Literature DB >> 28056765

Comparison of DNA quantification methodology used in the DNA extraction protocol for the UK Biobank cohort.

Samantha Welsh1, Tim Peakman2, Simon Sheard2, Rachael Almond2.   

Abstract

BACKGROUND: UK Biobank is a large prospective cohort study in the UK established by the Medical Research Council (MRC) and the Wellcome Trust to enable approved researchers to investigate the role of genetic factors, environmental exposures and lifestyle in the causes of major diseases of late and middle age. A wide range of phenotypic data has been collected at recruitment and has recently been enhanced by the UK Biobank Genotyping Project. All UK Biobank participants (500,000) have been genotyped on either the UK Biobank Axiom® Array or the Affymetrix UK BiLEVE Axiom® Array and the workflow for preparing samples for genotyping is described. The genetic data is hoped to provide further insight into the genetics of disease. All data, including the genetic data, is available for access to approved researchers. Data for two methods of DNA quantification (ultraviolet-visible spectroscopy [UV/Vis]) measured on the Trinean DropSense™ 96 and PicoGreen®) were compared by two laboratories (UK Biobank and Affymetrix).
RESULTS: The sample processing workflow established at UK Biobank, for genotyping on the custom Affymetrix Axiom® array, resulted in high quality DNA (average DNA concentration 38.13 ng/μL, average 260/280 absorbance 1.91). The DNA generated high quality genotype data (average call rate 99.48% and pass rate 99.45%). The DNA concentration measured on the Trinean DropSense™ 96 at UK Biobank correlated well with DNA concentration measured by PicoGreen® at Affymetrix (r = 0.85).
CONCLUSIONS: The UK Biobank Genotyping Project demonstrated that the high throughput DNA extraction protocol described generates high quality DNA suitable for genotyping on the Affymetrix Axiom array. The correlation between DNA concentration derived from UV/Vis and PicoGreen® quantification methods suggests, in large-scale genetic studies involving two laboratories, it may be possible to remove the DNA quantification step in one laboratory without affecting downstream analyses. This would result in reductions in cost and time to complete the project, allowing generation of genetic data faster and cheaper.

Entities:  

Keywords:  Affymetrix; DNA concentration; Genotyping; PicoGreen; Quantification; Trinean; UK Biobank; UV/Vis

Mesh:

Substances:

Year:  2017        PMID: 28056765      PMCID: PMC5217214          DOI: 10.1186/s12864-016-3391-x

Source DB:  PubMed          Journal:  BMC Genomics        ISSN: 1471-2164            Impact factor:   3.969


Background

UK Biobank is a resource containing samples and a wide range of data on 500,000 UK participants. The resource is available for approved researchers to apply to use for the purposes of ‘improving health of future generations’ [1-5]. An enhancement project was initiated in 2013 which aimed to genotype the entire UK Biobank cohort using a high density array and subsequent imputation. The majority of participants (~440,000) were genotyped on the UK Biobank Axiom® Array [6], with 50,000 participants genotyped on the Affymetrix UK BiLEVE Axiom® Array [7], which has > 95% content overlap with the UK Biobank Axiom® Array. A bespoke sample processing workflow was designed to ensure the ~500,000 samples could be processed to a high quality within 18 months. The pipeline involved three main stages (and three collaborating entities); preparation of the DNA was performed at UK Biobank, genotyping at Affymetrix and quality control (QC) of the data at the Wellcome Trust Centre for Human Genetics (WTCHG). The Clinical Trial Service Unit (CTSU) at the University of Oxford is responsible for the storage and distribution of the genotype data (and all other UK Biobank data) to approved researchers. It is well reported that the concentration obtained from DNA quantification can differ significantly dependent upon the DNA quantification method used, DNA extraction methodology and laboratory [8-11]. Since DNA was extracted at UK Biobank and genotyped at Affymetrix (following normalisation), both laboratories included a method to quantify the DNA for QC purposes. This is typical of assays where the DNA sample is not extracted in-house. UK Biobank used the Trinean DropSense™ 96 droplet reader in combination with DropPlate-S (a UV/Vis-based plate method) and Affymetrix used PicoGreen® (a fluorescent dye that intercalates with DNA). This project provided a unique opportunity to compare two commonly used DNA quantification methods across a large sample number. A comparison of these two DNA quantification methods across two laboratories is presented.

