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Home	Populations	HLA	KIR	Other polymorphisms	HLA-ADR	KDDB

MIC » MIC Allele Frequency Search

Please specify your search by selecting options from boxes. Then, click "Search" to find MIC allele / gene frequencies that match your criteria. Remember at least one option must be selected.

Locus:  All loci MICA MICB Starting Allele:  A4 A5 A5.1 A6 A9 A9.1 A10 MICA*001 MICA*002 MICA*002:01 MICA*002:02 MICA*002:03 MICA*002:04 MICA*004 MICA*005 MICA*006 MICA*007 MICA*007:01 MICA*007:02 MICA*007:03 MICA*007:04 MICA*007:05 MICA*007:06 MICA*008 MICA*008:01 MICA*008:01:01 MICA*008:01:02 MICA*008:02 MICA*008:03 MICA*008:04 MICA*008:05 MICA*009 MICA*009:01 MICA*009:02 MICA*010 MICA*010:01 MICA*010:02 MICA*011 MICA*012 MICA*012:01 MICA*012:02 MICA*012:03 MICA*012:04 MICA*013 MICA*014 MICA*015 MICA*016 MICA*017 MICA*018 MICA*018:01 MICA*018:02 MICA*019 MICA*020 MICA*022 MICA*023 MICA*024 MICA*025 MICA*026 MICA*027 MICA*028 MICA*029 MICA*030 MICA*031 MICA*032 MICA*033 MICA*034 MICA*035 MICA*036 MICA*037 MICA*038 MICA*039 MICA*040 MICA*041 MICA*042 MICA*043 MICA*044 MICA*045 MICA*046 MICA*047 MICA*048 MICA*049 MICA*050 MICA*051 MICA*052 MICA*053 MICA*054 MICA*055 MICA*056 MICA*057 MICA*058 MICA*059 MICA*060 MICA*061 MICA*062 MICA*063N MICA*064N MICA*065 MICA*066 MICA*067 MICA*068 MICA*069 MICA*070 MICA*072 MICA*073 MICA*074 MICA*075 MICA*076 MICA*077 MICA*078 MICA*079 MICA*080 MICA*081 MICA*082 MICA*083 MICA*084 MICA*085 MICB*001 MICB*002 MICB*002:01 MICB*002:01:01 MICB*002:01:02 MICB*003 MICB*004 MICB*004:01 MICB*004:01:01 MICB*004:01:02 MICB*005 MICB*005:01 MICB*005:02 MICB*005:02:01 MICB*005:02:02 MICB*005:02:03 MICB*005:02:04 MICB*005:03 MICB*005:04 MICB*005:05 MICB*005:06 MICB*005:07 MICB*005:08 MICB*006 MICB*007 MICB*008 MICB*009N MICB*010 MICB*011 MICB*012 MICB*013 MICB*014 MICB*015 MICB*016 MICB*018 MICB*019 MICB*020 MICB*021N MICB*022 MICB*023 MICB*024 MICB*025 MICB*026 MICB*027 MICB*028 MICB*029 MICB*030 Ending Allele:  A4 A5 A5.1 A6 A9 A9.1 A10 MICA*001 MICA*002 MICA*002:01 MICA*002:02 MICA*002:03 MICA*002:04 MICA*004 MICA*005 MICA*006 MICA*007 MICA*007:01 MICA*007:02 MICA*007:03 MICA*007:04 MICA*007:05 MICA*007:06 MICA*008 MICA*008:01 MICA*008:01:01 MICA*008:01:02 MICA*008:02 MICA*008:03 MICA*008:04 MICA*008:05 MICA*009 MICA*009:01 MICA*009:02 MICA*010 MICA*010:01 MICA*010:02 MICA*011 MICA*012 MICA*012:01 MICA*012:02 MICA*012:03 MICA*012:04 MICA*013 MICA*014 MICA*015 MICA*016 MICA*017 MICA*018 MICA*018:01 MICA*018:02 MICA*019 MICA*020 MICA*022 MICA*023 MICA*024 MICA*025 MICA*026 MICA*027 MICA*028 MICA*029 MICA*030 MICA*031 MICA*032 MICA*033 MICA*034 MICA*035 MICA*036 MICA*037 MICA*038 MICA*039 MICA*040 MICA*041 MICA*042 MICA*043 MICA*044 MICA*045 MICA*046 MICA*047 MICA*048 MICA*049 MICA*050 MICA*051 MICA*052 MICA*053 