DOCSLIB.ORG

Example 1: a Friend Sends You a Manuscript from a Student and Wants You to Comment on It

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Example 1: a Friend Sends You a Manuscript from a Student and Wants You to Comment on It Example 1: A friend sends you a manuscript from a student and wants you to comment on it. The paper discusses results from a Genome-wide association study (GWAS) for milk yields in three different cattle breeds, Swedish Red, Holstein Frisian and Gir/Gyr (number of animals: 200, 150 and 50, respectively). The paper states that all cattle were genotyped using the same marker-panel, a cattle SNP array with 60,000 SNPs. There is no further information on the markers/genotypes. The graph (a so-called Manhattan plot) shows three significant peaks, one is a single SNP (p- value 0.000001); at the second peak a total of 10 SNPs exceeded the significance threshold of which five were highly significant. The third peak included 20 significant as well as five highly significant SNPs. How could your friend test the reliability of the results? a) Which steps are required when analysing results from a SNP array? How can the reliability of SNPs be evaluated (name at least four and add detailed arguments of their relevance for at least two)? Quality control (QC): call rate, minor allele frequency, HWE, inheritance (if possible); double controls on the platform; CR might be problem due to calling in the platform algorithms Doing a validation study using data from another population Make sure the p-values were corrected for false positives (e.g. permutation) b) There were different populations and numbers of individuals included in the analysis. What could be solutions to check for the impact of the data structure on the results? There might be a biased; breeds differ; different numbers of observations The population structure should be checked and included in the analysis, alternatively could be analysis be performed for each population separately c) Do you think information on the linkage disequilibrium (LD) is required in the study? Which result suggests that this might be needed? Explain briefly what LD implements One peak included only a single SNP, this could indicate a problem with the SNP; also for the others multiple SNP were significant, it might be useful to plot the LD and check if those with the same p values are in complete LD exists usually between two close markers; two linked loci, association between variants; if marker in LD with disease locus → trait associated with marker; mutation is shared by affected individuals through common descent; markers in LD = haplotype; shared alleles at nearby loci representing haplotype of the ancestral chromosome on which the mutation first occurred; measured in R2 or D’ d) Make a final comment on how you would suggest the study to be performed, including information on which steps are relevant when doing a Genome-wide association study. Run the analysis for each population separately, compare with the combined results. 1. Plan the study: choose the appropriate design to target your hypothesis 2. Collect data (based on the design choose the data, e.g. number of markers) 3. Remove ‘problematic’ data from the dataset (prepare genotypes but also phenotypes) 4. Identify other pedigree related problems e.g. population stratification 5. Association analysis 6. Correction of results to minimize false positives Example 2: You have received the cow data during the exercise. Please run the GWAS using Plink. a) How many cows were included in the analysis? 100 males and 400 females: Open plink, go to the right directory, add plink --file cow --no-pheno --pheno cow.phe --noweb b) Where any individuals removed because of poor quality? How many and what is the reason? You could add criteria for correction, e.g. plink --file cow --no-pheno --pheno cow.phe –-noweb --geno 0.25 --maf 0.05 --mind 0.25 for example would give you: 0 animals were removed c) How many SNPs were included in the analysis and how many passed the quality filter? You can also use the information from b) 852 SNP failed the test for minor allele frequency, none the other tests d) How many SNP are significantly associated with the different phenotypes before and after adjustment? plink --file cow --no-pheno --pheno cow.phe –-noweb --geno 0.25 --maf 0.05 --mind 0.25 –all-pheno --assoc --qt-means -- mperm 1000 you can alternatively run the adjustment statement plink --file cow --no-pheno --pheno cow.phe –-noweb --geno 0.25 --maf 0.05 --mind 0.25 –all-pheno --assoc --qt-means – adjust transfer the results in Excel and check how many SNP exceeded a p-value of 0.05 e) Choose one trait and check if the 10 most significant markers are in LD e.g. for trait 1 (and I sued the non-adjusted data): SNP9309, SNP9295, SNP9308, SNP7145, SNP9497, SNP1683, SNP2946, SNP9321, SNP8151, SNP8599 plink --file cow --no-pheno --pheno cow.phe –-noweb --ld SNP9309 SNP9295 run this for each pair of SNP f) Let’s assume the trait number 3 is related to fat content in the milk. One significantly associated SNP1169 would be marker rs110075862. Please identify 5 candidate genes in the region of the marker and discuss the most interesting of the markers in relation to fat content in the milk. Check the position of the marker on Ensembl and then identify genes in close approximation. Examples of genes: DGAT1, HSF1, BOP1, SCRT1, TMEM249 Check for example publications on NCBI/ pubmed: ‘DGAT1, cattle, milk fat’, or just the function of the gene in genecards or OMIM DGAT1 is a strong candidate for fat content in the milk Example 3: You ran over many years a number of studies to identify genetic regions for an inherited disease (BLAD) and for milk yield in cattle. You have found that both traits have a genetic background, thus selection is possible. Now the cattle industry wants to apply your results in their breeding program. Which method(s) would you suggest as being useful for the selection of cattle for trait 1 (inherited disease) and for trait 2 (milk yield)? Describe briefly the methods and the information needed in order to use the information for selection in breeding populations. Methods suggested would be a marker-assisted selection or the use of a marker test to select breeding stock and avoid the inherited disease. BLAD (Bovine leukocyte adhesion deficiency) is a recessive disorder and a gene test is available. Depending on the test and the mutation, such a gene test can be used only within or across populations. It can be a direct or indirect (LD or linkage) test. For the other trait, milk yield, this is likely a complex trait. Having found that there is a genetic background, selection is possible. However, a marker test is unlikely for such a complex trait, suggested is therefore the use of genomic selection. For genomic selection we need a training (or reference) population and the selection individuals. Animals in the training need to have phenotypes and genotypes, while the selection candidates need to be genotyped. Genomic breeding values (GEBV) will be estimated for all selection candidates based on the marker effects derived from the training population. There are certain assumptions which need to be considered. Further information required are pedigree information, good genotype data (to cover the LD), reliable phenotypes… .
Load more

Recommended publications
  • Universidade Estadual De Campinas Instituto De Biologia
    UNIVERSIDADE ESTADUAL DE CAMPINAS INSTITUTO DE BIOLOGIA VERÔNICA APARECIDA MONTEIRO SAIA CEREDA O PROTEOMA DO CORPO CALOSO DA ESQUIZOFRENIA THE PROTEOME OF THE CORPUS CALLOSUM IN SCHIZOPHRENIA CAMPINAS 2016 1 VERÔNICA APARECIDA MONTEIRO SAIA CEREDA O PROTEOMA DO CORPO CALOSO DA ESQUIZOFRENIA THE PROTEOME OF THE CORPUS CALLOSUM IN SCHIZOPHRENIA Dissertação apresentada ao Instituto de Biologia da Universidade Estadual de Campinas como parte dos requisitos exigidos para a obtenção do Título de Mestra em Biologia Funcional e Molecular na área de concentração de Bioquímica. Dissertation presented to the Institute of Biology of the University of Campinas in partial fulfillment of the requirements for the degree of Master in Functional and Molecular Biology, in the area of Biochemistry. ESTE ARQUIVO DIGITAL CORRESPONDE À VERSÃO FINAL DA DISSERTAÇÃO DEFENDIDA PELA ALUNA VERÔNICA APARECIDA MONTEIRO SAIA CEREDA E ORIENTADA PELO DANIEL MARTINS-DE-SOUZA. Orientador: Daniel Martins-de-Souza CAMPINAS 2016 2 Agência(s) de fomento e nº(s) de processo(s): CNPq, 151787/2F2014-0 Ficha catalográfica Universidade Estadual de Campinas Biblioteca do Instituto de Biologia Mara Janaina de Oliveira - CRB 8/6972 Saia-Cereda, Verônica Aparecida Monteiro, 1988- Sa21p O proteoma do corpo caloso da esquizofrenia / Verônica Aparecida Monteiro Saia Cereda. – Campinas, SP : [s.n.], 2016. Orientador: Daniel Martins de Souza. Dissertação (mestrado) – Universidade Estadual de Campinas, Instituto de Biologia. 1. Esquizofrenia. 2. Espectrometria de massas. 3. Corpo caloso.