Methods

Methods below describe the DNA extraction/genotyping workflow for the ~440,000 UK Biobank samples run on the UK Biobank Axiom® Array. Further detail on the 50,000 UK Biobank samples run on the UK BiLEVE Axiom® Array is available elsewhere [7].

UK Biobank

Selection of source sample for DNA extraction

UK Biobank participants provide a wide range of biological samples [4, 5] that are aliquotted into 850 μL, 2D bar-coded micro tubes prior to storage. The buffy coat aliquot, derived from 10 ml of whole blood collected into an ethylenediaminetetraacetic acid (EDTA) vacutainer, was selected for DNA extraction as it was available for the majority of participants and was expected to yield the required 10 ng/μL concentration required for genotyping. Saliva was considered but due to the lower number of participants providing this sample type to the UK Biobank study, this was not used. During retrieval of buffy coat samples for DNA extraction it was important to maximise the picking speed whilst avoiding clustering of participants by time or date of collection, collection centre, geography (UK Biobank recruited from 22 assessment centres across the UK), or any participant phenotype (typically only 2 assessment centres were represented on each stored plate). An algorithm was developed to pick the samples in a way that increased the number of assessment centres per plate to reduce potential systematic bias from the way the samples were originally collected and processed that could affect downstream genotyping.

DNA extraction

A custom DNA extraction system was developed by the Tecan Integration Group (TIG) [12] to enable DNA extraction and quantification in a single process (Fig. 1). DNA was extracted from 850 μL of buffy coat using a cartridge-based, magnetic bead extraction methodology (Maxwell® 16 Blood DNA Purification Kit, Promega, AS1010X and Maxwell® 16 Research Instrument [Promega, AS200-HS]).
Fig. 1

UK Biobank DNA Extraction System. This system comprises 1) Ziath® DataPaq High Speed Single Rack Scanner ZTS-A6 for sample tracking, 2) two modified hemi-skinned Promega Maxwell® 16 instruments for DNA extraction, 3) Trinean DropSense™ 96 for DNA quantification, 4) Brooks Tube Auditor™ for volume measurement and 5) Liconic STX44 Automated Incubator to keep DNA at 4 °C after extraction

UK Biobank DNA Extraction System. This system comprises 1) Ziath® DataPaq High Speed Single Rack Scanner ZTS-A6 for sample tracking, 2) two modified hemi-skinned Promega Maxwell® 16 instruments for DNA extraction, 3) Trinean DropSense™ 96 for DNA quantification, 4) Brooks Tube Auditor™ for volume measurement and 5) Liconic STX44 Automated Incubator to keep DNA at 4 °C after extraction DNA extraction occurs within the pre-filled cartridge (supplied in Promega, AS1010X) which is split into wells containing lysis buffer, MagneSil™ Paramagnetic Particles (PMPs) and wash buffers. Briefly, DNA is moved between wells by the PMPs and the application of a magnetic force to disposable plungers. Cells are lysed by a guanidine-based lysis buffer alongside mechanical lysis from the disposable plungers. DNA adsorbed by silica coated magnetic beads is moved through a series of wash wells by a plunger. Salts from lysis and other impurities that may inhibit downstream processing (e.g. haem, proteins etc.) are removed during the washing. Once purified, DNA is eluted from the beads in Tris-EDTA-based lysis buffer, is assisted by heating at 56 °C. To maximise the DNA yield and purity from the source material (standard protocol extracts from 250 μL buffy coat), the lysis buffer (Promega, A826E) was increased by 600 μL, the wash buffer (Promega, MD1412) by 1 mL in two wash wells and the cycle through the cartridge was repeated a further two times. Following extraction, the DNA was aliquotted across three tubes; one for primary storage at UK Biobank (425 μL), one for back-up storage at UK Biobank back-up centre (425 μL) and one for genotyping (50 μL). The primary aliquot was quantified on the Trinean DropSense™ 96. The quality metrics for a 96-well plate to automatically proceed to genotyping on the Axiom® array was 80% of the plate must have a DNA concentration > 10 ng/μL. Prior to shipping a plate for genotyping, the measured DNA concentration and quality (by absorbance at 260/280) of the stock DNA (sibling to the DNA shipped for genotyping) were assessed from the concentration obtained from the Trinean DropSense™ 96. Results from QC checks were entered into the UK Biobank Laboratory Information Management System (LIMS) and are a data field that can be requested by researchers.