MICA*054 MICA*055 MICA*056 MICA*057 MICA*058 MICA*059 MICA*060 MICA*061 MICA*062 MICA*063N MICA*064N MICA*065 MICA*066 MICA*067 MICA*068 MICA*069 MICA*070 MICA*072 MICA*073 MICA*074 MICA*075 MICA*076 MICA*077 MICA*078 MICA*079 MICA*080 MICA*081 MICA*082 MICA*083 MICA*084 MICA*085 MICB*001 MICB*002 MICB*002:01 MICB*002:01:01 MICB*002:01:02 MICB*003 MICB*004 MICB*004:01 MICB*004:01:01 MICB*004:01:02 MICB*005 MICB*005:01 MICB*005:02 MICB*005:02:01 MICB*005:02:02 MICB*005:02:03 MICB*005:02:04 MICB*005:03 MICB*005:04 MICB*005:05 MICB*005:06 MICB*005:07 MICB*005:08 MICB*006 MICB*007 MICB*008 MICB*009N MICB*010 MICB*011 MICB*012 MICB*013 MICB*014 MICB*015 MICB*016 MICB*018 MICB*019 MICB*020 MICB*021N MICB*022 MICB*023 MICB*024 MICB*025 MICB*026 MICB*027 MICB*028 MICB*029 MICB*030    (A4, A5, A5.1, etc. refer to data on frequencies of microsatellites)
Select specific alleles    (If you want to pick specific alleles, make sure your alleles are within the Start-End range above)
Clear Select your alleles   Clear All   Hide A4 A5 A5.1 A6 A9 A9.1 A10 MICA*001 MICA*002 MICA*002:01 MICA*002:02 MICA*002:03 MICA*002:04 MICA*004 MICA*005 MICA*006 MICA*007 MICA*007:01 MICA*007:02 MICA*007:03 MICA*007:04 MICA*007:05 MICA*007:06 MICA*008 MICA*008:01 MICA*008:01:01 MICA*008:01:02 MICA*008:02 MICA*008:03 MICA*008:04 MICA*008:05 MICA*009 MICA*009:01 MICA*009:02 MICA*010 MICA*010:01 MICA*010:02 MICA*011 MICA*012 MICA*012:01 MICA*012:02 MICA*012:03 MICA*012:04 MICA*013 MICA*014 MICA*015 MICA*016 MICA*017 MICA*018 MICA*018:01 MICA*018:02 MICA*019 MICA*020 MICA*022 MICA*023 MICA*024 MICA*025 MICA*026 MICA*027 MICA*028 MICA*029 MICA*030 MICA*031 MICA*032 MICA*033 MICA*034 MICA*035 MICA*036 MICA*037 MICA*038 MICA*039 MICA*040 MICA*041 MICA*042 MICA*043 MICA*044 MICA*045 MICA*046 MICA*047 MICA*048 MICA*049 MICA*050 MICA*051 MICA*052 MICA*053 MICA*054 MICA*055 MICA*056 MICA*057 MICA*058 MICA*059 MICA*060 MICA*061 MICA*062 MICA*063N MICA*064N MICA*065 MICA*066 MICA*067 MICA*068 MICA*069 MICA*070 MICA*072 MICA*073 MICA*074 MICA*075 MICA*076 MICA*077 MICA*078 MICA*079 MICA*080 MICA*081 MICA*082 MICA*083 MICA*084 MICA*085 MICB*001 MICB*002 MICB*002:01 MICB*002:01:01 MICB*002:01:02 MICB*003 MICB*004 MICB*004:01 MICB*004:01:01 MICB*004:01:02 MICB*005 MICB*005:01 MICB*005:02 MICB*005:02:01 MICB*005:02:02 MICB*005:02:03 MICB*005:02:04 MICB*005:03 MICB*005:04 MICB*005:05 MICB*005:06 MICB*005:07 MICB*005:08 MICB*006 MICB*007 MICB*008 MICB*009N MICB*010 MICB*011 MICB*012 MICB*013 MICB*014 MICB*015 MICB*016 MICB*018 MICB*019 MICB*020 MICB*021N MICB*022 MICB*023 MICB*024 MICB*025 MICB*026 MICB*027 MICB*028 MICB*029 MICB*030
