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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 |
301 |
A*01:02 |  | Germany DKMS - Turkey minority | | 0.0002 |  | 4,856 | See |  |  | |
302 |
A*01:02 |  | Germany DKMS - United Kingdom minority | | 0.0010 |  | 1,043 | See |  |  | |
303 |
A*01:02 |  | Germany pop 6 | | 0.0002 |  | 8,862 | See |  |  | |
304 |
A*01:02 |  | Guinea Bissau | | 0.0080 |  | 65 | See |  |  | |
305 |
A*01:02 |  | Guinea Bissau Balanta | | 0.0210 |  | 48 | See |  |  | |
306 |
A*01:02 |  | Guinea Bissau Bijago | | 0 |  | 23 | See |  |  | |
307 |
A*01:02 |  | Guinea Bissau Fula | | 0 |  | 31 | See |  |  | |
308 |
A*01:02 |  | Guinea Bissau Papel | | 0.0200 |  | 25 | See |  |  | |
309 |
A*01:02 |  | India Delhi pop 2 | 6.7 | 0.0330 |  | 90 | See |  |  | |
310 |
A*01:02 |  | Ireland Northern | 0.0 | 0 |  | 1,000 | See |  |  | |
311 |
A*01:02 |  | Italy North pop 3 | 0.0 | 0 |  | 97 | See |  |  | |
312 |
A*01:02 |  | Italy pop 5 | | 0.0030 |  | 975 | See |  |  | |
313 |
A*01:02 |  | Kenya Luo | | 0.0040 |  | 265 | See |  |  | |
314 |
A*01:02 |  | Kenya Nandi | | 0.0270 |  | 240 | See |  |  | |
315 |
A*01:02 |  | Madeira | | 0.0080 |  | 185 | See |  |  | |
316 |
A*01:02 |  | Mali Bandiagara | | 0.0110 |  | 138 | See |  |  | |
317 |
A*01:02 |  | Mexico Mestizo | 0.0 | 0 |  | 41 | See |  |  | |
318 |
A*01:02 |  | Mexico Mexico City Mestizo pop 2 | | 0.0043 |  | 234 | See |  |  | |
319 |
A*01:02 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
320 |
A*01:02 |  | Morocco Settat Chaouya | 1.4 | 0.0070 |  | 98 | See |  |  | |
321 |
A*01:02 |  | Oman | 0.0 | 0 |  | 118 | See |  |  | |
322 |
A*01:02 |  | Pakistan Baloch | | 0 |  | 66 | See |  |  | |
323 |
A*01:02 |  | Pakistan Brahui | | 0 |  | 104 | See |  |  | |
324 |
A*01:02 |  | Pakistan Burusho | | 0 |  | 92 | See |  |  | |
325 |
A*01:02 |  | Pakistan Kalash | | 0 |  | 69 | See |  |  | |
326 |
A*01:02 |  | Pakistan Karachi Parsi | | 0 |  | 91 | See |  |  | |
327 |
A*01:02 |  | Pakistan Mixed Pathan | | 0 |  | 100 | See |  |  | |
328 |
A*01:02 |  | Russia Tuva pop 2 | | 0.0030 |  | 169 | See |  |  | |
329 |
A*01:02 |  | Saudi Arabia pop 5 | 1.3 | 0.0063 |  | 158 | See |  |  | |
330 |
A*01:02 |  | Senegal Niokholo Mandenka | | 0.0160 |  | 165 | See |  |  | |
331 |
A*01:02 |  | Singapore Chinese | 0.0 | 0 |  | 149 | See |  |  | |
332 |
A*01:02 |  | South Africa Natal Zulu | 0.0 | 0 |  | 100 | See |  |  | |
333 |
A*01:02 |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
334 |
A*01:02 |  | Switzerland Aargau-Solothurn | | 0 |  | 1,838 | See |  |  | |
335 |
A*01:02 |  | Tunisia | 2.0 | 0.0100 |  | 100 | See |  |  | |
336 |
A*01:02 |  | Uganda Kampala | | 0.0030 |  | 161 | See |  |  | |
337 |
A*01:02 |  | Uganda Kampala pop 2 | | 0.0060 |  | 175 | See |  |  | |
338 |
A*01:02 |  | USA African American pop 3 | | 0.0070 |  | 564 | See |  |  | |
339 |
A*01:02 |  | USA African American pop 4 | | 0.0065 |  | 2,411 | See |  |  | |
340 |
A*01:02 |  | USA Alaska Yupik | | 0 |  | 252 | See |  |  | |
341 |
A*01:02 |  | USA Asian pop 2 | | 0 |  | 1,772 | See |  |  | |
342 |
A*01:02 |  | USA Caucasian pop 4 | | 0.0009 |  | 1,070 | See |  |  | |
343 |
A*01:02 |  | USA Hispanic pop 2 | | 0.0030 |  | 1,999 | See |  |  | |
344 |
A*01:02 |  | Zambia Lusaka | | 0 |  | 44 | See |  |  | |
345 |
A*01:03 |  | Bulgaria | | 0 |  | 55 | See |  |  | |
346 |
A*01:03 |  | China Beijing Shijiazhuang Tianjian Han | | 0.0020 |  | 618 | See |  |  | |
347 |
A*01:03 |  | China Guangdong Province Meizhou Han | | 0.0050 |  | 100 | See |  |  | |
348 |
A*01:03 |  | China North Han | | 0 |  | 105 | See |  |  | |
349 |
A*01:03 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
350 |
A*01:03 |  | Germany DKMS - Austria minority | | 0.0009 |  | 1,698 | See |  |  | |