Shipment

Following DNA extraction and quantification, DNA was stored at −80 °C. Plates were sent for genotyping to the Affymetrix Research Services Lab, Santa Clara, CA, USA (ARSL) approximately weekly. Plates were shipped on dry ice and accompanied by an electronic sample manifest containing an anonymised participant identifier plus the gender, ethnicity and geographical location of the participant to which the sample pertains (latter for QC purposes).

Pre-genotyping at affymetrix

Genotyping was performed at the ARSL as per the Manufacturer’s Instructions [13] using the UK Biobank Axiom® Array or UK BiLEVE Axiom® Array. In summary, samples were thawed and homogenised (incubated at 37 °C for 2 h) prior to PicoGreen® quantification to establish the volume of DNA required for normalisation to a concentration of 10 ng/μL. Plates where < 80% of samples had a concentration of > 10 ng/μL required authorisation before proceeding to genotyping. After normalisation, two controls were added to the plate and samples entered the Axiom® assay workflow [14]. Samples failing initial QC (<95% of markers measured could be confidently genotyped [call rate]) were re-processed. If samples failed re-processing a second sample from the participant was extracted, where available. Data analysis was performed as per Manufacturer’s Guidelines [15]. Any deviations from standard protocol are documented in [14].

Data QC at WTCHG

QC procedures applied to the UK Biobank genotyped data are described in [16]. Upon completion of QC, data is passed to UK Biobank for release to approved researchers.

Results and discussion

The sample processing workflow described aimed to generate high quality genotypes from as many UK Biobank participants as possible. At UK Biobank, it was important to design a workflow that generated high quality DNA for genotyping on the Axiom® Array (and use in future downstream applications), whilst maintaining a high throughput so data were available within a short timeframe. Results are presented for 484,325 samples. Approximately 1% of samples (5016 samples) were excluded from the analysis because DNA quantification data was not available from both Trinean and PicoGreen quantification.

Success of picking algorithm

The picking algorithm was designed to prevent clustering of phenotypes (specifically participant assessment centre). The success of the algorithm was assessed by selecting 14 plates picked consecutively at the start, middle and end of the project and counting the number of assessment centres represented on each plate. Of the 42 plates checked, each plate contained samples from participants from at least five assessment centres (Fig. 2). This demonstrates the picking algorithm was successful in avoiding clustering of samples by assessment centre and reducing sample bias on the genotyping plate.
Fig. 2

Assessment centres per picked plate. Number of assessments centres per picked plate from three sets of 14 plates from time-points during picking (start, middle and end)

Assessment centres per picked plate. Number of assessments centres per picked plate from three sets of 14 plates from time-points during picking (start, middle and end)

Success of DNA extraction—DNA quantity and quality assessment at UK Biobank

The novel methodology to extract DNA from 850 μL of buffy coat was assessed via the DNA concentration and 260/280, using the Trinean DropSense™ 96 (a UV/Vis, plate-based DNA quantification system). The average DNA concentration was 38.13 ng/μL (0.02 to 634.99 ng/μL) and average 260/280 was 1.91 (distribution shown in Fig. 3). The results from quantification at UK Biobank indicated the DNA extraction protocol generated good quality DNA. From DNA quantification performed at UK Biobank, 1.31% of samples fell outside the required 10 ng/μL threshold for automatic pass into the genotyping process.
Fig. 3

DNA quality assessed by 260/280. Spread of 260/280 values across 481,772 DNA samples (results from 2553 samples outside 1.5–2.5 not displayed)

DNA quality assessed by 260/280. Spread of 260/280 values across 481,772 DNA samples (results from 2553 samples outside 1.5–2.5 not displayed)