    [Show full text]
  • Distinguishing Pleiotropy from Linked QTL Between Milk Production Traits
    Cai et al. Genet Sel Evol (2020) 52:19 https://doi.org/10.1186/s12711-020-00538-6 Genetics Selection Evolution RESEARCH ARTICLE Open Access Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle Zexi Cai1*†, Magdalena Dusza2†, Bernt Guldbrandtsen1, Mogens Sandø Lund1 and Goutam Sahana1 Abstract Background: Production and health traits are central in cattle breeding. Advances in next-generation sequencing technologies and genotype imputation have increased the resolution of gene mapping based on genome-wide association studies (GWAS). Thus, numerous candidate genes that afect milk yield, milk composition, and mastitis resistance in dairy cattle are reported in the literature. Efect-bearing variants often afect multiple traits. Because the detection of overlapping quantitative trait loci (QTL) regions from single-trait GWAS is too inaccurate and subjective, multi-trait analysis is a better approach to detect pleiotropic efects of variants in candidate genes. However, large sample sizes are required to achieve sufcient power. Multi-trait meta-analysis is one approach to deal with this prob- lem. Thus, we performed two multi-trait meta-analyses, one for three milk production traits (milk yield, protein yield and fat yield), and one for milk yield and mastitis resistance. Results: For highly correlated traits, the power to detect pleiotropy was increased by multi-trait meta-analysis com- pared with the subjective assessment of overlapping of single-trait QTL confdence intervals. Pleiotropic efects of lead single nucleotide polymorphisms (SNPs) that were detected from the multi-trait meta-analysis were confrmed by bivariate association analysis. The previously reported pleiotropic efects of variants within the DGAT1 and MGST1 genes on three milk production traits, and pleiotropic efects of variants in GHR on milk yield and fat yield were con- frmed.
    [Show full text]
  • CSE642 Final Version
    Eindhoven University of Technology MASTER Dimensionality reduction of gene expression data Arts, S. Award date: 2018 Link to publication Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain Eindhoven University of Technology MASTER THESIS Dimensionality Reduction of Gene Expression Data Author: S. (Sako) Arts Daily Supervisor: dr. V. (Vlado) Menkovski Graduation Committee: dr. V. (Vlado) Menkovski dr. D.C. (Decebal) Mocanu dr. N. (Nikolay) Yakovets May 16, 2018 v1.0 Abstract The focus of this thesis is dimensionality reduction of gene expression data. I propose and test a framework that deploys linear prediction algorithms resulting in a reduced set of selected genes relevant to a specified case. Abstract In cancer research there is a large need to automate parts of the process of diagnosis, this is mainly to reduce cost, make it faster and more accurate.
    [Show full text]
  • Looking for Missing Proteins in the Proteome Of
    Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update Yves Vandenbrouck, Lydie Lane, Christine Carapito, Paula Duek, Karine Rondel, Christophe Bruley, Charlotte Macron, Anne Gonzalez de Peredo, Yohann Coute, Karima Chaoui, et al. To cite this version: Yves Vandenbrouck, Lydie Lane, Christine Carapito, Paula Duek, Karine Rondel, et al.. Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update. Journal of Proteome Research, American Chemical Society, 2016, 15 (11), pp.3998-4019. 10.1021/acs.jproteome.6b00400. hal-02191502 HAL Id: hal-02191502 https://hal.archives-ouvertes.fr/hal-02191502 Submitted on 19 Mar 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Journal of Proteome Research 1 2 3 Looking for missing proteins in the proteome of human spermatozoa: an 4 update 5 6 Yves Vandenbrouck1,2,3,#,§, Lydie Lane4,5,#, Christine Carapito6, Paula Duek5, Karine Rondel7, 7 Christophe Bruley1,2,3, Charlotte Macron6, Anne Gonzalez de Peredo8, Yohann Couté1,2,3, 8 Karima Chaoui8, Emmanuelle Com7, Alain Gateau5, AnneMarie Hesse1,2,3, Marlene 9 Marcellin8, Loren Méar7, Emmanuelle MoutonBarbosa8, Thibault Robin9, Odile Burlet- 10 Schiltz8, Sarah Cianferani6, Myriam Ferro1,2,3, Thomas Fréour10,11, Cecilia Lindskog12,Jérôme 11 1,2,3 7,§ 12 Garin , Charles Pineau .
