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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 |
2,801 |
A*03:01:01 | | Morocco Settat Chaouya | 9.4 | 0.0470 | | 98 | See | | | |
2,802 |
A*03:01:01 | | South Korea pop 3 | | 0 | | 485 | See | | | |
2,803 |
A*03:01:01 | | USA African American pop 3 | | 0.0860 | | 564 | See | | | |
2,804 |
A*03:01:01 | | USA San Francisco Caucasian | | 0.1480 | | 220 | See | | | |
2,805 |
A*03:01:01:01 | | China North Han | | 0 | | 105 | See | | | |
2,806 |
A*03:01:01:01 | | Indonesia Java pop 2 | | 0.0140 | | 36 | See | | | |
2,807 |
A*03:01:01:01 | | Indonesia Sundanese and Javanese | | 0.0250 | | 201 | See | | | |
2,808 |
A*03:01:01:01 | | Mexico Guadalajara Mestizo pop 2 | | 0.0290 | | 103 | See | | | |
2,809 |
A*03:01:01:01 | | Saudi Arabia pop 5 | 5.1 | 0.0253 | | 158 | See | | | |
2,810 |
A*03:01:01:01 | | USA Eastern European | | 0.1290 | | 558 | See | | | |
2,811 |
A*03:01:01:01 | | USA Mexican American Mestizo | | 0.0680 | | 553 | See | | |
|
2,812 |
A*03:01:01:01 | | USA San Francisco Caucasian | | 0.1480 | | 220 | See | | | |
2,813 |
A*03:01:01:02N | | China North Han | | 0 | | 105 | See | | | |
2,814 |
A*03:01:02 | | Bulgaria | | 0 | | 55 | See | | | |
2,815 |
A*03:01:02 | | Cameroon Yaounde | | 0.0060 | | 92 | See | | | |
2,816 |
A*03:01:02 | | China North Han | | 0 | | 105 | See | | | |
2,817 |
A*03:01:02 | | India Khandesh Region Pawra | | 0 | | 50 | See | | | |
2,818 |
A*03:01:02 | | India Mumbai Maratha | | 0 | | 91 | See | | | |
2,819 |
A*03:01:02 | | India West Bhil | | 0.0500 | | 50 | See | | | |
2,820 |
A*03:01:02 | | India West Coast Parsi | | 0.0100 | | 50 | See | | | |
2,821 |
A*03:01:02 | | Mexico Guadalajara Mestizo pop 2 | | 0.0050 | | 103 | See | | | |
2,822 |
A*03:01:02 | | Morocco Nador Metalsa pop 2 | | 0 | | 73 | See | | | |
2,823 |
A*03:01:02 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,824 |
A*03:01:02 | | South Korea pop 3 | | 0 | | 485 | See | | | |
2,825 |
A*03:01:03 | | Bulgaria | | 0 | | 55 | See | | | |
2,826 |
A*03:01:03 | | Cape Verde Northwestern Islands | | 0.0080 | | 62 | See | | | |
2,827 |
A*03:01:03 | | Cape Verde Southeastern Islands | | 0 | | 62 | See | | | |
2,828 |
A*03:01:03 | | China North Han | | 0 | | 105 | See | | | |
2,829 |
A*03:01:03 | | Guinea Bissau | | 0 | | 65 | See | | | |
2,830 |
A*03:01:03 | | Morocco Nador Metalsa pop 2 | | 0 | | 73 | See | | | |
2,831 |
A*03:01:03 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,832 |
A*03:01:03 | | South Korea pop 3 | | 0 | | 485 | See | | | |
2,833 |
A*03:01:04 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,834 |
A*03:01:05 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,835 |
A*03:01:06 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,836 |
A*03:01:07 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,837 |
A*03:01:08 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,838 |
A*03:01:09 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,839 |
A*03:01:10 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,840 |
A*03:01:11 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,841 |
A*03:01:12 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,842 |
A*03:01:13 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,843 |
A*03:01:14 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,844 |
A*03:01:15 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,845 |
A*03:01:16 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,846 |
A*03:01:17 | | Morocco Settat Chaouya | 0.0 | 0 | | 98 | See | | | |
2,847 |
A*03:02 | | Brazil Belo Horizonte Caucasian | 1.1 | 0.0050 | | 95 | See | | | |
2,848 |
A*03:02 | | Bulgaria | | 0 | | 55 | See | | | |
2,849 |
A*03:02 | | China Beijing Shijiazhuang Tianjian Han | | 0.0020 | | 618 | See | | | |
2,850 |
A*03:02 | | China Inner Mongolia Region | | 0.0050 | | 102 | See | | | |
