Genetic studies of plasma analytes identify novel potential biomarkers for several complex traits

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Deming, Yuetiva ^a   Xia, Jian ^b   Cai, Yefei ^d   Lord, Jenny ^a   Del Aguila, Jorge L ^a   Fernandez, Maria Victoria ^a   Carrell, David ^e   Black, Kathleen ^a   Budde, John ^a   Ma, Shengmei ^b   Saef, Benjamin ^c   Howells, Bill ^d   Bertelsen, Sarah ^a   Bailey, Matthew ^a   Ridge, Perry G ^a   Holtzman, David ^a   Morris, John C ^b   Bales, Kelly ^a   Pickering, Eve H ^a   Lee, Jin Moo ^a   Heitsch, Laura ^e   Kauwe, John ^c   Goate, Alison ^a   Piccio, Laura ^b   Cruchaga, Carlos ^a   more..

^a WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b CENTRAL SOUTH UNIVERSITY   (China)

^c WELLCOME TRUST SANGER INSTITUTE   (United Kingdom)

^d BRIGHAM YOUNG UNIVERSITY   (United States)

^e Chemical Research and Development   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84952940877     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep18092     Document Type: Article

References (64)

1. Garge, N. et al. Identification of quantitative trait loci underlying proteome variation in human lymphoblastoid cells. Mol Cell Proteomics 9, 1383-1399, doi: 10.1074/mcp.M900378-MCP200 (2010)
2. Lourdusamy, A. et al. Identification of cis-regulatory variation influencing protein abundance levels in human plasma. Hum Mol Genet 21, 3719-3726, doi: 10.1093/hmg/dds186 (2012)
3. Civelek, M. &Lusis, A. J. Systems genetics approaches to understand complex traits. Nat Rev Genet 15, 34-48, doi: 10.1038/nrg3575 (2014)
4. Shin, S. Y. et al. An atlas of genetic influences on human blood metabolites. Nat Genet 46, 543-550, doi: 10.1038/ng.2982 (2014)
5. Li, Y., Huang, J. &Amos, C. I. Genetic association analysis of complex diseases incorporating intermediate phenotype information. PLoS One 7, e46612, doi: 10.1371/journal.pone.0046612 (2012)
6. Melzer, D. et al. A genome-wide association study identifies protein quantitative trait loci (pQTLs). PLoS Genet 4, e1000072, doi: 10.1371/journal.pgen.1000072 (2008)
7. Gieger, C. et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet 4, e1000282, doi: 10.1371/journal.pgen.1000282 (2008)
8. Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat Genet 44, 269-276, doi: 10.1038/ng.1073 (2012)
9. Kim, S. et al. Influence of genetic variation on plasma protein levels in older adults using a multi-Analyte panel. PLoS One 8, e70269, doi: 10.1371/journal.pone.0070269 (2013)
10. Yu, B. et al. Genetic determinants influencing human serum metabolome among African Americans. PLoS Genet 10, e1004212, doi: 10.1371/journal.pgen.1004212 (2014)
11. Skol, A. D., Scott, L. J., Abecasis, G. R. &Boehnke, M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet 38, 209-213, doi: 10.1038/ng1706 (2006)
12. Harari, O. et al. Phosphorylated tau-Abeta42 ratio as a continuous trait for biomarker discovery for early-stage Alzheimer's disease in multiplex immunoassay panels of cerebrospinal fluid. Biol Psychiatry 75, 723-731, doi: 10.1016/j.biopsych.2013.11.032 (2014)
13. Yang, Q., Kathiresan, S., Lin, J. P., Tofler, G. H. &O'Donnell, C. J. Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study. BMC Med Genet 8 Suppl 1, S12, doi: 10.1186/1471-2350-8-S1-S12 (2007)
14. Hwang, S. J., Yang, Q., Meigs, J. B., Pearce, E. N. &Fox, C. S. A genome-wide association for kidney function and endocrine-related traits in the NHLBI's Framingham Heart Study. BMC Med Genet 8 Suppl 1, S10, doi: 10.1186/1471-2350-8-S1-S10 (2007)
15. Paterson, A. D. et al. Genome-wide association identifies the ABO blood group as a major locus associated with serum levels of soluble E-selectin. Arterioscler Thromb Vasc Biol 29, 1958-1967, doi: 10.1161/ATVBAHA.109.192971 (2009)
16. Benyamin, B. et al. Variants in TF and HFE explain approximately 40% of genetic variation in serum-transferrin levels. Am J Hum Genet 84, 60-65, doi: 10.1016/j.ajhg.2008.11.011 (2009)
17. Qi, L. et al. Genetic variants in ABO blood group region, plasma soluble E-selectin levels and risk of type 2 diabetes. Hum Mol Genet 19, 1856-1862, doi: 10.1093/hmg/ddq057 (2010)
18. Surakka, I. et al. A genome-wide association study of monozygotic twin-pairs suggests a locus related to variability of serum highdensity lipoprotein cholesterol. Twin Res Hum Genet 15, 691-699, doi: 10.1017/thg.2012.63 (2012)
