The Allele Frequencies Website [Search HLA Allele Frequencies]

HLA » Allele Frequency Search

Displaying all results from your selection.

Line	Allele	Population	% of individuals that have the allele	Allele Frequency	Sample Size	Location
1	A*24:02	American Samoa		0.3300	51	14_18_S_170_42_W
2	A*24:02	Argentina Gran Chaco Eastern Toba	9.5	0.0480	135	26_11_S_58_11_W
3	A*24:02	Argentina Gran Chaco Mataco Wichi	20.5	0.1020	49	27_27_S_58_59_W
4	A*24:02	Argentina Gran Chaco Western Toba Pilaga	0.0	0.0000	19	27_27_S_58_59_W
5	A*24:02	Argentina Rosario Toba	5.8	0.0350	86	32_57_S_60_39_W
6	A*24:02	Armenia combined Regions		0.1200	100	40_11_N_44_31_E
7	A*24:02	Australia Cape York Peninsula Aborigine		0.2230	103	10_41_S_142_32_E
8	A*24:02	Australia Groote Eylandt Aborigine		0.2930	75	13_58_S_136_35_E
9	A*24:02	Australia Kimberly Aborigine		0.0830	41	15_46_S_128_44_E
10	A*24:02	Australia New South Wales Caucasian		0.0820	134	33_0_S_146_0_E
11	A*24:02	Australia Yuendumu Aborigine		0.2980	191	22_15_S_131_47_E
12	A*24:02	Austria	20.0	0.1010	200	48_13_N_16_21_E
13	A*24:02	Azores Central Islands		0.1250	59	38_0_N_28_0_W
14	A*24:02	Azores Oriental Islands		0.1030	43	36_58_N_25_6_W
15	A*24:02	Azores Terceira Island		0.1130	130	38_44_N_27_19_W
16	A*24:02	Belgium	12.5	0.0660	99	50_50_N_4_21_E
17	A*24:02	Brazil Puyanawa	15.3	0.0770	150	10_52_S_53_5_W
18	A*24:02	Brazil Terena	15.0	0.0750	60	20_0_S_55_0_W
19	A*24:02	Brazil Vale do Ribeira Quilombos	0.1	0.0000	144	24_36_S_48_15_W
20	A*24:02	Cameroon Baka Pygmy		0.1000	10	8_30_N_14_0_E
21	A*24:02	Cameroon Bamileke		0.0000	77	5_28_N_10_25_E
22	A*24:02	Cameroon Beti		0.0030	174	2_55_N_11_9_E
23	A*24:02	Cameroon Sawa		0.0770	13	4_50_N_10_0_E
24	A*24:02	Cameroon Yaounde		0.0050	92	3_52_N_11_31_E
25	A*24:02	Cape Verde Northwestern Islands		0.0480	62	16_0_N_24_0_W
26	A*24:02	Cape Verde Southeastern Islands		0.0890	62	16_0_N_24_0_W
27	A*24:02	Chile Easter Island		0.3580	21	27_7_S_109_22_W
28	A*24:02	Chile Mapuche		0.0308	66	37_48_S_73_23_W
29	A*24:02	Chile Santiago Mixed	16.0		70	33_26_S_70_39_W
30	A*24:02	China Beijing		0.1490	67	39_58_N_116_24_E
31	A*24:02	China Beijing Shijiazhuang Tianjian Han		0.1050	618	39_54_N_116_24_E
32	A*24:02	China Canton Han		0.1630	264	23_6_N_113_15_E
33	A*24:02	China Guangxi Region Maonan		0.1340	108	23_48_N_108_59_E
34	A*24:02	China Guangzhou		0.1520	102	23_6_N_113_15_E
35	A*24:02	China Guizhou Province Bouyei		0.1390	109	26_50_N_106_50_E
36	A*24:02	China Guizhou Province Miao pop 2		0.1470	85	26_50_N_106_50_E
37	A*24:02	China Guizhou Province Shui		0.2430	153	26_50_N_106_50_E
38	A*24:02	China Han HIV negative		0.1560	72	36_34_N_103_49_E
39	A*24:02	China Henan HIV negative		0.1560	16	34_0_N_113_40_E
40	A*24:02	China Hubei Han	31.8	0.1590	3,732	30_45_N_114_15_E
41	A*24:02	China Inner Mongolia Region		0.1960	102	36_0_N_102_0_E
42	A*24:02	China Jiangsu Han		0.1510	3,238	32_54_N_119_48_E
43	A*24:02	China Jiangsu Province Han		0.1742	334	32_54_N_119_48_E
44	A*24:02	China Marrow Donor Registry		0.1520	600	39_54_N_116_23_E
45	A*24:02	China North Han		0.0000	105	39_54_N_116_24_E
46	A*24:02	China Qinghai Province Hui		0.1640	110	36_0_N_102_0_E
47	A*24:02	China Shanxi HIV negative		0.0680	22	36_5_N_111_50_E
48	A*24:02	China Sichuan HIV negative		0.1760	34	30_50_N_102_49_E
49	A*24:02	China Southwest Dai		0.0810	124	22_0_N_100_48_E
50	A*24:02	China Tibet Region Tibetan		0.2720	158	29_39_N_91_7_E
51	A*24:02	China Uyghur HIV negative		0.2110	19	36_34_N_103_49_E
52	A*24:02	China Yunnan Bulang		0.2370	116	21_57_N_100_27_E
53	A*24:02	China Yunnan Hani		0.0900	150	21_59_N_100_49_E
54	A*24:02	China Yunnan Province Han		0.1630	101	24_2_N_105_3_E
55	A*24:02	China Yunnan Province Lisu		0.1180	111	24_30_N_101_30_E
56	A*24:02	China Yunnan Province Nu		0.1140	107	24_30_N_101_30_E
57	A*24:02	Colombia Bogotá Cord Blood	38.2	0.2081	1,463	4_39_N_73_52_W
58	A*24:02	Colombia North Chimila Amerindians		0.4574	47	10_1_N_74_13_W
59	A*24:02	Colombia North Wiwa El Encanto		0.4327	52	10_10_N_73_53_W
60	A*24:02	Croatia		0.1600	150	45_0_N_15_0_E
61	A*24:02	Croatia pop 4		0.1098	4,000	45_48_N_15_57_E
62	A*24:02	Czech Republic NMDR		0.0960	5,099	49_48_N_15_30_E
