HLA Allele Imputation with Multitask Deep Convolutional Neural Network

Chi, C.

doi:10.1101/2021.06.03.447012

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bioRxiv · 10.1101/2021.06.03.447012

HLA Allele Imputation with Multitask Deep Convolutional Neural Network

Chi, C.

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Abstract

MotivationThe Human leukgocyte antigen (HLA) system is a highly polymorphic gene complex encoding the major histocompatibility complex proteins in humans. HLA alleles are of strong epidemiological interest for their large effect sizes in associations with autoimmune diseases, infectious diseases, severe drug reactions, and transplant medicine. Since HLA genotyping can be time-consuming and cost-prohibitive, methods to impute HLA alleles from SNP genotype data have been developed, including HLA Genotype Imputation with Attribute Bagging (HIBAG), HLA*IMP:02, and SNP2HLA. However, limitations of these imputation programs include imputation accuracy, computational runtime, and ability to impute HLA allele haplotypes. ResultsWe present a deep learning framework for HLA allele imputation using a multitask convolutional neural network (CNN) architecture. In this approach, we use phased SNP genotype data flanking {+/-}250 kb from each HLA locus to simultaneously impute HLA allele haplotyes across loci HLA-A, -B, -C, -DQA1, -DQB1, -DPA1, -DPB1, and -DRB1. We start by tokenizing phased genotype sequences into k-mers that serve as input to the model. The CNN architecture starts with a shared embedding layer for learning low-dimensional representations of k-mers, shared convolutional layers for detecting genotype motifs, and branches off into separate densely-connected layers for imputing each HLA loci. We present evidence that the CNN used information from known tag SNPs to impute HLA alleles, and demonstrate the architecture is robust against a selection of hyperparameters. On the T1DGC dataset, our model achieved 97.6% imputation accuracy, which was superior to SNP2HLAs performance and comparable to HIBAGs performance. However, unlike HIBAG, our method can impute an entire HLA haplotype sequence instead of imputing one locus at a time. Additionally, by separating the training and inference steps, our imputation program provides user flexibility to reduce usage time. AvailabilityThe source code is available at https://github.com/CalvinTChi/HLA_imputation Contactcalvin.chi@berkeley.edu

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Chi, C.. 2021-06-05. HLA Allele Imputation with Multitask Deep Convolutional Neural Network. https://doi.org/10.1101/2021.06.03.447012

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