G2Cdb::Gene report

Gene id
G00001883
Gene symbol
NEGR1 (HGNC)
Species
Homo sapiens
Description
neuronal growth regulator 1
Orthologue
G00000634 (Mus musculus)

Databases (6)

Gene
ENSG00000172260 (Ensembl human gene)
257194 (Entrez Gene)
1065 (G2Cdb plasticity & disease)
NEGR1 (GeneCards)
Marker Symbol
HGNC:17302 (HGNC)
Protein Sequence
Q7Z3B1 (UniProt)

Synonyms (4)

  • IGLON4
  • KILON
  • MGC46680
  • Ntra

Literature (16)

Pubmed - other

  • Novel obesity risk loci do not determine distribution of body fat depots: a whole-body MRI/MRS study.

    Haupt A, Thamer C, Heni M, Machicao F, Machann J, Schick F, Stefan N, Fritsche A, Häring HU and Staiger H

    Department of Internal Medicine, Division of Endocrinology, Diabetology, Angiology, Nephrology and Clinical Chemistry, Eberhard-Karls-University Tübingen, Tübingen, Germany.

    A recent meta-analysis of genome-wide association studies has identified six new risk-loci for common obesity. We studied whether these risk loci influence the distribution of body fat depots. We genotyped 1,469 nondiabetic subjects for the single-nucleotide polymorphisms (SNPs) TMEM18 rs6548238, KCTD15 rs11084753, GNPDA2 rs10938397, SH2B1 rs7498665, MTCH2 rs10838738, and NEGR1 rs2815752. We assessed BMI, waist circumference, total body fat, and lean body mass (bioimpedance). All subjects underwent an oral glucose tolerance test (OGTT) for estimation of insulin sensitivity. In 332 subjects, we measured total adipose tissue (TAT), visceral adipose tissue (VAT), nonvisceral adipose tissue (NVAT), liver fat content, and intramyocellular lipids (IMCLs) using whole-body magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). In the dominant inheritance model, the risk alleles of TMEM18 rs6548238 and MTCH2 rs10838738 were nominally associated with higher BMI (P = 0.04, both). The risk allele of TMEM18 rs6548238 was additionally associated with higher waist circumference and total body fat (P <or= 0.03), the risk allele of NEGR1 rs2815752 with higher waist circumference (P = 0.05) and unexpectedly with lower BMI (P = 0.01). In the MR cohort, we found an association of the risk allele of SH2B1 rs7498665 with higher VAT (P = 0.009) and of GNPDA2 rs10938397 with increased IMCLs (P = 0.03). After Bonferroni correction for multiple comparisons (corrected alpha-level: P = 0.0085), none of the SNPs was significantly associated with measures of adiposity or body fat distribution (all P > 0.009, dominant inheritance model). Therefore, our results suggest that these new obesity SNPs, despite their influence on BMI, are neither associated with a metabolically unfavorable nor with a favorable body composition.

    Obesity (Silver Spring, Md.) 2010;18;6;1212-7

  • Are recently identified genetic variants regulating BMI in the general population associated with anorexia nervosa?

    Brandys MK, van Elburg AA, Loos RJF, Bauer F, Hendriks J, van der Schouw YT and Adan RAH

    Department of Neuroscience & Pharmacology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands.

    The influence of body mass index (BMI) on susceptibility to anorexia nervosa (AN) is not clear. Recently published genome-wide association (GWA) studies of the general population identified several variants influencing BMI. We genotyped these variants in an AN sample to test for association and to investigate a combined effect of BMI-increasing alleles (as determined in the original GWA studies) on the risk of developing the disease. Individual single nucleotide polymorphisms (SNPs) were tested for association with AN in a sample of 267 AN patients and 1,636 population controls. A logistic regression for the combined effect of BMI-increasing alleles included 225 cases and 1,351 controls. We found no significant association between individual SNPs and AN. The analysis of a combined effect of BMI-increasing alleles showed absence of association with the investigated condition. The percentages of BMI-increasing alleles were equal between cases and controls. This study found no evidence that genetic variants regulating BMI in the general population are significantly associated with susceptibility to AN.

    Funded by: Medical Research Council: MC_U106188470

    American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics 2010;153B;2;695-699

  • Cumulative effects and predictive value of common obesity-susceptibility variants identified by genome-wide association studies.

    Li S, Zhao JH, Luan J, Luben RN, Rodwell SA, Khaw KT, Ong KK, Wareham NJ and Loos RJ

    MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, UK.

    Background: Large-scale genome-wide association studies have identified 12 genetic loci that are robustly associated with body mass index (BMI).

    Objectives: We examined associations and compared effect sizes of these newly identified obesity susceptibility loci with various anthropometric traits and assessed their cumulative effects and predictive value for obesity risk.

    Design: We genotyped 12 single nucleotide polymorphisms (SNPs) from each locus in 20,431 individuals (age: 39-79 y) from the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort. General linear model and logistic regression were used to examine associations, and the area under the receiver operating characteristic curve (AUC) was used to assess the predictive value of these variants for obesity risk.