63	A*24:02	Ecuador Amerindians		0.1190	63	0_13_S_78_30_W
64	A*24:02	Ecuador Cayapa		0.6140	183	1_3_N_79_12_W
65	A*24:02	England North West	13.8	0.0690	298	53_0_N_3_0_W
66	A*24:02	Finland		0.0940	91	63_29_N_25_16_E
67	A*24:02	France French Bone Marrow Donor Registry		0.0935	42,623	48_51_N_2_21_E
68	A*24:02	France Southeast	20.0	0.1060	130	46_0_N_5_0_E
69	A*24:02	Gaza	11.9	0.0595	42	31_22_N_34_18_E
70	A*24:02	Georgia Svaneti Region Svan		0.1310	80	42_54_N_43_0_E
71	A*24:02	Germany DKMS - Austria minority		0.0854	1,698	48_31_N_9_3_E
72	A*24:02	Germany DKMS - Bosnia and Herzegovina minority		0.0841	1,028	48_31_N_9_3_E
73	A*24:02	Germany DKMS - China minority		0.1446	1,282	48_31_N_9_3_E
74	A*24:02	Germany DKMS - Croatia minority		0.1124	2,057	48_31_N_9_3_E
75	A*24:02	Germany DKMS - France minority		0.0889	1,406	48_31_N_9_3_E
76	A*24:02	Germany DKMS - German donors		0.0911	3,456,066	48_31_N_9_3_E
77	A*24:02	Germany DKMS - Greece minority		0.1434	1,894	48_31_N_9_3_E
78	A*24:02	Germany DKMS - Italy minority		0.1199	1,159	48_31_N_9_3_E
79	A*24:02	Germany DKMS - Netherlands minority		0.0955	1,374	48_31_N_9_3_E
80	A*24:02	Germany DKMS - Portugal minority		0.0903	1,176	48_31_N_9_3_E
81	A*24:02	Germany DKMS - Romania minority		0.1079	1,234	48_31_N_9_3_E
82	A*24:02	Germany DKMS - Spain minority		0.0901	1,107	48_31_N_9_3_E
83	A*24:02	Germany DKMS - Turkey minority		0.1447	4,856	48_31_N_9_3_E
84	A*24:02	Germany DKMS - United Kingdom minority		0.0859	1,043	48_31_N_9_3_E
85	A*24:02	Germany pop 6		0.0887	8,862	51_6_N_10_23_E
86	A*24:02	Germany pop 8		0.0954	39,689	48_24_N_9_58_E
87	A*24:02	Greece pop 6		0.1178	242	37_58_N_23_43_E
88	A*24:02	Greece pop 8	21.7	0.1084	83	37_58_N_23_43_E
89	A*24:02	Guinea Bissau		0.0150	65	12_0_N_15_0_W
90	A*24:02	Guinea Bissau Balanta		0.0000	48	11_52_N_15_36_W
91	A*24:02	Guinea Bissau Bijago		0.0000	23	11_52_N_15_36_W
92	A*24:02	Guinea Bissau Fula		0.0970	31	11_52_N_15_36_W
93	A*24:02	Guinea Bissau Papel		0.0000	25	11_52_N_15_36_W
94	A*24:02	Hong Kong Chinese BMDR		0.1472	7,595	22_15_N_114_10_E
95	A*24:02	Hong Kong Chinese cord blood registry		0.1525	3,892	22_15_N_114_10_E
96	A*24:02	India Delhi pop 2	2.2	0.0170	90	28_36_N_77_13_E
97	A*24:02	India Khandesh Region Pawra		0.1600	50	20_54_N_74_46_E
98	A*24:02	India Mumbai Maratha		0.1670	91	18_58_N_72_49_E
99	A*24:02	India Tamil Nadu		0.1629	2,492	11_32_N_78_52_E
100	A*24:02	India West Bhil		0.0000	50	20_54_N_74_46_E
101	A*24:02	India West Coast Parsi		0.0000	50	18_0_N_74_0_E
102	A*24:02	Indonesia Java Western	25.8		236	7_30_S_111_15_E
103	A*24:02	Iran Baloch		0.0340	100	29_29_N_60_52_E
104	A*24:02	Iran Gorgan		0.1250	64	36_50_N_54_43_E
105	A*24:02	Iran Kurd pop 2		0.1760	60	36_17_N_46_17_E
106	A*24:02	Iran Saqqez-Baneh Kurds		0.1750	60	36_14_N_46_16_E
107	A*24:02	Iran Tabriz Azeris		0.1237	97	38_4_N_46_17_E
108	A*24:02	Iran Yazd		0.0982	56	31_53_N_54_21_E
109	A*24:02	Ireland South	13.2	0.0680	250	53_20_N_6_15_W
110	A*24:02	Israel Arab pop 2		0.1013	12,301	32_8_N_34_52_E
111	A*24:02	Israel Argentina Jews		0.0139	4,307	32_8_N_34_52_E
112	A*24:02	Israel Ashkenazi Jews pop 3		0.0147	4,625	32_8_N_34_52_E
113	A*24:02	Israel Bukhara Jews		0.0191	2,317	32_8_N_34_52_E
114	A*24:02	Israel Druze		0.0165	5,914	32_41_N_35_17_E
115	A*24:02	Israel Ethiopia Jews		0.0282	5,928	32_8_N_34_52_E
116	A*24:02	Israel Georgia Jews		0.0107	4,471	32_10_N_34_53_E
117	A*24:02	Israel Iran Jews		0.0098	8,153	32_8_N_34_52_E
118	A*24:02	Israel Iraq Jews		0.0883	13,270	32_10_N_34_52_E
119	A*24:02	Israel Kavkazi Jews		0.1496	2,840	32_10_N_34_52_E
120	A*24:02	Israel Libya Jews		0.0135	3,739	32_10_N_34_52_E
121	A*24:02	Israel Morocco Jews		0.1143	36,718	32_10_N_34_52_E
122	A*24:02	Israel Poland Jews		0.0896	13,871	32_10_N_34_52_E
123	A*24:02	Israel Tunisia Jews		0.1201	9,070	32_10_N_34_52_E
124	A*24:02	Israel USA Jews		0.0121	6,058	32_10_N_34_52_E
125	A*24:02	Israel USSR Jews		0.0177	45,681	32_9_N_34_50_E
126	A*24:02	Israel YemenJews		0.0140	15,542	32_9_N_34_50_E
127	A*24:02	Italy North pop 3	11.5	0.0580	97	43_0_N_12_0_E
128	A*24:02	Italy pop 5		0.1220	975	45_28_N_9_11_E
129	A*24:02	Japan Central		0.3790	371	35_41_N_139_46_E
130	A*24:02	Japan Hokkaido Ainu		0.2400	50	43_0_N_142_0_E
