YEASTRACT - Genes Grouped by TF, Ordered by the Percentage of Genes Regulated by TF, Relative to the Total Number of Genes in the List 08/07/14 16:17
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Analysis of Gene Expression Data for Gene Ontology
ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION A Thesis Presented to The Graduate Faculty of The University of Akron In Partial Fulfillment of the Requirements for the Degree Master of Science Robert Daniel Macholan May 2011 ANALYSIS OF GENE EXPRESSION DATA FOR GENE ONTOLOGY BASED PROTEIN FUNCTION PREDICTION Robert Daniel Macholan Thesis Approved: Accepted: _______________________________ _______________________________ Advisor Department Chair Dr. Zhong-Hui Duan Dr. Chien-Chung Chan _______________________________ _______________________________ Committee Member Dean of the College Dr. Chien-Chung Chan Dr. Chand K. Midha _______________________________ _______________________________ Committee Member Dean of the Graduate School Dr. Yingcai Xiao Dr. George R. Newkome _______________________________ Date ii ABSTRACT A tremendous increase in genomic data has encouraged biologists to turn to bioinformatics in order to assist in its interpretation and processing. One of the present challenges that need to be overcome in order to understand this data more completely is the development of a reliable method to accurately predict the function of a protein from its genomic information. This study focuses on developing an effective algorithm for protein function prediction. The algorithm is based on proteins that have similar expression patterns. The similarity of the expression data is determined using a novel measure, the slope matrix. The slope matrix introduces a normalized method for the comparison of expression levels throughout a proteome. The algorithm is tested using real microarray gene expression data. Their functions are characterized using gene ontology annotations. The results of the case study indicate the protein function prediction algorithm developed is comparable to the prediction algorithms that are based on the annotations of homologous proteins. -
Allele-Specific Expression of Ribosomal Protein Genes in Interspecific Hybrid Catfish
Allele-specific Expression of Ribosomal Protein Genes in Interspecific Hybrid Catfish by Ailu Chen A dissertation submitted to the Graduate Faculty of Auburn University in partial fulfillment of the requirements for the Degree of Doctor of Philosophy Auburn, Alabama August 1, 2015 Keywords: catfish, interspecific hybrids, allele-specific expression, ribosomal protein Copyright 2015 by Ailu Chen Approved by Zhanjiang Liu, Chair, Professor, School of Fisheries, Aquaculture and Aquatic Sciences Nannan Liu, Professor, Entomology and Plant Pathology Eric Peatman, Associate Professor, School of Fisheries, Aquaculture and Aquatic Sciences Aaron M. Rashotte, Associate Professor, Biological Sciences Abstract Interspecific hybridization results in a vast reservoir of allelic variations, which may potentially contribute to phenotypical enhancement in the hybrids. Whether the allelic variations are related to the downstream phenotypic differences of interspecific hybrid is still an open question. The recently developed genome-wide allele-specific approaches that harness high- throughput sequencing technology allow direct quantification of allelic variations and gene expression patterns. In this work, I investigated allele-specific expression (ASE) pattern using RNA-Seq datasets generated from interspecific catfish hybrids. The objective of the study is to determine the ASE genes and pathways in which they are involved. Specifically, my study investigated ASE-SNPs, ASE-genes, parent-of-origins of ASE allele and how ASE would possibly contribute to heterosis. My data showed that ASE was operating in the interspecific catfish system. Of the 66,251 and 177,841 SNPs identified from the datasets of the liver and gill, 5,420 (8.2%) and 13,390 (7.5%) SNPs were identified as significant ASE-SNPs, respectively. -
Supplementary Figures 1-14 and Supplementary References
