An integrative systems genetics approach reveals potential causal genes and pathways related to obesity

Genome Medicine

Volumn 7, Issue 1, 2015, Pages 1-15

  Kogelman, Lisette J A ^a   Zhernakova, Daria V ^b   Westra, Harm Jan ^c,d,e   Cirera, Susanna ^a   Fredholm, Merete ^a   Franke, Lude ^b   Kadarmideen, Haja N ^a  

^a UNIVERSITY OF COPENHAGEN   (Denmark)

^b UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^c BRIGHAM AND WOMEN S HOSPITAL   (United States)

^d LABORATORY FOR MOLECULAR MEDICINE   (United States)

^e BROAD INSTITUTE OF MIT AND HARVARD   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ABHD12B GENE; ANIMAL EXPERIMENT; ANIMAL MODEL; ANIMAL TISSUE; ARTICLE; CONTROLLED STUDY; CTSL GENE; ENPP1 GENE; FEMALE; GENE; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE FUNCTION; GENE IDENTIFICATION; GENE LOCATION; GENE REGULATORY NETWORK; GENETIC ASSOCIATION; GENETIC TRANSCRIPTION; GENETIC VARIABILITY; MALE; MOLECULAR GENETICS; NONHUMAN; OBESITY; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; QUANTITATIVE TRAIT LOCUS MAPPING; RNA SEQUENCE; SINGLE NUCLEOTIDE POLYMORPHISM; SUBCUTANEOUS FAT; ADIPOSE TISSUE; ANIMAL; GENETIC PREDISPOSITION; GENETICS; GENOMICS; METABOLISM; PIG; QUANTITATIVE TRAIT LOCUS; SEQUENCE ANALYSIS;

ADIPOSE TISSUE; ANIMALS; FEMALE; GENE EXPRESSION PROFILING; GENETIC PREDISPOSITION TO DISEASE; GENOMICS; MALE; OBESITY; POLYMORPHISM, SINGLE NUCLEOTIDE; QUANTITATIVE TRAIT LOCI; SEQUENCE ANALYSIS, RNA; SWINE;

EID: 84944873789     PISSN: None     EISSN: 1756994X     Source Type: Journal    
DOI: 10.1186/s13073-015-0229-0     Document Type: Article

References (94)