DNA quantification—comparison of methods

This project provided a unique opportunity to compare two methods of DNA quantification across a large number of samples. DNA concentration measured via the UV/Vis on the Trinean DropSense™ 96 during DNA extraction at UK Biobank were exported alongside the PicoGreen® derived concentrations included in the summary metrics from Affymetrix for each sample from the UK Biobank LIMS. Out of the 484,325 samples where data were available from both quantification methods, the average DNA concentration measured using the Trinean DropSense™ 96 was 38.13 ng/μL (0.02 to 634.99 ng/μl) and via PicoGreen® was 37.13 ng/μL (0.01 to 730.52 ng/μL; Fig. 4).
Fig. 4

DNA concentration measured by UV/Vis (Trinean) and PicoGreen®. Spread of DNA concentration in 484,325 samples quantified using Trinean DropSense™ 96 and PicoGreen®

DNA concentration measured by UV/Vis (Trinean) and PicoGreen®. Spread of DNA concentration in 484,325 samples quantified using Trinean DropSense™ 96 and PicoGreen® Correlation of the two methods was assessed between the DNA concentration obtained from Trinean DropSense™ 96 and PicoGreen® quantification methods from 482,638 samples (r = 0.85; Fig. 5; Additional file 1). Some samples were excluded from the analysis (1687 samples; 0.3%) as there was > 60 ng/μL difference between the DNA concentration measured by UV/Vis and PicoGreen quantification methods. This difference in a small number of samples can be explained by the lack of homogeneity in some of the DNA samples. The DNA samples were quantified by UV/Vis immediately after extraction; it is expected that the DNA is largely homogenous at this stage as it has recently been eluted using a combination of mechanical mixing with a plunger and heat (with the exception of highly viscous samples). Conversely, PicoGreen® quantification was performed after storage at −80 °C, shipping on dry ice and homogenisation by heating. It is possible that a small number of viscous samples were heterogeneous during one or both of the DNA quantifications resulting in large differences in the measured DNA concentrations [17].
Fig. 5

Correlation between UV/Vis (Trinean) and PicoGreen® methods of DNA quantification. Correlation between DNA concentration measured via Trinean DropSense™ 96 and PicoGreen® quantification methods in 482,638 samples (r = 0.85)

Correlation between UV/Vis (Trinean) and PicoGreen® methods of DNA quantification. Correlation between DNA concentration measured via Trinean DropSense™ 96 and PicoGreen® quantification methods in 482,638 samples (r = 0.85) Additionally, there were a small number of samples where one method quantified the DNA near 0 ng/μL and the other method quantified DNA > 10 ng/μL (87 samples were quantified as < 2 ng/μL via UV/Vis and > 10 ng/μL via PicoGreen® and 233 samples were quantified as < 2 ng/μL via PicoGreen® and > 10 ng/μL via UV/Vis [Additional file 2]). The most likely explanation for the difference would be failure to transfer DNA to the quantification plate in one of the quantification methods during automation. The correlation demonstrates that whilst others have observed differences between DNA concentration methodologies [10, 11], the preparation of DNA as described in this paper yields similar DNA concentrations whether measured using a UV/Vis method (on the Trinean DropSense™ 96) or via PicoGreen®. The findings from this comparison may assist other laboratories when considering the sample workflow and whether repeat DNA quantifications of the same sample are required.

Success of genotyping on axiom® array

Results from DNA concentration checks at UK Biobank indicated that the majority of samples would meet the requirements for genotyping of DNA concentration > 10 ng/μL and 260/280 > 1.8 (98.7% of samples > 10 ng/μL, 84.3% 260/280 > 1.8). Results from genotyping (after QC by Affymetrix, prior to WTCHG QC) for 484,325 samples (average call rate 99.48% and pass rate 99.45%) demonstrate the DNA generated from the workflow described is of high quality and is suitable for downstream genetic analysis; Axiom genotyping in this case. The average DNA concentration of the 2675 failed samples was 36.54 ng/μL (UV/Vis) and 37.15 ng/μL (PicoGreen®) compared with 38.14 ng/μL and 34.71 ng/μL (UV/Vis and PicoGreen®, respectively) for samples that passed. The average 260/280 of failed samples was 1.93 and 1.91 for samples that passed. DNA concentration metrics from pass and fail samples do not demonstrate that DNA quantity or quality was not responsible for the small percentage of failures at the genotyping step.