131	A*24:02	Japan pop 16		0.3648	18,604	35_41_N_139_46_E
132	A*24:02	Japan pop 3		0.3620	1,018	35_41_N_139_46_E
133	A*24:02	Japan pop 5		0.3270	117	35_41_N_139_46_E
134	A*24:02	Jordan Amman		0.0590	146	31_35_N_36_20_E
135	A*24:02	Kenya Luo		0.0060	265	0_0_S_34_6_E
136	A*24:02	Kenya Nandi		0.0100	240	0_0_S_35_0_E
137	A*24:02	Kenya, Nyanza Province, Luo tribe	2.0	0.0100	100	0_5_S_34_46_E
138	A*24:02	Kosovo	21.8	0.1169	124	42_34_N_21_0_E
139	A*24:02	Madeira		0.1300	185	32_44_N_16_58_W
140	A*24:02	Malaysia Champa	24.1	0.1380	29	2_30_N_112_30_E
141	A*24:02	Malaysia Jelebu Temuan		0.1040	25	2_56_N_102_5_E
142	A*24:02	Malaysia Kedah Baling Kensiu		0.1400	25	5_40_N_100_55_E
143	A*24:02	Malaysia Kedah Kensiu	14.3	0.0710	21	5_40_N_100_55_E
144	A*24:02	Malaysia Kelantan	32.1	0.1790	28	2_30_N_112_30_E
145	A*24:02	Malaysia Mandailing	33.3	0.2040	27	2_30_N_112_30_E
146	A*24:02	Malaysia Pahang Semai	36.8	0.1970	38	4_11_N_102_3_E
147	A*24:02	Malaysia Patani	20.0	0.1200	25	2_30_N_112_30_E
148	A*24:02	Malaysia Peninsular Chinese	28.9	0.1598	194	3_9_N_101_57_E
149	A*24:02	Malaysia Peninsular Indian	31.4	0.1679	271	3_9_N_101_57_E
150	A*24:02	Malaysia Peninsular Malay	35.3	0.1977	951	3_9_N_101_57_E
151	A*24:02	Malaysia Perak Grik Jehai		0.1200	25	5_25_N_101_8_E
152	A*24:02	Malaysia Sarawak Bau Bidayuh		0.0400	25	1_25_N_110_9_E
153	A*24:02	Mali Bandiagara		0.0000	138	14_4_N_3_6_W
154	A*24:02	Mexico Chiapas Lacandon Mayans		0.1904	218	16_54_N_92_5_W
155	A*24:02	Mexico Chihuahua Chihuahua City Pop 2	31.8	0.1591	88	28_48_N_106_26_W
156	A*24:02	Mexico Chihuahua Tarahumara		0.3750	44	27_30_N_107_45_W
157	A*24:02	Mexico Mexico City Mestizo pop 2		0.1688	234	19_26_N_99_8_W
158	A*24:02	Mexico Mexico City Mestizo population		0.1434	143	19_25_N_99_7_W
159	A*24:02	Mexico Mexico City Tlalpan	32.1	0.1712	330	19_25_N_99_8_W
160	A*24:02	Mexico Mixtec		0.2530	97	23_57_N_103_29_W
161	A*24:02	Mexico Oaxaca Jamiltepec Mixtec		0.2550	96	16_25_N_97_58_W
162	A*24:02	Mexico Oaxaca Mixe		0.0570	55	17_5_N_96_45_W
163	A*24:02	Mexico Oaxaca Mixtec		0.2450	103	17_5_N_96_45_W
164	A*24:02	Mexico Oaxaca Zapotec		0.2310	90	17_5_N_96_45_W
165	A*24:02	Mexico Sonora Seri		0.0450	34	28_57_N_112_34_W
166	A*24:02	Mexico Veracruz Xalapa	28.6	0.1429	84	19_32_N_96_55_W
167	A*24:02	Morocco Atlantic Coast Chaouya		0.0410	98	30_22_N_7_22_W
168	A*24:02	Netherlands Leiden		0.0900	1,305	52_9_N_4_31_E
169	A*24:02	Netherlands UMCU	28.1	0.1484	64	52_5_N_5_10_E
170	A*24:02	New Caledonia		0.6070	65	21_0_S_165_0_E
171	A*24:02	New Zealand Maori with Admixed History	51.4	0.2910	105	42_0_S_174_0_E
172	A*24:02	New Zealand Maori with Full Ancestry	65.2	0.3800	46	42_0_S_174_0_E
173	A*24:02	New Zealand Polynesians with Admixed History	59.3	0.3330	27	42_0_S_174_0_E
174	A*24:02	New Zealand Polynesians with Full Ancestry	57.1	0.3100	21	42_0_S_174_0_E
175	A*24:02	Nicaragua Managua	28.4	0.1512	339	12_7_N_86_10_W
176	A*24:02	Pakistan Baloch		0.0800	66	30_0_N_67_0_E
177	A*24:02	Pakistan Brahui		0.0400	104	30_0_N_67_0_E
178	A*24:02	Pakistan Burusho		0.1250	92	36_0_N_73_0_E
179	A*24:02	Pakistan Kalash		0.0750	69	32_0_N_70_0_E
180	A*24:02	Pakistan Karachi Parsi		0.0440	91	24_49_N_66_59_E
181	A*24:02	Pakistan Mixed Pathan		0.1020	100	32_0_N_70_0_E
182	A*24:02	Pakistan Mixed Sindhi		0.1070	101	24_0_N_68_0_E
183	A*24:02	Panama		0.2240	462	8_59_N_79_37_W
184	A*24:02	Papua New Guinea East New Britain Rabaul		0.3160	60	4_12_S_152_11_E
185	A*24:02	Papua New Guinea Eastern Highlands Goroka Asaro		0.7440	57	6_5_S_145_23_E
186	A*24:02	Papua New Guinea Karimui Plateau Pawaia		0.7440	80	7_14_S_145_20_E
187	A*24:02	Papua New Guinea Madang		0.1530	65	5_13_S_145_48_E
188	A*24:02	Papua New Guinea Wanigela Keapara		0.6270	66	10_5_S_147_50_E
189	A*24:02	Papua New Guinea West Schrader Ranges Haruai		0.1420	55	5_5_S_144_10_E
190	A*24:02	Papua New Guinea Wosera Abelam		0.5130	131	3_48_S_143_2_E
191	A*24:02	Peru Titikaka Lake Uro		0.2810	105	15_50_S_69_42_W
192	A*24:02	Peru Titikaka Lake Uros		0.2810	105	15_50_S_70_1_W
193	A*24:02	Philippines Ivatan	58.0	0.3200	50	20_22_N_121_56_E
194	A*24:02	Poland		0.1250	200	51_7_N_17_2_E
195	A*24:02	Poland DKMS		0.0943	20,653	52_8_N_19_23_E
196	A*24:02	Portugal Azores Terceira Island	22.8	0.1140	130	38_43_N_27_13_W