SUPPORTING INFORMATION Spatial Cross-Talk Between Oxidative Stress and DNA Replication in Human Fibroblasts Marko Radulovic,1,2 Noor O Baqader,1 Kai Stoeber,3† and Jasminka Godovac-Zimmermann1* 1Division of Medicine, University College London, Center for Nephrology, Royal Free Campus, Rowland Hill Street, London, NW3 2PF, UK. 2Insitute of Oncology and Radiology, Pasterova 14, 11000 Belgrade, Serbia 3Research Department of Pathology and UCL Cancer Institute, Rockefeller Building, University College London, University Street, London WC1E 6JJ, UK †Present Address: Shionogi Europe, 33 Kingsway, Holborn, London WC2B 6UF, UK TABLE OF CONTENTS 1. Supplementary Figures 1-14 and Supplementary References. Figure S-1. Network and joint spatial razor plot for 18 enzymes of glycolysis and the pentose phosphate shunt. Figure S-2. Correlation of SILAC ratios between OXS and OAC for proteins assigned to the SAME class. Figure S-3. Overlap matrix (r = 1) for groups of CORUM complexes containing 19 proteins of the 49-set. Figure S-4. Joint spatial razor plots for the Nop56p complex and FIB-associated complex involved in ribosome biogenesis. Figure S-5. Analysis of the response of emerin nuclear envelope complexes to OXS and OAC. Figure S-6. Joint spatial razor plots for the CCT protein folding complex, ATP synthase and V-Type ATPase. Figure S-7. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated by GO to nucleocytoplasmic transport (GO:0006913). Figure S-8. Joint spatial razor plots showing changes in subcellular abundance and compartmental distribution for proteins annotated to endocytosis (GO:0006897). Figure S-9. Joint spatial razor plots for 401-set proteins annotated by GO to small GTPase mediated signal transduction (GO:0007264) and/or GTPase activity (GO:0003924). -
1 AGING Supplementary Table 2
SUPPLEMENTARY TABLES Supplementary Table 1. Details of the eight domain chains of KIAA0101. Serial IDENTITY MAX IN COMP- INTERFACE ID POSITION RESOLUTION EXPERIMENT TYPE number START STOP SCORE IDENTITY LEX WITH CAVITY A 4D2G_D 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ B 4D2G_E 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ C 6EHT_D 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ D 6EHT_E 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ E 6GWS_D 41-72 41 72 100 100 3.2Å PCNA X-RAY DIFFRACTION √ F 6GWS_E 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ G 6GWS_F 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ H 6IIW_B 2-11 2 11 100 100 1.699Å UHRF1 X-RAY DIFFRACTION √ www.aging-us.com 1 AGING Supplementary Table 2. Significantly enriched gene ontology (GO) annotations (cellular components) of KIAA0101 in lung adenocarcinoma (LinkedOmics). Leading Description FDR Leading Edge Gene EdgeNum RAD51, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, CENPW, HJURP, NDC80, CDCA5, NCAPH, BUB1, ZWILCH, CENPK, KIF2C, AURKA, CENPN, TOP2A, CENPM, PLK1, ERCC6L, CDT1, CHEK1, SPAG5, CENPH, condensed 66 0 SPC24, NUP37, BLM, CENPE, BUB3, CDK2, FANCD2, CENPO, CENPF, BRCA1, DSN1, chromosome MKI67, NCAPG2, H2AFX, HMGB2, SUV39H1, CBX3, TUBG1, KNTC1, PPP1CC, SMC2, BANF1, NCAPD2, SKA2, NUP107, BRCA2, NUP85, ITGB3BP, SYCE2, TOPBP1, DMC1, SMC4, INCENP. RAD51, OIP5, CDK1, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, ESCO2, CENPW, HJURP, TTK, NDC80, CDCA5, BUB1, ZWILCH, CENPK, KIF2C, AURKA, DSCC1, CENPN, CDCA8, CENPM, PLK1, MCM6, ERCC6L, CDT1, HELLS, CHEK1, SPAG5, CENPH, PCNA, SPC24, CENPI, NUP37, FEN1, chromosomal 94 0 CENPL, BLM, KIF18A, CENPE, MCM4, BUB3, SUV39H2, MCM2, CDK2, PIF1, DNA2, region CENPO, CENPF, CHEK2, DSN1, H2AFX, MCM7, SUV39H1, MTBP, CBX3, RECQL4, KNTC1, PPP1CC, CENPP, CENPQ, PTGES3, NCAPD2, DYNLL1, SKA2, HAT1, NUP107, MCM5, MCM3, MSH2, BRCA2, NUP85, SSB, ITGB3BP, DMC1, INCENP, THOC3, XPO1, APEX1, XRCC5, KIF22, DCLRE1A, SEH1L, XRCC3, NSMCE2, RAD21. -
GIGSEA: Genotype Imputed Gene Set Enrichment Analysis