Population:  All populations Argentina Formosa Toba MIC (n=94) Argentina Formosa Wichi MIC (n=42) Brazil Limao Verde and Corrego Reserve Terena MIC (n=60) Brazil Mato Grosso do Sul Kaiowa MIC (n=174) Brazil Mato Grosso do Sul Nandeva MIC (n=89) Brazil Parana Guarani M'byá MIC (n=100) Brazil Parana Japanese MIC (n=190) Brazil Parana Kaingang MIC (n=245) Brazil Parana Mixed MIC (n=201) Brazil Sao Paulo Mixed MIC (n=200) China Baotou Han MIC (n=104) China Guangxi Zhuang MIC (n=209) China Guangzhou Han MIC (n=106) China Hui MIC (n=44) China Hunan Province Han MIC (n=85) China North Han MIC (n=32) China Shanghai Han MIC (n=183) China Sichuan Province Han MIC (n=200) China Uygur MIC (n=49) China Xinjiang Province Uygur MIC (n=66) China Yunnan Province Dai MIC (n=41) China Zhangjiajie Tujias MIC (n=187) China Zhejiang Han MIC (n=400) China Zhejiang Province Han MIC (n=100) England Bedfordshire MIC (n=354) England MIC (n=118) England Northwest MIC (n=250) Germany MIC (n=154) Greece Athens MIC (n=27) Iran Tehran MIC (n=18) Italy MIC (n=101) Italy MIC pop 2 (n=98) Italy Perugia MIC (n=158) Italy Rome MIC (n=26) Japan MIC (n=130) Japan MIC pop 2 (n=152) Japan MIC pop 3 (n=114) Japan Tokyo MIC (n=352) Kazakhstan MIC (n=38) Latvia MIC (n=108) Morocco Nador Metalsa MIC (n=82) Nigeria South Efik MIC (n=32) Nigeria South Igbo MIC (n=46) Nigeria South Yoruba MIC (n=74) Saudi Arabia Riyadh MIC (n=18) Slovenia MIC (n=70) South Korea MIC (n=199) South Korea Seoul MIC (n=139) Spain Majorca Chueta MIC (n=95) Spain Majorca MIC (n=165) Spain Malaga MIC (n=241) Spain MIC (n=116) Spain MIC pop 2 (n=342) Spain Murcia MIC (n=154) Sweden MIC (n=503) Thailand North East MIC (n=255) Thailand North East MIC pop 2 (n=100) Turkey MIC (n=65) USA African American MIC (n=201) USA African American MIC pop 2 (n=39) USA Boston African American MIC (n=60) USA Caucasian MIC (n=103) USA Caucasian MIC pop2 (n=242) Wales MIC (n=166) Country:  All countries Argentina Brazil China England Germany Greece Iran Italy Japan Kazakhstan Korea, South Latvia Morocco Nigeria Saudi Arabia Slovenia South Africa Spain Sweden Thailand Turkey United States Wales Source of dataset:  All Sources Literature Proceedings of IHWs Unpublished
Region:  All regions Central Asia Europe North Africa North America North-East Asia South and Central America South-East Asia Sub-Saharan Africa Western Asia Ethnic Origin:  All ethnicities Amerindian Arab Asian Black Caucasoid Mixed Oriental    Type of study:  All Studies Anthropology Study Blood Donor Bone Marrow Registry Controls for Disease Study Other Sort by:  Allele/Gene Allele/Gene, Highest to Lowest Frequency Population Population, Highest to Lowest Frequency
Sample Size:  = <> < > <= >= All 50 100 500 1000 5000     Sample Year:  = <> < > <= >= All years 1995 1996 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2011 2012 2013 2014 2017     Level of resolution :  = <> < > <= >= All 0 1 2 3  (Click here for further details)
Show frequencies: All Only positives Only negatives