197	A*24:02	Portugal Center		0.0800	50	39_0_N_8_0_W
198	A*24:02	Portugal North		0.0540	46	41_0_N_8_0_W
199	A*24:02	Portugal South		0.1020	49	37_0_N_8_0_W
200	A*24:02	Romania	23.8	0.1270	348	46_0_N_25_0_E
201	A*24:02	Russia Belgorod region	0.7	0.0033	153	50_46_N_37_27_E
202	A*24:02	Russia Bering Island Aleuts		0.2163	104	55_0_N_166_15_E
203	A*24:02	Russia Karelia		0.1191	1,075	63_9_N_32_59_E
204	A*24:02	Russia Tuva pop 2		0.2150	169	51_30_N_95_5_E
205	A*24:02	Sao Tome Island Angolar		0.0470	32	0_20_N_6_44_E
206	A*24:02	Sao Tome Island Forro		0.0150	66	0_20_N_6_44_E
207	A*24:02	Saudi Arabia Guraiat and Hail	11.7	0.0630	213	27_31_N_41_41_E
208	A*24:02	Senegal Niokholo Mandenka		0.0050	165	12_35_N_12_9_W
209	A*24:02	Singapore Riau Malay		0.0080	132	1_24_N_103_45_E
210	A*24:02	Singapore SGVP Chinese CHS		0.1360	96	1_21_N_103_50_E
211	A*24:02	Singapore SGVP Malay MAS		0.1670	89	1_21_N_103_50_E
212	A*24:02	Singapore SGVP. Indian INS		0.1180	86	1_21_N_103_50_E
213	A*24:02	South Africa Worcester	11.0	0.0570	159	33_39_S_19_27_E
214	A*24:02	South Korea pop 10		0.2209	4,128	37_33_N_126_58_E
215	A*24:02	South Korea pop 3		0.2170	485	37_33_N_126_59_E
216	A*24:02	Spain (Catalunya, Navarra, Extremadura, Aaragón, Cantabria,	17.4	0.0908	4,335	41_22_N_2_11_E
217	A*24:02	Sri Lanka Colombo	30.3	0.1670	714	6_56_N_79_50_E
218	A*24:02	Sudan Central Shaigiya Mixed	2.7		36	18_0_N_33_0_E
219	A*24:02	Sudan East Rashaida	18.5		27	14_0_N_35_0_E
220	A*24:02	Sudan Mixed	9.5	0.0530	200	5_0_N_35_0_E
221	A*24:02	Sweden Northern Sami		0.2120	154	67_0_N_18_30_E
222	A*24:02	Sweden Southern Sami		0.1320	130	60_52_N_14_44_E
223	A*24:02	Taiwan Ami	84.7	0.6280	98	23_0_N_121_0_E
224	A*24:02	Taiwan Atayal	82.1	0.6180	106	24_0_N_121_0_E
225	A*24:02	Taiwan Bunun	84.2	0.5840	101	23_46_N_121_0_E
226	A*24:02	Taiwan Hakka	25.5	0.1450	55	24_0_N_121_0_E
227	A*24:02	Taiwan Han Chinese		0.1580	504	23_46_N_121_0_E
228	A*24:02	Taiwan Minnan pop 1	34.3	0.1860	102	24_0_N_121_0_E
229	A*24:02	Taiwan Paiwan	96.1	0.8630	51	23_0_N_121_0_E
230	A*24:02	Taiwan Pazeh	58.2	0.3360	55	25_0_N_121_30_E
231	A*24:02	Taiwan pop 2		0.1640	364	23_46_N_121_0_E
232	A*24:02	Taiwan pop 3		0.1790	212	23_46_N_121_0_E
233	A*24:02	Taiwan Puyuma	88.0	0.6400	50	23_0_N_121_0_E
234	A*24:02	Taiwan Rukai	96.0	0.7600	50	23_0_N_121_0_E
235	A*24:02	Taiwan Saisiat	86.3	0.5690	51	24_0_N_121_0_E
236	A*24:02	Taiwan Siraya	78.4	0.4710	51	24_0_N_121_0_E
237	A*24:02	Taiwan Tao	78.0	0.5400	50	24_0_N_121_0_E
238	A*24:02	Taiwan Taroko	72.7	0.4450	55	24_0_N_121_0_E
239	A*24:02	Taiwan Thao	90.0	0.6000	30	24_0_N_121_0_E
240	A*24:02	Taiwan Tsou	98.0	0.7840	51	24_0_N_121_0_E
241	A*24:02	Taiwan Tzu Chi Morrow Donor Registry		0.1604	46,682	25_2_N_121_38_E
242	A*24:02	Taiwan Tzu Chi Morrow Donor Registry Aborigine		0.3541	233	25_2_N_121_38_E
243	A*24:02	Taiwan Tzu Chi Cord Blood Bank		0.1700	710	23_46_N_121_0_E
244	A*24:02	Tanzania Maasai	9.0	0.0448	336	3_11_S_37_4_E
245	A*24:02	Thailand		0.0390	142	16_7_N_102_0_E
246	A*24:02	Tunisia	13.0	0.0670	100	35_0_N_9_0_E
247	A*24:02	Tunisia Gabes		0.0947	95	33_53_N_10_6_E
248	A*24:02	Uganda Kampala		0.0430	161	0_3_N_32_6_E
249	A*24:02	Uganda Kampala pop 2		0.0090	175	0_20_N_32_30_E
250	A*24:02	United Arab Emirates Abu Dhabi		0.0385	52	24_28_N_54_22_E
251	A*24:02	USA African American		0.0280	252	30_0_N_95_0_W
252	A*24:02	USA African American Bethesda	8.9	0.0440	187	38_0_N_78_0_W
253	A*24:02	USA African American pop 2		0.0200	149	32_46_N_96_46_W
254	A*24:02	USA African American pop 3		0.0200	564	38_53_N_77_2_W
255	A*24:02	USA African American pop 4		0.0221	2,411	44_58_N_93_15_W
256	A*24:02	USA Alaska Yupik		0.5810	252	60_48_N_161_26_W
257	A*24:02	USA Arizona Gila River Amerindian		0.3410	492	33_4_N_111_44_W
258	A*24:02	USA Asian		0.1890	358	30_0_N_95_0_W
259	A*24:02	USA Asian pop 2		0.1824	1,772	44_58_N_93_15_W
260	A*24:02	USA Caucasian Bethesda	20.6	0.1070	307	38_0_N_78_0_W
261	A*24:02	USA Caucasian pop 2		0.0660	265	30_0_N_95_0_W
262	A*24:02	USA Caucasian pop 3		0.1310	88	32_46_N_96_48_W
263	A*24:02	USA Caucasian pop 4		0.0953	1,070	38_53_N_77_2_W
264	A*24:02	USA Hawaii Okinawa		0.3430	106	21_0_N_157_0_W
265	A*24:02	USA Hispanic		0.1220	234	30_0_N_95_0_W
