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Monocular and binocular visual impairment in the UK Biobank study: prevalence, associations and diagnoses.

BMJ open ophthalmology

McKibbin M, Farragher TM, Shickle D.
PMID: 29657974
BMJ Open Ophthalmol. 2018 Jan 31;3(1):e000076. doi: 10.1136/bmjophth-2017-000076. eCollection 2018.

OBJECTIVE: To determine the prevalence of, associations with and diagnoses leading to mild visual impairment or worse (logMAR >0.3) in middle-aged adults in the UK Biobank study.METHODS AND ANALYSIS: Prevalence estimates for monocular and binocular visual impairment were determined...

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McKibbin M, Farragher TM, Shickle D. Monocular and binocular visual impairment in the UK Biobank study: prevalence, associations and diagnoses. BMJ Open Ophthalmol. 2018;3(1):e000076doi: 10.1136/bmjophth-2017-000076.
McKibbin, M., Farragher, T. M., & Shickle, D. (2018). Monocular and binocular visual impairment in the UK Biobank study: prevalence, associations and diagnoses. BMJ open ophthalmology, 3(1), e000076. https://doi.org/10.1136/bmjophth-2017-000076
McKibbin, Martin, et al. "Monocular and binocular visual impairment in the UK Biobank study: prevalence, associations and diagnoses." BMJ open ophthalmology vol. 3,1 (2018): e000076. doi: https://doi.org/10.1136/bmjophth-2017-000076
McKibbin M, Farragher TM, Shickle D. Monocular and binocular visual impairment in the UK Biobank study: prevalence, associations and diagnoses. BMJ Open Ophthalmol. 2018 Jan 31;3(1):e000076. doi: 10.1136/bmjophth-2017-000076. eCollection 2018. PMID: 29657974; PMCID: PMC5895967.
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Risk factors and mortality associated with multimorbidity in people with stroke or transient ischaemic attack: a study of 8,751 UK Biobank participants.

Journal of comorbidity

Gallacher KI, McQueenie R, Nicholl B, Jani BD, Lee D, Mair FS.
PMID: 29492397
J Comorb. 2018 Feb 19;8(1):1-8. doi: 10.15256/joc.2018.8.129. eCollection 2018.

BACKGROUND: Multimorbidity is common in stroke, but the risk factors and effects on mortality remain poorly understood.OBJECTIVE: To examine multimorbidity and its associations with sociodemographic/lifestyle risk factors and all-cause mortality in UK Biobank participants with stroke or transient ischaemic...

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Gallacher KI, McQueenie R, Nicholl B, et al. Risk factors and mortality associated with multimorbidity in people with stroke or transient ischaemic attack: a study of 8,751 UK Biobank participants. J Comorb. 2018;8(1):1-8doi: 10.15256/joc.2018.8.129.
Gallacher, K. I., McQueenie, R., Nicholl, B., Jani, B. D., Lee, D., & Mair, F. S. (2018). Risk factors and mortality associated with multimorbidity in people with stroke or transient ischaemic attack: a study of 8,751 UK Biobank participants. Journal of comorbidity, 8(1), 1-8. https://doi.org/10.15256/joc.2018.8.129
Gallacher, Katie I, et al. "Risk factors and mortality associated with multimorbidity in people with stroke or transient ischaemic attack: a study of 8,751 UK Biobank participants." Journal of comorbidity vol. 8,1 (2018): 1-8. doi: https://doi.org/10.15256/joc.2018.8.129
Gallacher KI, McQueenie R, Nicholl B, Jani BD, Lee D, Mair FS. Risk factors and mortality associated with multimorbidity in people with stroke or transient ischaemic attack: a study of 8,751 UK Biobank participants. J Comorb. 2018 Feb 19;8(1):1-8. doi: 10.15256/joc.2018.8.129. eCollection 2018. PMID: 29492397; PMCID: PMC5827474.
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Anatomical selectivity in overlap of chronic facial and bodily pain.

Pain reports

Slade GD, Rosen JD, Ohrbach R, Greenspan JD, Fillingim RB, Parisien M, Khoury S, Diatchenko L, Maixner W, Bair E.
PMID: 31583346
Pain Rep. 2019 May 29;4(3):e729. doi: 10.1097/PR9.0000000000000729. eCollection 2019.

BACKGROUND: Chronic facial pain often overlaps with pain experienced elsewhere in the body, although previous studies have focused on a few, selected pain conditions when assessing the degree of overlap.AIM: To quantify the degree of overlap between facial pain...