    Results: Effect sizes of the risk alleles ranged between 0.058 and 0.329 for BMI (in kg/m(2)), between 0.094 and 0.866 kg for weight, and between 0.085 and 0.819 cm for waist circumference, with rs1121980 (FTO locus) showing the largest effect. Risk alleles of rs7132908 (FAIM2 locus) and rs17782313 (MC4R locus) were also associated with taller height. On average, each additional risk allele was associated with increases of 0.149 in BMI (P = 1.54 x 10(-22)), 0.444 kg in body weight (P = 9.88 x 10(-22)), and 0.357 cm in waist circumference (P = 1.10 x 10(-18)) and 10.8% (P = 9.83 x 10(-16)) and 5.5% (P = 3.38 x 10(-10)) increased risks of obesity and overweight, respectively. All SNPs combined explained 0.9% of BMI variation, with an AUC of 0.574 (95% CI: 0.559, 0.590) for prediction of obesity.

    Conclusions: Common variants for BMI have small effects on obesity measures and show different association patterns with anthropometric traits, with the largest effect shown for the FTO locus. These variants have cumulative effects, yet their predictive value for obesity risk is limited.

    Funded by: Cancer Research UK; Medical Research Council: G0401527, G0701863, MC_U105630924, MC_U106179471, MC_U106179472, MC_U106188470

    The American journal of clinical nutrition 2010;91;1;184-90

  • Association between obesity and polymorphisms in SEC16B, TMEM18, GNPDA2, BDNF, FAIM2 and MC4R in a Japanese population.

    Hotta K, Nakamura M, Nakamura T, Matsuo T, Nakata Y, Kamohara S, Miyatake N, Kotani K, Komatsu R, Itoh N, Mineo I, Wada J, Masuzaki H, Yoneda M, Nakajima A, Funahashi T, Miyazaki S, Tokunaga K, Kawamoto M, Ueno T, Hamaguchi K, Tanaka K, Yamada K, Hanafusa T, Oikawa S, Yoshimatsu H, Nakao K, Sakata T, Matsuzawa Y, Kamatani N and Nakamura Y

    Laboratory for Endocrinology and Metabolism, Center for Genomic Medicine, RIKEN, Yokohama, Japan. kikuko@src.riken.jp

    There is evidence that the obesity phenotype in the Caucasian populations is associated with variations in several genes, including neuronal growth regulator 1 (NEGR1), SEC16 homolog B (SCE16B), transmembrane protein 18 (TMEM18), ets variant 5 (ETV5), glucosamine-6-phosphate deaminase 2 (GNPDA2), prolactin (PRL), brain-derived neurotrophic factor (BDNF), mitochondrial carrier homolog 2 (MTCH2), Fas apoptotic inhibitory molecule 2 (FAIM2), SH2B adaptor protein 1 (SH2B1), v-maf musculoaponeurotic fibrosarcoma oncogene homolog (MAF), Niemann-Pick disease, type C1 (NPC1), melanocortin 4 receptor (MC4R) and potassium channel tetramerisation domain containing 15 (KCTD15). To investigate the relationship between obesity and these genes in the Japanese population, we genotyped 27 single-nucleotide polymorphisms (SNPs) in 14 genes from obese subjects (n=1129, body mass index (BMI) > or =30 kg m(-2)) and normal-weight control subjects (n=1736, BMI <25 kg m(-2)). The SNP rs10913469 in SEC16B (P=0.000012) and four SNPs (rs2867125, rs6548238, rs4854344 and rs7561317) in the TMEM18 gene (P=0.00015), all of which were in almost absolute linkage disequilibrium, were significantly associated with obesity in the Japanese population. SNPs in GNPDA2, BDNF, FAIM2 and MC4R genes were marginally associated with obesity (P<0.05). Our data suggest that some SNPs identified by genome-wide association studies in the Caucasians also confer susceptibility to obesity in Japanese subjects.

    Journal of human genetics 2009;54;12;727-31

  • Obesity genes identified in genome-wide association studies are associated with adiposity measures and potentially with nutrient-specific food preference.

    Bauer F, Elbers CC, Adan RA, Loos RJ, Onland-Moret NC, Grobbee DE, van Vliet-Ostaptchouk JV, Wijmenga C and van der Schouw YT

    Complex Genetics Section, Department of Medical Genetics, University Medical Center Utrecht, Utrecht, Netherlands.

    Background: New genetic loci, most of which are expressed in the brain, have recently been reported to contribute to the development of obesity. The brain, especially the hypothalamus, is strongly involved in regulating weight and food intake.

    Objectives: We investigated whether the recently reported obesity loci are associated with measures of abdominal adiposity and whether these variants affect dietary energy or macronutrient intake.

    Design: We studied 1700 female Dutch participants in the European Prospective Investigation into Cancer and Nutrition (EPIC). Their anthropometric measurements and intake of macronutrients were available. Genotyping was performed by using KASPar chemistry. A linear regression model, with an assumption of an additive effect, was used to analyze the association between genotypes of 12 single nucleotide polymorphisms (SNPs) and adiposity measures and dietary intake.