1. WHO. World Health Organization: Obesity and overweight, Fact sheet No. 311. 2012. Updated March 2013. Available at http://www.who.int/mediacentre/factsheets/fs311/en/.
2. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):937-48. doi: 10.1038/ng.686.
3. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518(7538):197-206. doi: 10.1038/nature14177.
4. Adams JU. Transcriptome: connecting the genome to gene function. Nat Educ. 2008;1(1):195.
5. Oshlack A, Robinson M, Young M. From RNA-seq reads to differential expression results. Genome Biol. 2010;11:220. doi: 10.1186/gb-2010-11-12-220.
6. Cirera S, Jensen MS, Elbrønd VS, Moesgaard SG, Christoffersen BØ, Kadarmideen HN, et al. Expression studies of six human obesity-related genes in seven tissues from divergent pig breeds. Anim Genet. 2014;45(1):59-66. doi: 10.1111/age.12082.
7. Rodríguez-Acebes S, Palacios N, Botella-Carretero J, Olea N, Crespo L, Peromingo R, et al. Gene expression profiling of subcutaneous adipose tissue in morbid obesity using a focused microarray: distinct expression of cell-cycle- and differentiation-related genes. BMC Med Genomics. 2010;3(1):1-15. doi: 10.1186/1755-8794-3-61.
8. Lee YH, Nair S, Rousseau E, Allison DB, Page GP, Tataranni PA, et al. Microarray profiling of isolated abdominal subcutaneous adipocytes from obese vs non-obese Pima Indians: increased expression of inflammation-related genes. Diabetologia. 2005;48(9):1776-83. doi: 10.1007/s00125-005-1867-3.
9. Marrades MP, Milagro FI, Martínez JA, Moreno-Aliaga MJ. Differential expression of aquaporin 7 in adipose tissue of lean and obese high fat consumers. Biochem Biophys Res Commun. 2006;339(3):785-9. doi: 10.1016/j.bbrc.2005.11.080.
10. Baranova A, Collantes R, Gowder S, Elariny H, Schlauch K, Younoszai A, et al. Obesity-related differential gene expression in the visceral adipose tissue. Obes Surg. 2005;15(6):758-65. doi: 10.1381/0960892054222876.
11. Wang, Z, Gerstein M, and Snyder M, RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57-63. doi: 10.1038/nrg2484.
12. Marioni J, Mason C, Mane S, Stephens M, Gilad Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 2008;18(9):1509-17. doi: 10.1101/gr.079558.108.
13. Kadarmideen H, von Rohr P, Janss L. From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding. Mamm Genome. 2006;17(6):548-64. doi: 10.1007/s00335-005-0169-x.
14. Civelek M, Lusis AJ. Systems genetics approaches to understand complex traits. Nat Rev Genet. 2014;15(1):34-48. doi: 10.1038/nrg3575.
15. Kadarmideen HN, Reverter A. Combined genetic, genomic and transcriptomic methods in the analysis of animal traits. CAB Reviews. 2007;2(042):16pp.
16. Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M. Mapping complex disease traits with global gene expression. Nat Rev Genet. 2009;10(3):184-94. doi: 10.1038/nrg2537.
17. Dixon AL, Liang L, Moffatt MF, Chen W, Heath S, Wong KCC, et al. A genome-wide association study of global gene expression. Nat Genet. 2007;39(10):1202-7. doi: 10.1038/ng2109.
18. Visscher PM, Hill WG, Wray NR. Heritability in the genomics era--concepts and misconceptions. Nat Rev Genet. 2008;9(4):255-66. doi: 10.1038/nrg2322.
19. Ponsuksili S, Murani E, Brand B, Schwerin M, Wimmers K. Integrating expression profiling and whole-genome association for dissection of fat traits in a porcine model. J Lipid Res. 2011;52(4):668-78. doi: 10.1194/jlr.M013342.
20. Ghazalpour A, Doss S, Sheth S, Ingram-Drake L, Schadt E, Lusis A, et al. Genomic analysis of metabolic pathway gene expression in mice. Genome Biol. 2005;6(7):R59.