266	A*24:02	USA Hispanic pop 2		0.1232	1,999	44_58_N_93_15_W
267	A*24:02	USA NMDP African		0.0220	28,557	38_45_N_76_6_W
268	A*24:02	USA NMDP African American pop 2		0.0245	416,581	38_45_N_76_6_W
269	A*24:02	USA NMDP Alaska Native or Aleut		0.2290	1,376	38_45_N_76_6_W
270	A*24:02	USA NMDP American Indian South or Central America		0.1120	5,926	38_45_N_76_6_W
271	A*24:02	USA NMDP Caribean Black		0.0251	33,328	38_45_N_76_6_W
272	A*24:02	USA NMDP Caribean Hispanic		0.0938	115,374	38_45_N_76_6_W
273	A*24:02	USA NMDP Caribean Indian		0.0950	14,339	38_45_N_76_6_W
274	A*24:02	USA NMDP Chinese		0.1519	99,672	38_45_N_76_6_W
275	A*24:02	USA NMDP European Caucasian		0.0846	1,242,890	38_45_N_76_6_W
276	A*24:02	USA NMDP Filipino		0.2399	50,614	38_45_N_76_6_W
277	A*24:02	USA NMDP Hawaiian or other Pacific Islander		0.2393	11,499	38_45_N_76_6_W
278	A*24:02	USA NMDP Hispanic South or Central American		0.1316	146,714	38_45_N_76_6_W
279	A*24:02	USA NMDP Japanese		0.3530	24,582	38_45_N_76_6_W
280	A*24:02	USA NMDP Korean		0.2211	77,584	38_45_N_76_6_W
281	A*24:02	USA NMDP Mexican or Chicano		0.1304	261,235	38_45_N_76_6_W
282	A*24:02	USA NMDP Middle Eastern or North Coast of Africa		0.1174	70,890	38_45_N_76_6_W
283	A*24:02	USA NMDP North American Amerindian		0.1292	35,791	38_45_N_76_6_W
284	A*24:02	USA NMDP South Asian Indian		0.1362	185,391	38_45_N_76_6_W
285	A*24:02	USA NMDP Southeast Asian		0.1380	27,978	38_45_N_76_6_W
286	A*24:02	USA NMDP Vietnamese		0.1109	43,540	38_45_N_76_6_W
287	A*24:02	USA North American Native		0.2270	187	30_0_N_95_0_W
288	A*24:02	USA Philadelphia Caucasian	20.0	0.1040	141	39_56_N_75_10_W
289	A*24:02	USA San Antonio Caucasian		0.0843	222	29_23_N_98_33_W
290	A*24:02	USA San Diego	20.0	0.1110	496	32_50_N_117_15_W
291	A*24:02	USA San Francisco Caucasian		0.0780	220	37_46_N_122_25_W
292	A*24:02	USA South Dakota Lakota Sioux		0.2620	302	44_30_N_100_0_W
293	A*24:02	USA South Texas Hispanic		0.1520	194	29_0_N_98_0_W
294	A*24:02	Venezuela Sierra de Perija Yucpa		0.2330	73	9_0_N_72_0_W
295	A*24:02	Vietnam Hanoi Kinh pop 2		0.1380	170	21_2_N_105_51_E
296	A*24:02	Zambia Lusaka		0.0000	44	15_25_S_28_17_E
297	A*24:02	Zimbabwe Harare Shona		0.0040	230	17_51_S_31_1_E
298	A*24:02:01	Brazil Barra Mansa Rio State Black		0.0274	73	22_32_S_44_10_W
299	A*24:02:01	Brazil Barra Mansa Rio State Caucasian		0.0815	405	22_32_S_44_10_W
300	A*24:02:01	Brazil Rio de Janeiro Black		0.0809	68	22_54_S_43_12_W
301	A*24:02:01	Brazil Rio de Janeiro Caucasian		0.0864	521	22_54_S_43_12_W
302	A*24:02:01	Brazil Rio de Janeiro Parda		0.0765	170	22_54_S_43_12_W
303	A*24:02:01	China Jingpo Minority	26.2	0.1410	105	25_0_N_102_0_E
304	A*24:02:01	China Zhejiang Han		0.1670	1,734	27_2_N_118_1_E
305	A*24:02:01	Costa Rica African -Caribbean (G)	6.8	0.0390	102	9_59_N_83_1_W
306	A*24:02:01	Costa Rica Amerindians (G)	56.5	0.3510	125	9_34_N_83_25_W
307	A*24:02:01	Costa Rica Central Valley Mestizo (G)	25.4	0.1360	221	9_55_N_84_5_W
308	A*24:02:01	Costa Rica Guanacaste Mestizo (G)	28.2	0.1500	110	10_16_N_85_37_W
309	A*24:02:01	England Blood Donors of Mixed Ethnicity	12.3	0.0645	519	52_21_N_1_10_W
310	A*24:02:01	Hong Kong Chinese HKBMDR HLA 11 loci	25.0	0.1346	5,266	22_25_N_114_17_E
311	A*24:02:01	India Andhra Pradesh Telugu Speaking	25.8	0.1371	186	13_4_N_80_14_E
312	A*24:02:01	India Karnataka Kannada Speaking	29.3	0.1550	174	12_57_N_77_36_E
313	A*24:02:01	India Kerala Malayalam speaking	28.1	0.1544	356	13_1_N_80_15_E
314	A*24:02:01	Japan Okinawa Ryukyuan		0.3350	143	26_4_N_127_8_E
315	A*24:02:01	Mexico Hidalgo Mezquital Valley/ Otomi	29.2	0.1875	72	20_28_N_99_13_W
316	A*24:02:01	Mongolia Buryat		0.2000	141	47_6_N_118_6_E
317	A*24:02:01	Nicaragua Mestizo (G)	25.9	0.1390	155	12_40_N_85_13_W
318	A*24:02:01	Poland BMR	17.8	0.0920	23,595	52_0_N_19_22_E
319	A*24:02:01	Russia Belgorod region	19.6	0.1046	153	50_46_N_37_27_E
320	A*24:02:01	Russia Bashkortostan, Bashkirs	13.3	0.0792	120	54_0_N_55_0_E
321	A*24:02:01	Russia Bashkortostan, Tatars	1.0	0.0052	192	54_13_N_56_9_E
322	A*24:02:01	Russia Nizhny Novgorod, Russians	3.4	0.0169	1,510	56_19_N_43_59_E
323	A*24:02:01	Russian Federation Vologda Region	17.6	0.1008	119	59_13_N_39_54_E