GIGSEA: Genotype Imputed Gene Set Enrichment Analysis Shijia Zhu Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA August 28, 2017 Contents Abstract . .1 1. Import packages . .1 2. Quick start . .2 3. One example of MetaXcan output . .2 4. Load data of gene sets . .4 4.1 Discrete-valued gene sets: . .4 4.2 Continuous-valued gene sets: . .6 4.3 User self-defined gene set . .7 5. Gene set enrichment analysis . .9 5.1 Gene set enrichment analysis using weighted simple linear regression . .9 5.2 Gene set enrichment analysis using weighted multiple regression model . 10 5.3 One-step weightedGSEA . 11 Abstract We presented the Genotype Imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, andmultiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to test the enrichment, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real biological signal. -
MRPL24 Polyclonal Antibody
PRODUCT DATA SHEET Bioworld Technology,Inc. MRPL24 polyclonal antibody Catalog: BS65388 Host: Rabbit Reactivity: Human,Mouse,Ra BackGround: term. Avoid freeze-thaw cycles. mitochondrial ribosomal protein L24(MRPL24) Homo Specificity: sapiens Mammalian mitochondrial ribosomal proteins are MRP-L24 Polyclonal Antibody detects endogenous levels encoded by nuclear genes and help in protein synthesis of MRP-L24 protein. within the mitochondrion. Mitochondrial ribosomes (mi- DATA: toribosomes) consist of a small 28S subunit and a large 39S subunit. They have an estimated 75% protein to rRNA composition compared to prokaryotic ribosomes, where this ratio is reversed. Another difference between mammalian mitoribosomes and prokaryotic ribosomes is that the latter contain a 5S rRNA. Among different spe- cies, the proteins comprising the mitoribosome differ greatly in sequence, and sometimes in biochemical prop- Western Blot analysis of various cells using MRP-L24 Polyclonal Anti- erties, which prevents easy recognition by sequence ho- body diluted at 1:2000 mology. This gene encodes a 39S subunit protein which is more than twice the size of its E.coli counterpart (EcoL24). Sequence analysis identified two transcript variants that encode the same protein. [provided by Ref- Seq, Jul 2008], Product: Liquid in PBS containing 50% glycerol, 0.5% BSA and 0.02% sodium azide. Immunohistochemistry analysis of paraffin-embedded human breast Molecular Weight: carcinoma tissue, using MRPL24 Antibody. The picture on the right is ~ 32 kDa blocked with the synthesized peptide. Swiss-Prot: Q96A35 Purification&Purity: The antibody was affinity-purified from rabbit antiserum by affinity-chromatography using epitope-specific im- munogen. Applications: Western blot analysis of lysates from HUVEC cells, using MRPL24 An- Western Blot: 1/500 - 1/2000. -
Supplementary Dataset S2
mitochondrial translational termination MRPL28 MRPS26 6 MRPS21 PTCD3 MTRF1L 4 MRPL50 MRPS18A MRPS17 2 MRPL20 MRPL52 0 MRPL17 MRPS33 MRPS15 −2 MRPL45 MRPL30 MRPS27 AURKAIP1 MRPL18 MRPL3 MRPS6 MRPS18B MRPL41 MRPS2 MRPL34 GADD45GIP1 ERAL1 MRPL37 MRPS10 MRPL42 MRPL19 MRPS35 MRPL9 MRPL24 MRPS5 MRPL44 MRPS23 MRPS25 ITB ITB ITB ITB ICa ICr ITL original ICr ICa ITL ICa ITL original ICr ITL ICr ICa mitochondrial translational elongation MRPL28 MRPS26 6 MRPS21 PTCD3 MRPS18A 4 MRPS17 MRPL20 2 MRPS15 MRPL45 MRPL52 0 MRPS33 MRPL30 −2 MRPS27 AURKAIP1 MRPS10 MRPL42 MRPL19 MRPL18 MRPL3 MRPS6 MRPL24 MRPS35 MRPL9 MRPS18B MRPL41 MRPS2 MRPL34 MRPS5 MRPL44 MRPS23 MRPS25 MRPL50 MRPL17 GADD45GIP1 ERAL1 MRPL37 ITB ITB ITB ITB ICa ICr original ICr ITL ICa ITL ICa ITL original ICr ITL ICr ICa translational termination MRPL28 MRPS26 6 MRPS21 PTCD3 C12orf65 4 MTRF1L MRPL50 MRPS18A 2 MRPS17 MRPL20 0 MRPL52 MRPL17 MRPS33 −2 MRPS15 MRPL45 MRPL30 MRPS27 AURKAIP1 MRPL18 MRPL3 MRPS6 MRPS18B MRPL41 MRPS2 MRPL34 GADD45GIP1 ERAL1 MRPL37 MRPS10 MRPL42 MRPL19 MRPS35 MRPL9 MRPL24 MRPS5 MRPL44 MRPS23 MRPS25 ITB ITB ITB ITB ICa ICr original ICr ITL ICa ITL ICa ITL original ICr ITL ICr ICa translational elongation DIO2 MRPS18B MRPL41 6 MRPS2 MRPL34 GADD45GIP1 4 ERAL1 MRPL37 2 MRPS10 MRPL42 MRPL19 0 MRPL30 MRPS27 AURKAIP1 −2 MRPL18 MRPL3 MRPS6 MRPS35 MRPL9 EEF2K MRPL50 MRPS5 MRPL44 MRPS23 MRPS25 MRPL24 MRPS33 MRPL52 EIF5A2 MRPL17 SECISBP2 MRPS15 MRPL45 MRPS18A MRPS17 MRPL20 MRPL28 MRPS26 MRPS21 PTCD3 ITB ITB ITB ITB ICa ICr ICr ITL original ITL ICa ICa ITL ICr ICr ICa original -
