Comparative study for haplotype block partitioning methods – Evidence from chromosome 6 of the North American Rheumatoid Arthritis Consortium (NARAC) dataset

Haplotype-based methods compete with “one-SNP-at-a-time” approaches on being preferred for association studies. Chromosome 6 contains most of the known genetic biomarkers for rheumatoid arthritis (RA) disease. Therefore, chromosome 6 serves as a benchmark for the haplotype methods testing. The aim of this study is to test the North American Rheumatoid Arthritis Consortium (NARAC) dataset to find out if haplotype block methods or single-locus approaches alone can sufficiently provide the significant single nucleotide polymorphisms (SNPs) associated with RA. In addition, could we be satisfied with only one method of the haplotype block methods for partitioning chromosome 6 of the NARAC dataset? In the NARAC dataset, chromosome 6 comprises 35,574 SNPs for 2,062 individuals (868 cases, 1,194 controls). Individual SNP approach and three haplotype block methods were applied to the NARAC dataset to identify the RA biomarkers. We employed three haplotype partitioning methods which are confidence interval test (CIT), four gamete test (FGT), and solid spine of linkage disequilibrium (SSLD). P-values after stringent Bonferroni correction for multiple testing were measured to assess the strength of association between the genetic variants and RA susceptibility. Moreover, the block size (in base pairs (bp) and number of SNPs included), number of blocks, percentage of uncovered SNPs by the block method, percentage of significant blocks from the total number of blocks, number of significant haplotypes and SNPs were used to compare among the three haplotype block methods. Individual SNP, CIT, FGT, and SSLD methods detected 432, 1,086, 1,099, and 1,322 associated SNPs, respectively. Each method identified significant SNPs that were not detected by any other method (Individual SNP: 12, FGT: 37, CIT: 55, and SSLD: 189 SNPs). 916 SNPs were discovered by all the three haplotype block methods. 367 SNPs were discovered by the haplotype block methods and the individual SNP approach. The P-values of these 367 SNPs were lower than those of the SNPs uniquely detected by only one method. The 367 SNPs detected by all the methods represent promising candidates for RA susceptibility. They should be further investigated for the European population. A hybrid technique including the four methods should be applied to detect the significant SNPs associated with RA for chromosome 6 of the NARAC dataset. Moreover, SSLD method may be preferred for its favored benefits in case of selecting only one method.

基于单倍型的方法（Haplotype-based methods）与“单SNP逐次分析（one-SNP-at-a-time）”方法在关联研究中同为常用的优选方案。6号染色体包含了类风湿关节炎（rheumatoid arthritis, RA）已知的绝大多数遗传生物标志物，因此可作为单倍型方法性能测试的基准数据集。本研究旨在针对北美类风湿关节炎联盟（North American Rheumatoid Arthritis Consortium, NARAC）数据集展开分析，以探究仅使用单倍型区块方法或单基因座分析方法，是否足以识别出与RA相关的具有统计学意义的单核苷酸多态性（single nucleotide polymorphisms, SNPs）。此外，本研究同时探讨仅采用一种单倍型区块划分方法处理NARAC数据集的6号染色体数据，是否能够满足分析需求。NARAC数据集的6号染色体包含2062名个体（其中868名为病例组，1194名为对照组）的35574个SNPs。研究团队采用了单SNP分析方法与三种单倍型区块方法，对该数据集进行RA相关生物标志物的识别。本次研究所使用的三种单倍型划分方法分别为：置信区间检验法（confidence interval test, CIT）、四配子检验法（four gamete test, FGT）以及连锁不平衡坚实脊法（solid spine of linkage disequilibrium, SSLD）。为评估遗传变异与RA易感性之间的关联强度，本研究对多重检验实施了严格的邦费罗尼校正（Bonferroni correction），并计算了校正后的P值以作为评估指标。为对比三种单倍型区块方法的性能，本研究选取了多项评价指标：区块大小（以碱基对base pairs, bp为单位及所包含的SNPs数量）、区块总数、区块方法未覆盖的SNPs占比、显著区块占总区块数的比例、显著单倍型数量以及显著SNPs数量。实验结果显示：单SNP分析、CIT、FGT及SSLD方法分别检出了432、1086、1099与1322个与RA相关的SNPs。每种方法均可识别出其他方法未检出的显著SNPs（单SNP分析：12个，FGT：37个，CIT：55个，SSLD：189个）。三种单倍型区块方法共同检出了916个SNPs，单倍型区块方法与单SNP分析方法共同检出了367个SNPs。上述367个SNPs的P值低于仅被单一方法独有的SNPs的P值。所有方法均检出的这367个SNPs，是极具潜力的RA易感性候选标志物，需针对欧洲人群开展进一步的验证研究。综上，针对NARAC数据集的6号染色体数据，应采用包含四种方法在内的混合分析技术，以精准识别与RA相关的显著SNPs。若仅选择单一方法开展分析，SSLD方法凭借其独特优势，可作为优选方案。