Regulation of expression quantitative trait loci by SVA retrotransposons within the major histocompatibility complex

Abstract

Genomic and transcriptomic studies of expression quantitative trait loci (eQTL) revealed that SINE-VNTR-Alu (SVA) retrotransposon insertion polymorphisms (RIPs) within human genomes markedly affect the co-expression of many coding and noncoding genes by coordinated regulatory processes. This study examined the polymorphic SVA modulation of gene co-expression within the major histocompatibility complex (MHC) genomic region where more than 160 coding genes are involved in innate and adaptive immunity. We characterized the modulation of SVA RIPs utilizing the genomic and transcriptomic sequencing data obtained from whole blood of 1266 individuals in the Parkinson’s Progression Markers Initiative (PPMI) cohort that included an analysis of human leukocyte antigen ( HLA) -A regulation in a subpopulation of the cohort. The regulatory properties of eight SVAs located within the class I and class II MHC regions were associated with differential co-expression of 71 different genes within and 75 genes outside the MHC region. Some of the same genes were affected by two or more different SVA. Five SVA are annotated in the human genomic reference sequence GRCh38.p14/hg38, whereas the other three were novel insertions within individuals. We also examined and found distinct structural effects (long and short variants and the CT internal variants) for one of the SVA ( R_SVA_24) insertions on the differential expression of the HLA-A gene within a subpopulation (550 individuals) of the PPMI cohort. This is the first time that many HLA and non-HLA genes (multilocus expression units) and splicing mechanisms have been shown to be regulated by eight structurally polymorphic SVA within the MHC genomic region by applying precise statistical analysis of RNA data derived from the blood samples of a human cohort population. This study shows that SVA within the MHC region are important regulators or rheostats of gene co-expression that might have potential roles in diversity, health, and disease.

Exploring SVA Insertion Polymorphisms in Shaping Differential Gene Expression in the Central Nervous System

“…A recent large-scale GWAS conducted by Van Rheenen et al has identified the HLA region as a locus significantly associated with ALS, further highlighting the importance of this locus [31]. Previous studies within our group have highlighted the capability of SVAs to modulate HLA gene expression; analysis of whole genome sequencing and transcriptomic data obtained from the whole blood of individuals within the PPMI cohort discovered that SVA_24, SVA_25 and SVA_27 modulate the expression of HLA-A, HLA-B and HLA-C, and HLA-DRB1 and HLA-DRB5, respectively [33,51]. This suggests that the modulation of HLA genes by SVAs could be a common mechanism within neurodegenerative disease.…”

Regulatory SVA retrotransposons and classical HLA genotyped-transcripts associated with Parkinson’s disease

“…The eQTL SVA transcripts expressed at eight MHC loci ( NR_SVA_377, R_SVA_24, R_SVA_25, R_SVA_26, NR_SVA_380, R_SVA_27, R_SVA_85, NR_SVA_381 ) that are shown in Figure 1 had statistically inferred regulatory effects on classical class I and class II gene transcription levels and their different isoforms ( 44 ). The MHC SVA genotype frequencies and their influence on classical class I and class II HLA genes and transcripts based on a previous study ( 44 ) are shown in Supplementary Table 5 . The number and percentage frequency of the 64 SVA-phased haplotypes with the eight MHC genotyped SVA as present or absent insertions in the present study are shown in Supplementary Table 6 .…”

“…Regulatory effects of SVA on HLA transcription levels were inferred statistically by eQTL analysis using the Matrix eQTL software ( 49 ) and described previously ( 44 ). Fastq files of whole-blood RNAseq were downloaded from the PPMI database and the referenced SVA (R_SVA) and non-referenced SVA (NR_SVA) ( Figure 1 ) were located, genotyped and identified within or outside the MHC genomic region with the assistance of the software tools, Delly2 structural variant caller and the transcript counters Salmon and DESeq2 , as previously described ( 41 , 44 ). All the transcripts’ of 1521 individuals downloaded as PPMI blood RNAseq.bam files were used to identify the genotypes of ten classical class I and class II HLA genes using the arcasHLA software tool described by Orenbach et al.…”

“…Recently, expression quantitative trait loci (eQTL) of different SVAs and their effect on the regulation of gene expression were identified and described for a Parkinson’s Progression Markers Initiative (PPMI) cohort using whole genome sequence and transcriptome data obtained from the blood of more than a thousand individuals ( 43 ). Also, there are SVAs within the MHC genomic region that are expressed and can regulate the expression of HLA genes ( 44 ). At least eighteen SVA polymorphic insertions were mapped previously within the MHC class I, II and III regions, and some were found to be haplotypic or haplospecific for particular HLA gene alleles that varied in frequency between European, Japanese and African American populations ( 45 ).…”

“…A PPMI clinical protocol was established in 2010 to acquire comprehensive longitudinal within-participant clinical, imaging, genomic, transcriptomic and biomarker data for three main cohorts, (1) PD with and without genetic risk variants, (2) prodromes (nonmotor features) at risk of PD, and (3) healthy controls with no neurological disorder and no first degree relative, currently aimed at enrolling 4000 participants at about 50 sites worldwide ( 48 ). We associated the regulatory properties of 8 SVA RIPs located within the class I and class II MHC regions of the PPMI cohort with the differential co-expression of 71 genes within and 75 genes outside of the MHC region, including all the classical class I and class II genes ( 44 ). A limitation of this SVA-HLA eQTL study was the absence of HLA allelic data to associate with the SVA genotypes and for stratifying the statistical differences between PD, prodromes and healthy controls within the PPMI cohort.…”