19. Froguel, P. et al. A genome-wide association study identifies rs2000999 as a strong genetic determinant of circulating haptoglobin levels. PLoS One 7, e32327, doi: 10.1371/journal.pone.0032327 (2012)
20. Qi, Q., Workalemahu, T., Zhang, C., Hu, F. B. &Qi, L. Genetic variants, plasma lipoprotein(a) levels, and risk of cardiovascular morbidity and mortality among two prospective cohorts of type 2 diabetes. Eur Heart J 33, 325-334, doi: 10.1093/eurheartj/ehr350 (2012)
21. He, M. et al. A genome wide association study of genetic loci that influence tumour biomarkers cancer antigen 19-9, carcinoembryonic antigen and alpha fetoprotein and their associations with cancer risk. Gut 63, 143-151, doi: 10.1136/gutjnl-2012-303434 (2014)
22. Liang, Y. et al. Genetic variations affecting serum carcinoembryonic antigen levels and status of regional lymph nodes in patients with sporadic colorectal cancer from Southern China. PLoS One 9, e97923, doi: 10.1371/journal.pone.0097923 (2014)
23. Akerblom, A. et al. Polymorphism of the cystatin C gene in patients with acute coronary syndromes: Results from the PLATelet inhibition and patient Outcomes study. Am Heart J 168, 96-102 e102, doi: 10.1016/j.ahj.2014.03.010 (2014)
24. Kocarnik, J. M. et al. Multiancestral analysis of inflammation-related genetic variants and C-reactive protein in the population architecture using genomics and epidemiology study. Circ Cardiovasc Genet 7, 178-188, doi: 10.1161/CIRCGENETICS.113.000173 (2014)
25. Boyle, A. P. et al. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res 22, 1790-1797, doi: 10.1101/gr.137323.112 (2012)
26. Chung, C. M. et al. A genome-wide association study identifies new loci for ACE activity: potential implications for response to ACE inhibitor. Pharmacogenomics J 10, 537-544, doi: 10.1038/tpj.2009.70 (2010)
27. Desch, K. C. et al. Linkage analysis identifies a locus for plasma von Willebrand factor undetected by genome-wide association. Proc Natl Acad Sci USA 110, 588-593, doi: 10.1073/pnas.1219885110 (2013)
28. Repapi, E. et al. Genome-wide association study identifies five loci associated with lung function. Nat Genet 42, 36-44, doi: 10.1038/ng.501 (2010)
29. Jabaudon, M. et al. Effects of a recruitment maneuver on plasma levels of soluble RAGE in patients with diffuse acute respiratory distress syndrome: a prospective randomized crossover study. Intensive Care Med 41, 846-855, doi: 10.1007/s00134-015-3726-0 (2015)
30. International Multiple Sclerosis Genetics, C. et al. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214-219, doi: 10.1038/nature10251 (2011)
31. Patsopoulos, N. A. et al. Genome-wide meta-Analysis identifies novel multiple sclerosis susceptibility loci. Ann Neurol 70, 897-912, doi: 10.1002/ana.22609 (2011)
32. Australia &New Zealand Multiple Sclerosis Genetics, C. Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet 41, 824-828, doi: 10.1038/ng.396 (2009)
33. Consortium, E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57-74, doi: 10.1038/nature11247 (2012)
34. Rivas, M. A. et al. Human genomics. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science 348, 666-669, doi: 10.1126/science.1261877 (2015)
35. Samson, M., Labbe, O., Mollereau, C., Vassart, G. &Parmentier, M. Molecular cloning and functional expression of a new human CC-chemokine receptor gene. Biochemistry 35, 3362-3367, doi: 10.1021/bi952950g (1996)
36. Duell, E. J. et al. Variation at ABO histo-blood group and FUT loci and diffuse and intestinal gastric cancer risk in a European population. Int J Cancer 136, 880-893, doi: 10.1002/ijc.29034 (2015)
37. Polanski, M. &Anderson, N. L. A list of candidate cancer biomarkers for targeted proteomics. Biomark Insights 1, 1-48 (2007)
38. Replication, D. G. et al. Genome-wide trans-Ancestry meta-Analysis provides insight into the genetic architecture of type 2 diabetes susceptibility. Nat Genet 46, 234-244, doi: 10.1038/ng.2897 (2014)
39. Kwon, H. et al. Adipocyte-specific IKKbeta signaling suppresses adipose tissue inflammation through an IL-13-dependent paracrine feedback pathway. Cell Rep 9, 1574-1583, doi: 10.1016/j.celrep.2014.10.068 (2014)
40. Walz, A., Schmutz, P., Mueller, C. &Schnyder-Candrian, S. Regulation and function of the CXC chemokine ENA-78 in monocytes and its role in disease. J Leukoc Biol 62, 604-611 (1997)