21. Davis RC, van Nas A, Castellani LW, Zhao Y, Zhou Z, Wen P, et al. Systems genetics of susceptibility to obesity-induced diabetes in mice. Physiol Genomics. 2012;44(1):1-13. doi: 10.1152/physiolgenomics.00003.2011.
22. Safari-Alighiarloo N, Taghizadeh M, Rezaei-Tavirani M, Goliaei B, Peyvandi AA. Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol Hepatol Bed Bench. 2014;7(1):17-31.
23. Sevimoglu T, Arga KY. The role of protein interaction networks in systems biomedicine. Comput Struct Biotechnol J. 2014;11(18):22-7. doi: 10.1016/j.csbj.2014.08.008.
24. Spurlock ME, Gabler NK. The development of porcine models of obesity and the metabolic syndrome. J Nutr. 2008;138(2):397-402.
25. Rocha D, Plastow G. Using commercial pigs in the search for genes behind human obesity. Nat Rev Genet. 2005;6(3). doi: 10.1038/nrg1556-c1.
26. Kogelman LJA, Kadarmideen HN, Mark T, Karlskov-Mortensen P, Bruun CS, Cirera S, et al. An F2 pig resource population as a model for genetic studies of obesity and obesity-related diseases in humans: design and genetic parameters. Front Genet. 2013;4:29. doi: 10.3389/fgene.2013.00029.
27. Kogelman LJA, Pant SD, Fredholm M, Kadarmideen HN. Systems genetics of obesity in an F2 pig model by genome-wide association, genetic network and pathway analyses. Front Genet. 2014;5:214. doi: 10.3389/fgene.2014.00214.
28. Kogelman LJA, Cirera S, Zhernakova D, Fredholm M, Franke L, Kadarmideen H. Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model. BMC Med Genomics. 2014;7(1):57. doi: 10.1186/1755-8794-7-57.
29. Westra H-J, Franke L. From genome to function by studying eQTLs. Biochim Biophys Acta. 2014;1842(10):1896-902. doi: 10.1038/ncb1623.
30. Westra H-J, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45(10):1238-43. doi: 10.1038/ng.2756.
31. Cameron ND. Selection indices and prediction of genetic merit in animal breeding. New York: CAB International; 1997.
32. Nettleton D, Wang D. Selective transcriptional profiling for trait-based eQTL mapping. Anim Genet. 2006;37 Suppl 1:13-7. doi: 10.1111/j.1365-2052.2006.01478.x.
33. Cardoso FF, Rosa GJ, Steibel JP, Ernst CW, Bates RO, Tempelman RJ. Selective transcriptional profiling and data analysis strategies for expression quantitative trait loci mapping in outbred F2 populations. Genetics. 2008;180:1679-90. doi: 10.1534/genetics.108.090969.
34. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-21. doi: 10.1093/bioinformatics/bts635.
35. Anders S, Pyl PT, Huber W. HTSeq - A Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166-9. doi: 10.1093/bioinformatics/btu638.
36. Łabaj PP, Leparc GG, Linggi BE, Markillie LM, Wiley HS, Kreil DP. Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling. Bioinformatics. 2011;27(13):i383-i91. doi: 10.1093/bioinformatics/btr247.
37. Anders S. Analysing RNA-Seq data with the DESeq Package. 2010. Available at http://www.bioconductor.org/help/course-materials/2011/BioC2011/LabStuff/DESeq.pdf.. Accessed 15 Oct 2015.
38. Smyth GK. Limma: Linear models for microarray data. Bioinformatics and computational biology solutions using R and Bioconductor. New York: Springer; 2005. p. 397-420.
39. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289-300.
40. Haider S, Ballester B, Smedley D, Zhang J, Rice P, Kasprzyk A. BioMart Central Portal-unified access to biological data. Nucleic Acids Res. 2009;37 suppl 2:W23-W7. doi: 10.1093/nar/gkp265.
