Genetic and Genomic Medicine

Trumpet plots: Visualizing The Relationship Between Allele Frequency And Effect Size In Genetic Association Studies

1. Lucia Corte
2. Lathan Liou
3. Paul F. O’Reilly
4. Judit García-González

• Curated by GigaByte

  **Editors Assessment: **

  This work presents a new standardized graphical approach for visualizing genetic associations across a wide range of allele frequencies. These proposed TrumpetPlots have a distinctive trumpet shape, hence the proposed name. With the majority of variants having low frequency and small effects, while a small number of variants have higher frequency and larger effects, this view can help to provide new and valuable insights into the genetic basis of traits and diseases, and also help prioritize efforts to discover new risk variants. The tool is provided as a novel R package and R Shiny application and to demonstrate its use the article illustrates the distribution of variant effect sizes across the allele frequency range for over 100 continuous traits available in the UK Biobank. After some problems in testing the package is now available and easy to deploy via CRAN.

  *This assessment refers to version 1 of this preprint. *

Reviewed by GigaByte

Association between APOL1 risk variants and the occurrence of sepsis in Black patients hospitalized with infections: a retrospective cohort study

1. Lan Jiang
2. Ge Liu
3. Annette Oeser
4. Andrea Ihegword
5. Alyson L Dickson
6. Laura L Daniel
7. Adriana M Hung
8. Nancy J Cox
9. Cecilia P Chung
10. Wei-Qi Wei
11. C Michael Stein
12. Qiping Feng

• Curated by eLife

  eLife assessment

  In this valuable study, patients homozygous for both minor frequency alleles of the APOL1 gene are shown to be at significant risk for progression into sepsis after infection. The study has enrolled a significant number of subjects and provides solid results. The study addresses to infectious diseases and critical care experts and one major weakness is the lack of inclusion of non-Black patients.

Reviewed by eLife

PYCR1 Levels Track with Premature and Chronological Skin Aging

1. Kortessa Sotiropoulou
2. Saniye Yumlu
3. Tomoko Hirano
4. Michael Maier
5. Abigail Loh
6. Peh Fern Ong
7. Onn Siong Yim
8. Chunping Liu
9. Emmanuel Vial
10. Umut Altunoğlu
11. Sheela Nampoothiri
12. Deepthi de Silva
13. Björn Fischer-Zirnsak
14. Hülya Kayserili
15. Poh San Lai
16. Oliver Dreesen
17. Kenji Kabashima
18. Uwe Kornak
19. Nathalie Escande-Beillard
20. Bruno Reversade

• Curated by eLife

  eLife assessment

  This manuscript uses genetic mouse modeling to delve deeper into a rare human disease of aging. The targeted approaches employed lend greater pathophysiologic insight and makes this paper valuable to the field art large. Additionally, the approaches used are rigorous and solid in supporting their conclusions. Some minor weaknesses were noted along with suggestions to add greater clarity.

Reviewed by eLife, preLights

Genetic associations for two biological age measures point to distinct aging phenotypes

1. Chia‐Ling Kuo
2. Luke C. Pilling
3. Zuyun Liu
4. Janice L. Atkins
5. Morgan E. Levine

Reviewed by ScreenIT

SARS-CoV-2 spread across the Colombian-Venezuelan border

1. Alberto Paniz-Mondolfi
2. Marina Muñoz
3. Carolina Florez
4. Sergio Gomez
5. Angelica Rico
6. Lisseth Pardo
7. Esther C. Barros
8. Carolina Hernández
9. Lourdes Delgado
10. Jesús E. Jaimes
11. Luis Pérez
12. Aníbal A. Teherán
13. Hala Alejel Alshammary
14. Ajay Obla
15. Zenab Khan
16. Jayeeta Dutta
17. Adriana van de Guchte
18. Ana S. Gonzalez-Reiche
19. Matthew M. Hernandez
20. Emilia Mia Sordillo
21. Viviana Simon
22. Harm van Bakel
23. Martin S. Llewellyn
24. Juan David Ramírez

Reviewed by ScreenIT

Polymorphisms in the ACE2 Locus Associate with Severity of COVID-19 Infection

1. Luke Wooster
2. Christopher J. Nicholson
3. Haakon H. Sigurslid
4. Christian L. Lino Cardenas
5. Rajeev Malhotra

Reviewed by ScreenIT

Metabolomics and computational analysis of the role of monoamine oxidase activity in delirium and SARS-COV-2 infection

1. Miroslava Cuperlovic-Culf
2. Emma L. Cunningham
3. Hossen Teimoorinia
4. Anuradha Surendra
5. Xiaobei Pan
6. Steffany A. L. Bennett
7. Mijin Jung
8. Bernadette McGuiness
9. Anthony Peter Passmore
10. David Beverland
11. Brian D. Green

Reviewed by ScreenIT

Analysis of Genetic Host Response Risk Factors in Severe COVID-19 Patients

1. Krystyna Taylor
2. Sayoni Das
3. Matthew Pearson
4. James Kozubek
5. Marcin Pawlowski
6. Claus Erik Jensen
7. Zbigniew Skowron
8. Gert Lykke Møller
9. Mark Strivens
10. Steve Gardner

Reviewed by ScreenIT