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Line |
Allele |
Population |
% of individuals
that have the allele |
Allele
Frequency
(in_decimals) |
Sample
Size |
IMGT/HLA¹
Database |
Distribution² |
Haplotype³
Association |
Notesª |
101 |
A*01 | | South Korea pop 8 | | 0.0190 | | 7,096 | See | | | |
102 |
A*01 | | Spain Arratia Valley Basque | | 0.1920 | | 83 | See | | | |
103 |
A*01 | | Spain Catalonia Girona | | 0.0860 | | 88 | See | | | |
104 |
A*01 | | Spain Gipuzkoa Basque | | 0.1350 | | 100 | See | | | |
105 |
A*01 | | Spain Ibiza | | 0.1380 | | 88 | See | | | |
106 |
A*01 | | Spain Majorca | | 0.1060 | | 407 | See | | | |
107 |
A*01 | | Spain Majorca and Minorca | | 0.0930 | | 90 | See | | | |
108 |
A*01 | | Spain Majorcans Jews | | 0.1590 | | 103 | See | | | |
109 |
A*01 | | Spain Minorca | | 0.0600 | | 94 | See | | | |
110 |
A*01 | | Spain Murcia | | 0.0950 | | 173 | See | | | |
111 |
A*01 | | Spain North Cabuerniga | | 0.0760 | | 95 | See | | | |
112 |
A*01 | | Spain North Cantabria | | 0.1080 | | 83 | See | | | |
113 |
A*01 | | Spain Pas Valley | | 0.0380 | | 88 | See | | | |
114 |
A*01 | | Sweden Stockholm | 25.7 | 0.1360 | | 1,347 | See | | | |
115 |
A*01 | | Sweden Uppsala | 24.3 | 0.1299 | | 300 | See | | | |
116 |
A*01 | | Taiwan Aborigine | | 0 | | 111 | See | | | |
117 |
A*01 | | Taiwan Chinese immigrants from Middle China | | 0.0090 | | 211 | See | | | |
118 |
A*01 | | Taiwan Chinese immigrants from North China | | 0.0300 | | 152 | See | | | |
119 |
A*01 | | Taiwan Chinese immigrants from South China | | 0.0150 | | 172 | See | | | |
120 |
A*01 | | Taiwan Hakka pop 2 | | 0.0020 | | 714 | See | | | |
121 |
A*01 | | Taiwan Minnan and Hakka | | 0.0030 | | 190 | See | | | |
122 |
A*01 | | Taiwan Minnan pop 2 | | 0.0030 | | 7,137 | See | | | |
123 |
A*01 | | Thailand Northeast | | 0.0240 | | 66 | See | | | |
124 |
A*01 | | Thailand Northeast pop 2 | | 0.0160 | | 400 | See | | | |
125 |
A*01 | | Thailand pop 4 | | 0.0220 | | 16,807 | See | | | |
126 |
A*01 | | Tunisia Ghannouch | | 0.1100 | | 82 | See | | | |
127 |
A*01 | | Tunisia pop 3 | | 0.0770 | | 104 | See | | | |
128 |
A*01 | | Turkey pop 2 | | 0.0660 | | 228 | See | | | |
129 |
A*01 | | United Kingdom pop 2 | | 0.2050 | | 101 | See | | | |
130 |
A*01 | | USA OPTN African American | | 0.0550 | | 1,510 | See | | | |
131 |
A*01 | | USA OPTN Asian | | 0.0320 | | 261 | See | | | |
132 |
A*01 | | USA OPTN Caucasian | | 0.1570 | | 8,525 | See | | | |
133 |
A*01 | | USA OPTN Hispanic | | 0.0630 | | 1,580 | See | | | |
134 |
A*01 | | Venezuela Caracas Mestizo | | 0.0700 | | 50 | See | | | |
135 |
A*01 | | Venezuela Colonia Tovar | | 0.1221 | | 86 | See | | | |
136 |
A*01 | | Vietnam Hanoi | | 0.0300 | | 50 | See | | | |
137 |
A*01 | | Wales | 37.4 | 0.2110 | | 1,798 | See | | | |
138 |
A*01:01 | | Australia Cape York Peninsula Aborigine | | 0.0530 | | 103 | See | | | |
