Open AccessIdentification of cis-regulatory variation influencing protein abundance levels in human plasma
Author(s) -
Anbarasu Lourdusamy,
Stephen J. Newhouse,
Katie Lun,
Petroula Proitsi,
John Powell,
Angela Hodges,
Sally K. Nelson,
A. Keith Stewart,
Stephen Williams,
Iwona Kłoszewska,
Patrizia Mecocci,
Hilkka Soininen,
Magda Tsolaki,
Bruno Vellas,
Simon Lovestone,
Richard Dobson
Publication year - 2012
Publication title -
human molecular genetics online/human molecular genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.811
H-Index - 276
eISSN - 1460-2083
pISSN - 0964-6906
DOI - 10.1093/hmg/dds186
Subject(s) - biology , genetics , genetic association , gene , genetic variation , single nucleotide polymorphism , genome wide association study , quantitative trait locus , computational biology , genotype
Proteins are central to almost all cellular processes, and dysregulation of expression and function is associated with a range of disorders. A number of studies in human have recently shown that genetic factors significantly contribute gene expression variation. In contrast, very little is known about the genetic basis of variation in protein abundance in man. Here, we assayed the abundance levels of proteins in plasma from 96 elderly Europeans using a new aptamer-based proteomic technology and performed genome-wide local (cis-) regulatory association analysis to identify protein quantitative trait loci (pQTL). We detected robust cis-associations for 60 proteins at a false discovery rate of 5%. The most highly significant single nucleotide polymorphism detected was rs7021589 (false discovery rate, 2.5 × 10(-12)), mapped within the gene coding sequence of Tenascin C (TNC). Importantly, we identified evidence of cis-regulatory variation for 20 previously disease-associated genes encoding protein, including variants with strong evidence of disease association show significant association with protein abundance levels. These results demonstrate that common genetic variants contribute to the differences in protein abundance levels in human plasma. Identification of pQTLs will significantly enhance our ability to discover and comprehend the biological and functional consequences of loci identified from genome-wide association study of complex traits. This is the first large-scale genetic association study of proteins in plasma measured using a novel, highly multiplexed slow off-rate modified aptamer (SOMAmer) proteomic platform.