    Results: Seven SNPs were associated (P < 0.05) with weight, body mass index (BMI), and waist circumference (unadjusted for BMI). They were in or near to 6 loci: FTO, MC4R, KCTD15, MTCH2, NEGR1, and BDNF. Five SNPs were associated with dietary intake (P < 0.05) and were in or near 5 loci: SH2B1 (particularly with increased fat), KCTD15 (particularly with carbohydrate intake), MTCH2, NEGR1, and BDNF.

    Conclusions: We confirmed some of the findings for the newly identified obesity loci that are associated with general adiposity in a healthy Dutch female population. Our results suggest that these loci are not specifically associated with abdominal adiposity but more generally with obesity. We also found that some of the SNPs were associated with macronutrient-specific food intake.

    Funded by: Medical Research Council: MC_U106188470

    The American journal of clinical nutrition 2009;90;4;951-9

  • Replication and extension of genome-wide association study results for obesity in 4923 adults from northern Sweden.

    Renström F, Payne F, Nordström A, Brito EC, Rolandsson O, Hallmans G, Barroso I, Nordström P, Franks PW and GIANT Consortium

    Department of Public Health and Clinical Medicine, Umeå University Hospital, Umeå, Sweden.

    Recent genome-wide association studies (GWAS) have identified multiple risk loci for common obesity (FTO, MC4R, TMEM18, GNPDA2, SH2B1, KCTD15, MTCH2, NEGR1 and PCSK1). Here we extend those studies by examining associations with adiposity and type 2 diabetes in Swedish adults. The nine single nucleotide polymorphisms (SNPs) were genotyped in 3885 non-diabetic and 1038 diabetic individuals with available measures of height, weight and body mass index (BMI). Adipose mass and distribution were objectively assessed using dual-energy X-ray absorptiometry in a sub-group of non-diabetics (n = 2206). In models with adipose mass traits, BMI or obesity as outcomes, the most strongly associated SNP was FTO rs1121980 (P < 0.001). Five other SNPs (SH2B1 rs7498665, MTCH2 rs4752856, MC4R rs17782313, NEGR1 rs2815752 and GNPDA2 rs10938397) were significantly associated with obesity. To summarize the overall genetic burden, a weighted risk score comprising a subset of SNPs was constructed; those in the top quintile of the score were heavier (+2.6 kg) and had more total (+2.4 kg), gynoid (+191 g) and abdominal (+136 g) adipose tissue than those in the lowest quintile (all P < 0.001). The genetic burden score significantly increased diabetes risk, with those in the highest quintile (n = 193/594 cases/controls) being at 1.55-fold (95% CI 1.21-1.99; P < 0.0001) greater risk of type 2 diabetes than those in the lowest quintile (n = 130/655 cases/controls). In summary, we have statistically replicated six of the previously associated obese-risk loci and our results suggest that the weight-inducing effects of these variants are explained largely by increased adipose accumulation.

    Funded by: Wellcome Trust: 090532

    Human molecular genetics 2009;18;8;1489-96

  • Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity.

    Thorleifsson G, Walters GB, Gudbjartsson DF, Steinthorsdottir V, Sulem P, Helgadottir A, Styrkarsdottir U, Gretarsdottir S, Thorlacius S, Jonsdottir I, Jonsdottir T, Olafsdottir EJ, Olafsdottir GH, Jonsson T, Jonsson F, Borch-Johnsen K, Hansen T, Andersen G, Jorgensen T, Lauritzen T, Aben KK, Verbeek AL, Roeleveld N, Kampman E, Yanek LR, Becker LC, Tryggvadottir L, Rafnar T, Becker DM, Gulcher J, Kiemeney LA, Pedersen O, Kong A, Thorsteinsdottir U and Stefansson K

    deCODE Genetics, Reykjavik, Iceland. thorleif@decode.is

    Obesity results from the interaction of genetic and environmental factors. To search for sequence variants that affect variation in two common measures of obesity, weight and body mass index (BMI), both of which are highly heritable, we performed a genome-wide association (GWA) study with 305,846 SNPs typed in 25,344 Icelandic, 2,998 Dutch, 1,890 European Americans and 1,160 African American subjects and combined the results with previously published results from the Diabetes Genetics Initiative (DGI) on 3,024 Scandinavians. We selected 43 variants in 19 regions for follow-up in 5,586 Danish individuals and compared the results to a genome-wide study on obesity-related traits from the GIANT consortium. In total, 29 variants, some correlated, in 11 chromosomal regions reached a genome-wide significance threshold of P < 1.6 x 10(-7). This includes previously identified variants close to or in the FTO, MC4R, BDNF and SH2B1 genes, in addition to variants at seven loci not previously connected with obesity.

    Funded by: NCRR NIH HHS: M01-RR000052; NHLBI NIH HHS: HL072518, HL087698

    Nature genetics 2009;41;1;18-24

  • Six new loci associated with body mass index highlight a neuronal influence on body weight regulation.

