The search for sexually antagonistic genes : Practical insights from studies of local adaptation and statistical genomics
Ruzicka, Filip ; Dutoit, Ludovic ; Czuppon, Peter ; Jordan, Crispin Y. ; Li, Xiang‐yi ; Olito, Colin LU ; Runemark, Anna LU ; Svensson, Erik LU
; Yazdi, Homa Papoli
LU
and Connallon, Tim
(2020)
In Evolution letters
4(5).
p.398-415
- Abstract
- Sexually antagonistic (SA) genetic variation—in which alleles favored in one sex are disfavored in the other—is predicted to be common and has been documented in several animal and plant populations, yet we currently know little about its pervasiveness among species or its population genetic basis. Recent applications of genomics in studies of SA genetic variation have highlighted considerable methodological challenges to the identification and characterization of SA genes, raising questions about the feasibility of genomic approaches for inferring SA selection. The related fields of local adaptation and statistical genomics have previously dealt with similar challenges, and lessons from these disciplines can therefore help overcome... (More)
- Sexually antagonistic (SA) genetic variation—in which alleles favored in one sex are disfavored in the other—is predicted to be common and has been documented in several animal and plant populations, yet we currently know little about its pervasiveness among species or its population genetic basis. Recent applications of genomics in studies of SA genetic variation have highlighted considerable methodological challenges to the identification and characterization of SA genes, raising questions about the feasibility of genomic approaches for inferring SA selection. The related fields of local adaptation and statistical genomics have previously dealt with similar challenges, and lessons from these disciplines can therefore help overcome current difficulties in applying genomics to study SA genetic variation. Here, we integrate theoretical and analytical concepts from local adaptation and statistical genomics research—including FST and FIS statistics, genome‐wide association studies, pedigree analyses, reciprocal transplant studies, and evolve‐and‐resequence experiments—to evaluate methods for identifying SA genes and genome‐wide signals of SA genetic variation. We begin by developing theoretical models for between‐sex FST and FIS, including explicit null distributions for each statistic, and using them to critically evaluate putative multilocus signals of sex‐specific selection in previously published datasets. We then highlight new statistics that address some of the limitations of FST and FIS, along with applications of more direct approaches for characterizing SA genetic variation, which incorporate explicit fitness measurements. We finish by presenting practical guidelines for the validation and evolutionary analysis of candidate SA genes and discussing promising empirical systems for future work. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/05bb333e-a80f-47f2-991c-b6428ac1f0fb
- author
- Ruzicka, Filip
; Dutoit, Ludovic
; Czuppon, Peter
; Jordan, Crispin Y.
; Li, Xiang‐yi
; Olito, Colin
LU
; Runemark, Anna
LU
; Svensson, Erik
LU
; Yazdi, Homa Papoli
LU
and Connallon, Tim
- organization
- publishing date
- 2020-10-01
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Evolution letters
- volume
- 4
- issue
- 5
- pages
- 398 - 415
- publisher
- Wiley-Blackwell
- external identifiers
-
- pmid:33014417
- scopus:85096314188
- ISSN
- 2056-3744
- DOI
- 10.1002/evl3.192
- language
- English
- LU publication?
- yes
- id
- 05bb333e-a80f-47f2-991c-b6428ac1f0fb
- date added to LUP
- 2020-12-18 07:45:00
- date last changed
- 2024-04-19 19:03:16
@article{05bb333e-a80f-47f2-991c-b6428ac1f0fb,
abstract = {{Sexually antagonistic (SA) genetic variation—in which alleles favored in one sex are disfavored in the other—is predicted to be common and has been documented in several animal and plant populations, yet we currently know little about its pervasiveness among species or its population genetic basis. Recent applications of genomics in studies of SA genetic variation have highlighted considerable methodological challenges to the identification and characterization of SA genes, raising questions about the feasibility of genomic approaches for inferring SA selection. The related fields of local adaptation and statistical genomics have previously dealt with similar challenges, and lessons from these disciplines can therefore help overcome current difficulties in applying genomics to study SA genetic variation. Here, we integrate theoretical and analytical concepts from local adaptation and statistical genomics research—including FST and FIS statistics, genome‐wide association studies, pedigree analyses, reciprocal transplant studies, and evolve‐and‐resequence experiments—to evaluate methods for identifying SA genes and genome‐wide signals of SA genetic variation. We begin by developing theoretical models for between‐sex FST and FIS, including explicit null distributions for each statistic, and using them to critically evaluate putative multilocus signals of sex‐specific selection in previously published datasets. We then highlight new statistics that address some of the limitations of FST and FIS, along with applications of more direct approaches for characterizing SA genetic variation, which incorporate explicit fitness measurements. We finish by presenting practical guidelines for the validation and evolutionary analysis of candidate SA genes and discussing promising empirical systems for future work.}},
author = {{Ruzicka, Filip and Dutoit, Ludovic and Czuppon, Peter and Jordan, Crispin Y. and Li, Xiang‐yi and Olito, Colin and Runemark, Anna and Svensson, Erik and Yazdi, Homa Papoli and Connallon, Tim}},
issn = {{2056-3744}},
language = {{eng}},
month = {{10}},
number = {{5}},
pages = {{398--415}},
publisher = {{Wiley-Blackwell}},
series = {{Evolution letters}},
title = {{The search for sexually antagonistic genes : Practical insights from studies of local adaptation and statistical genomics}},
url = {{http://dx.doi.org/10.1002/evl3.192}},
doi = {{10.1002/evl3.192}},
volume = {{4}},
year = {{2020}},
}