Literature DB >> 35842778

Including diverse and admixed populations in genetic epidemiology research.

Amke Caliebe1, Fasil Tekola-Ayele2, Burcu F Darst3,4, Xuexia Wang5, Yeunjoo E Song6, Jiang Gui7, Ronnie A Sebro8, David J Balding9, Mohamad Saad10,11, Marie-Pierre Dubé12,13.   

Abstract

The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.
© 2022 The Authors. Genetic Epidemiology published by Wiley Periodicals LLC.

Entities:  

Keywords:  admixture; diversity; genetic association; genome-wide association study; inclusion

Mesh:

Year:  2022        PMID: 35842778      PMCID: PMC9452464          DOI: 10.1002/gepi.22492

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.344


  202 in total

1.  Genetic similarities within and between human populations.

Authors:  D J Witherspoon; S Wooding; A R Rogers; E E Marchani; W S Watkins; M A Batzer; L B Jorde
Journal:  Genetics       Date:  2007-03-04       Impact factor: 4.562

2.  RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference.

Authors:  Brian K Maples; Simon Gravel; Eimear E Kenny; Carlos D Bustamante
Journal:  Am J Hum Genet       Date:  2013-08-01       Impact factor: 11.025

3.  Testing for ancient admixture between closely related populations.

Authors:  Eric Y Durand; Nick Patterson; David Reich; Montgomery Slatkin
Journal:  Mol Biol Evol       Date:  2011-02-15       Impact factor: 16.240

4.  Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.

Authors:  Zhihong Zhu; Futao Zhang; Han Hu; Andrew Bakshi; Matthew R Robinson; Joseph E Powell; Grant W Montgomery; Michael E Goddard; Naomi R Wray; Peter M Visscher; Jian Yang
Journal:  Nat Genet       Date:  2016-03-28       Impact factor: 38.330

5.  Integrative approaches for large-scale transcriptome-wide association studies.

Authors:  Alexander Gusev; Arthur Ko; Huwenbo Shi; Gaurav Bhatia; Wonil Chung; Brenda W J H Penninx; Rick Jansen; Eco J C de Geus; Dorret I Boomsma; Fred A Wright; Patrick F Sullivan; Elina Nikkola; Marcus Alvarez; Mete Civelek; Aldons J Lusis; Terho Lehtimäki; Emma Raitoharju; Mika Kähönen; Ilkka Seppälä; Olli T Raitakari; Johanna Kuusisto; Markku Laakso; Alkes L Price; Päivi Pajukanta; Bogdan Pasaniuc
Journal:  Nat Genet       Date:  2016-02-08       Impact factor: 38.330

6.  Variation in estimated recombination rates across human populations.

Authors:  Jan Graffelman; David J Balding; Anna Gonzalez-Neira; Jaume Bertranpetit
Journal:  Hum Genet       Date:  2007-07-03       Impact factor: 4.132

7.  Whole-Genome Restriction Mapping by "Subhaploid"-Based RAD Sequencing: An Efficient and Flexible Approach for Physical Mapping and Genome Scaffolding.

Authors:  Jinzhuang Dou; Huaiqian Dou; Chuang Mu; Lingling Zhang; Yangping Li; Jia Wang; Tianqi Li; Yuli Li; Xiaoli Hu; Shi Wang; Zhenmin Bao
Journal:  Genetics       Date:  2017-05-03       Impact factor: 4.562

8.  Does big data require a methodological change in medical research?

Authors:  Amke Caliebe; Friedhelm Leverkus; Gerd Antes; Michael Krawczak
Journal:  BMC Med Res Methodol       Date:  2019-06-17       Impact factor: 4.615

9.  RaPID: ultra-fast, powerful, and accurate detection of segments identical by descent (IBD) in biobank-scale cohorts.

Authors:  Ardalan Naseri; Xiaoming Liu; Kecong Tang; Shaojie Zhang; Degui Zhi
Journal:  Genome Biol       Date:  2019-07-25       Impact factor: 13.583

10.  Putting RFMix and ADMIXTURE to the test in a complex admixed population.

Authors:  Caitlin Uren; Eileen G Hoal; Marlo Möller
Journal:  BMC Genet       Date:  2020-04-07       Impact factor: 2.797

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  1 in total

1.  Including diverse and admixed populations in genetic epidemiology research.

Authors:  Amke Caliebe; Fasil Tekola-Ayele; Burcu F Darst; Xuexia Wang; Yeunjoo E Song; Jiang Gui; Ronnie A Sebro; David J Balding; Mohamad Saad; Marie-Pierre Dubé
Journal:  Genet Epidemiol       Date:  2022-07-16       Impact factor: 2.344

  1 in total

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