Lie, B. A. | Search bioRxiv

bioRxiv2021

Allele imputation for the Killer cell Immunoglobulin-like Receptor KIR3DL1/S1

Highly polymorphic interactions of KIR3DL1 and KIR3DS1 with HLA class I ligands modulates the effector functions of natural killer (NK) cells and some T cells. This genetically determined diversity affects severity of infections, immune-mediated diseases, and some cancers, and impacts the course of cancer treatment, including transplantation. KIR3DL1 is an inhibitory receptor, and KIR3DS1 is an activating receptor encoded by the KIR3DL1/S1 gene that has more than 200 diverse and divergent alleles. Determination of KIR3DL1/S1 genotypes for medical application is hampered by complex sequence and structural variation that distinguishes individuals and populations, requiring targeted approaches to generate and analyze high-resolution allele data. To overcome these obstacles, we developed and optimized a model for imputing KIR3DL1/S1 alleles at high-resolution from whole-genome SNP data, and designed to represent a substantial component of human genetic diversity. We show that our Global model is effective at imputing KIR3DL1/S1 alleles with an accuracy ranging from 89% in Africans to 97% in East Asians, with mean specificity of 99.8% and sensitivity of 99% for named alleles >1% frequency. We used the established algorithm of the HIBAG program, in a modification named Pulling Out Natural killer cell Genomics (PONG). Because HIBAG was designed to impute HLA alleles also from whole-genome SNP data, PONG allows combinatorial diversity of KIR3DL1/S1 and HLA-A and B to be analyzed using complementary techniques on a single data source. The use of PONG thus negates the need for targeted sequencing data in very large-scale association studies where such methods might not be tractable. All code, imputation models, test data and documentation are available at https://github.com/NormanLabUCD/PONG. Author SummaryNatural killer (NK) cells are cytotoxic lymphocytes that identify and kill infected or malignant cells and guide immune responses. The effector functions of NK cells are modulated through polymorphic interactions of KIR3DL1/S1 on their surface with the human leukocyte antigens (HLA) that are found on most other cell types in the body. KIR3DL1/S1 is highly polymorphic and differentiated across human populations, affecting susceptibility and course of multiple immune-mediated diseases and their treatments. Genotyping KIR3DL1/S1 for direct medical application or research has been encumbered by the complex sequence and structural variation, which requires targeted approaches and extensive domain expertise to generate and validate high-resolution allele calls. We therefore developed Pulling Out Natural Killer Cell Genomics (PONG) to impute KIR3DL1/S1 alleles from whole genome SNP data, and which we implemented as an open-source R package. We assessed imputation performance using data from five broad population groups that represent a substantial portion of human genetic diversity. We can impute KIR3DL1/S1 alleles with an accuracy ranging from 89% in Africans and South Asians to 97% in East Asians. Globally, imputation of KIR3DL1/S1 alleles having frequency >1% has a mean sensitivity of 94% and specificity of 99.8%. Thus, the PONG method both enables highly sensitive individual-level calling and makes large scale medical genetic studies of KIR3DL1/S1 possible.

immunology↗

bioRxiv2020

An extension to: Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing has emerged as the favoured method to study microRNA (miRNA) expression, but biases introduced during library preparation have been reported. To assist researchers choose the most appropriate library preparation kit, we recently compared the performance of six commercially-available kits on synthetic miRNAs and human RNA, where library preparation was performed by the vendors. We hereby supplement this study with data from two further commonly used kits (NEBNext, NEXTflex) whose manufacturers initially declined to participate. As before, performance was assessed with respect to sensitivity, reliability, titration response and differential expression. Despite NEXTflex employing partially-randomised adapter sequences to minimise bias, we reaffirm that biases in miRNA abundance are kit-specific, complicating the comparison of miRNA datasets generated using different kits.

molecular biology↗

bioRxiv2019

Systematic assessment of commercially available low-input miRNA library preparation kits

High-throughput sequencing is increasingly favoured to assay the presence and abundance of micro RNAs (miRNAs) in biological samples, even from low RNA amounts, and a number of commercial vendors now offer kits that allow miRNA sequencing from sub-nanogram (ng) inputs. However, although biases introduced during library preparation have been documented, the relative performance of current reagent kits has not been investigated in detail. Here, six commercial kits capable of handling <100ng total RNA input were used for library preparation, performed by kit manufactures, on synthetic miRNAs of known quantities and human biological total RNA samples. We compared the performance of miRNA detection sensitivity, reliability, titration response and the ability to detect differentially expressed miRNAs. In addition, we assessed the use of unique molecular identifiers sequence (UMI) tags in one kit. We observed differences in detection sensitivity and ability to identify differentially expressed miRNAs between the kits, but none were able to detect the full repertoire of expected miRNAs. The reliability within the replicates of all kits was good, while larger differences were observed between the kits, although none could accurately quantify the majority of miRNAs. UMI tags, at least within the input ranges tested, offered little advantage to improve data utility. In conclusion, biases in miRNA abundance are heavily influenced by the kit used for library preparation, suggesting that comparisons of datasets prepared by different procedures should be made with caution. This article is intended to assist researchers select the most appropriate kit for their experimental conditions.

bioinformatics↗