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Slade GD, Rosen JD, Ohrbach R, et al. Anatomical selectivity in overlap of chronic facial and bodily pain. Pain Rep. 2019;4(3):e729doi: 10.1097/PR9.0000000000000729.
Slade, G. D., Rosen, J. D., Ohrbach, R., Greenspan, J. D., Fillingim, R. B., Parisien, M., Khoury, S., Diatchenko, L., Maixner, W., & Bair, E. (2019). Anatomical selectivity in overlap of chronic facial and bodily pain. Pain reports, 4(3), e729. https://doi.org/10.1097/PR9.0000000000000729
Slade, Gary D, et al. "Anatomical selectivity in overlap of chronic facial and bodily pain." Pain reports vol. 4,3 (2019): e729. doi: https://doi.org/10.1097/PR9.0000000000000729
Slade GD, Rosen JD, Ohrbach R, Greenspan JD, Fillingim RB, Parisien M, Khoury S, Diatchenko L, Maixner W, Bair E. Anatomical selectivity in overlap of chronic facial and bodily pain. Pain Rep. 2019 May 29;4(3):e729. doi: 10.1097/PR9.0000000000000729. eCollection 2019. PMID: 31583346; PMCID: PMC6749913.
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Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers.

JNCI cancer spectrum

Jia G, Lu Y, Wen W, Long J, Liu Y, Tao R, Li B, Denny JC, Shu XO, Zheng W.
PMID: 32596635
JNCI Cancer Spectr. 2020 Mar 12;4(3):pkaa021. doi: 10.1093/jncics/pkaa021. eCollection 2020 Jun.

BACKGROUND: Genome-wide association studies have identified common genetic risk variants in many loci associated with multiple cancers. We sought to systematically evaluate the utility of these risk variants in identifying high-risk individuals for eight common cancers.METHODS: We constructed polygenic...

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Jia G, Lu Y, Wen W, et al. Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers. JNCI Cancer Spectr. 2020;4(3):pkaa021doi: 10.1093/jncics/pkaa021.
Jia, G., Lu, Y., Wen, W., Long, J., Liu, Y., Tao, R., Li, B., Denny, J. C., Shu, X. O., & Zheng, W. (2020). Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers. JNCI cancer spectrum, 4(3), pkaa021. https://doi.org/10.1093/jncics/pkaa021
Jia, Guochong, et al. "Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers." JNCI cancer spectrum vol. 4,3 (2020): pkaa021. doi: https://doi.org/10.1093/jncics/pkaa021
Jia G, Lu Y, Wen W, Long J, Liu Y, Tao R, Li B, Denny JC, Shu XO, Zheng W. Evaluating the Utility of Polygenic Risk Scores in Identifying High-Risk Individuals for Eight Common Cancers. JNCI Cancer Spectr. 2020 Mar 12;4(3):pkaa021. doi: 10.1093/jncics/pkaa021. eCollection 2020 Jun. PMID: 32596635; PMCID: PMC7306192.
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On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments.

Journal of the American Statistical Association

Windmeijer F, Farbmacher H, Davies N, Davey Smith G.
PMID: 31708716
J Am Stat Assoc. 2018 Nov 13;114(527):1339-1350. doi: 10.1080/01621459.2018.1498346. eCollection 2019.

We investigate the behavior of the Lasso for selecting invalid instruments in linear instrumental variables models for estimating causal effects of exposures on outcomes, as proposed recently by Kang et al. Invalid instruments are such that they fail the...

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Windmeijer F, Farbmacher H, Davies N, et al. On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments. J Am Stat Assoc. 2018;114(527):1339-1350doi: 10.1080/01621459.2018.1498346.
Windmeijer, F., Farbmacher, H., Davies, N., & Davey Smith, G. (2019). On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments. Journal of the American Statistical Association, 114(527), 1339-1350. https://doi.org/10.1080/01621459.2018.1498346
Windmeijer, Frank, et al. "On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments." Journal of the American Statistical Association vol. 114,527 (2019): 1339-1350. doi: https://doi.org/10.1080/01621459.2018.1498346
Windmeijer F, Farbmacher H, Davies N, Davey Smith G. On the Use of the Lasso for Instrumental Variables Estimation with Some Invalid Instruments. J Am Stat Assoc. 2018 Nov 13;114(527):1339-1350. doi: 10.1080/01621459.2018.1498346. eCollection 2019. PMID: 31708716; PMCID: PMC6817329.
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Platelet Measurements and Type 2 Diabetes: Investigations in Two Population-Based Cohorts.