Conclusions

The UK Biobank Genotyping project was established to generate genotype data from as many of the 500,000 participants as possible. Data from 150,000 participants is already available to approved UK Biobank researchers and will be followed in 2016 by the data from the remaining participants upon completion of QC and imputation by WTCHG. The sample processing workflow described has generated high quality DNA for the UK Biobank Genotyping Project and for use in future UK Biobank projects. The comparison between DNA quantification has demonstrated Trinean DropSense™ 96 quantification performed at UK Biobank and PicoGreen® performed at Affymetrix generates similar results (r = 0.85) which may be improved further by thorough optimisation of the DNA homogenisation protocol following thawing. The correlation between DNA concentration derived from UV/Vis and PicoGreen® quantification methods in this workflow suggests, in large-scale genetic studies involving two or more laboratories, it may be possible to remove repeat DNA quantification steps without affecting downstream analyses. It is recommended that during the early phases of a project, the correlation between quantification methods employed in each laboratory is assessed with the aim of removing one of the quantification measures if the correlation is good. Removing a quantification step in the workflow would lead to increased throughput, a decrease in consumable costs and a reduction in staff required which would ultimately allow for generation of genetic data faster and cheaper.
  9 in total

Review 1.  Quantification of DNA in forensic samples.

Authors:  Janice A Nicklas; Eric Buel
Journal:  Anal Bioanal Chem       Date:  2003-05-09       Impact factor: 4.142

2.  UK Biobank: from concept to reality.

Authors:  William Ollier; Tim Sprosen; Tim Peakman
Journal:  Pharmacogenomics       Date:  2005-09       Impact factor: 2.533

3.  The UK Biobank sample handling and storage validation studies.

Authors:  Tim C Peakman; Paul Elliott
Journal:  Int J Epidemiol       Date:  2008-04       Impact factor: 7.196

4.  The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine.

Authors:  Paul Elliott; Tim C Peakman
Journal:  Int J Epidemiol       Date:  2008-04       Impact factor: 7.196

5.  Comparison of five DNA quantification methods.

Authors:  Karsten Nielsen; Helle Smidt Mogensen; Johannes Hedman; Harald Niederstätter; Walther Parson; Niels Morling
Journal:  Forensic Sci Int Genet       Date:  2008-04-15       Impact factor: 4.882

6.  UK biobank data: come and get it.

Authors:  Naomi E Allen; Cathie Sudlow; Tim Peakman; Rory Collins
Journal:  Sci Transl Med       Date:  2014-02-19       Impact factor: 17.956

7.  UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.

Authors:  Cathie Sudlow; John Gallacher; Naomi Allen; Valerie Beral; Paul Burton; John Danesh; Paul Downey; Paul Elliott; Jane Green; Martin Landray; Bette Liu; Paul Matthews; Giok Ong; Jill Pell; Alan Silman; Alan Young; Tim Sprosen; Tim Peakman; Rory Collins
Journal:  PLoS Med       Date:  2015-03-31       Impact factor: 11.069

8.  Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank.

Authors:  Louise V Wain; Nick Shrine; Suzanne Miller; Victoria E Jackson; Ioanna Ntalla; María Soler Artigas; Charlotte K Billington; Abdul Kader Kheirallah; Richard Allen; James P Cook; Kelly Probert; Ma'en Obeidat; Yohan Bossé; Ke Hao; Dirkje S Postma; Peter D Paré; Adaikalavan Ramasamy; Reedik Mägi; Evelin Mihailov; Eva Reinmaa; Erik Melén; Jared O'Connell; Eleni Frangou; Olivier Delaneau; Colin Freeman; Desislava Petkova; Mark McCarthy; Ian Sayers; Panos Deloukas; Richard Hubbard; Ian Pavord; Anna L Hansell; Neil C Thomson; Eleftheria Zeggini; Andrew P Morris; Jonathan Marchini; David P Strachan; Martin D Tobin; Ian P Hall
Journal:  Lancet Respir Med       Date:  2015-09-27       Impact factor: 30.700