A Homozygous MRPL24 Mutation Causes a Complex Movement Disorder and Affects the Mitoribosome Assembly T
Neurobiology of Disease 141 (2020) 104880 Contents lists available at ScienceDirect Neurobiology of Disease journal homepage: www.elsevier.com/locate/ynbdi A homozygous MRPL24 mutation causes a complex movement disorder and affects the mitoribosome assembly T Michela Di Nottiaa,1, Maria Marcheseb,1, Daniela Verrignia, Christian Daniel Muttic, Alessandra Torracoa, Romina Olivad, Erika Fernandez-Vizarrac, Federica Moranib, Giulia Trania, Teresa Rizzaa, Daniele Ghezzie,f, Anna Ardissoneg,h, Claudia Nestib, Gessica Vascoi, Massimo Zevianic, Michal Minczukc, Enrico Bertinia, Filippo Maria Santorellib,2, ⁎ Rosalba Carrozzoa, ,2 a Unit of Muscular and Neurodegenerative Disorders, Laboratory of Molecular Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy b Molecular Medicine & Neurogenetics, IRCCS Fondazione Stella Maris, Pisa, Italy c MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge, UK d Department of Sciences and Technologies, University Parthenope of Naples, Naples, Italy e Unit of Medical Genetics and Neurogenetics, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy f Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy g Child Neurology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy h Department of Molecular and Translational Medicine DIMET, University of Milan-Bicocca, Milan, Italy i Department of Neurosciences, IRCCS Bambino Gesù Children Hospital, Rome, Italy ARTICLE INFO ABSTRACT Keywords: Mitochondrial ribosomal protein large 24 (MRPL24) is 1 of the 82 protein components of mitochondrial ribo- Mitochondrial disorders somes, playing an essential role in the mitochondrial translation process. Movement disorder We report here on a baby girl with cerebellar atrophy, choreoathetosis of limbs and face, intellectual dis- MRPL24 ability and a combined defect of complexes I and IV in muscle biopsy, caused by a homozygous missense mu- Mitoribosomes tation identified in MRPL24. -
C6orf203 Controls OXPHOS Function Through Modulation of Mitochondrial Protein Biosynthesis
bioRxiv preprint doi: https://doi.org/10.1101/704403; this version posted July 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. C6orf203 controls OXPHOS function through modulation of mitochondrial protein biosynthesis number of characters excluding Materials and Methods: 40,651 Sara Palacios-Zambrano1,2, Luis Vázquez-Fonseca1,2, Cristina González-Páramos1,2, Laura Mamblona1,2, Laura Sánchez-Caballero3, Leo Nijtmans3, Rafael Garesse1,2 and Miguel Angel Fernández-Moreno1,2,* 1 Departamento de Bioquímica, Instituto de Investigaciones Biomédicas “Alberto Sols” UAM CSIC and Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER). Facultad de Medicina, Universidad Autónoma de Madrid. Madrid 28029, Spain. 2 Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid 28041, Spain. 3 Department of Pediatrics, Radboud Center for Mitochondrial Medicine, Radboud University Medical Center, Nijmegen, The Netherlands. * To whom correspondence should be addressed. Tel:+34 91 497 31 29; Email: [email protected] Running title “C6orf203 controls mt-proteins synthesis” bioRxiv preprint doi: https://doi.org/10.1101/704403; this version posted July 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. ABSTRACT Mitochondria are essential organelles present in the vast majority of eukaryotic cells. Their central function is to produce cellular energy through the OXPHOS system, and functional alterations provoke so-called mitochondrial OXPHOS diseases. It is estimated that several hundred mitochondrial proteins have unknown functions. Very recently, C6orf203 was described to participate in mitochondrial transcription under induced mitochondrial DNA depletion stress conditions. -
Fhl1p Protein, a Positive Transcription Factor in Pichia Pastoris, Enhances