41. Fehrmann RS, Jansen RC, Veldink JH, Westra HJ, Arends D, Bonder MJ, et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet. 2011;7(8):e1002197. doi: 10.1371/journal.pgen.1002197.
42. Ponsuksili S, Du Y, Murani E, Schwerin M, Wimmers K. Elucidating molecular networks that either affect or respond to plasma cortisol concentration in target tissues of liver and muscle. Genetics. 2012;192(3):1109-22. doi: 10.1534/genetics.112.143081.
43. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010;26(16):2069-70. doi: 10.1093/bioinformatics/btq330.
44. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. doi: 10.1186/1471-2105-9-559.
45. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics. 2008;24(5):719-20. doi: 10.1093/bioinformatics/btm563.
46. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-504. doi: 10.1101/gr.1239303.
47. Orchard S, Ammari M, Aranda B, Breuza L, Briganti L, Broackes-Carter F et al. The MIntAct project-IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. 2013. doi: 10.1093/nar/gkt1115.
48. Su G, Kuchinsky A, Morris JH, States DJ, Meng F. GLay: community structure analysis of biological networks. Bioinformatics. 2010;26(24):3135-7. doi: 10.1093/bioinformatics/btq596.
49. Young M, Wakefield M, Smyth G, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):R14. doi: 10.1186/gb-2010-11-2-r14.
50. Fehrmann RSN, Karjalainen JM, Krajewska M, Westra H-J, Maloney D, Simeonov A, et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat Genet. 2015;47(2):115-25. doi: 10.1038/ng.3173.
51. Kadarmideen HN. Genomics to systems biology in animal and veterinary sciences: progress, lessons and opportunities. Livest Sci. 2014;166:232-48. doi: 10.1016/j.livsci.2014.04.028.
52. Kadarmideen HN, Watson-Haigh NS, Andronicos NM. Systems biology of ovine intestinal parasite resistance: disease gene modules and biomarkers. Mol BioSyst. 2011;7(1):235-46. doi: 10.1039/c0mb00190b.
53. Max M, Shanker YG, Huang L, Rong M, Liu Z, Campagne F, et al. Tas1r3, encoding a new candidate taste receptor, is allelic to the sweet responsiveness locus Sac. Nat Genet. 2001;28(1):58-63. doi: 10.1038/ng0501-58.
54. Bachmanov AA, Beauchamp GK. Taste receptor genes. Annu Rev Nutr. 2007;27:389-414. doi: 10.1146/annurev.nutr.26.061505.111329.
55. Grimm ER, Steinle NI. Genetics of eating behavior: established and emerging concepts. Nutr Rev. 2011;69(1):52-60. doi: 10.1111/j.1753-4887.2010.00361.x.
56. Salbe AD, DelParigi A, Pratley RE, Drewnowski A, Tataranni PA. Taste preferences and body weight changes in an obesity-prone population. Am J Clin Nutr. 2004;79(3):372-8.
57. Do DN, Strathe AB, Ostersen T, Jensen J, Mark T, Kadarmideen HN. Genome-wide association study reveals genetic architecture of eating behavior in pigs and its implications for humans obesity by comparative mapping. PLoS One. 2013;8(8):e71509. doi: 10.1371/journal.pone.0071509.
58. Zheng J, Wu C, Ma W, Zhang Y, Hou T, Xu H, et al. Abnormal expression of chondroitin sulphate N-acetylgalactosaminyltransferase 1 and Hapln-1 in cartilage with Kashin-Beck disease and primary osteoarthritis. Int Orthop. 2013;37(10):2051-9. doi: 10.1007/s00264-013-1937-y.
59. McDonough CW, Palmer ND, Hicks PJ, Roh BH, An SS, Cooke JN, et al. A genome-wide association study for diabetic nephropathy genes in African Americans. Kidney Int. 2011;79(5):563-72. doi: 10.1038/ki.2010.467.
60. Namba K, Nishio M, Mori K, Miyamoto N, Tsurudome M, Ito M, et al. Involvement of ADAM9 in multinucleated giant cell formation of blood monocytes. Cell Immunol. 2001;213(2):104-13. doi: 10.1006/cimm.2001.1873.