    Willer CJ, Speliotes EK, Loos RJ, Li S, Lindgren CM, Heid IM, Berndt SI, Elliott AL, Jackson AU, Lamina C, Lettre G, Lim N, Lyon HN, McCarroll SA, Papadakis K, Qi L, Randall JC, Roccasecca RM, Sanna S, Scheet P, Weedon MN, Wheeler E, Zhao JH, Jacobs LC, Prokopenko I, Soranzo N, Tanaka T, Timpson NJ, Almgren P, Bennett A, Bergman RN, Bingham SA, Bonnycastle LL, Brown M, Burtt NP, Chines P, Coin L, Collins FS, Connell JM, Cooper C, Smith GD, Dennison EM, Deodhar P, Elliott P, Erdos MR, Estrada K, Evans DM, Gianniny L, Gieger C, Gillson CJ, Guiducci C, Hackett R, Hadley D, Hall AS, Havulinna AS, Hebebrand J, Hofman A, Isomaa B, Jacobs KB, Johnson T, Jousilahti P, Jovanovic Z, Kraft P, Kuokkanen M, Kuusisto J, Laitinen J, Lakatta EG, Luan J, Luben RN, Mangino M, McArdle WL, Meitinger T, Mulas A, Munroe PB, Narisu N, Ness AR, Northstone K, O'Rahilly S, Purmann C, Rees MG, Ridderstråle M, Ring SM, Rivadeneira F, Ruokonen A, Sandhu MS, Saramies J, Scott LJ, Scuteri, Silander K, Sims MA, Song K, Stephens J, Stevens S, Stringham HM, Tung YC, Valle TT, Van Duijn CM, Vimaleswaran KS, Vollenweider P, Waeber G, Wallace C, Watanabe RM, Waterworth DM, Watkins N, Wellcome Trust Case Control Consortium, Witteman JC, Zeggini E, Zhai G, Zillikens MC, Altshuler D, Caulfield MJ, Chanock SJ, Farooqi IS, Ferrucci L, Guralnik JM, Hattersley AT, Hu FB, Jarvelin MR, Laakso M, Mooser V, Ong KK, Ouwehand WH, Salomaa V, Samani NJ, Spector TD, Tuomi T, Tuomilehto J, Uda M, Uitterlinden AG, Wareham NJ, Deloukas P, Frayling TM, Groop LC, Hayes RB, Hunter DJ, Mohlke KL, Peltonen L, Schlessinger D, Strachan DP, Wichmann HE, McCarthy MI, Boehnke M, Barroso I, Abecasis GR, Hirschhorn JN and Genetic Investigation of ANthropometric Traits Consortium

    Genetic Investigation of ANthropometric Traits Consortium.

    Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 x 10(-8)): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.

    Funded by: British Heart Foundation: FS/05/061/19501; Intramural NIH HHS; Medical Research Council: G0000649, G0000934, G0400874, G0401527, G0600705, G0601261, G0701863, G0800582, G9521010, G9815508, G9824984, MC_U105630924, MC_U106179471, MC_U106179472, MC_U106188470, MC_U147585819, MC_UP_A620_1014, U.1475.00.003.00010.02 (85819); NCI NIH HHS: 5UO1CA098233, CA49449, CA50385, CA65725, CA67262, CA87969, P01 CA087969, R01 CA049449, R01 CA050385, R01 CA065725, R01 CA067262, U01 CA049449, U01 CA067262, U01 CA098233; NHGRI NIH HHS: 01-HG-65403, 1Z01 HG000024, HG02651, N01HG65403, R01 HG002651, Z01 HG000024; NHLBI NIH HHS: HL084729, HL087679, R01 HL087679, U01 HL084729; NIA NIH HHS: N01-AG-1-2109; NIDDK NIH HHS: DK062370, DK072193, DK075787, F32 DK079466, F32 DK079466-01, K23 DK067288, K23 DK080145, K23 DK080145-01, R01 DK029867, R01 DK062370, R01 DK072193, R01 DK075787, R56 DK062370, T32 DK007191, T32DK07191, U01 DK062370; NIMH NIH HHS: 1RL1MH083268, RL1 MH083268; Wellcome Trust: 068545/Z/02, 076113, 076467/Z/05/Z, 077011, 077016, 079557, 082390, 089061, 090532

    Nature genetics 2009;41;1;25-34

  • Association of systemic lupus erythematosus with C8orf13-BLK and ITGAM-ITGAX.

    Hom G, Graham RR, Modrek B, Taylor KE, Ortmann W, Garnier S, Lee AT, Chung SA, Ferreira RC, Pant PV, Ballinger DG, Kosoy R, Demirci FY, Kamboh MI, Kao AH, Tian C, Gunnarsson I, Bengtsson AA, Rantapää-Dahlqvist S, Petri M, Manzi S, Seldin MF, Rönnblom L, Syvänen AC, Criswell LA, Gregersen PK and Behrens TW

    Genentech, South San Francisco, CA 94080, USA.

    Background: Systemic lupus erythematosus (SLE) is a clinically heterogeneous disease in which the risk of disease is influenced by complex genetic and environmental contributions. Alleles of HLA-DRB1, IRF5, and STAT4 are established susceptibility genes; there is strong evidence for the existence of additional risk loci.