Frontiers in cardiovascular medicine

Rodriguez BAT, Johnson AD.
PMID: 32754618
Front Cardiovasc Med. 2020 Jul 10;7:118. doi: 10.3389/fcvm.2020.00118. eCollection 2020.

Type 2 diabetes is a major risk factor for cardiovascular disease. Given the contribution of platelets to atherothrombosis-which in turn is a major contributor to cardiac events, there may be cause to consider platelet function in management of diabetes....

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Rodriguez BAT, Johnson AD. Platelet Measurements and Type 2 Diabetes: Investigations in Two Population-Based Cohorts. Front Cardiovasc Med. 2020;7:118doi: 10.3389/fcvm.2020.00118.
Rodriguez, B. A. T., & Johnson, A. D. (2020). Platelet Measurements and Type 2 Diabetes: Investigations in Two Population-Based Cohorts. Frontiers in cardiovascular medicine, 7118. https://doi.org/10.3389/fcvm.2020.00118
Rodriguez, Benjamin A T, and Johnson, Andrew D. "Platelet Measurements and Type 2 Diabetes: Investigations in Two Population-Based Cohorts." Frontiers in cardiovascular medicine vol. 7 (2020): 118. doi: https://doi.org/10.3389/fcvm.2020.00118
Rodriguez BAT, Johnson AD. Platelet Measurements and Type 2 Diabetes: Investigations in Two Population-Based Cohorts. Front Cardiovasc Med. 2020 Jul 10;7:118. doi: 10.3389/fcvm.2020.00118. eCollection 2020. PMID: 32754618; PMCID: PMC7365849.
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Fully Automated Myocardial Strain Estimation from Cardiovascular MRI-tagged Images Using a Deep Learning Framework in the UK Biobank.

Radiology. Cardiothoracic imaging

Ferdian E, Suinesiaputra A, Fung K, Aung N, Lukaschuk E, Barutcu A, Maclean E, Paiva J, Piechnik SK, Neubauer S, Petersen SE, Young AA.
PMID: 32715298
Radiol Cardiothorac Imaging. 2020 Feb 27;2(1):e190032. doi: 10.1148/ryct.2020190032.

PURPOSE: To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac MRI-tagged images.MATERIALS AND METHODS: In this retrospective cross-sectional study, 4508 cases from the U.K. Biobank were split randomly...

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Ferdian E, Suinesiaputra A, Fung K, et al. Fully Automated Myocardial Strain Estimation from Cardiovascular MRI-tagged Images Using a Deep Learning Framework in the UK Biobank. Radiol Cardiothorac Imaging. 2020;2(1):e190032doi: 10.1148/ryct.2020190032.
Ferdian, E., Suinesiaputra, A., Fung, K., Aung, N., Lukaschuk, E., Barutcu, A., Maclean, E., Paiva, J., Piechnik, S. K., Neubauer, S., Petersen, S. E., & Young, A. A. (2020). Fully Automated Myocardial Strain Estimation from Cardiovascular MRI-tagged Images Using a Deep Learning Framework in the UK Biobank. Radiology. Cardiothoracic imaging, 2(1), e190032. https://doi.org/10.1148/ryct.2020190032
Ferdian, Edward, et al. "Fully Automated Myocardial Strain Estimation from Cardiovascular MRI-tagged Images Using a Deep Learning Framework in the UK Biobank." Radiology. Cardiothoracic imaging vol. 2,1 (2020): e190032. doi: https://doi.org/10.1148/ryct.2020190032
Ferdian E, Suinesiaputra A, Fung K, Aung N, Lukaschuk E, Barutcu A, Maclean E, Paiva J, Piechnik SK, Neubauer S, Petersen SE, Young AA. Fully Automated Myocardial Strain Estimation from Cardiovascular MRI-tagged Images Using a Deep Learning Framework in the UK Biobank. Radiol Cardiothorac Imaging. 2020 Feb 27;2(1):e190032. doi: 10.1148/ryct.2020190032. PMID: 32715298; PMCID: PMC7051160.
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Association of Genetic Variants Related to Combined Exposure to Lower Low-Density Lipoproteins and Lower Systolic Blood Pressure With Lifetime Risk of Cardiovascular Disease.

JAMA

Ference BA, Bhatt DL, Catapano AL, Packard CJ, Graham I, Kaptoge S, Ference TB, Guo Q, Laufs U, Ruff CT, Cupido A, Hovingh GK, Danesh J, Holmes MV, Smith GD, Ray KK, Nicholls SJ, Sabatine MS.
PMID: 31475726
JAMA. 2019 Oct 08;322(14):1381-1391. doi: 10.1001/jama.2019.14120.

