• Age-specific difference
• Cardiovascular disease
• Genetic heterogeneity by age
• Genetic susceptibility
• Prediabetes

• Hodgkin lymphoma
• NF-kB
• expression quantitative trait locus
• genetic etiology
• genetic heterogeneity
• genetic lesion
• genome-wide association study
• hodgkin and reed-sternberg cell

• Hodgkin lymphoma
• Reed–Sternberg cell
• expression quantitative trait loci
• gene expression
• mixed model

• Humans
• Hodgkin Disease/genetics
• Quantitative Trait Loci
• Genome-Wide Association Study
• Precision Medicine
• Gene Expression
• Aminopeptidases/genetics
• Minor Histocompatibility Antigens

Heterogeneity of expression quantitative trait locus (eQTL) effects have been shown across gene expression processes. Knowledge on how to produce the heterogeneity is quite limited. This study aims to examine fluctuations in differential gene expression by alleles of sequence variants across expression processes.

Genome-wide eQTL analyses with transcriptome-wide gene expression data revealed 20 cis-acting eQTLs associated simultaneously with mRNA expression, ribosome occupancy, and protein abundance. A 97 kb-long eQTL signal for mitochondrial ribosomal protein L43 (MRPL43) covered the gene, showing a heterogeneous effect size on gene products across expression stages. One allele of the eQTL was associated with increased mRNA expression and ribosome occupancy but decreased protein abundance. We examined the heterogeneity and found that the eQTL can be attributed to the independent functions of three nucleotide variants, with a strong linkage. NC_000010.11:g.100987606G > T, upstream of MRPL43, may regulate the binding affinity of transcription factors. NC_000010.11:g.100986746C > G, 3 bp from an MRPL43 splice donor site, may alter the splice site. NC_000010.11:g.100978794A > G, in the isoform with a long 3′-UTR, may strengthen the binding affinity of the microRNA. Individuals with the TGG haplotype at these three variants had higher levels of mRNA expression and ribosome occupancy than individuals with the GCA haplotype but lower protein levels, producing the flipped effect throughout the expression process.

These findings suggest that multiple functional variants in a linkage exert their regulatory functions at different points in the gene expression process, producing a complexity of single eQTLs.

The online version contains supplementary material available at 10.1186/s12863-022-01057-7.

• Expression quantitative trait locus
• Functional variant
• Mitochondrial ribosomal protein L43
• Mixed model
• Regulation of gene expression

The discovery of expression quantitative trait loci (eQTLs) and their target genes (eGenes) has not only compensated for the limitations of genome-wide association studies for complex phenotypes but has also provided a basis for predicting gene expression. Efforts have been made to develop analytical methods in statistical genetics, a key discipline in eQTL analysis. In particular, mixed model– and deep learning–based analytical methods have been extremely beneficial in mapping eQTLs and predicting gene expression. Nevertheless, we still face many challenges associated with eQTL discovery. Here, we discuss two key aspects of these challenges: 1, the complexity of eTraits with various factors such as polygenicity and epistasis and 2, the voluminous work required for various types of eQTL profiles. The properties and prospects of statistical methods, including the mixed model method, Bayesian inference, the deep learning method, and the integration method, are presented as future directions for eQTL discovery. This review will help expedite the design and use of efficient methods for eQTL discovery and eTrait prediction.

• Gibbs sampling
• Markov chain Monte Carlo
• expression quantitative trait locus
• genetic association
• mixed model
• polygenic variance component
• posterior
• random effect

• expression quantitative trait locus
• genetic association
• genetic variance
• heritability
• mixed model

• S100β protein
• basal ganglia hemorrhage
• vascular dementia