Cosetta Minelli

Genetics · 2010–2019

A researcher in the Science corpus: 7 papers; trusts 18 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Genetics — 101 citations into it; #899 of 6,854 by trust here, #1,786 by citations
• Statistics and Probability — 45 citations into it; #12,201 of 12,932 by trust here, #7,508 by citations
• Rheumatology — 13 citations into it
• Surgery — 8 citations into it
• Health, Toxicology and Mutagenesis — 7 citations into it
• Pediatrics, Perinatology and Child Health — 7 citations into it
• Health — 6 citations into it

Whom Cosetta Minelli trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. George Davey Smith
2. Stephen Burgess
3. J. Bowden
4. Julian P. T. Higgins
5. S. Thompson
6. Vanessa Didelez
7. Nuala Sheehan
8. Shah Ebrahim
9. Deborah Lawlor
10. Adam S. Butterworth

Who trusts Cosetta Minelli overall

1. J. Bowden
2. George Davey Smith
3. Gibran Hemani
4. Stephen Burgess
5. Michael Holmes
6. F. Del Greco M
7. F. Hartwig
8. E. Sanderson
9. Wes Spiller
10. Qingyuan Zhao

Papers from Cosetta Minelli's perspective

Ordered by how strongly the researchers Cosetta Minelli trusts endorse each paper. Same corpus, a different order for every researcher.

1. Mendelian randomization as an instrumental variable approach to causal inference (Statistical Methods in Medical Research)
2. Mendelian randomization: prospects, potentials, and limitations. (International Journal of Epidemiology)
3. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. (International Journal of Epidemiology)
4. On Instrumental Variables Estimation of Causal Odds Ratios
5. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression (International Journal of Epidemiology)
6. Mendelian randomization: genetic anchors for causal inference in epidemiological studies (Human Molecular Genetics)
7. Efficient Design for Mendelian Randomization Studies: Subsample and 2-Sample Instrumental Variable Estimators (American Journal of Epidemiology)
8. THE ESTIMATION OF ECONOMIC RELATIONSHIPS USING INSTRUMENTAL VARIABLES
9. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome (Statistics in Medicine)
10. Commentary: Heterogeneity in meta-analysis should be expected and appropriately quantified. (International Journal of Epidemiology)

Their papers

• Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption (2018) — 879 citations
• Multiple New Loci Associated with Kidney Function and Chronic Kidney Disease: The CKDGen consortium (2010) — 799 citations
• Guidelines for performing Mendelian randomization investigations (2019) — 2,022 citations
• Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018) — 561 citations
• Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression (2018) — 441 citations
• A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization (2017) — 1,630 citations
• Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome (2015) — 1,439 citations