2,851 |
A*03:02 | | China North Han | | 0 | | 105 | See | | | |
2,852 |
A*03:02 | | China Qinghai Province Hui | | 0 | | 110 | See | | | |
2,853 |
A*03:02 | | China Tibet Region Tibetan | | 0 | | 158 | See | | | |
2,854 |
A*03:02 | | Cuba Caucasian | 0.0 | 0 | | 70 | See | | | |
2,855 |
A*03:02 | | Cuba Mixed Race | 0.0 | 0 | | 42 | See | | | |
2,856 |
A*03:02 | | England North West | 0.7 | 0.0030 | | 298 | See | | | |
2,857 |
A*03:02 | | Georgia Svaneti Region Svan | | 0.0060 | | 80 | See | | | |
2,858 |
A*03:02 | | Germany DKMS - Austria minority | | 0.0038 | | 1,698 | See | | | |
2,859 |
A*03:02 | | Germany DKMS - Bosnia and Herzegovina minority | | 0.0010 | | 1,028 | See | | | |
2,860 |
A*03:02 | | Germany DKMS - China minority | | 0.0031 | | 1,282 | See | | | |
2,861 |
A*03:02 | | Germany DKMS - Croatia minority | | 0.0039 | | 2,057 | See | | | |
2,862 |
A*03:02 | | Germany DKMS - France minority | | 0.0036 | | 1,406 | See | | | |
2,863 |
A*03:02 | | Germany DKMS - Greece minority | | 0.0071 | | 1,894 | See | | | |
2,864 |
A*03:02 | | Germany DKMS - Italy minority | | 0.0039 | | 1,159 | See | | | |
2,865 |
A*03:02 | | Germany DKMS - Netherlands minority | | 0.0033 | | 1,374 | See | | | |
2,866 |
A*03:02 | | Germany DKMS - Portugal minority | | 0.0030 | | 1,176 | See | | | |
2,867 |
A*03:02 | | Germany DKMS - Romania minority | | 0.0061 | | 1,234 | See | | | |
2,868 |
A*03:02 | | Germany DKMS - Spain minority | | 0.0036 | | 1,107 | See | | | |
2,869 |
A*03:02 | | Germany DKMS - Turkey minority | | 0.0282 | | 4,856 | See | | | |
2,870 |
A*03:02 | | Germany DKMS - United Kingdom minority | | 0.0014 | | 1,043 | See | | | |
2,871 |
A*03:02 | | Germany pop 6 | | 0.0024 | | 8,862 | See | | | |
2,872 |
A*03:02 | | Greece pop 6 | | 0.0083 | | 242 | See | | | |
2,873 |
A*03:02 | | India Delhi pop 2 | 4.4 | 0.0220 | | 90 | See | | | |
2,874 |
A*03:02 | | India Khandesh Region Pawra | | 0 | | 50 | See | | | |
2,875 |
A*03:02 | | India Mumbai Maratha | | 0 | | 91 | See | | | |
2,876 |
A*03:02 | | India North pop 2 | | 0.0100 | | 72 | See | | | |
2,877 |
A*03:02 | | India West Bhil | | 0 | | 50 | See | | | |
2,878 |
A*03:02 | | India West Coast Parsi | | 0.0200 | | 50 | See | | | |
2,879 |
A*03:02 | | Iran Baloch | | 0.0060 | | 100 | See | | | |
2,880 |
A*03:02 | | Ireland Northern | 0.3 | 0.0020 | | 1,000 | See | | | |
2,881 |
A*03:02 | | Italy North pop 3 | 0.0 | 0 | | 97 | See | | | |
2,882 |
A*03:02 | | Italy pop 5 | | 0.0150 | | 975 | See | | | |
2,883 |
A*03:02 | | Japan Central | | 0.0010 | | 371 | See | | | |
2,884 |
A*03:02 | | Japan pop 3 | | 0.0010 | | 1,018 | See | | | |
2,885 |
A*03:02 | | Kenya Luo | | 0 | | 265 | See | | | |
2,886 |
A*03:02 | | Kenya Nandi | | 0 | | 240 | See | | | |
2,887 |
A*03:02 | | Madeira | | 0.0030 | | 185 | See | | | |
2,888 |
A*03:02 | | Mali Bandiagara | | 0 | | 138 | See | | | |
2,889 |
A*03:02 | | Mexico Mestizo | 0.0 | 0 | | 41 | See | | | |
2,890 |
A*03:02 | | Mexico Mexico City Mestizo pop 2 | | 0.0064 | | 234 | See | | | |
2,891 |
A*03:02 | | Morocco Nador Metalsa pop 2 | | 0.0410 | | 73 | See | | | |
2,892 |
A*03:02 | | Morocco Settat Chaouya | 1.4 | 0.0070 | | 98 | See | | | |
2,893 |
A*03:02 | | Oman | 2.5 | 0.0130 | | 118 | See | | | |
2,894 |
A*03:02 | | Pakistan Baloch | | 0.0160 | | 66 | See | | | |
2,895 |
A*03:02 | | Pakistan Burusho | | 0 | | 92 | See | | | |
2,896 |
A*03:02 | | Pakistan Kalash | | 0.0170 | | 69 | See | | | |
2,897 |
A*03:02 | | Pakistan Karachi Parsi | | 0.0280 | | 91 | See | | | |
2,898 |
A*03:02 | | Pakistan Mixed Pathan | | 0.0100 | | 100 | See | | | |
2,899 |
A*03:02 | | Pakistan Mixed Sindhi | | 0.0130 | | 101 | See | | | |
2,900 |
A*03:02 | | Poland | | 0.0030 | | 200 | 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 2,801 to 2,900
(from 60,683) records |
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