    Methods: We genotyped more than 500,000 single-nucleotide polymorphisms (SNPs) in DNA samples from 1311 case subjects with SLE and 1783 control subjects; all subjects were North Americans of European descent. Genotypes from 1557 additional control subjects were obtained from public data repositories. We measured the association between the SNPs and SLE after applying strict quality-control filters to reduce technical artifacts and to correct for the presence of population stratification. Replication of the top loci was performed in 793 case subjects and 857 control subjects from Sweden.

    Results: Genetic variation in the region upstream from the transcription initiation site of the gene encoding B lymphoid tyrosine kinase (BLK) and C8orf13 (chromosome 8p23.1) was associated with disease risk in both the U.S. and Swedish case-control series (rs13277113; odds ratio, 1.39; P=1x10(-10)) and also with altered levels of messenger RNA in B-cell lines. In addition, variants on chromosome 16p11.22, near the genes encoding integrin alpha M (ITGAM, or CD11b) and integrin alpha X (ITGAX), were associated with SLE in the combined sample (rs11574637; odds ratio, 1.33; P=3x10(-11)).

    Conclusions: We identified and then confirmed through replication two new genetic loci for SLE: a promoter-region allele associated with reduced expression of BLK and increased expression of C8orf13 and variants in the ITGAM-ITGAX region.

    Funded by: NCRR NIH HHS: 5-M01-RR00079, M01-RR00052; NHLBI NIH HHS: HL54900, HL74165; NIAID NIH HHS: N01-AI95386; NIAMS NIH HHS: AR050267, AR43737, K23-AR051044, K24-AR02175, N01-AR1-2256, P60 AR053308, R01-AR046588, R01-AR44804

    The New England journal of medicine 2008;358;9;900-9

  • Genome-wide association and linkage analyses of hemostatic factors and hematological phenotypes in the Framingham Heart Study.

    Yang Q, Kathiresan S, Lin JP, Tofler GH and O'Donnell CJ

    The National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA. qyang@bu.edu

    Background: Increased circulating levels of hemostatic factors as well as anemia have been associated with increased risk of cardiovascular disease (CVD). Known associations between hemostatic factors and sequence variants at genes encoding these factors explain only a small proportion of total phenotypic variation. We sought to confirm known putative loci and identify novel loci that may influence either trait in genome-wide association and linkage analyses using the Affymetrix GeneChip 100K single nucleotide polymorphism (SNP) set.

    Methods: Plasma levels of circulating hemostatic factors (fibrinogen, factor VII, plasminogen activator inhibitor-1, von Willebrand factor, tissue plasminogen activator, D-dimer) and hematological phenotypes (platelet aggregation, viscosity, hemoglobin, red blood cell count, mean corpuscular volume, mean corpuscular hemoglobin concentration) were obtained in approximately 1000 Framingham Heart Study (FHS) participants from 310 families. Population-based association analyses using the generalized estimating equations (GEE), family-based association test (FBAT), and multipoint variance components linkage analyses were performed on the multivariable adjusted residuals of hemostatic and hematological phenotypes.

    Results: In association analysis, the lowest GEE p-value for hemostatic factors was p = 4.5*10(-16) for factor VII at SNP rs561241, a variant located near the F7 gene and in complete linkage disequilibrium (LD) (r2 = 1) with the Arg353Gln F7 SNP previously shown to account for 9% of total phenotypic variance. The lowest GEE p-value for hematological phenotypes was 7*10(-8) at SNP rs2412522 on chromosome 4 for mean corpuscular hemoglobin concentration. We presented top 25 most significant GEE results with p-values in the range of 10(-6) to 10(-5) for hemostatic or hematological phenotypes. In relating 100K SNPs to known can 116e didate genes, we identified two SNPs (rs1582055, rs4897475) in erythrocyte membrane protein band 4.1-like 2 (EPB41L2) associated with hematological phenotypes (GEE p < 10(-3)). In linkage analyses, the highest linkage LOD score for hemostatic factors was 3.3 for factor VII on chromosome 10 around 15 Mb, and for hematological phenotypes, LOD 3.4 for hemoglobin on chromosome 4 around 55 Mb. All GEE and FBAT association and variance components linkage results can be found at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007 webcite.

    Conclusion: Using genome-wide association methodology, we have successfully identified a SNP in complete LD with a sequence variant previously shown to be strongly associated with factor VII, providing proof of principle for this approach. Further study of additional strongly associated SNPs and linked regions may identify novel variants that influence the inter-individual variability in hemostatic factors and hematological phenotypes.

    Funded by: NCRR NIH HHS: 1S10RR163736-01A1; NHLBI NIH HHS: N01-HC-25195, N01HC25195

    BMC medical genetics 2007;8 Suppl 1;S12

  • The DNA sequence and biological annotation of human chromosome 1.