139 |
A*01:01 | | Australia Groote Eylandt Aborigine | | 0.0270 | | 75 | See | | | |
140 |
A*01:01 | | Australia New South Wales Caucasian | | 0.1870 | | 134 | See | | | |
141 |
A*01:01 | | Australia Yuendumu Aborigine | | 0.0080 | | 191 | See | | | |
142 |
A*01:01 | | Azores Central Islands | | 0.0800 | | 59 | See | | | |
143 |
A*01:01 | | Azores Oriental Islands | | 0.1150 | | 43 | See | | | |
144 |
A*01:01 | | Belgium | 29.2 | 0.1550 | | 99 | See | | | |
145 |
A*01:01 | | Brazil Belo Horizonte Caucasian | 13.7 | 0.0790 | | 95 | See | | | |
146 |
A*01:01 | | Cameroon Baka Pygmy | | 0 | | 10 | See | | | |
147 |
A*01:01 | | Cameroon Bamileke | | 0.0260 | | 77 | See | | | |
148 |
A*01:01 | | Cameroon Beti | | 0.0110 | | 174 | See | | | |
149 |
A*01:01 | | Cameroon Sawa | | 0 | | 13 | See | | | |
150 |
A*01:01 | | Cameroon Yaounde | | 0.0110 | | 92 | See | | | |
151 |
A*01:01 | | Central African Republic Mbenzele Pygmy | 0.0 | 0 | | 36 | See | | | |
152 |
A*01:01 | | China Beijing | | 0.0370 | | 67 | See | | | |
153 |
A*01:01 | | China Beijing Shijiazhuang Tianjian Han | | 0.0340 | | 618 | See | | | |
154 |
A*01:01 | | China Canton Han | | 0.0060 | | 264 | See | | | |
155 |
A*01:01 | | China Guangzhou | | 0.0100 | | 102 | See | | | |
156 |
A*01:01 | | China Guizhou Province Bouyei | | 0.0050 | | 109 | See | | | |
157 |
A*01:01 | | China Guizhou Province Miao pop 2 | | 0 | | 85 | See | | | |
158 |
A*01:01 | | China Guizhou Province Shui | | 0 | | 153 | See | | | |
159 |
A*01:01 | | China Inner Mongolia Region | | 0.0540 | | 102 | See | | | |
160 |
A*01:01 | | China North Han | | 0 | | 105 | See | | | |
161 |
A*01:01 | | China Qinghai Province Hui | | 0.0550 | | 110 | See | | | |
162 |
A*01:01 | | China Yunnan Province Han | | 0.0150 | | 101 | See | | | |
163 |
A*01:01 | | Cuba Caucasian | 15.7 | 0.0790 | | 70 | See | | | |
164 |
A*01:01 | | Cuba Mixed Race | 14.3 | 0.0710 | | 42 | See | | | |
165 |
A*01:01 | | England North West | 38.6 | 0.2080 | | 298 | See | | | |
166 |
A*01:01 | | Georgia Svaneti Region Svan | | 0.0560 | | 80 | See | | | |
167 |
A*01:01 | | Germany DKMS - Austria minority | | 0.1561 | | 1,698 | See | | | |
168 |
A*01:01 | | Germany DKMS - Bosnia and Herzegovina minority | | 0.1513 | | 1,028 | See | | | |
169 |
A*01:01 | | Germany DKMS - China minority | | 0.0380 | | 1,282 | See | | | |
170 |
A*01:01 | | Germany DKMS - Croatia minority | | 0.1213 | | 2,057 | See | | | |
171 |
A*01:01 | | Germany DKMS - France minority | | 0.1376 | | 1,406 | See | | | |
172 |
A*01:01 | | Germany DKMS - Greece minority | | 0.1038 | | 1,894 | See | | | |
173 |
A*01:01 | | Germany DKMS - Italy minority | | 0.1330 | | 1,159 | See | | | |
174 |
A*01:01 | | Germany DKMS - Netherlands minority | | 0.1681 | | 1,374 | See | | | |
175 |
A*01:01 | | Germany DKMS - Portugal minority | | 0.1158 | | 1,176 | See | | | |
176 |