351 |
A*01:03 |  | Germany DKMS - France minority | | 0.0007 |  | 1,406 | See |  |  | |
352 |
A*01:03 |  | Germany DKMS - Greece minority | | 0.0008 |  | 1,894 | See |  |  | |
353 |
A*01:03 |  | Germany DKMS - Netherlands minority | | 0.0004 |  | 1,374 | See |  |  | |
354 |
A*01:03 |  | Germany DKMS - Portugal minority | | 0.0004 |  | 1,176 | See |  |  | |
355 |
A*01:03 |  | Germany DKMS - Spain minority | | 0.0005 |  | 1,107 | See |  |  | |
356 |
A*01:03 |  | Germany DKMS - Turkey minority | | 0.0009 |  | 4,856 | See |  |  | |
357 |
A*01:03 |  | Germany pop 6 | | 0.0002 |  | 8,862 | See |  |  | |
358 |
A*01:03 |  | Iran Baloch | | 0.0060 |  | 100 | See |  |  | |
359 |
A*01:03 |  | Italy Bergamo | 0.0 | 0 |  | 101 | See |  |  | |
360 |
A*01:03 |  | Italy North pop 3 | 0.0 | 0 |  | 97 | See |  |  | |
361 |
A*01:03 |  | Kenya Luo | | 0 |  | 265 | See |  |  | |
362 |
A*01:03 |  | Kenya Nandi | | 0.0170 |  | 240 | See |  |  | |
363 |
A*01:03 |  | Mali Bandiagara | | 0 |  | 138 | See |  |  | |
364 |
A*01:03 |  | Mexico Mexico City Mestizo pop 2 | | 0.0021 |  | 234 | See |  |  | |
365 |
A*01:03 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
366 |
A*01:03 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
367 |
A*01:03 |  | Poland DKMS | | 0.0002 |  | 20,653 | See |  |  | |
368 |
A*01:03 |  | Saudi Arabia pop 5 | 1.3 | 0.0063 |  | 158 | See |  |  | |
369 |
A*01:03 |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
370 |
A*01:03 |  | Taiwan Han Chinese | | 0.0020 |  | 504 | See |  |  | |
371 |
A*01:03 |  | Tunisia | 0.0 | 0 |  | 100 | See |  |  | |
372 |
A*01:03 |  | Uganda Kampala | | 0 |  | 161 | See |  |  | |
373 |
A*01:03 |  | Uganda Kampala pop 2 | | 0.0030 |  | 175 | See |  |  | |
374 |
A*01:03 |  | USA African American pop 4 | | 0.0002 |  | 2,411 | See |  |  | |
375 |
A*01:03 |  | USA Alaska Yupik | | 0 |  | 252 | See |  |  | |
376 |
A*01:03 |  | USA Asian pop 2 | | 0 |  | 1,772 | See |  |  | |
377 |
A*01:03 |  | USA Hispanic pop 2 | | 0 |  | 1,999 | See |  |  | |
378 |
A*01:03 |  | Zambia Lusaka | | 0 |  | 44 | See |  |  | |
379 |
A*01:04N |  | Bulgaria | | 0 |  | 55 | See |  |  | |
380 |
A*01:04N |  | China North Han | | 0 |  | 105 | See |  |  | |
381 |
A*01:04N |  | Italy Bergamo | 0.0 | 0 |  | 101 | See |  |  | |
382 |
A*01:04N |  | Italy North pop 3 | 0.0 | 0 |  | 97 | See |  |  | |
383 |
A*01:04N |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
384 |
A*01:04N |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
385 |
A*01:04N |  | USA Alaska Yupik | | 0 |  | 252 | See |  |  | |
386 |
A*01:06 |  | Bulgaria | | 0 |  | 55 | See |  |  | |
387 |
A*01:06 |  | China North Han | | 0 |  | 105 | See |  |  | |
388 |
A*01:06 |  | China Tibet Region Tibetan | | 0 |  | 158 | See |  |  | |
389 |
A*01:06 |  | England North West | 0.3 | 0.0020 |  | 298 | See |  |  | |
390 |
A*01:06 |  | Germany DKMS - Turkey minority | | 0.0001 |  | 4,856 | See |  |  | |
391 |
A*01:06 |  | India Khandesh Region Pawra | | 0.0100 |  | 50 | See |  |  | |
392 |
A*01:06 |  | India Mumbai Maratha | | 0 |  | 91 | See |  |  | |
393 |
A*01:06 |  | India West Bhil | | 0 |  | 50 | See |  |  | |
394 |
A*01:06 |  | India West Coast Parsi | | 0.0800 |  | 50 | See |  |  | |
395 |
A*01:06 |  | Morocco Nador Metalsa pop 2 | | 0 |  | 73 | See |  |  | |
396 |
A*01:06 |  | Morocco Settat Chaouya | 0.0 | 0 |  | 98 | See |  |  | |
397 |
A*01:06 |  | Poland DKMS | | 0.0000700 |  | 20,653 | See |  |  | |
398 |
A*01:06 |  | South Korea pop 3 | | 0 |  | 485 | See |  |  | |
399 |
A*01:07 |  | Bulgaria | | 0 |  | 55 | See |  |  | |
400 |
A*01:07 |  | China North Han | | 0 |  | 105 | 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 301 to 400
(from 60,683) records |
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