41. Z'Graggen, K., Walz, A., Mazzucchelli, L., Strieter, R. M. &Mueller, C. The C-X-C chemokine ENA-78 is preferentially expressed in intestinal epithelium in inflammatory bowel disease. Gastroenterology 113, 808-816 (1997)
42. Jostins, L. et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119-124, doi: 10.1038/nature11582 (2012)
43. Inouye, M. et al. Novel Loci for metabolic networks and multi-tissue expression studies reveal genes for atherosclerosis. PLoS Genet 8, e1002907, doi: 10.1371/journal.pgen.1002907 (2012)
44. Seppala, I. et al. Genome-wide association study on dimethylarginines reveals novel AGXT2 variants associated with heart rate variability but not with overall mortality. Eur Heart J 35, 524-531, doi: 10.1093/eurheartj/eht447 (2014)
45. Krumbholz, M. et al. Chemokines in multiple sclerosis: CXCL12 and CXCL13 up-regulation is differentially linked to CNS immune cell recruitment. Brain 129, 200-211, doi: 10.1093/brain/awh680 (2006)
46. Sellebjerg, F. et al. Increased cerebrospinal fluid concentrations of the chemokine CXCL13 in active MS. Neurology 73, 2003-2010, doi: 10.1212/WNL.0b013e3181c5b457 (2009)
47. Piccio, L. et al. Changes in B-And T-lymphocyte and chemokine levels with rituximab treatment in multiple sclerosis. Arch Neurol 67, 707-714, doi: 10.1001/archneurol.2010.99 (2010)
48. Morris, J. C. The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology 43, 2412-2414 (1993)
49. Hu, W. T. et al. Plasma multianalyte profiling in mild cognitive impairment and Alzheimer disease. Neurology 79, 897-905, doi: 10.1212/WNL.0b013e318266fa70 (2012)
50. Saykin, A. J. et al. Alzheimer's Disease Neuroimaging Initiative biomarkers as quantitative phenotypes: Genetics core aims, progress, and plans. Alzheimers Dement 6, 265-273, doi: 10.1016/j.jalz.2010.03.013 (2010)
51. Cruchaga, C. et al. GWAS of cerebrospinal fluid tau levels identifies risk variants for Alzheimer's disease. Neuron 78, 256-268, doi: 10.1016/j.neuron.2013.02.026 (2013)
52. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559-575, doi: 10.1086/519795 (2007)
53. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38, 904-909, doi: 10.1038/ng1847 (2006)
54. Browning, S. R. &Browning, B. L. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet 81, 1084-1097, doi: 10.1086/521987 (2007)
55. Kauwe, J. S. et al. Genome-wide association study of CSF levels of 59 alzheimer's disease candidate proteins: significant associations with proteins involved in amyloid processing and inflammation. PLoS Genet 10, e1004758, doi: 10.1371/journal.pgen.1004758 (2014)
56. Kauwe, J. S. et al. Fine mapping of genetic variants in BIN1, CLU, CR1 and PICALM for association with cerebrospinal fluid biomarkers for Alzheimer's disease. PLoS One 6, e15918, doi: 10.1371/journal.pone.0015918 (2011)
57. Kauwe, J. S. et al. Alzheimer's disease risk variants show association with cerebrospinal fluid amyloid beta. Neurogenetics 10, 13-17, doi: 10.1007/s10048-008-0150-4 (2009)
58. Cruchaga, C. et al. Cerebrospinal fluid APOE levels: an endophenotype for genetic studies for Alzheimer's disease. Hum Mol Genet 21, 4558-4571, doi: 10.1093/hmg/dds296 (2012)
59. Kauwe, J. S. et al. Validating predicted biological effects of Alzheimer's disease associated SNPs using CSF biomarker levels. J Alzheimers Dis 21, 833-842, doi: 10.3233/JAD-2010-091711 (2010)
60. Surucu, B. &Koc, E. Assessing the validity of a statistical distribution: some illustrative examples from dermatological research. Clin Exp Dermatol 33, 239-242, doi: 10.1111/j.1365-2230.2007.02629.x (2008)
61. Pruim, R. J. et al. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26, 2336-2337, doi: 10.1093/bioinformatics/btq419 (2010)
62. Willer, C. J., Li, Y. &Abecasis, G. R. METAL: fast and efficient meta-Analysis of genomewide association scans. Bioinformatics 26, 2190-2191, doi: 10.1093/bioinformatics/btq340 (2010)
63. Dayem Ullah, A. Z., Lemoine, N. R. &Chelala, C. SNPnexus: a web server for functional annotation of novel and publicly known genetic variants (2012 update). Nucleic Acids Res 40, W65-70, doi: 10.1093/nar/gks364 (2012)
64. Wang, K., Li, M. &Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 38, e164, doi: 10.1093/nar/gkq603 (2010)