    Gregory SG, Barlow KF, McLay KE, Kaul R, Swarbreck D, Dunham A, Scott CE, Howe KL, Woodfine K, Spencer CC, Jones MC, Gillson C, Searle S, Zhou Y, Kokocinski F, McDonald L, Evans R, Phillips K, Atkinson A, Cooper R, Jones C, Hall RE, Andrews TD, Lloyd C, Ainscough R, Almeida JP, Ambrose KD, Anderson F, Andrew RW, Ashwell RI, Aubin K, Babbage AK, Bagguley CL, Bailey J, Beasley H, Bethel G, Bird CP, Bray-Allen S, Brown JY, Brown AJ, Buckley D, Burton J, Bye J, Carder C, Chapman JC, Clark SY, Clarke G, Clee C, Cobley V, Collier RE, Corby N, Coville GJ, Davies J, Deadman R, Dunn M, Earthrowl M, Ellington AG, Errington H, Frankish A, Frankland J, French L, Garner P, Garnett J, Gay L, Ghori MR, Gibson R, Gilby LM, Gillett W, Glithero RJ, Grafham DV, Griffiths C, Griffiths-Jones S, Grocock R, Hammond S, Harrison ES, Hart E, Haugen E, Heath PD, Holmes S, Holt K, Howden PJ, Hunt AR, Hunt SE, Hunter G, Isherwood J, James R, Johnson C, Johnson D, Joy A, Kay M, Kershaw JK, Kibukawa M, Kimberley AM, King A, Knights AJ, Lad H, Laird G, Lawlor S, Leongamornlert DA, Lloyd DM, Loveland J, Lovell J, Lush MJ, Lyne R, Martin S, Mashreghi-Mohammadi M, Matthews L, Matthews NS, McLaren S, Milne S, Mistry S, Moore MJ, Nickerson T, O'Dell CN, Oliver K, Palmeiri A, Palmer SA, Parker A, Patel D, Pearce AV, Peck AI, Pelan S, Phelps K, Phillimore BJ, Plumb R, Rajan J, Raymond C, Rouse G, Saenphimmachak C, Sehra HK, Sheridan E, Shownkeen R, Sims S, Skuce CD, Smith M, Steward C, Subramanian S, Sycamore N, Tracey A, Tromans A, Van Helmond Z, Wall M, Wallis JM, White S, Whitehead SL, Wilkinson JE, Willey DL, Williams H, Wilming L, Wray PW, Wu Z, Coulson A, Vaudin M, Sulston JE, Durbin R, Hubbard T, Wooster R, Dunham I, Carter NP, McVean G, Ross MT, Harrow J, Olson MV, Beck S, Rogers J, Bentley DR, Banerjee R, Bryant SP, Burford DC, Burrill WD, Clegg SM, Dhami P, Dovey O, Faulkner LM, Gribble SM, Langford CF, Pandian RD, Porter KM and Prigmore E

    The Wellcome Trust Sanger Institute, The Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SA, UK. sgregory@chg.duhs.duke.edu

    The reference sequence for each human chromosome provides the framework for understanding genome function, variation and evolution. Here we report the finished sequence and biological annotation of human chromosome 1. Chromosome 1 is gene-dense, with 3,141 genes and 991 pseudogenes, and many coding sequences overlap. Rearrangements and mutations of chromosome 1 are prevalent in cancer and many other diseases. Patterns of sequence variation reveal signals of recent selection in specific genes that may contribute to human fitness, and also in regions where no function is evident. Fine-scale recombination occurs in hotspots of varying intensity along the sequence, and is enriched near genes. These and other studies of human biology and disease encoded within chromosome 1 are made possible with the highly accurate annotated sequence, as part of the completed set of chromosome sequences that comprise the reference human genome.

    Funded by: Medical Research Council: G0000107; Wellcome Trust

    Nature 2006;441;7091;315-21

  • Modification-specific proteomics of plasma membrane proteins: identification and characterization of glycosylphosphatidylinositol-anchored proteins released upon phospholipase D treatment.

    Elortza F, Mohammed S, Bunkenborg J, Foster LJ, Nühse TS, Brodbeck U, Peck SC and Jensen ON

    Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, DK-5230 Odense M, Denmark.

    Plasma membrane proteins are displayed through diverse mechanisms, including anchoring in the extracellular leaflet via glycosylphosphatidylinositol (GPI) molecules. GPI-anchored membrane proteins (GPI-APs) are a functionally and structurally diverse protein family, and their importance is well-recognized as they are candidate cell surface biomarker molecules with potential diagnostic and therapeutic applications in molecular medicine. GPI-APs have also attracted interest in plant biotechnology because of their role in root development and cell remodeling. Using a shave-and-conquer concept, we demonstrate that phospholipase D (PLD) treatment of human and plant plasma membrane fractions leads to the release of GPI-anchored proteins that were identified and characterized by capillary liquid chromatography and tandem mass spectrometry. In contrast to phospholipase C, the PLD enzyme is not affected by structural heterogeneity of the GPI moiety, making PLD a generally useful reagent for proteomic investigations of GPI-anchored proteins in a variety of cells, tissues, and organisms. A total of 11 human GPI-APs and 35 Arabidopsis thaliana GPI-APs were identified, representing a significant addition to the number of experimentally detected GPI-APs in both species. Computational GPI-AP sequence analysis tools were investigated for the characterization of the identified GPI-APs, and these demonstrated that there is some discrepancy in their efficiency in classification of GPI-APs and the exact assignment of omega-sites. This study highlights the efficiency of an integrative proteomics approach that combines experimental and computational methods to provide the selectivity, specificity, and sensitivity required for characterization of post-translationally modified membrane proteins.