IMPORTANCE: The relationship between exposure to lower low-density lipoprotein cholesterol (LDL-C) and lower systolic blood pressure (SBP) with the risk of cardiovascular disease has not been reliably quantified.OBJECTIVE: To assess the association of lifetime exposure to the combination of...

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Ference BA, Bhatt DL, Catapano AL, et al. Association of Genetic Variants Related to Combined Exposure to Lower Low-Density Lipoproteins and Lower Systolic Blood Pressure With Lifetime Risk of Cardiovascular Disease. JAMA. 2019;322(14):1381-1391doi: 10.1001/jama.2019.14120.
Ference, B. A., Bhatt, D. L., Catapano, A. L., Packard, C. J., Graham, I., Kaptoge, S., Ference, T. B., Guo, Q., Laufs, U., Ruff, C. T., Cupido, A., Hovingh, G. K., Danesh, J., Holmes, M. V., Smith, G. D., Ray, K. K., Nicholls, S. J., & Sabatine, M. S. (2019). Association of Genetic Variants Related to Combined Exposure to Lower Low-Density Lipoproteins and Lower Systolic Blood Pressure With Lifetime Risk of Cardiovascular Disease. JAMA, 322(14), 1381-1391. https://doi.org/10.1001/jama.2019.14120
Ference, Brian A, et al. "Association of Genetic Variants Related to Combined Exposure to Lower Low-Density Lipoproteins and Lower Systolic Blood Pressure With Lifetime Risk of Cardiovascular Disease." JAMA vol. 322,14 (2019): 1381-1391. doi: https://doi.org/10.1001/jama.2019.14120
Ference BA, Bhatt DL, Catapano AL, Packard CJ, Graham I, Kaptoge S, Ference TB, Guo Q, Laufs U, Ruff CT, Cupido A, Hovingh GK, Danesh J, Holmes MV, Smith GD, Ray KK, Nicholls SJ, Sabatine MS. Association of Genetic Variants Related to Combined Exposure to Lower Low-Density Lipoproteins and Lower Systolic Blood Pressure With Lifetime Risk of Cardiovascular Disease. JAMA. 2019 Oct 08;322(14):1381-1391. doi: 10.1001/jama.2019.14120. PMID: 31475726; PMCID: PMC6724415.
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Frequency and distribution of corneal astigmatism and keratometry features in adult life: Methodology and findings of the UK Biobank study.

PloS one

Pontikos N, Chua S, Foster PJ, Tuft SJ, Day AC.
PMID: 31536508
PLoS One. 2019 Sep 19;14(9):e0218144. doi: 10.1371/journal.pone.0218144. eCollection 2019.

PURPOSE: To describe corneal astigmatism in the UK Biobank population and to look for associations with other biometric variables and socio-demographic factors.METHODS: This analysis included a subsample of 107,452 participants of the UK Biobank study who underwent an enhanced...

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Pontikos N, Chua S, Foster PJ, et al. Frequency and distribution of corneal astigmatism and keratometry features in adult life: Methodology and findings of the UK Biobank study. PLoS One. 2019;14(9):e0218144doi: 10.1371/journal.pone.0218144.
Pontikos, N., Chua, S., Foster, P. J., Tuft, S. J., Day, A. C. (2019). Frequency and distribution of corneal astigmatism and keratometry features in adult life: Methodology and findings of the UK Biobank study. PloS one, 14(9), e0218144. https://doi.org/10.1371/journal.pone.0218144
Pontikos, Nikolas, et al. "Frequency and distribution of corneal astigmatism and keratometry features in adult life: Methodology and findings of the UK Biobank study." PloS one vol. 14,9 (2019): e0218144. doi: https://doi.org/10.1371/journal.pone.0218144
Pontikos N, Chua S, Foster PJ, Tuft SJ, Day AC. Frequency and distribution of corneal astigmatism and keratometry features in adult life: Methodology and findings of the UK Biobank study. PLoS One. 2019 Sep 19;14(9):e0218144. doi: 10.1371/journal.pone.0218144. eCollection 2019. PMID: 31536508; PMCID: PMC6752876.
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Exome Sequencing in .

Frontiers in genetics

Glentis S, Dimopoulos AC, Rouskas K, Ntritsos G, Evangelou E, Narod SA, Mes-Masson AM, Foulkes WD, Rivera B, Tonin PN, Ragoussis J, Dimas AS.
PMID: 31681433
Front Genet. 2019 Oct 18;10:1005. doi: 10.3389/fgene.2019.01005. eCollection 2019.