9.  Performance of high-throughput DNA quantification methods.

Authors:  Kashif A Haque; Ruth M Pfeiffer; Michael B Beerman; Jeff P Struewing; Stephen J Chanock; Andrew W Bergen
Journal:  BMC Biotechnol       Date:  2003-10-28       Impact factor: 2.563

  9 in total
  30 in total

1.  Assessing thyroid cancer risk using polygenic risk scores.

Authors:  Sandya Liyanarachchi; Julius Gudmundsson; Egil Ferkingstad; Huiling He; Jon G Jonasson; Vinicius Tragante; Folkert W Asselbergs; Li Xu; Lambertus A Kiemeney; Romana T Netea-Maier; Jose I Mayordomo; Theo S Plantinga; Hannes Hjartarson; Jon Hrafnkelsson; Erich M Sturgis; Pamela Brock; Fadi Nabhan; Gudmar Thorleifsson; Matthew D Ringel; Kari Stefansson; Albert de la Chapelle
Journal:  Proc Natl Acad Sci U S A       Date:  2020-03-04       Impact factor: 11.205

2.  Distinction between the effects of parental and fetal genomes on fetal growth.

Authors:  Thorhildur Juliusdottir; Valgerdur Steinthorsdottir; Lilja Stefansdottir; Gardar Sveinbjornsson; Erna V Ivarsdottir; Rosa B Thorolfsdottir; Jon K Sigurdsson; Vinicius Tragante; Kristjan E Hjorleifsson; Anna Helgadottir; Michael L Frigge; Gudmundur Thorgeirsson; Rafn Benediktsson; Emil L Sigurdsson; David O Arnar; Thora Steingrimsdottir; Ingileif Jonsdottir; Hilma Holm; Daniel F Gudbjartsson; Gudmar Thorleifsson; Unnur Thorsteinsdottir; Kari Stefansson
Journal:  Nat Genet       Date:  2021-07-19       Impact factor: 38.330

3.  FLT3 stop mutation increases FLT3 ligand level and risk of autoimmune thyroid disease.

Authors:  Saedis Saevarsdottir; Thorunn A Olafsdottir; Erna V Ivarsdottir; Gisli H Halldorsson; Kristbjorg Gunnarsdottir; Asgeir Sigurdsson; Ari Johannesson; Jon K Sigurdsson; Thorhildur Juliusdottir; Sigrun H Lund; Asgeir O Arnthorsson; Edda L Styrmisdottir; Julius Gudmundsson; Gerdur M Grondal; Kristjan Steinsson; Lars Alfredsson; Johan Askling; Rafn Benediktsson; Ragnar Bjarnason; Arni J Geirsson; Bjorn Gudbjornsson; Hallgrimur Gudjonsson; Haukur Hjaltason; Astradur B Hreidarsson; Lars Klareskog; Ingrid Kockum; Helga Kristjansdottir; Thorvardur J Love; Bjorn R Ludviksson; Tomas Olsson; Pall T Onundarson; Kjartan B Orvar; Leonid Padyukov; Bardur Sigurgeirsson; Vinicius Tragante; Kristbjorg Bjarnadottir; Thorunn Rafnar; Gisli Masson; Patrick Sulem; Daniel F Gudbjartsson; Pall Melsted; Gudmar Thorleifsson; Gudmundur L Norddahl; Unnur Thorsteinsdottir; Ingileif Jonsdottir; Kari Stefansson
Journal:  Nature       Date:  2020-06-24       Impact factor: 49.962

4.  Effects of genetic ancestry and socioeconomic deprivation on ethnic differences in serum creatinine.

Authors:  Leonardo Mariño-Ramírez; Shivam Sharma; Lavanya Rishishwar; Andrew B Conley; Shashwat Deepali Nagar; I King Jordan
Journal:  Gene       Date:  2022-06-27       Impact factor: 3.913

