Line |
Haplotype |
Population |
Frequency (%) |
Sample Size |
Distribution¹ |
1 | A*66-B*41-DRB1*11-DQB1*03:01 | | Mexico San Luis Potosi Rural | 2.2989 | | 87 |
|
2 | A*11-B*41-DRB1*11-DQB1*03:01 | | Mexico Jalisco, Tlajomulco | 1.6667 | | 30 |
|
3 | A*02-B*41-DRB1*11-DQB1*03:01 | | Mexico Queretaro, Queretaro city | 1.1111 | | 45 |
|
4 | A*02-B*41-DRB1*11-DQB1*03:01 | | Mexico Mexico City East | 0.6250 | | 79 |
|
5 | A*02-B*41-DRB1*11-DQB1*03:01 | | Mexico Veracruz, Veracruz city | 0.5814 | | 171 |
|
6 | A*11-B*41-DRB1*11-DQB1*03:01 | | Guatemala, Guatemala City Mixed Ancestry | 0.3900 | | 127 |
|
7 | A*23:01:01-B*41:01:01-C*17:01:01-DRB1*11:01:02-DQB1*03:01:01-DPA1*02:01:01-DPB1*04:01:01 | | Brazil Rio de Janeiro Caucasian | 0.3891 | | 521 |
|
8 | A*24:02:01-B*41:02:01-C*17:03:01-DRB1*11:01:01-DQA1*05:05:01-DQB1*03:01-DPA1*01:03:01-DPB1*04:01:01 | | Russia Belgorod region | 0.3268 | | 153 |
|
9 | A*66-B*41-DRB1*11-DQB1*03:01 | | Mexico Mexico City Center | 0.3247 | | 152 |
|
10 | A*24-B*41-DRB1*11-DQB1*03:01 | | Mexico Guanajuato Rural | 0.3067 | | 162 |
|
11 | A*02:05-B*41:01-C*17:01-DRB1*11:01-DQA1*03:01-DQB1*03:01-DPB1*01:01 | | South Africa Worcester | 0.3000 | | 159 |
|
12 | A*24-B*41-DRB1*11-DQB1*03:01 | | Mexico Veracruz, Xalapa | 0.2674 | | 187 |
|
13 | A*23-B*41-DRB1*11-DQB1*03:01 | | Mexico Sonora Rural | 0.2538 | | 197 |
|
14 | A*02-B*41-DRB1*11-DQB1*03:01 | | Mexico Chihuahua Rural | 0.2092 | | 236 |
|
15 | A*03:02-B*41:02:01-C*17:01-DRB1*11:04:01-DQB1*03:01 | | England North West | 0.2000 | | 298 |
|
16 | A*23-B*41-DRB1*11-DQB1*03:01 | | Mexico Zacatecas Rural | 0.1859 | | 266 |
|
17 | A*02:01-B*41:01-C*07:328-DRB1*11:01-DQB1*03:01-DPB1*233:01 | | Tanzania Maasai | 0.1597 | | 336 |
|
18 | A*23:01-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01-DPB1*04:01 | | Tanzania Maasai | 0.1597 | | 336 |
|
19 | A*26-B*41-DRB1*11-DQB1*03:01 | | Mexico Michoacan Rural | 0.1433 | | 348 |
|
20 | A*02:02-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | Italy pop 5 | 0.1400 | | 975 |
|
21 | A*02:05-B*41:01-C*07:01-DRB1*11:01-DQB1*03:01 | | Italy pop 5 | 0.1400 | | 975 |
|
22 | A*30-B*41-DRB1*11-DQB1*03:01 | | Mexico Coahuila, Torreon | 0.1250 | | 396 |
|
23 | A*02:01-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | Germany DKMS - Italy minority | 0.0860 | | 1,159 |
|
24 | A*11-B*41-DRB1*11-DQB1*03:01 | | Mexico Jalisco Rural | 0.0853 | | 585 |
|
25 | A*24-B*41-DRB1*11:01-DQA1*05:05-DQB1*03:01 | | Brazil Paraná Caucasian | 0.0780 | | 641 |
|
26 | A*32-B*41-DRB1*11:04-DQA1*05:01-DQB1*03:01 | | Brazil Paraná Caucasian | 0.0780 | | 641 |
|
27 | A*02:01:01:01-B*41:01:01-C*17:01:01-DRB1*11:04:01-DQB1*03:01 | | Russia Nizhny Novgorod, Russians | 0.0662 | | 1,510 |
|
28 | A*11-B*41-DRB1*11-DQB1*03:01 | | Ecuador Andes Mixed Ancestry | 0.0607 | | 824 |
|
29 | A*23-B*41-DRB1*11-DQB1*03:01 | | Ecuador Andes Mixed Ancestry | 0.0607 | | 824 |
|
30 | A*02-B*41-DRB1*11-DQB1*03:01 | | Mexico Puebla Rural | 0.0600 | | 833 |
|
31 | A*03:01-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01-DPB1*13:01 | | Russia Karelia | 0.0565 | | 1,075 |
|
32 | A*03:01-B*41:01-C*07:01-DRB1*11:01-DQB1*03:01-DPB1*02:01 | | Russia Karelia | 0.0558 | | 1,075 |
|
33 | A*23:01-B*41:02-C*17:01-DRB1*11:04-DQB1*03:01 | | USA Hispanic pop 2 | 0.0470 | | 1,999 |
|
34 | A*01:01-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | Germany DKMS - Italy minority | 0.0430 | | 1,159 |