324	A*24:02:01	Saudi Arabia pop 6 (G)		0.0747	28,927	23_52_N_45_4_E
325	A*24:02:01	South Africa Caucasians		0.0700	102	30_0_S_25_0_E
326	A*24:02:01	South African Black	4.2	0.0211	142	29_9_S_24_39_E
327	A*24:02:01	South African Indian population	28.0	0.1600	50	29_22_S_24_31_E
328	A*24:02:01	South African Mixed ancestry	18.0	0.0900	50	29_22_S_24_31_E
329	A*24:02:01	Spain, Canary Islands, Gran canaria island	17.7	0.0930	215	27_57_N_15_36_W
330	A*24:02:01	USA Arizona Gila River Pima		0.3552	3,000	33_4_N_111_43_W
331	A*24:02:01	USA Mexican American Mestizo		0.1460	553	38_53_N_77_2_W
332	A*24:02:01	USA San Francisco Caucasian		0.0750	220	37_46_N_122_25_W
333	A*24:02:01	Vietnam Kinh	26.0	0.1287	101	14_5_N_108_27_E
334	A*24:02:01:01	Bolivia/Chile Aymara NA-DHS_13 (G)	35.0	0.1750	20	22_0_S_70_0_W
335	A*24:02:01:01	Bolivia/Peru Quechua NA-DHS_12 (G)	28.6	0.1429	21	14_0_S_74_0_W
336	A*24:02:01:01	Brazil Belo Horizonte Caucasian	22.1	0.1160	95	19_55_S_43_56_W
337	A*24:02:01:01	Brazil Mixed		0.0860	108	23_10_S_47_48_W
338	A*24:02:01:01	Bulgaria		0.1180	55	42_0_N_23_0_E
339	A*24:02:01:01	Bulgaria Romani		0.0830	13	42_44_N_23_20_E
340	A*24:02:01:01	Canada Chipewyan NA-DHS_2 (G)	48.0	0.2600	25	59_36_N_107_20_W
341	A*24:02:01:01	Canada Cree NA-DHS_3 (G)	38.9	0.2222	18	50_20_N_102_30_W
342	A*24:02:01:01	Canada Ojibwa NA-DHS_4 (G)	33.3	0.1667	16	46_0_N_81_0_W
343	A*24:02:01:01	Chile Huilliche NA-DHS_14 (G)	25.0	0.1500	20	41_0_S_73_0_W
344	A*24:02:01:01	China Guangdong Province Meizhou Han		0.2220	100	24_27_N_116_7_E
345	A*24:02:01:01	China North Han		0.1520	105	39_54_N_116_24_E
346	A*24:02:01:01	China South Han		0.1720	284	23_1_N_113_3_E
347	A*24:02:01:01	China Yunnan Province Jinuo		0.1830	109	24_30_N_101_30_E
348	A*24:02:01:01	China Yunnan Province Wa		0.1300	119	24_30_N_101_30_E
349	A*24:02:01:01	Colombia Arhuaco NA-DHS_16 (G)	64.7	0.4412	17	11_0_N_73_0_W
350	A*24:02:01:01	Colombia Embera NA-DHS_19 (G)	92.9	0.5357	14	7_0_N_76_0_W
351	A*24:02:01:01	Colombia Inga NA-DHS_11 (G)	53.3	0.3667	16	1_0_N_77_0_W
352	A*24:02:01:01	Colombia Kogi NA-DHS_17 (G)	66.7	0.4333	15	11_0_N_74_0_W
353	A*24:02:01:01	Colombia Waunana NA-DHS_20 (G)	85.0	0.6000	20	5_0_N_77_0_W
354	A*24:02:01:01	Colombia Wayuu NA-DHS_15 (G)	40.0	0.2000	15	11_0_N_73_0_W
355	A*24:02:01:01	Colombia Zenu NA-DHS_18 (G)	75.0	0.4167	16	9_0_N_75_0_W
356	A*24:02:01:01	Colombia/Brazil Ticuna Arara NA-DHS_21 (G)	66.7	0.5000	17	4_0_S_70_0_W
357	A*24:02:01:01	Colombia/Brazil Ticuna Tarapaca NA-DHS_22 (G)	73.7	0.5263	19	4_0_S_70_0_W
358	A*24:02:01:01	Costa Rica Cabecar NA-DHS_9 (G)	52.6	0.3421	19	10_0_N_84_0_W
359	A*24:02:01:01	Costa Rica Guaymi NA-DHS_10 (G)	72.2	0.3611	18	9_0_N_82_0_W
360	A*24:02:01:01	Cuba Caucasian	21.4	0.1140	70	23_8_N_82_22_W
361	A*24:02:01:01	Cuba Mixed Race	9.5	0.0480	42	23_8_N_82_22_W
362	A*24:02:01:01	Czech Republic		0.0100	106	50_0_N_14_0_E
363	A*24:02:01:01	Georgia Tibilisi		0.1710	109	41_41_N_44_57_E
364	A*24:02:01:01	Georgia Tibilisi Kurd		0.1170	31	41_41_N_44_57_E
365	A*24:02:01:01	Guatemala Kaqchikel NA-DHS_8 (G)	47.4	0.2368	19	15_0_N_91_0_W
366	A*24:02:01:01	Hong Kong Chinese	28.0	0.1540	569	22_16_N_114_9_E
367	A*24:02:01:01	India Andhra Pradesh Golla		0.1420	111	17_5_N_78_5_E
368	A*24:02:01:01	India New Delhi		0.1140	71	28_37_N_77_13_E
369	A*24:02:01:01	India North pop 2		0.1920	72	25_0_N_74_0_E
370	A*24:02:01:01	India Tamil Nadu Nadar		0.1560	61	8_0_N_77_0_E
371	A*24:02:01:01	Indonesia Java pop 2		0.1390	36	6_8_S_106_10_E
372	A*24:02:01:01	Indonesia Sundanese and Javanese		0.1440	201	6_8_S_106_10_E
373	A*24:02:01:01	Ireland Northern	12.0	0.0610	1,000	54_40_N_6_45_W
374	A*24:02:01:01	Israel Arab Druze		0.0800	101	31_8_N_35_2_E
375	A*24:02:01:01	Israel Ashkenazi and Non Ashkenazi Jews		0.0770	146	31_8_N_35_2_E
376	A*24:02:01:01	Kenya		0.0210	144	1_28_S_36_8_E
377	A*24:02:01:01	Libya Cyrenaica		0.0850	118	32_7_N_20_4_E
378	A*24:02:01:01	Mexico Guadalajara Mestizo pop 2		0.1650	103	20_40_N_103_21_W
379	A*24:02:01:01	Mexico Mestizo	24.4	0.1340	41	19_24_N_99_9_W
380	A*24:02:01:01	Mexico Mixe NA-DHS_6 (G)	10.0	0.0750	20	17_0_N_96_0_W
381	A*24:02:01:01	Mexico Mixtec NA-DHS_5 (G)	45.0	0.2250	20	17_0_N_97_0_W