    [Show full text]
  • Additional File 1
    Additional File 1 1. Supplementary Methods .......................................................................................................................................... 2 1.1 Cell homogeneity .............................................................................................................................................. 2 1.2 Statistical analysis ............................................................................................................................................. 2 2. Supplementary Tables............................................................................................................................................... 3 2.1 Supplementary Table 1: Sample Characteristics ............................................................................................... 3 2.2 Supplementary Table 2: Patients for WGBS ..................................................................................................... 3 2.3 Supplementary Table 3: Primer Sequences ...................................................................................................... 4 2.4 Supplementary Table 4: WGBS descriptive information .................................................................................. 6 2.5 Supplementary Table 5: DMRs in the PTSD cohort identified with camel ....................................................... 7 2.6 Supplementary Table 6: DMRs in the PTSD cohort identified with metilene ................................................... 8 2.7 Supplementary Table
    [Show full text]
  • Identification of Key Pathways and Genes in Dementia Via Integrated Bioinformatics Analysis
    bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. 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. Identification of Key Pathways and Genes in Dementia via Integrated Bioinformatics Analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. 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 To provide a better understanding of dementia at the molecular level, this study aimed to identify the genes and key pathways associated with dementia by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing dataset GSE153960 derived from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between patients with dementia and healthy controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed, analyzed and visualized, with which the hub genes miRNAs and TFs nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic curve (ROC) analysis.
    [Show full text]
  • Overexpressed HSF1 Cancer Signature Genes Cluster in Human Chromosome 8Q Christopher Q
    Zhang et al. Human Genomics (2017) 11:35 DOI 10.1186/s40246-017-0131-5 PRIMARY RESEARCH Open Access Overexpressed HSF1 cancer signature genes cluster in human chromosome 8q Christopher Q. Zhang1,4, Heinric Williams3,4, Thomas L. Prince3,4† and Eric S. Ho1,2*† Abstract Background: HSF1 (heat shock factor 1) is a transcription factor that is found to facilitate malignant cancer development and proliferation. In cancer cells, HSF1 mediates a set of genes distinct from heat shock that contributes to malignancy. This set of genes is known as the HSF1 Cancer Signature genes or simply HSF1-CanSig genes. HSF1-CanSig genes function and operate differently than typical cancer-causing genes, yet it is involved in fundamental oncogenic processes. Results: By utilizing expression data from 9241 cancer patients, we identified that human chromosome 8q21-24 is a location hotspot for the most frequently overexpressed HSF1-CanSig genes. Intriguingly, the strength of the HSF1 cancer program correlates with the number of overexpressed HSF1-CanSig genes in 8q, illuminating the essential role of HSF1 in mediating gene expression in different cancers. Chromosome 8q21-24 is found under selective pressure in preserving gene order as it exhibits strong synteny among human, mouse, rat, and bovine, although the biological significance remains unknown. Statistical modeling, hierarchical clustering, and gene ontology-based pathway analyses indicate crosstalk between HSF1-mediated responses and pre-mRNA 3′ processing in cancers. Conclusions: Our results confirm the unique role of chromosome 8q mediated by the master regulator HSF1 in cancer cases. Additionally, this study highlights the connection between cellular processes triggered by HSF1 and pre-mRNA 3′ processing in cancers.