    Journal of proteome research 2006;5;4;935-43

  • Complete sequencing and characterization of 21,243 full-length human cDNAs.

    Ota T, Suzuki Y, Nishikawa T, Otsuki T, Sugiyama T, Irie R, Wakamatsu A, Hayashi K, Sato H, Nagai K, Kimura K, Makita H, Sekine M, Obayashi M, Nishi T, Shibahara T, Tanaka T, Ishii S, Yamamoto J, Saito K, Kawai Y, Isono Y, Nakamura Y, Nagahari K, Murakami K, Yasuda T, Iwayanagi T, Wagatsuma M, Shiratori A, Sudo H, Hosoiri T, Kaku Y, Kodaira H, Kondo H, Sugawara M, Takahashi M, Kanda K, Yokoi T, Furuya T, Kikkawa E, Omura Y, Abe K, Kamihara K, Katsuta N, Sato K, Tanikawa M, Yamazaki M, Ninomiya K, Ishibashi T, Yamashita H, Murakawa K, Fujimori K, Tanai H, Kimata M, Watanabe M, Hiraoka S, Chiba Y, Ishida S, Ono Y, Takiguchi S, Watanabe S, Yosida M, Hotuta T, Kusano J, Kanehori K, Takahashi-Fujii A, Hara H, Tanase TO, Nomura Y, Togiya S, Komai F, Hara R, Takeuchi K, Arita M, Imose N, Musashino K, Yuuki H, Oshima A, Sasaki N, Aotsuka S, Yoshikawa Y, Matsunawa H, Ichihara T, Shiohata N, Sano S, Moriya S, Momiyama H, Satoh N, Takami S, Terashima Y, Suzuki O, Nakagawa S, Senoh A, Mizoguchi H, Goto Y, Shimizu F, Wakebe H, Hishigaki H, Watanabe T, Sugiyama A, Takemoto M, Kawakami B, Yamazaki M, Watanabe K, Kumagai A, Itakura S, Fukuzumi Y, Fujimori Y, Komiyama M, Tashiro H, Tanigami A, Fujiwara T, Ono T, Yamada K, Fujii Y, Ozaki K, Hirao M, Ohmori Y, Kawabata A, Hikiji T, Kobatake N, Inagaki H, Ikema Y, Okamoto S, Okitani R, Kawakami T, Noguchi S, Itoh T, Shigeta K, Senba T, Matsumura K, Nakajima Y, Mizuno T, Morinaga M, Sasaki M, Togashi T, Oyama M, Hata H, Watanabe M, Komatsu T, Mizushima-Sugano J, Satoh T, Shirai Y, Takahashi Y, Nakagawa K, Okumura K, Nagase T, Nomura N, Kikuchi H, Masuho Y, Yamashita R, Nakai K, Yada T, Nakamura Y, Ohara O, Isogai T and Sugano S

    Helix Research Institute, 1532-3 Yana, Kisarazu, Chiba 292-0812, Japan.

    As a base for human transcriptome and functional genomics, we created the "full-length long Japan" (FLJ) collection of sequenced human cDNAs. We determined the entire sequence of 21,243 selected clones and found that 14,490 cDNAs (10,897 clusters) were unique to the FLJ collection. About half of them (5,416) seemed to be protein-coding. Of those, 1,999 clusters had not been predicted by computational methods. The distribution of GC content of nonpredicted cDNAs had a peak at approximately 58% compared with a peak at approximately 42%for predicted cDNAs. Thus, there seems to be a slight bias against GC-rich transcripts in current gene prediction procedures. The rest of the cDNAs unique to the FLJ collection (5,481) contained no obvious open reading frames (ORFs) and thus are candidate noncoding RNAs. About one-fourth of them (1,378) showed a clear pattern of splicing. The distribution of GC content of noncoding cDNAs was narrow and had a peak at approximately 42%, relatively low compared with that of protein-coding cDNAs.

    Nature genetics 2004;36;1;40-5

  • The secreted protein discovery initiative (SPDI), a large-scale effort to identify novel human secreted and transmembrane proteins: a bioinformatics assessment.