|
35 | A*11-B*41-DRB1*11-DQB1*03:01 | | Ecuador Mixed Ancestry | 0.0426 | | 1,173 |
|
36 | A*23-B*41-DRB1*11-DQB1*03:01 | | Ecuador Mixed Ancestry | 0.0426 | | 1,173 |
|
37 | A*66:01-B*41:02-C*17:01-DRB1*11:01-DQB1*03:01 | | Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, | 0.0340 | | 4,335 |
|
38 | A*68:01-B*41:02-C*17:01-DRB1*11:01-DQB1*03:01 | | Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria, | 0.0340 | | 4,335 |
|
39 | A*01:01:01:01-B*41:01:01-C*17:01:01-DRB1*11:01:01-DQB1*03:01 | | Russia Nizhny Novgorod, Russians | 0.0331 | | 1,510 |
|
40 | A*02:01:01:01-B*41:02:01-C*17:03-DRB1*11:03-DQB1*03:01 | | Russia Nizhny Novgorod, Russians | 0.0331 | | 1,510 |
|
41 | A*23:01:01-B*41:02:01-C*17:03-DRB1*11:04-DQB1*03:01 | | Russia Nizhny Novgorod, Russians | 0.0331 | | 1,510 |
|
42 | A*02:03:01-B*41:01:01-C*17:01:01-DRB1*11:04:01-DQB1*03:01:01 | | China Zhejiang Han | 0.0288 | | 1,734 |
|
43 | A*33:01-B*41:02-C*17:01-DRB1*11:02-DQB1*03:01 | | USA African American pop 4 | 0.0220 | | 2,411 |
|
44 | A*23:01-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | India North UCBB | 0.0171 | | 5,849 |
|
45 | A*66:01-B*41:02-C*17:01-DRB1*11:04-DQB1*03:01 | | Germany DKMS - Turkey minority | 0.0150 | | 4,856 |
|
46 | A*66:01:01-B*41:02:01-C*17:03-DRB1*11:01:01-DQB1*03:01:01 | | Poland BMR | 0.0142 | | 23,595 |
|
47 | A*01:01-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | Germany DKMS - Turkey minority | 0.0140 | | 4,856 |
|
48 | A*01:01-B*41:01-C*02:08-DRB1*11:01-DQB1*03:01 | | India Central UCBB | 0.0119 | | 4,204 |
|
49 | A*31:06-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | India Central UCBB | 0.0119 | | 4,204 |
|
50 | A*02:02-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | Germany DKMS - Turkey minority | 0.0100 | | 4,856 |
|
51 | A*23:01-B*41:02-C*17:01-DRB1*11:01-DQB1*03:01 | | Germany DKMS - Turkey minority | 0.0100 | | 4,856 |
|
52 | A*24:02-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | Germany DKMS - Turkey minority | 0.0100 | | 4,856 |
|
53 | A*30:02-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | Germany DKMS - Turkey minority | 0.0100 | | 4,856 |
|
54 | A*30:04-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | Germany DKMS - Turkey minority | 0.0100 | | 4,856 |
|
55 | A*01:01-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01 | | India North UCBB | 0.0085 | | 5,849 |
|
56 | A*02:02-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | India North UCBB | 0.0085 | | 5,849 |
|
57 | A*02:01:01-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01 | | Poland BMR | 0.0069 | | 23,595 |
|
58 | A*03:01:01-B*41:02:01-C*04:01:01-DRB1*11:01:01-DQB1*03:01:01 | | Poland BMR | 0.0056 | | 23,595 |
|
59 | A*02:01:01-B*41:01:01-C*17:01:01-DRB1*11:04:01-DQB1*03:01:01 | | Poland BMR | 0.0047 | | 23,595 |
|
60 | A*01:01:01-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01 | | Poland BMR | 0.0037 | | 23,595 |
|
61 | A*02:01:01-B*41:02:01-C*17:03-DRB1*11:01:01-DQB1*03:01:01 | | Poland BMR | 0.0032 | | 23,595 |
|
62 | A*26:01:01-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01 | | Poland BMR | 0.0031 | | 23,595 |
|
63 | A*02:05:01-B*41:01:01-C*07:01:01-DRB1*11:04:01-DQB1*03:01:01 | | Poland BMR | 0.0021 | | 23,595 |
|
64 | A*23:01:01-B*41:01:01-C*17:01:01-DRB1*11:01:01-DQB1*03:01:01 | | Poland BMR | 0.0021 | | 23,595 |
