Archives

  • 2018-07
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • br Results and discussion br Conclusions Full gene

    2020-07-30


    Results and discussion
    Conclusions Full-gene haplotypes of four genes encoding trans-acting T-metabolism proteins, UGT2B7, ABCB1, OPRM1, and COMT, were defined and characterized using substantially more polymorphic sites than previously employed in pharmacogenetic studies. In doing so, a large number of haplotypes were observed. The data presented demonstrate significant LDs between full-gene haplotypes of CYP2D6 and those of UGT2B7 and COMT; however, the functional effects of these findings need to be determined empirically. The relatively low frequency of each haplotype and associated diplotype may confound LD estimates simply because Iodoacetyl-LC-Biotin mg each haplotype was only observed in combination with one other haplotype. This study also proposed an extended ABCB1-Block -1, which included distal untranslated Iodoacetyl-LC-Biotin mg 1, and did not substantially increase acquired information over the truncated Block -1 reported by Sai et al. [30], [31]. Most individual haplotypes identified in this study were quite rare; however, relatively common haplotypes (≥1% global frequency) were identified which contain at least one damaging, or most likely damaging, polymorphism. It should be noted that copy number variation and CYP2D6/CYP2D7 gene conversion do occur in some individuals, primary UMs and may alter the presented LD and regression patterns [65]. These events were not considered herein for determining of CYP2D6 activity [11] due to the limitations of short read sequences that comprise 1000 Genomes Project data [66], [67]. It is likely that ongoing developments in longer read sequencing technologies will provide more confident interpretation of structural variation from existing short-read sequences [68], [69], [70], [71]. The variant effects of many polymorphisms included in these haplotype definitions have not been empirically evaluated by the pharmacogenetics/pharmacogenomics community. There are obvious limitations to using an algorithmic approach to variant effect [72]; however, the predicted implications on phenotype should not be overlooked, instead they can be used to narrow the pool of potentially causal variants/haplotypes to explore empirically. The inclusion of only self-reported healthy individuals in the 1000 Genomes Project means that additional functionally-relevant haplotypes may be selected against being represented in this dataset. This limiting factor may impact the analyses performed above. It is likely that additional polymorphisms and/or specific haplotypes may be enriched, or selected for, in affected, or T-exposed, cohorts [73], [74], [75]. As such, there potentially are additional damaging haplotypes in these affected groups that have not been observed herein so a full-gene interrogation of affected cohorts may provide greater resolution to damaging haplotype population distribution. This possibility lends support to utilizing a comprehensive genotyping approach, such as relatively long-read MPS or continuous-read nanopore technology in pharmacogenetic/pharmacogenomic interrogations [70], [71], [76].