PMID- 24476119
OWN - NLM
STAT- MEDLINE
DCOM- 20140829
LR  - 20221207
IS  - 1471-2164 (Electronic)
IS  - 1471-2164 (Linking)
VI  - 15
DP  - 2014 Jan 29
TI  - Predicting HLA genotypes using unphased and flanking single-nucleotide 
      polymorphisms in Han Chinese population.
PG  - 81
LID - 10.1186/1471-2164-15-81 [doi]
AB  - BACKGROUND: Genetic variation associated with human leukocyte antigen (HLA) genes 
      has immunological functions and is associated with autoimmune diseases. To date, 
      large-scale studies involving classical HLA genes have been limited by 
      time-consuming and expensive HLA-typing technologies. To reduce these costs, 
      single-nucleotide polymorphisms (SNPs) have been used to predict HLA-allele 
      types. Although HLA allelic distributions differ among populations, most 
      prediction model of HLA genes are based on Caucasian samples, with few reported 
      studies involving non-Caucasians. RESULTS: Our sample consisted of 437 Han 
      Chinese with Affymetrix 5.0 and Illumina 550 K SNPs, of whom 214 also had data on 
      Affymetrix 6.0 SNPs. All individuals had HLA typings at a 4-digit resolution. 
      Using these data, we have built prediction model of HLA genes that are specific 
      for a Han Chinese population. To optimize our prediction model of HLA genes, we 
      analyzed a number of critical parameters, including flanking-region size, 
      genotyping platform, and imputation. Predictive accuracies generally increased 
      both with sample size and SNP density. CONCLUSIONS: SNP data from the HapMap 
      Project are about five times more dense than commercially available genotype chip 
      data. Using chips to genotype our samples, however, only reduced the accuracy of 
      our HLA predictions by only ~3%, while saving a great deal of time and expense. 
      We demonstrated that classical HLA alleles can be predicted from SNP genotype 
      data with a high level of accuracy (80.37% (HLA-B) ~95.79% (HLA-DQB1)) in a Han 
      Chinese population. This finding offers new opportunities for researchers in 
      obtaining HLA genotypes via prediction using their already existing chip 
      datasets. Since the genetic variation structure (e.g. SNP, HLA, Linkage 
      disequilibrium) is different between Han Chinese and Caucasians, and has strong 
      impact in building prediction models for HLA genes, our findings emphasize the 
      importance of building ethnic-specific models when analyzing human populations.
FAU - Hsieh, Ai-Ru
AU  - Hsieh AR
FAU - Chang, Su-Wei
AU  - Chang SW
FAU - Chen, Pei-Lung
AU  - Chen PL
FAU - Chu, Chen-Chung
AU  - Chu CC
FAU - Hsiao, Ching-Lin
AU  - Hsiao CL
FAU - Yang, Wei-Shiung
AU  - Yang WS
FAU - Chang, Chien-Ching
AU  - Chang CC
FAU - Wu, Jer-Yuarn
AU  - Wu JY
FAU - Chen, Yuan-Tsong
AU  - Chen YT
FAU - Chang, Tien-Chun
AU  - Chang TC
AD  - Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan. 
      tienchunchang@ntu.edu.tw.
FAU - Fann, Cathy Sj
AU  - Fann CS
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20140129
PL  - England
TA  - BMC Genomics
JT  - BMC genomics
JID - 100965258
RN  - 0 (HLA Antigens)
RN  - 0 (HLA-B Antigens)
RN  - 0 (HLA-DQ beta-Chains)
RN  - 0 (HLA-DQB1 antigen)
SB  - IM
MH  - 3' Flanking Region
MH  - 5' Flanking Region
MH  - Alleles
MH  - Asian People/*genetics
MH  - China
MH  - Gene Frequency
MH  - Genotype
MH  - HLA Antigens/*genetics
MH  - HLA-B Antigens/genetics
MH  - HLA-DQ beta-Chains/genetics
MH  - HapMap Project
MH  - Humans
MH  - Linkage Disequilibrium
MH  - *Polymorphism, Single Nucleotide
PMC - PMC3909910
EDAT- 2014/01/31 06:00
MHDA- 2014/08/30 06:00
PMCR- 2014/01/29
CRDT- 2014/01/31 06:00
PHST- 2013/08/15 00:00 [received]
PHST- 2014/01/16 00:00 [accepted]
PHST- 2014/01/31 06:00 [entrez]
PHST- 2014/01/31 06:00 [pubmed]
PHST- 2014/08/30 06:00 [medline]
PHST- 2014/01/29 00:00 [pmc-release]
AID - 1471-2164-15-81 [pii]
AID - 10.1186/1471-2164-15-81 [doi]
PST - epublish
SO  - BMC Genomics. 2014 Jan 29;15:81. doi: 10.1186/1471-2164-15-81.