    [Show full text]
  • Open Dissertation Mollyhall.Pdf
    The Pennsylvania State University The Graduate School Eberly College of Science BEYOND GENOME-WIDE ASSOCIATION STUDIES (GWAS): EMERGING METHODS FOR INVESTIGATING COMPLEX ASSOCIATIONS FOR COMMON TRAITS A Dissertation in Biochemistry, Microbiology, and Molecular Biology by Molly A. Hall © 2015 Molly A. Hall Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy December 2015 The dissertation of Molly A. Hall was reviewed and approved* by the following: Marylyn D. Ritchie Paul Berg Professor of Biochemistry and Molecular Biology Dissertation Adviser Chair of Committee Scott B. Selleck Professor of Biochemistry and Molecular Biology Head of the Department of Biochemistry and Molecular Biology Ross Hardison T. Ming Chu Professor of Biochemistry and Molecular Biology Santhosh Girirajan Assistant Professor of Biochemistry and Molecular Biology Assistant Professor of Anthropology George H. Perry Assistant Professor of Anthropology and Biology Catherine A. McCarty Principal Research Scientist, Essentia Institute of Rural Health Special Member *Signatures are on file in the Graduate School. ii ABSTRACT Genome-wide association studies (GWAS) have identified numerous loci associated with human phenotypes. This approach, however, does not consider the richly diverse and complex environment with which humans interact throughout the life course, nor does it allow for interrelationships among genetic loci and across traits. Methods that embrace pleiotropy (the effect of one locus on more than one trait), gene-environment (GxE) and gene-gene (GxG) interactions will further unveil the impact of alterations in biological pathways and identify genes that are only involved with disease in the context of the environment. This valuable information can be used to assess personal risk and choose the most appropriate medical interventions based on an individual’s genotype and environment.
    [Show full text]
  • Transcriptomic Changes Resulting from STK32B Overexpression Identifies Pathways Potentially Relevant to Essential Tremor
    bioRxiv preprint doi: https://doi.org/10.1101/552901; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Transcriptomic changes resulting from 2 STK$%B overexpression identifies pathways 3 potentially relevant to essential tremor 4 Calwing Liao.,0, Faezeh Sarayloo.,0, Veikko Vuokila0, Daniel Rochefort0, Fulya Akçimen.,0, 5 Simone Diamond0, Alexandre D. Laporte0, Dan Spiegelman0, Qin HeJ, Hélène Catoire0, Patrick A. 6 Dion0,O, Guy A. Rouleau.,0,O 7 8 .Department of Human GeneticS, McGill University, Montréal, Quebec, Canada 9 0Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada. 10 JDepartment of Biomedical ScienceS, Université de Montréal, Montréal, Quebec, Canada 11 ODepartment of Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada 12 13 Corresponding Author: 14 Dr. Guy A. Rouleau, MD, PhD, FRCPC, OQ 15 Department of Neurology and Neurosurgery 16 McGill University 17 Montréal, Québec, Canada 18 HJA 0BO 19 E-mail: [email protected] 20 21 Keywords: STK$%B, eSSential tremor, transcriptome, FUS 22 Conflict of Interests: All authors report no conflict of intereStS. 23 Funding Sources: Canadian InstituteS of Health ReSearch 24 bioRxiv preprint doi: https://doi.org/10.1101/552901; this version posted May 10, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
    [Show full text]
  • Genetic Studies of Elite Athlete Status
    Wang, Guan (2013) Genetic studies of elite athlete status. PhD thesis. http://theses.gla.ac.uk/4594/ Copyright and moral rights for this work are retained by the author A copy can be downloaded for personal non-commercial research or study, without prior permission or charge This work cannot be reproduced or quoted extensively from without first obtaining permission in writing from the author The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given Enlighten: Theses https://theses.gla.ac.uk/ [email protected] Genetic Studies of Elite Athlete Status by Guan Wang, M.Res. A Doctoral Thesis Submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy August 2013 Institute of Cardiovascular and Medical Sciences College of Medical, Veterinary, and Life Sciences University of Glasgow © G. Wang 2013 2 Author’s Declaration I declare that this thesis is the result of my own work, except that subject recruitment in each country was orchestrated by the relevant PIs as follows: Dr. Yannis Pitsiladis (Jamaican sprint cohort), Dr. Krista Austin (USA sprint cohort), Dr. Rob Lee (Caucasian swim cohort), Dr. Noriyuki Fuku (Japanese swim and track-and-field athlete cohorts), and Dr. Sandy Hsieh (Taiwanese swim cohort); genotyping of Japanese and Taiwanese swim cohort was conducted by Dr. Eri Mikami and Dr. Li-Ling Chiu; genome-wide genotyping of Jamaican and USA sprint cohorts as well as Japanese track-and-field athlete cohort was overseen by Dr.
