Evidence of new intragenic HBB haplotypes model for the prediction of beta-thalassemia in the Malaysian population

This study sought to determine the potential role of HBB haplotypes to predict beta-thalassemia in the Malaysian population. A total of 543 archived samples were selected for this study. Five tagging SNPs in the beta-globin gene (HBB; NG_000007.3) were analyzed for SNP-based and haplotype association using SHEsis online software. Single-SNP-based association analysis showed three SNPs have a statistically significant association with beta-thalassemia. When Bonferroni correction was applied, four SNPs were found statistically significant with beta-thalassemia; IVS2-74T>G (padj = 0.047), IVS2-16G>C (padj = 0.017), IVS2-666C>T (padj = 0.017) and 3’UTR + 314G>A (padj = 0.002). However, 3'UTR + 233G>C did not yield a significant association with padj value = 0.076. Further investigation using combined five SNPs for haplotype association analysis revealed three susceptible haplotypes with significant p values of which, haplotypes 1-2-2-1-1 (p = 6.49 × 10−7, OR = 10.371 [3.345–32.148]), 1-2-1-1-1 (p = 0.009, OR = 1.423 [1.095–1.850] and 1-1-1-1-1 (p = 1.39 × 10−4, OR = 10.221 [2.345–44.555]). Three haplotypes showed protective effect with significant p value of which, 2-2-1-1-1 (p = 0.006, OR = 0.668 [0.500–0.893]), 1-1-2-2-1 (p = 0.013, OR = 0.357 [0.153–0.830]) and 1-1-2-1-1 (p = 0.033, OR = 0.745 [0.567–0.977]). This study has identified the potential use of intragenic polymorphic markers in the HBB gene, which were significantly associated with beta-thalassemia. Combining these five SNPs defined a new haplotype model for beta-thalassemia and further evaluation for predicting severity in beta-thalassemia.

Evidence of New Intragenic HBB Haplotypes Model for the Prediction of Beta-Thalassemia in the Malaysian Population

This study sought to determine the potential role of HBB haplotypes for the prediction of beta-thalassemia in the Malaysian population. A total of 543 archived samples were reviewed and selected for this study. Five tagging SNPs in the beta-globin gene (HBB; NG_000007.3) were recorded and analysed for SNP-based and haplotype association using SHEsis online software. Single-SNP-based association analysis showed three tagging SNPs have statistical significant association with beta-thalassemia; IVS2-16G>C (p=0.036), IVS2-666C>T (p=0.032) and 3’UTR +314G>A (p=0.004). However, two tagging SNPs assigned as IVS2-74T>G and 3’UTR +233G>C did not yield significant association with p-value 0.099 and 0.211, respectively. In a further investigation, combined five tagging SNPs for haplotype association analysis revealed three susceptible haplotypes with significant p-values of which, assigned as haplotypes 1-2-2-1-1 (p=6.49x10-7, OR=10.371 [3.345~32.148]), 1-2-1-1-1 (p= 0.009, OR=1.423 [1.095~1.850] and 1-1-1-1-1 (p=1.39x10-4, OR=10.221 [2.345~44.555]). Another three haplotypes were found to be protective haplotype with significant p-value of which assigned as haplotypes 2-2-1-1-1 (p=0.006, OR=0.668 [0.500~0.893]), 1-1-2-2-1 (p=0.013, OR=0.357 [0.153~0.830]) and 1-1-2-1-1 (p=0.033, OR=0.745 [0.567~0.977]). This study has identified the potential use of intragenic polymorphic markers in the HBB gene, which were significantly associated with beta-thalassemia. A combination of these five tagging SNPs defined a new haplotype model for beta-thalassemia and further evaluation for the prediction of severity in beta-thalassemia.

Suggestive Evidence of Slc2a9 Polymorphisms Association in Gouty Malay Males

Introduction: Solute carrier family 2, member 9 (SLC2A9) is thought to be an important urate transporter that influences the excretion and reabsorption of serum uric acid, thus has a strong effect on serum urate and risk of gout. SLC2A9 polymorphisms have been extensively studied in various populations in association with gout development. Our aim was to test for association of SLC2A9 SNPs with gout in Malay males. Methods: 78 gouty patients and 82 normal subjects were recruited and genotyped for rs3733591, rs5028843 and rs11942223 using PCR-RFLP technique. Single association and haplotype association analyses were conducted using SHEsis online software. Results: rs3733591 and rs5028843 showed association with gout with p value of 0.020 and 0.036, respectively, whilst rs11942223 yielded no association with p value of 0.08 with trend towards susceptibility projecting by OR=3.547, 3.667 and 2.732, respectively. It is noteworthy that haplotype 1/1/1 conferred protection in gout with p value 0.004 (OR=0.324 [0.147-0.716]). Conclusion: This study might suggest an evidence of association of SLC2A9 SNPs with gout among Malay males.

Exploring effective approaches for haplotype block phasing

Background Knowledge of phase, the specific allele sequence on each copy of homologous chromosomes, is increasingly recognized as critical for detecting certain classes of disease-associated mutations. One approach for detecting such mutations is through phased haplotype association analysis. While the accuracy of methods for phasing genotype data has been widely explored, there has been little attention given to phasing accuracy at haplotype block scale. Understanding the combined impact of the accuracy of phasing tool and the method used to determine haplotype blocks on the error rate within the determined blocks is essential to conduct accurate haplotype analyses. Results We present a systematic study exploring the relationship between seven widely used phasing methods and two common methods for determining haplotype blocks. The evaluation focuses on the number of haplotype blocks that are incorrectly phased. Insights from these results are used to develop a haplotype estimator based on a consensus of three tools. The consensus estimator achieved the most accurate phasing in all applied tests. Individually, EAGLE2, BEAGLE and SHAPEIT2 alternate in being the best performing tool in different scenarios. Determining haplotype blocks based on linkage disequilibrium leads to more correctly phased blocks compared to a sliding window approach. We find that there is little difference between phasing sections of a genome (e.g. a gene) compared to phasing entire chromosomes. Finally, we show that the location of phasing error varies when the tools are applied to the same data several times, a finding that could be important for downstream analyses. Conclusions The choice of phasing and block determination algorithms and their interaction impacts the accuracy of phased haplotype blocks. This work provides guidance and evidence for the different design choices needed for analyses using haplotype blocks. The study highlights a number of issues that may have limited the replicability of previous haplotype analysis.