Population-scale variability of the human UDP-glycosyltransferase gene family.

Human UDP-glycosyltransferases (UGTs) are responsible for the glucuronidation of a wide variety of endogenous substrates and multiple commonly prescribed drugs. Different genetic polymorphisms in UGT genes are implicated in interindividual differences in drug response and cancer risk. However, the genetic complexity beyond these variants has not been comprehensively assessed. We here leveraged whole-exome and whole-genome sequencing data from 141,456 unrelated individuals across seven major human populations to provide a comprehensive profile of genetic variability across the human UGT gene family. Overall, 9666 exonic variants were observed of which 98.9% were rare. To interpret the functional impact of UGT missense variants, we developed a gene family-specific variant effect predictor. This algorithm identified a total of 1208 deleterious variants, most of which were found in African and South Asian populations. Structural analysis corroborated the predicted effects for multiple variations in substrate binding sites. Combined, our analyses provide a systematic overview of UGT variability, which can yield insights into inter-individual differences in phase 2 metabolism and facilitate the translation of sequencing data into personalized predictions of UGT substrate disposition.

Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex.

Background: Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results: A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. Conclusions: Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.