A*01:01 | | Germany DKMS - Romania minority | | 0.1457 | | 1,234 | See | | | |
177 |
A*01:01 | | Germany DKMS - Spain minority | | 0.1189 | | 1,107 | See | | | |
178 |
A*01:01 | | Germany DKMS - Turkey minority | | 0.1032 | | 4,856 | See | | | |
179 |
A*01:01 | | Germany DKMS - United Kingdom minority | | 0.1803 | | 1,043 | See | | | |
180 |
A*01:01 | | Germany pop 6 | | 0.1541 | | 8,862 | See | | | |
181 |
A*01:01 | | Ghana Ga-Adangbe | 4.6 | 0.0229 | | 131 | See | | | |
182 |
A*01:01 | | Greece pop 6 | | 0.1136 | | 242 | See | | | |
183 |
A*01:01 | | Guinea Bissau Balanta | | 0.0210 | | 48 | See | | | |
184 |
A*01:01 | | Guinea Bissau Bijago | | 0.0220 | | 23 | See | | | |
185 |
A*01:01 | | Guinea Bissau Fula | | 0.0810 | | 31 | See | | | |
186 |
A*01:01 | | Guinea Bissau Papel | | 0.0800 | | 25 | See | | | |
187 |
A*01:01 | | India Delhi pop 2 | 20.0 | 0.1050 | | 90 | See | | | |
188 |
A*01:01 | | India Khandesh Region Pawra | | 0.0500 | | 50 | See | | | |
189 |
A*01:01 | | India Mumbai Maratha | | 0.1230 | | 91 | See | | | |
190 |
A*01:01 | | India North pop 2 | | 0.1150 | | 72 | See | | | |
191 |
A*01:01 | | India Tamil Nadu Nadar | | 0.0900 | | 61 | See | | | |
192 |
A*01:01 | | India West Bhil | | 0 | | 50 | See | | | |
193 |
A*01:01 | | India West Coast Parsi | | 0.0100 | | 50 | See | | | |
194 |
A*01:01 | | Indonesia Java Western | 5.1 | 0.0260 | | 236 | See | | | |
195 |
A*01:01 | | Ireland Northern | 36.4 | 0.2020 | | 1,000 | See | | | |
196 |
A*01:01 | | Italy North pop 3 | 26.9 | 0.1540 | | 97 | See | | | |
197 |
A*01:01 | | Italy pop 5 | | 0.1020 | | 975 | See | | | |
198 |
A*01:01 | | Japan Central | | 0.0090 | | 371 | See | | | |
199 |
A*01:01 | | Japan Hokkaido Ainu | | 0 | | 50 | See | | | |
200 |
A*01:01 | | Japan pop 3 | | 0.0020 | | 1,018 | See | | | |
Notes:
* Allele Frequency: Total number of copies of the allele in the population sample (Alleles / 2n) in decimal format.
Important: This field has been expanded to four decimals to better represent frequencies of large datasets (e.g. where sample size > 1000 individuals)
* % of individuals that have the allele: Percentage of individuals who have the allele in the population (Individuals / n).
* Allele Frequencies shown in
green were calculated from Phenotype Frequencies assuming Hardy-Weinberg proportions.
AF = 1-square_root(1-PF)
PF = 1-(1-AF)
2
AF = Allele Frequency; PF = Phenotype Frequency, i.e. (%) of the individuals carrying the allele.
* Allele Frequencies marked with (*) were calculated from all alleles in the corresponding
G group.
¹ IMGT/HLA Database - For more details of the allele.
² Distribution - Graphical distribution of the allele.
³ Haplotype Association - Find HLA haplotypes with this allele.
ª Notes - See notes for ambiguous combinations of alleles.
Displaying 101 to 200
(from 60,683) records |
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