5.  Genetic insight into sick sinus syndrome.

Authors:  Rosa B Thorolfsdottir; Gardar Sveinbjornsson; Hildur M Aegisdottir; Stefania Benonisdottir; Lilja Stefansdottir; Erna V Ivarsdottir; Gisli H Halldorsson; Jon K Sigurdsson; Christian Torp-Pedersen; Peter E Weeke; Søren Brunak; David Westergaard; Ole B Pedersen; Erik Sorensen; Kaspar R Nielsen; Kristoffer S Burgdorf; Karina Banasik; Ben Brumpton; Wei Zhou; Asmundur Oddsson; Vinicius Tragante; Kristjan E Hjorleifsson; Olafur B Davidsson; Sridharan Rajamani; Stefan Jonsson; Bjarni Torfason; Atli S Valgardsson; Gudmundur Thorgeirsson; Michael L Frigge; Gudmar Thorleifsson; Gudmundur L Norddahl; Anna Helgadottir; Solveig Gretarsdottir; Patrick Sulem; Ingileif Jonsdottir; Cristen J Willer; Kristian Hveem; Henning Bundgaard; Henrik Ullum; David O Arnar; Unnur Thorsteinsdottir; Daniel F Gudbjartsson; Hilma Holm; Kari Stefansson
Journal:  Eur Heart J       Date:  2021-05-21       Impact factor: 29.983

6.  Candidate genetic variants and antidepressant-related fall risk in middle-aged and older adults.

Authors:  A C Pronk; L J Seppala; K Trajanoska; N Stringa; B van de Loo; L C P G M de Groot; N M van Schoor; F Koskeridis; G Markozannes; E Ntzani; A G Uitterlinden; F Rivadeneira; B H Stricker; N van der Velde
Journal:  PLoS One       Date:  2022-04-14       Impact factor: 3.752

7.  Analysis of genetically independent phenotypes identifies shared genetic factors associated with chronic musculoskeletal pain conditions.

Authors:  Yakov A Tsepilov; Maxim B Freidin; Alexandra S Shadrina; Sodbo Z Sharapov; Elizaveta E Elgaeva; Jan van Zundert; Lennart С Karssen; Pradeep Suri; Frances M K Williams; Yurii S Aulchenko
Journal:  Commun Biol       Date:  2020-06-25

8.  Variants associating with uterine leiomyoma highlight genetic background shared by various cancers and hormone-related traits.

Authors:  Thorunn Rafnar; Bjarni Gunnarsson; Olafur A Stefansson; Patrick Sulem; Andres Ingason; Michael L Frigge; Lilja Stefansdottir; Jon K Sigurdsson; Vinicius Tragante; Valgerdur Steinthorsdottir; Unnur Styrkarsdottir; Simon N Stacey; Julius Gudmundsson; Gudny A Arnadottir; Asmundur Oddsson; Florian Zink; Gisli Halldorsson; Gardar Sveinbjornsson; Ragnar P Kristjansson; Olafur B Davidsson; Anna Salvarsdottir; Asgeir Thoroddsen; Elisabet A Helgadottir; Katrin Kristjansdottir; Orri Ingthorsson; Valur Gudmundsson; Reynir T Geirsson; Ragnheidur Arnadottir; Daniel F Gudbjartsson; Gisli Masson; Folkert W Asselbergs; Jon G Jonasson; Karl Olafsson; Unnur Thorsteinsdottir; Bjarni V Halldorsson; Gudmar Thorleifsson; Kari Stefansson
Journal:  Nat Commun       Date:  2018-09-07       Impact factor: 14.919

9.  Does Obesity Cause Thyroid Cancer? A Mendelian Randomization Study.

Authors:  Jonathan Mark Fussey; Robin N Beaumont; Andrew R Wood; Bijay Vaidya; Joel Smith; Jessica Tyrrell
Journal:  J Clin Endocrinol Metab       Date:  2020-07-01       Impact factor: 5.958

10.  Brugada Syndrome-Associated Genetic Loci Are Associated With J-Point Elevation and an Increased Risk of Cardiac Arrest.

Authors:  Laura Andreasen; Jonas Ghouse; Morten W Skov; Christian T Have; Gustav Ahlberg; Peter V Rasmussen; Allan Linneberg; Oluf Pedersen; Pyotr G Platonov; Stig Haunsø; Jesper H Svendsen; Torben Hansen; Jørgen K Kanters; Morten S Olesen
Journal:  Front Physiol       Date:  2018-07-10       Impact factor: 4.566

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.