Approximately 10% of breast cancer (BC) cases are hereditary BC (HBC), with HBC most commonly encountered in the context of hereditary breast and ovarian cancer (HBOC) syndrome. Although thousands of loss-of-function (LoF) alleles in over 20 genes have been...

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Glentis S, Dimopoulos AC, Rouskas K, et al. Exome Sequencing in . Front Genet. 2019;10:1005doi: 10.3389/fgene.2019.01005.
Glentis, S., Dimopoulos, A. C., Rouskas, K., Ntritsos, G., Evangelou, E., Narod, S. A., Mes-Masson, A. M., Foulkes, W. D., Rivera, B., Tonin, P. N., Ragoussis, J., & Dimas, A. S. (2019). Exome Sequencing in . Frontiers in genetics, 101005. https://doi.org/10.3389/fgene.2019.01005
Glentis, Stavros, et al. "Exome Sequencing in ." Frontiers in genetics vol. 10 (2019): 1005. doi: https://doi.org/10.3389/fgene.2019.01005
Glentis S, Dimopoulos AC, Rouskas K, Ntritsos G, Evangelou E, Narod SA, Mes-Masson AM, Foulkes WD, Rivera B, Tonin PN, Ragoussis J, Dimas AS. Exome Sequencing in . Front Genet. 2019 Oct 18;10:1005. doi: 10.3389/fgene.2019.01005. eCollection 2019. PMID: 31681433; PMCID: PMC6813924.
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Calibration of polygenic risk scores is required prior to clinical implementation: results of three common cancers in UKB.

Journal of medical genetics

Wei J, Shi Z, Na R, Resurreccion WK, Wang CH, Duggan D, Zheng SL, Hulick PJ, Helfand BT, Xu J.
PMID: 33443076
J Med Genet. 2020 Dec 21; doi: 10.1136/jmedgenet-2020-107286. Epub 2020 Dec 21.

BACKGROUND: SNP-based polygenic risk scores have recently been adopted in the clinic for risk assessment of some common diseases. Their validity is supported by a consistent trend between their OBJECTIVES: We assessed the reliability of polygenic score values to...

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Wei J, Shi Z, Na R, et al. Calibration of polygenic risk scores is required prior to clinical implementation: results of three common cancers in UKB. J Med Genet. 2020;doi: 10.1136/jmedgenet-2020-107286.
Wei, J., Shi, Z., Na, R., Resurreccion, W. K., Wang, C. H., Duggan, D., Zheng, S. L., Hulick, P. J., Helfand, B. T., & Xu, J. (2020). Calibration of polygenic risk scores is required prior to clinical implementation: results of three common cancers in UKB. Journal of medical genetics, . https://doi.org/10.1136/jmedgenet-2020-107286
Wei, Jun, et al. "Calibration of polygenic risk scores is required prior to clinical implementation: results of three common cancers in UKB." Journal of medical genetics vol. (2020). doi: https://doi.org/10.1136/jmedgenet-2020-107286
Wei J, Shi Z, Na R, Resurreccion WK, Wang CH, Duggan D, Zheng SL, Hulick PJ, Helfand BT, Xu J. Calibration of polygenic risk scores is required prior to clinical implementation: results of three common cancers in UKB. J Med Genet. 2020 Dec 21; doi: 10.1136/jmedgenet-2020-107286. Epub 2020 Dec 21. PMID: 33443076.
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A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes.

Bioinformatics (Oxford, England)

Majumdar A, Burch KS, Haldar T, Sankararaman S, Pasaniuc B, Gauderman WJ, Witte JS.
PMID: 33453114
Bioinformatics. 2021 Jan 16; doi: 10.1093/bioinformatics/btaa1083. Epub 2021 Jan 16.

MOTIVATION: While gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a...

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Majumdar A, Burch KS, Haldar T, et al. A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics. 2021;doi: 10.1093/bioinformatics/btaa1083.
Majumdar, A., Burch, K. S., Haldar, T., Sankararaman, S., Pasaniuc, B., Gauderman, W. J., & Witte, J. S. (2021). A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics (Oxford, England), . https://doi.org/10.1093/bioinformatics/btaa1083
Majumdar, Arunabha, et al. "A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes." Bioinformatics (Oxford, England) vol. (2021). doi: https://doi.org/10.1093/bioinformatics/btaa1083
Majumdar A, Burch KS, Haldar T, Sankararaman S, Pasaniuc B, Gauderman WJ, Witte JS. A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes. Bioinformatics. 2021 Jan 16; doi: 10.1093/bioinformatics/btaa1083. Epub 2021 Jan 16. PMID: 33453114.
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