Abstract
Proteins, widely studied as potential biomarkers, play important roles in numerous physiological functions and diseases. Genetic variation may modulate corresponding protein levels and point to the role of these variants in disease pathophysiology. Effects of individual single nucleotide polymorphisms (SNPs) within a gene were analyzed for corresponding plasma protein levels using genome-wide association study (GWAS) genotype data and proteomic panel data with 132 quality-controlled analytes from 521 Caucasian participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. Linear regression analysis detected 112 significant (Bonferroni threshold p = 2.44×10−5) associations between 27 analytes and 112 SNPs. 107 out of these 112 associations were tested in the Indiana Memory and Aging Study (IMAS) cohort for replication and 50 associations were replicated at uncorrected p<0.05 in the same direction of effect as those in the ADNI. We identified multiple novel associations including the association of rs7517126 with plasma complement factor H-related protein 1 (CFHR1) level at p<1.46×10−60, accounting for 40 percent of total variation of the protein level. We serendipitously found the association of rs6677604 with the same protein at p<9.29×10−112. Although these two SNPs were not in the strong linkage disequilibrium, 61 percent of total variation of CFHR1 was accounted for by rs6677604 without additional variation by rs7517126 when both SNPs were tested together. 78 other SNP-protein associations in the ADNI sample exceeded genome-wide significance (5×10−8). Our results confirmed previously identified gene-protein associations for interleukin-6 receptor, chemokine CC-4, angiotensin-converting enzyme, and angiotensinogen, although the direction of effect was reversed in some cases. This study is among the first analyses of gene-protein product relationships integrating multiplex-panel proteomics and targeted genes extracted from a GWAS array. With intensive searches taking place for proteomic biomarkers for many diseases, the role of genetic variation takes on new importance and should be considered in interpretation of proteomic results.
Highlights
Proteins play critical roles in numerous physiological functions and altered protein levels have been associated with disease [1,2,3,4,5,6,7,8]
Progress in biomarker discovery and confirmation is likely to be limited without a better understanding of the genetic basis of protein analyte levels which can be analyzed as continuous phenotypes or quantitative traits (QTs) because variations in genes, which contain the information to encode proteins, may affect the production of proteins leading to altered levels and potentially to disease
Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multisite longitudinal study, including more than 800 participants, aged 55 to 90, recruited from over 50 sites across the United States and Canada, The participants include approximately 200 cognitively normal older individuals to be followed for 3 years, 400 patients diagnosed with mild cognitive impairment (MCI) to be followed for 3 years, and 200 patients diagnosed with early AD to be followed for 2 years at 6- or 12- month intervals
Summary
Proteins play critical roles in numerous physiological functions and altered protein levels have been associated with disease [1,2,3,4,5,6,7,8]. Progress in biomarker discovery and confirmation is likely to be limited without a better understanding of the genetic basis of protein analyte levels which can be analyzed as continuous phenotypes or quantitative traits (QTs) because variations in genes, which contain the information to encode proteins, may affect the production of proteins leading to altered levels and potentially to disease. When proteins are known to play important roles in disease or treatment, influence of genetic variations associated with the encoded proteins should be considered. In this case, identified functional variants may be used to stratify protein analytes in their interpretation as diagnostic, prognostic, or therapeutic response biomarkers for disease or treatment
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