0.3486 | 0.0928 | 0.0107 | 0.0331 | 0.7406 | 0 | 4.470 | 3.875 |
CJB | 0.3641 | 0.0940 | 0.0081 | 0.0227 | 0.7411 | 0.1 | 3.065 | 3.794 |
MTS | 0.3806 | 0.0946 | 0.0120 | 0.0317 | 0.7419 | 0.2 | 4.281 | 3.713 |
YCB | 0.3522 | 0.0849 | 0.0098 | 0.0282 | 0.7414 | 0.1 | 3.808 | 3.808 |
XDH | 0.3976 | 0.0986 | 0.0110 | 0.0270 | 0.7421 | 0.2 | 3.646 | 3.646 |
FQS | 0.4361 | 0.1197 | 0.0123 | 0.0278 | 0.7420 | 0.2 | 3.754 | 3.551 |
DYS | 0.4985 | 0.1466 | 0.0119 | 0.0226 | 0.7420 | 0.2 | 3.052 | 3.362 |
f ii, average co-ancestries; DNei, Nei’s genetic distance; f, contribution to global co-ancestry; D, absolute contribution to the total genetic diversity; GDT|i, global diversity; loss/gain(%), the % loss/gain after removing a population from the pool; PC, proportional contribution to gene diversity; PC1 estimates are weighted by population size; PC2 estimates ignore sample size.
1) Values representing high contributions are shown in boldface. Mean co-ancestry within-breed, f = 0.363; mean Nei’s minimum distance in the metapopulation, D = 0.103; mean co-ancestry in the metapopulation, f = 0.246; global genetic diversity of the metapopulation, GDT = 0.741.
2) LLS, Longling yellow goat; MGS, Maguan poll goat; YLS, Yuling goat; ZTS, Zhaotong goat; GZS, Guizhou White goat; LNS, Liaoning Cashmere goat; CDM, Chengdu Brown goat; LLY, Longlin goat; LZS, Leizhou goat; MGR, Inner Mongolia Cashmere goat; CDS, Chaidamu goat; XJS, Xinjiang goat; XZS, Tibetan goat; ZWS, Zhongwei goat; SNB, Shannan White goat; HWS, Huanghuai goat; JNQ, Jining Gray goat; YMH, Yimeng Black goat; LBB, Lubei White goat; THS, Taihang goat; CJB, Yangtse River Delta White goat; MTS, Matou goat; YCB, Yichang White goat; XDH, Xiangdong Black goat; FQS, Fuqing goat; DYS, Daiyun goat.
WEDs, which vary based on weighted equal drift similarity; Bootstrap, WEDS with bootstrap procedure; WLM, weighted log-linear model; PCHe, proportion of expected heterozygosity; PCweitz, Weitzman approach; PCFst, aggregate methods based on Fst; and PC5:1, the Piyasation and Kinghorn formula. PC, proportional contribution to gene diversity; PC1 estimates are weighted by population size; PC2 estimates ignore sample size.
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