Use of the human reference genome 38 enhances integration of the multi‐tissue proteomics with genetics and disease

Abstract

Background: Most existing protein quantitative trait locus (pQTL) studies focus on a single tissue and use the reference Genome Reference Consortium Human Build 37 (GRCh37/hg19) published in 2009. GRCh38/hg38 was updated in 2013 with correcting thousands of sequencing artifacts that cause false SNPs and updating non-nuclear genomic sequence. Many papers strongly recommend switching to GRCh38/hg38. The goal of this study is to use multiple tissues (brain, CSF and plasma) to explore the genetic architecture of protein levels in neurologically relevant tissues and to update the human reference genome as hg38 to compare the findings. Method:In this study, 805 CSF samples and 869 proteins, 529 plasma samples and 953 proteins and 378 brain samples and 1,300 proteins passed quality control process.We performed genome-wide association analyses of over 8 million autosomal variants (MAF ≥ 0.01) imputed using the most updated hg38 TOPMed imputation panel to identify cis/trans pQTLs. We performed conditional analysis to identify independent pQTLs. After removing pleiotropic loci and including Alzheimer's disease (AD) variants, Mendelian randomization (MR) was applied to detect the casual associations between proteins and AD risk. To decrease unmeasured pleiotropy effect, we used colocalization analysis to get more supporting information among multiple traits. Finally, we replicated our findings in much larger studies. Result:In brain tissue, we identified 4269 pQTLs in hg38 compared to 2418 pQTLs in hg19 with a threshold of 5 × 10 −8 for cis-pQTLs and 5 × 10 −8 /(number of independent proteins) for trans-pQTLs. We found the independent SNPs and highlight the complexity of regions with multiple independent local pQTLs. Analysis for CSF and plasma is underway. Conclusion:This study is to apply the hg38 as the human reference genome to detect much more pQTLs and correct possible false findings in the previous hg19 study. With the following analysis, we could get more valuable and confirmed information about the additional GWAS loci and identify the function of certain proteins on the disease risk. The multiple tissue samples and multiple traits could help us detect the complex genetic architecture of protein levels in neurologically relevant tissues.