61. Cao JJ. Effects of obesity on bone metabolism. J Orthop Surg Res. 2011;6:30. doi: 10.1186/1749-799x-6-30.
62. Gimble JM, Robinson CE, Wu X, Kelly KA. The function of adipocytes in the bone marrow stroma: an update. Bone. 1996;19(5):421-8. doi: 10.1016/s8756-3282(96)00258-x.
63. Wu Y, Humphrey MB, Nakamura MC. Osteoclasts - the innate immune cells of the bone. Autoimmunity. 2008;41(3):183-94. doi: 10.1080/08916930701693180.
64. Pennisi E. Disease risk links to gene regulation. Science. 2011;332(6033):1031. doi: 10.1126/science.332.6033.1031.
65. Kumar V, Wijmenga C, Withoff S. From genome-wide association studies to disease mechanisms: celiac disease as a model for autoimmune diseases. Semin Immunopathol. 2012;34(4):567-80. doi: 10.1007/s00281-012-0312-1.
66. Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci. 2009;106(23):9362-7. doi: 10.1073/pnas.0903103106.
67. Schwenk RW, Eckel J. A novel method to monitor insulin-stimulated GTP-loading of Rab11a in cardiomyocytes. Cell Signal. 2007;19(4):825-30. doi: 10.1016/j.cellsig.2006.10.008.
68. Capobianco V, Nardelli C, Ferrigno M, Iaffaldano L, Pilone V, Forestieri P, et al. miRNA and protein expression profiles of visceral adipose tissue reveal miR-141/YWHAG and miR-520e/RAB11A as two potential miRNA/protein target pairs associated with severe obesity. J Proteome Res. 2012;11(6):3358-69. doi: 10.1021/pr300152z.
69. Pan W, Chandalia M, Abate N. New insights into the role of ENPP1 in insulin resistance. J Metabonomics Metab. 2012;1(1). doi: 10.4172/jmbm.1000e103.
70. Meyre D, Bouatia-Naji N, Tounian A, Samson C, Lecoeur C, Vatin V, et al. Variants of ENPP1 are associated with childhood and adult obesity and increase the risk of glucose intolerance and type 2 diabetes. Nat Genet. 2005;37(8):863-7. doi: 10.1038/ng1604.
71. Maddux BA, Goldfine ID. Membrane glycoprotein PC-1 inhibition of insulin receptor function occurs via direct interaction with the receptor alpha-subunit. Diabetes. 2000;49(1):13-9. doi: 10.2337/diabetes.49.1.13.
72. Yang M, Zhang Y, Pan J, Sun J, Liu J, Libby P, et al. Cathepsin L activity controls adipogenesis and glucose tolerance. Nat Cell Biol. 2007;9(8):970-7. doi: 10.1038/ncb1623.
73. Puri V, Konda S, Ranjit S, Aouadi M, Chawla A, Chouinard M, et al. Fat-specific protein 27, a novel lipid droplet protein that enhances triglyceride storage. J Biol Chem. 2007;282(47):34213-8. doi: 10.1074/jbc.M707404200.
74. Puri V, Virbasius JV, Guilherme A, Czech MP. RNAi screens reveal novel metabolic regulators: RIP140, MAP4k4 and the lipid droplet associated fat specific protein (FSP) 27. Acta Physiol. 2008;192(1):103-15. doi: 10.1111/j.1748-1716.2007.01786.x.
75. Toh SY, Gong J, Du G, Li JZ, Yang S, Ye J, et al. Up-regulation of mitochondrial activity and acquirement of brown adipose tissue-like property in the white adipose tissue of fsp27 deficient mice. PLoS One. 2008;3(8):e2890. doi: 10.1371/journal.pone.0002890.
76. Ito M, Nagasawa M, Omae N, Ide T, Akasaka Y, Murakami K. Differential regulation of CIDEA and CIDEC expression by insulin via Akt1/2- and JNK2-dependent pathways in human adipocytes. J Lipid Res. 2011;52(8):1450-60. doi: 10.1194/jlr.M012427.
77. Moreno-Navarrete JM, Ortega F, Serrano M, Rodriguez-Hermosa JI, Ricart W, Mingrone G, et al. CIDEC/FSP27 and PLIN1 gene expression run in parallel to mitochondrial genes in human adipose tissue, both increasing after weight loss. Int J Obes (Lond). 2014;38(6):865-72. doi: 10.1038/ijo.2013.171.