    Clark HF, Gurney AL, Abaya E, Baker K, Baldwin D, Brush J, Chen J, Chow B, Chui C, Crowley C, Currell B, Deuel B, Dowd P, Eaton D, Foster J, Grimaldi C, Gu Q, Hass PE, Heldens S, Huang A, Kim HS, Klimowski L, Jin Y, Johnson S, Lee J, Lewis L, Liao D, Mark M, Robbie E, Sanchez C, Schoenfeld J, Seshagiri S, Simmons L, Singh J, Smith V, Stinson J, Vagts A, Vandlen R, Watanabe C, Wieand D, Woods K, Xie MH, Yansura D, Yi S, Yu G, Yuan J, Zhang M, Zhang Z, Goddard A, Wood WI, Godowski P and Gray A

    Departments of Bioinformatics, Molecular Biology and Protein Chemistry, Genentech, Inc, South San Francisco, California 94080, USA. hclark@gene.com

    A large-scale effort, termed the Secreted Protein Discovery Initiative (SPDI), was undertaken to identify novel secreted and transmembrane proteins. In the first of several approaches, a biological signal sequence trap in yeast cells was utilized to identify cDNA clones encoding putative secreted proteins. A second strategy utilized various algorithms that recognize features such as the hydrophobic properties of signal sequences to identify putative proteins encoded by expressed sequence tags (ESTs) from human cDNA libraries. A third approach surveyed ESTs for protein sequence similarity to a set of known receptors and their ligands with the BLAST al b56 gorithm. Finally, both signal-sequence prediction algorithms and BLAST were used to identify single exons of potential genes from within human genomic sequence. The isolation of full-length cDNA clones for each of these candidate genes resulted in the identification of >1000 novel proteins. A total of 256 of these cDNAs are still novel, including variants and novel genes, per the most recent GenBank release version. The success of this large-scale effort was assessed by a bioinformatics analysis of the proteins through predictions of protein domains, subcellular localizations, and possible functional roles. The SPDI collection should facilitate efforts to better understand intercellular communication, may lead to new understandings of human diseases, and provides potential opportunities for the development of therapeutics.

    Genome research 2003;13;10;2265-70

  • Characterization of a novel rat brain glycosylphosphatidylinositol-anchored protein (Kilon), a member of the IgLON cell adhesion molecule family.

    Funatsu N, Miyata S, Kumanogoh H, Shigeta M, Hamada K, Endo Y, Sokawa Y and Maekawa S

    Department of Biotechnology, Kyoto Institute of Technology, Kyoto 606-8585, Japan.

    In the central nervous system, many cell adhesion molecules are known to participate in the establishment and remodeling of the neural circuit. Some of the cell adhesion molecules are known to be anchored to the membrane by the glycosylphosphatidylinositol (GPI) inserted to their C termini, and many GPI-anchored proteins are known to be localized in a Triton-insoluble membrane fraction of low density or so-called "raft." In this study, we surveyed the GPI-anchored proteins in the Triton-insoluble low density fraction from 2-week-old rat brain by solubilization with phosphatidylinositol-specific phospholipase C. By Western blotting and partial peptide sequencing after the deglycosylation with peptide N-glycosidase F, the presence of Thy-1, F3/contactin, and T-cadherin was shown. In addition, one of the major proteins, having an apparent molecular mass of 36 kDa after the peptide N-glycosidase F digestion, was found to be a novel protein. The result of cDNA cloning showed that the protein is an immunoglobulin superfamily member with three C2 domains and has six putative glycosylation sites. Since this protein shows high sequence similarity to IgLON family members including LAMP, OBCAM, neurotrimin, CEPU-1, AvGP50, and GP55, we termed this protein Kilon (a kindred of IgLON). Kilon-specific monoclonal antibodies were produced, and Western blotting analysis showed that expression of Kilon is restricted to brain, and Kilon has an apparent molecular mass of 46 kDa in SDS-polyacrylamide gel electrophoresis in its expressed form. In brain, the expression of Kilon is already detected in E16 stage, and its level gradually increases during development. Kilon immunostaining was observed in the cerebral cortex and hippocampus, in which the strongly stained puncta were observed on dendrites and soma of pyramidal neurons.

    The Journal of biological chemistry 1999;274;12;8224-30

Gene lists (8)

Gene List Source Species Name Description Gene count
L00000009 G2C Homo sapiens Human PSD Human orthologues of mouse PSD adapted from Collins et al (2006) 1080
L00000011 G2C Homo sapiens Human clathrin Human orthologues of mouse clathrin coated vesicle genes adapted from Collins et al (2006) 150
L00000012 G2C Homo sapiens Human Synaptosome Human orthologues of mouse synaptosome adapted from Collins et al (2006) 152
L00000016 G2C Homo sapiens Human PSP Human orthologues of mouse PSP adapted from Collins et al (2006) 1121
L00000059 G2C Homo sapiens BAYES-COLLINS-HUMAN-PSD-CONSENSUS Human cortex PSD consensus 748
L00000061 G2C Homo sapiens BAYES-COLLINS-MOUSE-PSD-CONSENSUS Mouse cortex PSD consensus (ortho) 984
L00000069 G2C Homo sapiens BAYES-COLLINS-HUMAN-PSD-FULL Human cortex biopsy PSD full list 1461
L00000071 G2C Homo sapiens BAYES-COLLINS-MOUSE-PSD-FULL Mouse cortex PSD full list (ortho) 1556
© G2C 2014. The Genes to Cognition Programme received funding from The Wellcome Trust and the EU FP7 Framework Programmes:
EUROSPIN (FP7-HEALTH-241498), SynSys (FP7-HEALTH-242167) and GENCODYS (FP7-HEALTH-241995).

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