A novel homozygous splice-site mutation of JK gene leads to Jk(a-b-) phenotype.

OBJECTIVES: This study aimed to investigate the molecular mechanism of the Jk(a-b-) phenotype in a Chinese transfusion patient. BACKGROUND: Many different mutation types relating to Jk(a-b-) phenotype have been reported. However, the splice-site mutation is relatively rare and the related functional verification is lacking. MATERIALS AND METHODS: In this study, the blood sample was collected from a transfusion patient with the Jk(a-b-) phenotype. Serotyping was performed using routine serological methods. The exons sequences and coding regions of the JK gene were amplified using polymerase chain reaction and directly sequenced. To perform a minigene splicing assay, the intronic mutation sequences were cloned into a pSPL3 splice reporting vector. The splicing reporter minigene assay was performed in HEK 293T cells. RESULTS: The Jk(a-b-) phenotype of the blood sample was identified through serological testing. Sequencing results revealed that the sample had a novel homozygous splice-site mutation JK*02N (NM_015865.7: c.663+3A>C). Further analysis, including cDNA sequencing and minigene splicing assay, confirmed that the novel splice-site mutation resulted in exon skipping. Interestingly, different numbers of exons being skipped were obtained by the two methods. CONCLUSION: This study revealed a novel homozygous splicing-site mutation associated with the Jk(a-b-) phenotype in Chinese population. Our results emphasise the importance of the in vitro functional method minigene splicing assay, while also acknowledging its potential limitations when compared to cDNA sequencing.

Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing.

BACKGROUND: Epigenome-wide association studies (EWAS), which seek the association between epigenetic marks and an outcome or exposure, involve multiple hypothesis testing. False discovery rate (FDR) control has been widely used for multiple testing correction. However, traditional FDR control methods do not use auxiliary covariates, and they could be less powerful if the covariates could inform the likelihood of the null hypothesis. Recently, many covariate-adaptive FDR control methods have been developed, but application of these methods to EWAS data has not yet been explored. It is not clear whether these methods can significantly improve detection power, and if so, which covariates are more relevant for EWAS data. RESULTS: In this study, we evaluate the performance of five covariate-adaptive FDR control methods with EWAS-related covariates using simulated as well as real EWAS datasets. We develop an omnibus test to assess the informativeness of the covariates. We find that statistical covariates are generally more informative than biological covariates, and the covariates of methylation mean and variance are almost universally informative. In contrast, the informativeness of biological covariates depends on specific datasets. We show that the independent hypothesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful, especially for sparse signals, and could improve the detection power by a median of 25% and 68% on real datasets, compared to the ST procedure. We further validate the findings in various biological contexts. CONCLUSIONS: Covariate-adaptive FDR control methods with informative covariates can significantly increase the detection power for EWAS. For sparse signals, IHW and CAMT are recommended.