Zheng et al. Microb Cell Fact (2019) 18:207 https://doi.org/10.1186/s12934-019-1256-0 Microbial Cell Factories RESEARCH Open Access Fhl1p protein, a positive transcription factor in Pichia pastoris, enhances the expression of recombinant proteins Xueyun Zheng1,2, Yimin Zhang1,2, Xinying Zhang1,2, Cheng Li1,2, Xiaoxiao Liu1,2, Ying Lin1,2* and Shuli Liang1,2* Abstract Background: The methylotrophic yeast Pichia pastoris is well-known for the production of a broad spectrum of functional types of heterologous proteins including enzymes, antigens, engineered antibody fragments, and next gen protein scafolds and many transcription factors are utilized to address the burden caused by the high expression of heterologous proteins. In this article, a novel P. pastoris transcription factor currently annotated as Fhl1p, an activator of ribosome biosynthesis processing, was investigated for promoting the expression of the recombinant proteins. Results: The function of Fhl1p of P. pastoris for improving the expression of recombinant proteins was verifed in strains expressing phytase, pectinase and mRFP, showing that the productivity was increased by 20–35%. RNA-Seq was used to study the Fhl1p regulation mechanism in detail, confrming Fhl1p involved in the regulation of rRNA pro- cessing genes, ribosomal small/large subunit biogenesis genes, Golgi vesicle transport genes, etc., which contributed to boosting the expression of foreign proteins. The overexpressed Fhl1p strain exhibited increases in the polysome and monosome levels, showing improved translation activities. Conclusion: This study illustrated that the transcription factor Fhl1p could efectively enhance recombinant protein expression in P. pastoris. Furthermore, we provided the evidence that overexpressed Fhl1p was related to more active translation state. -
Transcriptomic and Proteomic Landscape of Mitochondrial
TOOLS AND RESOURCES Transcriptomic and proteomic landscape of mitochondrial dysfunction reveals secondary coenzyme Q deficiency in mammals Inge Ku¨ hl1,2†*, Maria Miranda1†, Ilian Atanassov3, Irina Kuznetsova4,5, Yvonne Hinze3, Arnaud Mourier6, Aleksandra Filipovska4,5, Nils-Go¨ ran Larsson1,7* 1Department of Mitochondrial Biology, Max Planck Institute for Biology of Ageing, Cologne, Germany; 2Department of Cell Biology, Institute of Integrative Biology of the Cell (I2BC) UMR9198, CEA, CNRS, Univ. Paris-Sud, Universite´ Paris-Saclay, Gif- sur-Yvette, France; 3Proteomics Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany; 4Harry Perkins Institute of Medical Research, The University of Western Australia, Nedlands, Australia; 5School of Molecular Sciences, The University of Western Australia, Crawley, Australia; 6The Centre National de la Recherche Scientifique, Institut de Biochimie et Ge´ne´tique Cellulaires, Universite´ de Bordeaux, Bordeaux, France; 7Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden Abstract Dysfunction of the oxidative phosphorylation (OXPHOS) system is a major cause of human disease and the cellular consequences are highly complex. Here, we present comparative *For correspondence: analyses of mitochondrial proteomes, cellular transcriptomes and targeted metabolomics of five [email protected] knockout mouse strains deficient in essential factors required for mitochondrial DNA gene (IKu¨ ); expression, leading to OXPHOS dysfunction. Moreover, -
GIGSEA: Genotype Imputed Gene Set Enrichment Analysis
GIGSEA: Genotype Imputed Gene Set Enrichment Analysis Shijia Zhu Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA August 28, 2017 Contents Abstract . .1 1. Import packages . .1 2. Quick start . .2 3. One example of MetaXcan output . .2 4. Load data of gene sets . .4 4.1 Discrete-valued gene sets: . .4 4.2 Continuous-valued gene sets: . .6 4.3 User self-defined gene set . .7 5. Gene set enrichment analysis . .9 5.1 Gene set enrichment analysis using weighted simple linear regression . .9 5.2 Gene set enrichment analysis using weighted multiple regression model . 10 5.3 One-step weightedGSEA . 11 Abstract We presented the Genotype Imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, andmultiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to test the enrichment, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real biological signal.