Displaying 501 to 600 (from 745) records

Pages:

1 2 3 4 5 6 7 8 of 8

Line	Allele	Population		% of individuals that have the allele		Allele Frequency (3_decimals)		Sample Size	IMGT/HLA Database¹	Distribution²	MIC-HLA Associations³
501	MICA*019		Brazil Limao Verde and Corrego Reserve Terena MIC			0.000		60	See
502	MICA*019		Brazil Mato Grosso do Sul Kaiowa MIC			0.000		174	See
503	MICA*019		Brazil Mato Grosso do Sul Nandeva MIC			0.006		89	See
504	MICA*019		Brazil Parana Guarani M'byá MIC			0.000		100	See
505	MICA*019		Brazil Parana Japanese MIC			0.029		190	See
506	MICA*019		Brazil Parana Kaingang MIC			0.000		245	See
507	MICA*019		Brazil Parana Mixed MIC			0.010		201	See
508	MICA*019		Brazil Sao Paulo Mixed MIC		4.5	0.023		200	See
509	MICA*019		China Baotou Han MIC			0.039		104	See
510	MICA*019		China Guangxi Zhuang MIC			0.103		209	See
511	MICA*019		China Zhangjiajie Tujias MIC			0.206		187	See
512	MICA*019		China Zhejiang Province Han MIC			0.095		100	See
513	MICA*019		England Bedfordshire MIC			0.028		354	See
514	MICA*019		Japan MIC			0.035		130	See
515	MICA*019		Morocco Nador Metalsa MIC			0.012		82	See
516	MICA*019		South Korea Seoul MIC			0.004		139	See
517	MICA*019		Spain Majorca Chueta MIC			0.021		95	See
518	MICA*019		Spain Majorca MIC			0.018		165	See
519	MICA*019		Thailand North East MIC			0.153		255	See
520	MICA*019		USA African American MIC			0.010		201	See
521	MICA*019		USA Caucasian MIC			0.025		103	See
522	MICA*019		Wales MIC		3.0	0.021		166	See
523	MICA*020		Brazil Mato Grosso do Sul Kaiowa MIC			0.000		174	See
524	MICA*020		Brazil Mato Grosso do Sul Nandeva MIC			0.000		89	See
525	MICA*020		Brazil Parana Guarani M'byá MIC			0.005		100	See
526	MICA*020		Brazil Parana Kaingang MIC			0.165		245	See
527	MICA*020		China Zhangjiajie Tujias MIC			0.000		187	See
528	MICA*020		Thailand North East MIC			0.004		255	See
529	MICA*020		Turkey MIC		7.7	0.038		65	See
530	MICA*022		England Northwest MIC		0.0	0.000		250	See
531	MICA*022		Wales MIC		0.0	0.000		166	See
532	MICA*023		China Zhangjiajie Tujias MIC			0.000		187	See
533	MICA*023		England Northwest MIC		0.0	0.000		250	See
534	MICA*023		Spain Murcia MIC			0.012		154	See
535	MICA*023		Wales MIC		0.0	0.000		166	See
536	MICA*024		Brazil Mato Grosso do Sul Kaiowa MIC			0.000		174	See
537	MICA*024		Brazil Mato Grosso do Sul Nandeva MIC			0.000		89	See
538	MICA*024		Brazil Parana Guarani M'byá MIC			0.000		100	See
539	MICA*024		Brazil Parana Kaingang MIC			0.000		245	See
540	MICA*024		England Northwest MIC		0.0	0.000		250	See
541	MICA*024		Wales MIC		0.0	0.000		166	See
542	MICA*025		England Northwest MIC		0.0	0.000		250	See
543	MICA*025		Wales MIC		0.0	0.000		166	See
544	MICA*026		Brazil Mato Grosso do Sul Kaiowa MIC			0.000		174	See
545	MICA*026		Brazil Mato Grosso do Sul Nandeva MIC			0.000		89	See
546	MICA*026		Brazil Parana Guarani M'byá MIC			0.000		100	See
547	MICA*026		Brazil Parana Kaingang MIC			0.000		245	See
548	MICA*026		China Zhangjiajie Tujias MIC			0.000		187	See
549	MICA*026		Thailand North East MIC			0.014		255	See
550	MICA*027		Argentina Formosa Toba MIC			0.309		94	See
551	MICA*027		Argentina Formosa Wichi MIC			0.310		42	See
552	MICA*027		Brazil Limao Verde and Corrego Reserve Terena MIC			0.352		60	See
