Literature DB >> 32472059

From molecules to populations: appreciating and estimating recombination rate variation.

Joshua V Peñalba1, Jochen B W Wolf2.   

Abstract

Recombination is a central biological process with implications for many areas in the life sciences. Yet we are only beginning to appreciate variation in the recombination rate along the genome and among individuals, populations and species. Spurred by technological advances, we are now able to bring variation in this key biological parameter to centre stage. Here, we review the conceptual implications of recombination rate variation and guide the reader through the assumptions, strengths and weaknesses of genomic inference methods, including population-based, pedigree-based and gamete-based approaches. Appreciation of the differences and commonalities of these approaches is a prerequisite to formulate a unifying and comparative framework for understanding the molecular and evolutionary mechanisms shaping, and being shaped by, recombination.

Year:  2020        PMID: 32472059     DOI: 10.1038/s41576-020-0240-1

Source DB:  PubMed          Journal:  Nat Rev Genet        ISSN: 1471-0056            Impact factor:   53.242


  18 in total

1.  Imbalanced segregation of recombinant haplotypes in hybrid populations reveals inter- and intrachromosomal Dobzhansky-Muller incompatibilities.

Authors:  Juan Li; Molly Schumer; Claudia Bank
Journal:  PLoS Genet       Date:  2022-03-28       Impact factor: 5.917

2.  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

3.  Beyond editing, CRISPR/Cas9 for protein localization: an educational primer for use with "A dCas9-based system identifies a central role for Ctf19 in kinetochore-derived suppression of meiotic recombination".

Authors:  Shelby L McVey; Mischa A Olson; Wojciech P Pawlowski; Natalie J Nannas
Journal:  Genetics       Date:  2022-08-30       Impact factor: 4.402

4.  Adaptive Control of the Meiotic Recombination Landscape by DNA Site-dependent Hotspots With Implications for Evolution.

Authors:  Reine U Protacio; Mari K Davidson; Wayne P Wahls
Journal:  Front Genet       Date:  2022-06-22       Impact factor: 4.772

5.  Recombination landscape divergence between populations is marked by larger low-recombining regions in domesticated rye.

Authors:  Mona Schreiber; Yixuan Gao; Natalie Koch; Joerg Fuchs; Stefan Heckmann; Axel Himmelbach; Andreas Börner; Hakan Özkan; Andreas Maurer; Nils Stein; Martin Mascher; Steven Dreissig
Journal:  Mol Biol Evol       Date:  2022-06-11       Impact factor: 8.800

6.  Recommendations for improving statistical inference in population genomics.

Authors:  Parul Johri; Charles F Aquadro; Mark Beaumont; Brian Charlesworth; Laurent Excoffier; Adam Eyre-Walker; Peter D Keightley; Michael Lynch; Gil McVean; Bret A Payseur; Susanne P Pfeifer; Wolfgang Stephan; Jeffrey D Jensen
Journal:  PLoS Biol       Date:  2022-05-31       Impact factor: 9.593

7.  On the prospect of achieving accurate joint estimation of selection with population history.

Authors:  Parul Johri; Adam Eyre-Walker; Ryan N Gutenkunst; Kirk E Lohmueller; Jeffrey D Jensen
Journal:  Genome Biol Evol       Date:  2022-07-02       Impact factor: 4.065

Review 8.  Inferring recombination patterns in African populations.

Authors:  Gerald van Eeden; Caitlin Uren; Marlo Möller; Brenna M Henn
Journal:  Hum Mol Genet       Date:  2021-04-26       Impact factor: 6.150

9.  Consequences of Single-Locus and Tightly Linked Genomic Architectures for Evolutionary Responses to Environmental Change.

Authors:  Rebekah A Oomen; Anna Kuparinen; Jeffrey A Hutchings
Journal:  J Hered       Date:  2020-08-12       Impact factor: 2.645

10.  High-Density Linkage Maps Based on Genotyping-by-Sequencing (GBS) Confirm a Chromosome-Level Genome Assembly and Reveal Variation in Recombination Rate for the Pacific Oyster Crassostrea gigas.

Authors:  Xiaoshen Yin; Alberto Arias-Pérez; Tevfik Hamdi Kitapci; Dennis Hedgecock
Journal:  G3 (Bethesda)       Date:  2020-12-03       Impact factor: 3.154

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