382	A*24:02:01:01	Mexico Zapotec NA-DHS_7 (G)	20.0	0.1000	20	16_0_N_97_0_W
383	A*24:02:01:01	Morocco Nador Metalsa pop 2		0.0620	73	35_10_N_2_56_W
384	A*24:02:01:01	Morocco Settat Chaouya	8.2	0.0410	98	33_0_N_7_36_W
385	A*24:02:01:01	Oman	11.0	0.0590	118	22_0_N_56_0_E
386	A*24:02:01:01	Paraguay/Argentina Ache NA-DHS_24 (G)	15.4	0.0769	13	24_0_S_56_0_W
387	A*24:02:01:01	Paraguay/Argentina Guarani NA-DHS_23 (G)	30.0	0.1500	10	23_0_S_56_0_W
388	A*24:02:01:01	Russia Bashkortostan, Bashkirs	9.2	0.0500	120	54_0_N_55_0_E
389	A*24:02:01:01	Russia Bashkortostan, Tatars	22.4	0.1146	192	54_13_N_56_9_E
390	A*24:02:01:01	Russia Nizhny Novgorod, Russians	18.4	0.0947	1,510	56_19_N_43_59_E
391	A*24:02:01:01	Russia Tundra Nentsi NA-DHS_1 (G)	37.5	0.2188	16	66_0_N_76_0_E
392	A*24:02:01:01	Saudi Arabia pop 5	15.8	0.0854	158	24_38_N_46_43_E
393	A*24:02:01:01	Singapore Chinese	28.2	0.1540	149	1_24_N_103_45_E
394	A*24:02:01:01	Singapore Chinese Han		0.2150	94	1_17_N_103_51_E
395	A*24:02:01:01	Singapore Javaneses		0.1600	51	1_24_N_103_45_E
396	A*24:02:01:01	Singapore Riau Malay		0.1650	132	1_24_N_103_45_E
397	A*24:02:01:01	Singapore Thai		0.0820	100	1_24_N_103_45_E
398	A*24:02:01:01	South Africa Natal Tamil		0.1600	51	30_0_S_31_0_E
399	A*24:02:01:01	South Africa Natal Zulu	2.0	0.0100	100	29_0_S_31_0_E
400	A*24:02:01:01	South Korea pop 3		0.0000	485	37_33_N_126_59_E
401	A*24:02:01:01	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
402	A*24:02:01:01	USA Arizona Pima		0.3600	100	33_0_N_113_0_W
403	A*24:02:01:01	USA Eastern European		0.1020	558	38_53_N_77_2_W
404	A*24:02:01:01	USA Italy Ancestry		0.0840	273	38_0_N_77_0_W
405	A*24:02:01:01	USA New Mexico Canoncito Navajo		0.3050	42	34_9_N_107_3_W
406	A*24:02:01:01	USA San Francisco Caucasian		0.0750	220	37_46_N_122_25_W
407	A*24:02:01:01	USA Spain Ancestry		0.0020	279	38_0_N_77_0_W
408	A*24:02:01:01	Venezuela Perja Mountain Bari		0.6020	55	9_49_N_72_49_W
409	A*24:02:01:02L	Brazil Belo Horizonte Caucasian	0.0	0.0000	95	19_55_S_43_56_W
410	A*24:02:01:02L	Bulgaria		0.0000	55	42_0_N_23_0_E
411	A*24:02:01:02L	China North Han		0.0000	105	39_54_N_116_24_E
412	A*24:02:01:02L	Cuba Caucasian	0.0	0.0000	70	23_8_N_82_22_W
413	A*24:02:01:02L	Cuba Mixed Race	0.0	0.0000	42	23_8_N_82_22_W
414	A*24:02:01:02L	Czech Republic		0.0000	106	50_0_N_14_0_E
415	A*24:02:01:02L	France French Bone Marrow Donor Registry		0.0025	42,623	48_51_N_2_21_E
416	A*24:02:01:02L	Hong Kong Chinese HKBMDR HLA 11 loci	0.0	0.0001	5,266	22_25_N_114_17_E
417	A*24:02:01:02L	India North pop 2		0.0100	72	25_0_N_74_0_E
418	A*24:02:01:02L	Ireland Northern	0.0	0.0000	1,000	54_40_N_6_45_W
419	A*24:02:01:02L	Mexico Mestizo	0.0	0.0000	41	19_24_N_99_9_W
420	A*24:02:01:02L	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
421	A*24:02:01:02L	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
422	A*24:02:01:02L	Oman	0.9	0.0040	118	22_0_N_56_0_E
423	A*24:02:01:02L	Pakistan Baloch		0.0000	66	30_0_N_67_0_E
424	A*24:02:01:02L	Pakistan Brahui		0.0060	104	30_0_N_67_0_E
425	A*24:02:01:02L	Pakistan Burusho		0.0000	92	36_0_N_73_0_E
426	A*24:02:01:02L	Pakistan Kalash		0.0170	69	32_0_N_70_0_E
427	A*24:02:01:02L	Pakistan Karachi Parsi		0.0000	91	24_49_N_66_59_E
428	A*24:02:01:02L	Pakistan Mixed Pathan		0.0050	100	32_0_N_70_0_E
429	A*24:02:01:02L	Pakistan Mixed Sindhi		0.0060	101	24_0_N_68_0_E
430	A*24:02:01:02L	Singapore Chinese	0.0	0.0000	149	1_24_N_103_45_E
431	A*24:02:01:02L	South Africa Natal Zulu	0.0	0.0000	100	29_0_S_31_0_E
432	A*24:02:01:02L	South Korea pop 3		0.0000	485	37_33_N_126_59_E
433	A*24:02:01:02L	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
434	A*24:02:02	Brazil Rio de Janeiro Caucasian		0.0010	521	22_54_S_43_12_W
435	A*24:02:02	Bulgaria		0.0000	55	42_0_N_23_0_E
436	A*24:02:02	China North Han		0.0000	105	39_54_N_116_24_E
437	A*24:02:02	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
438	A*24:02:02	Morocco Nador Metalsa pop 2		0.0000	73	35_10_N_2_56_W
439	A*24:02:02	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
440	A*24:02:02	Saudi Arabia pop 5	1.3	0.0063	158	24_38_N_46_43_E
441	A*24:02:02	South Korea pop 3		0.0000	485	37_33_N_126_59_E
442	A*24:02:02	USA Alaska Yupik		0.0000	252	60_48_N_161_26_W
443	A*24:02:03Q	Bulgaria		0.0000	55	42_0_N_23_0_E