553	MICA*027		Brazil Mato Grosso do Sul Kaiowa MIC			0.112		174	See
554	MICA*027		Brazil Mato Grosso do Sul Nandeva MIC			0.136		89	See
555	MICA*027		Brazil Parana Guarani M'byá MIC			0.395		100	See
556	MICA*027		Brazil Parana Japanese MIC			0.121		190	See
557	MICA*027		Brazil Parana Kaingang MIC			0.008		245	See
558	MICA*027		Brazil Parana Mixed MIC			0.047		201	See
559	MICA*027		China Baotou Han MIC			0.053		104	See
560	MICA*027		China Guangxi Zhuang MIC			0.019		209	See
561	MICA*027		China Zhangjiajie Tujias MIC			0.000		187	See
562	MICA*027		China Zhejiang Province Han MIC			0.070		100	See
563	MICA*027		England Northwest MIC		1.6	0.008		250	See
564	MICA*027		Morocco Nador Metalsa MIC			0.006		82	See
565	MICA*027		Slovenia MIC		1.4	0.007		70	See
566	MICA*027		South Korea MIC			0.136		199	See
567	MICA*027		South Korea Seoul MIC			0.050		139	See
568	MICA*027		Spain Murcia MIC			0.025		154	See
569	MICA*027		USA African American MIC			0.005		201	See
570	MICA*027		USA Caucasian MIC			0.049		103	See
571	MICA*027		Wales MIC		1.2	0.006		166	See
572	MICA*028		Wales MIC		0.0	0.000		166	See
573	MICA*029		Brazil Mato Grosso do Sul Kaiowa MIC			0.000		174	See
574	MICA*029		Brazil Mato Grosso do Sul Nandeva MIC			0.000		89	See
575	MICA*029		Brazil Parana Guarani M'byá MIC			0.000		100	See
576	MICA*029		Brazil Parana Kaingang MIC			0.000		245	See
577	MICA*029		Brazil Sao Paulo Mixed MIC		0.5	0.003		200	See
578	MICA*029		Turkey MIC		1.5	0.008		65	See
579	MICA*029		Wales MIC		0.6	0.003		166	See
580	MICA*030		Brazil Mato Grosso do Sul Kaiowa MIC			0.000		174	See
581	MICA*030		Brazil Mato Grosso do Sul Nandeva MIC			0.000		89	See
582	MICA*030		Brazil Parana Guarani M'byá MIC			0.000		100	See
583	MICA*030		Brazil Parana Kaingang MIC			0.000		245	See
584	MICA*030		China Zhangjiajie Tujias MIC			0.000		187	See
585	MICA*030		Spain Murcia MIC			0.012		154	See
586	MICA*030		USA African American MIC			0.005		201	See
587	MICA*030		USA Caucasian MIC			0.000		103	See
588	MICA*030		Wales MIC		0.0	0.000		166	See
589	MICA*031		Wales MIC		0.0	0.000		166	See
590	MICA*032		Wales MIC		0.0	0.000		166	See
591	MICA*033		China Baotou Han MIC			0.005		104	See
592	MICA*033		China Guangxi Zhuang MIC			0.007		209	See
593	MICA*033		China Zhangjiajie Tujias MIC			0.000		187	See
594	MICA*033		Spain Murcia MIC			0.012		154	See
595	MICA*033		Wales MIC		0.0	0.000		166	See
596	MICA*034		Wales MIC		0.0	0.000		166	See
597	MICA*035		Brazil Mato Grosso do Sul Kaiowa MIC			0.000		174	See
598	MICA*035		Brazil Mato Grosso do Sul Nandeva MIC			0.000		89	See
599	MICA*035		Brazil Parana Guarani M'byá MIC			0.000		100	See
600	MICA*035		Brazil Parana Kaingang MIC			0.000		245	See

Notes:

- Allele Frequency: Total number of copies of the allele in the population sample (Alleles / 2n) in three decimal format.
- % of individuals that have the allele: Percentage of individuals who have the allele or gene (Individuals / n).
- Allele Frequencies shown in green were calculated from the % of the individuals carrying the allele, 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.
¹ IMGT/HLA Database - For more details of the allele.
² Distribution - Graphical distribution of the allele.
³ MICA-HLA-B Associations - Find MICA-HLA-B associations with this allele.