Predicting HLA genotypes using unphased and flanking single-nucleotide polymorphisms in Han Chinese population.
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.
Ai-Ru
AR
Hsieh
Su-Wei
SW
Chang
Pei-Lung
PL
Chen
Chen-Chung
CC
Chu
Ching-Lin
CL
Hsiao
Wei-Shiung
WS
Yang
Chien-Ching
CC
Chang
Jer-Yuarn
JY
Wu
Yuan-Tsong
YT
Chen
Institute of Biomedical Sciences, Academia Sinica, Nankang, Taipei, Taiwan. tienchunchang@ntu.edu.tw.
Tien-Chun
TC
Chang
Cathy Sj
CS
Fann
D024509:3' Flanking Region; D024506:5' Flanking Region; D000483:Alleles; D044466:Asian People / Q000235:genetics*; D002681:China; D005787:Gene Frequency; D005838:Genotype; D006680:HLA Antigens / Q000235:genetics*; D015235:HLA-B Antigens / Q000235:genetics; D059866:HLA-DQ beta-Chains / Q000235:genetics; D060148:HapMap Project; D006801:Humans; D015810:Linkage Disequilibrium; D020641:Polymorphism, Single Nucleotide
D006680:HLA Antigens; D015235:HLA-B Antigens; D059866:HLA-DQ beta-Chains; C069051:HLA-DQB1 antigen