78. Rohr PV, Friberg M, Kadarmideen HN. Prediction of transcription factor binding sites using results from genetical genomics investigations. J Bioinforma Comput Biol. 2007;5:773-93.
79. Thornton P, Hatcher JP, Robinson I, Sargent B, Franzén B, Martino G, et al. Artemin-GFRaα3 interactions partially contribute to acute inflammatory hypersensitivity. Neurosci Lett. 2013;545:23-8. doi: 10.1016/j.neulet.2013.04.007.
80. Blankman JL, Long JZ, Trauger SA, Siuzdak G, Cravatt BF. ABHD12 controls brain lysophosphatidylserine pathways that are deregulated in a murine model of the neurodegenerative disease PHARC. Proc Natl Acad Sci U S A. 2013;110(4):1500-5. doi: 10.1073/pnas.1217121110.
81. Yea K, Kim J, Lim S, Kwon T, Park HS, Park KS, et al. Lysophosphatidylserine regulates blood glucose by enhancing glucose transport in myotubes and adipocytes. Biochem Biophys Res Commun. 2009;378(4):783-8. doi: 10.1038/ncb1623.
82. Turturro S, Shen X, Shyam R, Yue B, Ying H. Effects of mutations and deletions in the human optineurin gene. Springer Plus. 2014;3(1):99. doi: 10.1186/2193-1801-3-99.
83. Sharma A, Huard C, Vernochet C, Ziemek D, Knowlton KM, Tyminski E, et al. Brown fat determination and development from muscle precursor cells by novel action of bone morphogenetic protein 6. PLoS One. 2014;9(3):e92608. doi: 10.1371/journal.pone.0092608.
84. Donaldson JG. Multiple roles for Arf6: sorting, structuring, and signaling at the plasma membrane. J Biol Chem. 2003;278(43):41573-6. doi: 10.1074/jbc.R300026200.
85. Lawrence JTR, Birnbaum MJ. ADP-ribosylation factor 6 regulates insulin secretion through plasma membrane phosphatidylinositol 4,5-bisphosphate. Proc Natl Acad Sci. 2003;100(23):13320-5. doi: 10.1073/pnas.2232129100.
86. Dong ZM, Gutierrez-Ramos J-C, Coxon A, Mayadas TN, Wagner DD. A new class of obesity genes encodes leukocyte adhesion receptors. Proc Natl Acad Sci. 1997;94(14):7526-30.
87. Babic AM, Wang HW, Lai MJ, Daniels TG, Felbinger TW, Burger PC, et al. ICAM-1 and beta 2 integrin deficiency impairs fat oxidation and insulin metabolism during fasting. Mol Med. 2004;10(7-12):72-9. doi: 10.2119/2004-00038.Wagner.
88. Awaya T, Yokosaki Y, Yamane K, Usui H, Kohno N, Eboshida A. Gene-environment association of an ITGB2 sequence variant with obesity in ethnic Japanese. Obesity. 2008;16(6):1463-6. doi: 10.1038/oby.2008.68.
89. Svenningsson P, Kim Y, Warner-Schmidt J, Oh Y-S, Greengard P. p11 and its role in depression and therapeutic responses to antidepressants. Nat Rev Neurosci. 2013;14(10):673-80. doi: 10.1038/nrn3564.
90. Vogt L, Schmitz N, Kurrer MO, Bauer M, Hinton HI, Behnke S, et al. VSIG4, a B7 family-related protein, is a negative regulator of T cell activation. J Clin Invest. 2006;116(10):2817-26. doi: 10.1172/JCI25673.
91. Holness C, Simmons D. Molecular cloning of CD68, a human macrophage marker related to lysosomal glycoproteins. Blood. 1993;81(6):1607-13.
92. Ramprasad MP, Terpstra V, Kondratenko N, Quehenberger O, Steinberg D. Cell surface expression of mouse macrosialin and human CD68 and their role as macrophage receptors for oxidized low density lipoprotein. Proc Natl Acad Sci U S A. 1996;93(25):14833-8.
93. Stewart TP, Kim HY, Saxton AM, Kim JH. Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J x TALLYHO/JngJ) F2 mice. BMC Genomics. 2010;11:713. doi: 10.1186/1471-2164-11-713.
94. Schadt EE, Lamb J, Yang X, Zhu J, Edwards S, GuhaThakurta D, et al. An integrative genomics approach to infer causal associations between gene expression and disease. Nat Genet. 2005;37(7):710-7. doi: 10.1038/ng1589.