    [Show full text]
  • Establishment and Characterization of New Tumor Xenografts and Cancer Cell Lines from EBV-Positive Nasopharyngeal Carcinoma
    UC Davis UC Davis Previously Published Works Title Establishment and characterization of new tumor xenografts and cancer cell lines from EBV-positive nasopharyngeal carcinoma. Permalink https://escholarship.org/uc/item/79j9621x Journal Nature communications, 9(1) ISSN 2041-1723 Authors Lin, Weitao Yip, Yim Ling Jia, Lin et al. Publication Date 2018-11-07 DOI 10.1038/s41467-018-06889-5 Peer reviewed eScholarship.org Powered by the California Digital Library University of California ARTICLE DOI: 10.1038/s41467-018-06889-5 OPEN Establishment and characterization of new tumor xenografts and cancer cell lines from EBV-positive nasopharyngeal carcinoma Weitao Lin 1, Yim Ling Yip 1, Lin Jia 1, Wen Deng2, Hong Zheng 3,4, Wei Dai3, Josephine Mun Yee Ko 3, Kwok Wai Lo 5, Grace Tin Yun Chung5, Kevin Y. Yip 6, Sau-Dan Lee6, Johnny Sheung-Him Kwan5, Jun Zhang1, Tengfei Liu1, Jimmy Yu-Wai Chan7, Dora Lai-Wan Kwong3, Victor Ho-Fun Lee3, John Malcolm Nicholls8, Pierre Busson 9, Xuefeng Liu 10,11,12, Alan Kwok Shing Chiang13, Kwai Fung Hui13, Hin Kwok14, Siu Tim Cheung 15, Yuk Chun Cheung1, Chi Keung Chan1, Bin Li1,16, 1234567890():,; Annie Lai-Man Cheung1, Pok Man Hau5, Yuan Zhou5, Chi Man Tsang1,5, Jaap Middeldorp 17, Honglin Chen 18, Maria Li Lung3 & Sai Wah Tsao1 The lack of representative nasopharyngeal carcinoma (NPC) models has seriously hampered research on EBV carcinogenesis and preclinical studies in NPC. Here we report the successful growth of five NPC patient-derived xenografts (PDXs) from fifty-eight attempts of trans- plantation of NPC specimens into NOD/SCID mice.
    [Show full text]
  • Supplementary Information
    1 Supplementary Information 2 “Large-scale genomic analysis identifies 12 loci harbouring genes for human reproductive 3 behaviour and infertility” 4 Table of Contents 5 TABLE OF CONTENTS ................................................................................................................................... 1 6 1. HUMAN REPRODUCTIVE BEHAVIOUR MOTIVATION AND PHENOTYPE DEFINITION .... 3 7 1.1 PHENOTYPE MOTIVATION ........................................................................................................................... 3 8 1.2 EVOLUTIONARY CAUSES OF GENETIC VARIANCE IN FERTILITY .................................................................. 5 9 1.3 ADDITIVE GENETIC VARIATION IN FERTILITY ............................................................................................. 6 10 1.4 DOMINANT GENETIC VARIATION IN FERTILITY .......................................................................................... 7 11 1.5 ENVIRONMENTAL VARIATION IN FERTILITY ............................................................................................... 8 12 1.6 PHENOTYPE DEFINITION .............................................................................................................................. 9 13 1.7 INSTRUCTIONS FOR CONTRIBUTING COHORTS ........................................................................................ 10 14 2. PRIMARY GWAS OF HUMAN FERTILITY ......................................................................................... 11 15 2.1 OVERVIEW OF HUMAN FERTILITY
    [Show full text]