Identifying genetic and environmental influences on proteins associated with age, cardiovascular risk, and other endpoints using the SomaScan® Assay
Protein quantitative trait locus (pQTL) studies identify genetic variants that are statistically associated with protein levels. Results from the growing number of pQTL studies can be combined with genome-wide association studies to identify proteins that underlie the genetic risk of disease, thus revealing the mechanisms of disease and potential drug targets.
The SomaScan® Assay is a highly multiplex protein assay that has facilitated the growth in pQTL studies. For affinity-based assays like SomaScan®, cis-pQTLs provide evidence of specificity. However, a pQTL association will only be identified for a given protein if enough of the variation in the protein is due to genetics. Non-genetic sources of variation, such as differences in lifestyle or exposure to environmental risk factors, also influence the risk of disease and can be captured by proteomics.
- Compiled a list of cis-pQTLs from two studies:
- 35,559 Icelanders in deCODE1
- 7,213 European Americans and 1,871 African Americans in ARIC2
- Calculated proportion of SomaScan® Assay aptamers with cis-pQTLs
- Compared to proportion of aptamers with cis-pQTLs that are also strongly associated (FDR < 10%) with age, four-year cardiovascular disease risk (CVD), and oral glucose tolerance tests3,4
- Based on the SomaScan® v4 Assay, which measures ~5,000 proteins
- The cumulative proportion of analytes that have a cis-pQTL decreases with progressively decreasing significance of association with three endpoints.
- Regardless of cis-pQTL status, each endpoint is associated (FDR < 10%) with a unique combination of analytes.
- pQTL association status (FDR < 10%), stratified by respective endpoint association. pQTLs are over-represented in endpoint association, but up to 40% of associated, measured proteins do not have pQTLs. Endpoint associations and pQTLs were identified using a 5K analyte assay, rather than the current 7K analyte assay.
- Endpoint-associated analytes are more likely to have a cis-pQTL than non-endpoint-associated analytes.
- Many endpoint-associated analytes do not have a cis-pQTL, indicating potential sensitivity to environmental effects
- Distinct sets of analytes respond to each endpoint.
- The SomaScan® Assay is highly multiplex and can identify hundreds to thousands of proteins associated with endpoints and with genetic variants.
Michael A. Hinterberg
David P. Astling
SomaLogic Operating Co., Inc., Boulder, CO USA
- Ferkingstad E, et al. Large-scale integration of the plasma proteome with genetics and disease. Nature Genetics 53.12 (2021):1712-1721.
- Zhang J, et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nature Genetics 54.5 (2022): 593-602.
- Williams S, et al. Plasma protein patterns as comprehensive indicators of health. Nature Medicine 25.12 (2019): 1851-1857.
PosterLatest research shows benefit of non-invasive, high-plex protein profiling for liver disease
Learn about using high-plex, aptamer-based protein profiling for NASH research through these three SomaLogic assets. Watch one webinar and download two posters that each highlight NASH research.
PosterProteomic Indicators of Metabolic Health in Diabetes and Social Deprivation
Understanding the health impacts of socioeconomic deprivation (SED) and its interaction with type 2 diabetes is important for patient care and effective public health initiatives. Large-scale proteomic profiling using aptamer-based technology to measure 7,000 proteins has facilitated the development of blood-based proteomic signatures for 11 cardiometabolic SomaSignalTM Tests (SST)
PosterHeritability, pQTLs, and environmental influence on proteins involved in age, cardiovascular risk, and glucose tolerance using the SomaScan® Assay
Protein quantitative trait locus (“pQTL”) studies identify genetic variants that are statistically associated with protein levels. Results from the growing number of pQTL studies can be combined with genome-wide association studies to identify proteins that underlie the genetic risk of disease, thus revealing the mechanisms of disease and potential drug targets.