|
65 | A*23:01:01-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01 | | Poland BMR | 0.0021 | | 23,595 |
|
66 | A*31:01:02-B*41:02:01-C*17:03-DRB1*11:04:01-DQB1*03:01:01 | | Poland BMR | 0.0021 | | 23,595 |
|
67 | A*24:02-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | India Tamil Nadu | 0.0017 | | 2,492 |
|
68 | A*24:03-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | India Tamil Nadu | 0.0017 | | 2,492 |
|
69 | A*24:07-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | India Tamil Nadu | 0.0017 | | 2,492 |
|
70 | A*24:10-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | India Tamil Nadu | 0.0017 | | 2,492 |
|
71 | A*24:17-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | India Tamil Nadu | 0.0017 | | 2,492 |
|
72 | A*24:32-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | India Tamil Nadu | 0.0017 | | 2,492 |
|
73 | A*24:55-B*41:01-C*17:01-DRB1*11:01-DQB1*03:01 | | India Tamil Nadu | 0.0017 | | 2,492 |
|
74 | A*23:01:01-B*41:01:01-C*07:01:01-DRB1*11:01:01-DQB1*03:01:01 | | Poland BMR | 0.0009993 | | 23,595 |
|
75 | A*24:02-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
76 | A*24:02-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
77 | A*24:02-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
78 | A*24:02-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
79 | A*24:03-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
80 | A*24:03-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
81 | A*24:03-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
82 | A*24:03-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
83 | A*24:07-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
84 | A*24:07-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
85 | A*24:07-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
86 | A*24:07-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
87 | A*24:10-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
88 | A*24:10-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
89 | A*24:10-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
90 | A*24:10-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
91 | A*24:17-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
92 | A*24:17-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
93 | A*24:17-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
94 | A*24:17-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
95 | A*24:32-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
96 | A*24:32-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
97 | A*24:32-B*41:01-C*17:01-DRB1*11:08-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
98 | A*24:32-B*41:01-C*17:01-DRB1*11:11-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
99 | A*24:55-B*41:01-C*17:01-DRB1*11:04-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
100 | A*24:55-B*41:01-C*17:01-DRB1*11:06-DQB1*03:01 | | India Tamil Nadu | 0.0003000 | | 2,492 |
|
* Haplotype Frequencies: Total number of copies of the haplotype in the population sample (Haplotypes / 2n) shown in percentages (%).
: This field has been expanded to two decimals to better represent frequencies of large datasets (e.g. where sample size > 1000 individuals)
¹ Distribution - Shows the geographic distribution in overlaid maps of the complete haplotype (left icon) or the input alleles if low level resolution was entered (right icon).