444	A*24:02:03Q	China North Han		0.0000	105	39_54_N_116_24_E
445	A*24:02:03Q	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
446	A*24:02:03Q	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
447	A*24:02:04	Bulgaria		0.0000	55	42_0_N_23_0_E
448	A*24:02:04	China North Han		0.0000	105	39_54_N_116_24_E
449	A*24:02:04	Italy North pop 3	0.0	0.0000	97	43_0_N_12_0_E
450	A*24:02:04	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
451	A*24:02:04	Saudi Arabia pop 6 (G)		0.0002	28,927	23_52_N_45_4_E
452	A*24:02:05	China North Han		0.0000	105	39_54_N_116_24_E
453	A*24:02:05	India Karnataka Kannada Speaking	0.6	0.0030	174	12_57_N_77_36_E
454	A*24:02:05	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
455	A*24:02:05	Saudi Arabia pop 6 (G)		0.0008	28,927	23_52_N_45_4_E
456	A*24:02:06	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
457	A*24:02:07	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
458	A*24:02:08	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
459	A*24:02:09	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
460	A*24:02:10	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
461	A*24:02:10	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
462	A*24:02:11	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
463	A*24:02:12	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
464	A*24:02:13	India Andhra Pradesh Telugu Speaking	2.7	0.0134	186	13_4_N_80_14_E
465	A*24:02:13	India Kerala Malayalam speaking	0.3	0.0010	356	13_1_N_80_15_E
466	A*24:02:13	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
467	A*24:02:13	Poland BMR	0.0	0.0001	23,595	52_0_N_19_22_E
468	A*24:02:13	Russia Bashkortostan, Bashkirs	0.8	0.0042	120	54_0_N_55_0_E
469	A*24:02:13	Russia Nizhny Novgorod, Russians	0.1	0.0007	1,510	56_19_N_43_59_E
470	A*24:02:13	Vietnam Kinh	1.0	0.0050	101	14_5_N_108_27_E
471	A*24:02:14	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
472	A*24:02:15	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
473	A*24:02:16	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
474	A*24:02:17	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
475	A*24:02:18	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
476	A*24:02:19	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
477	A*24:02:19	Poland BMR	0.0	0.0000	23,595	52_0_N_19_22_E
478	A*24:02:19	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
479	A*24:02:20	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
480	A*24:02:21	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
481	A*24:02:22	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
482	A*24:02:23	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
483	A*24:02:24	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
484	A*24:02:25	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
485	A*24:02:26	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
486	A*24:02:27	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
487	A*24:02:28	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
488	A*24:02:29	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
489	A*24:02:30	Morocco Settat Chaouya	0.0	0.0000	98	33_0_N_7_36_W
490	A*24:02:31	China Zhejiang Han		0.0003	1,734	27_2_N_118_1_E
491	A*24:02:31	Hong Kong Chinese HKBMDR HLA 11 loci	0.1	0.0005	5,266	22_25_N_114_17_E
492	A*24:02:40	China Zhejiang Han		0.0003	1,734	27_2_N_118_1_E
493	A*24:02:40	Hong Kong Chinese HKBMDR HLA 11 loci	0.4	0.0022	5,266	22_25_N_114_17_E
494	A*24:02:40	Vietnam Kinh	1.0	0.0050	101	14_5_N_108_27_E
495	A*24:02:46	USA NMDP Black South or Central American		0.0019	4,889	38_45_N_76_6_W
496	A*24:02:48	Saudi Arabia pop 6 (G)		0.0000	28,927	23_52_N_45_4_E
497	A*24:02:54	USA NMDP Black South or Central American		0.0336	4,889	38_45_N_76_6_W
498	A*24:02:102	USA Arizona Gila River Pima		0.0002	3,000	33_4_N_111_43_W

©2022 Allele Frequencies Website.

Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools
Gonzalez-Galarza FF, McCabe A, Santos EJ, Jones J, Takeshita LY, Ortega-Rivera ND, Del Cid-Pavon GM, Ramsbottom K, Ghattaoraya GS, Alfirevic A, Middleton D and Jones AR Nucleic Acid Research 2020, 48:D783-8.
Liverpool, U.K.

support@allelefrequencies.net