1 | INTRODUCTION
The adaptation of a population to a new environment can involve traits
controlled by only a few genes that have a major effect, but such
oligogenic adaption is relatively rare
(Bell, 2009; van’t Hof
et al., 2011; Bastide et al., 2016). Indeed, many adaptive traits are
genetically complex and involve large numbers of loci, each of which
contributes little to the phenotype
(Pritchard et al.,
2010; Sella & Barton, 2019). With the large amount of genomic data now
available, many authors have been able to identify the genetic basis of
complex adaptive traits in different organisms
(Daborn, 2002; Cook et
al., 2012; Linnen et al., 2013) but identifying the genetic basis of a
polygenic trait is not sufficient to understand adaptive potential of a
species. In addition, the effect size of the genes (i.e. their
contribution to the genetic variance of a trait,
(Park et al., 2010)),
interactions between genes (i.e. additivity, dominance, epistasis and
pleiotropy, (Hansen,
2006)) and redundancy (i.e. when several genotypes share the same
phenotype by accumulating different combinations of mutations
(Barghi et al., 2020))
need to be evaluated.
Identifying the genetic architecture of adaptive traits has been the
main focus of two fields of evolutionary biology
(Höllinger et al.,
2019; Barghi et al., 2020). The first approach is based on molecular
population genetics and assumes that adaptive traits result in the
directional selection of a limited number of beneficial mutations that
have major effects on the traits concerned. A hitchhiking effect on
other linked loci leads to loss of diversity in the surrounding genomic
regions; this footprint is called a “selective sweep”
(Maynard-Smith &
Haigh, 1974; Messer & Petrov, 2013). Genome scan methods have been
developed to detect this footprint across the genome by measuring
differentiation between populations, by detecting variations in the site
frequency spectrum (SFS) and/or identifying haplotypes under strong
linkage disequilibrium (reviewed by
(Vitti et al., 2013,
Vatsiou et al., 2016
and Pavlidis &
Alachiotis, 2017). The second approach is based on quantitative genetics
and focuses on the phenotype to identify the genes responsible for
phenotypic variation
(Bazakos et al.,
2017). Evolution of a polygenic trait is supposed to be the result of a
collective effect of a large number of loci with infinitesimally small
variations, leading to more subtle footprints called “shifts”
(Barton et al., 2017;
Boyle et al., 2017). Analyses of quantitative trait loci (QTL) or genome
wide association studies (GWAS) are used to decipher the genetic
architecture of a phenotypic trait by identifying correlations between
loci and the phenotype
(Barton & Keightley,
2002; Visscher et al., 2017). Molecular population genetics and
quantitative genetics views are not incompatible. Pritchard and Di
Rienzo in 2010 proposed a unifying view of polygenic adaptation as the
result of sweeps and shifts acting simultaneously. Thus, combining the
two approaches could be a good way to decipher the genetic architecture
underlying polygenic adaptation
(Gagnaire &
Gaggiotti, 2016).
Genetic architecture of traits can be viewed as the genetic potential
for phenotype variation through mutation. However, this concept is not
sufficient to fully understand adaptation in natural populations, and
Barghi et al. 2020 recently proposed the notion of adaptive architecture
to better describe the adaptive potential of species. This notion
extends the genetic architecture concept by including other factors
involved in population adaptation such as the frequency of contributing
alleles, pleiotropy fitness constraints, and genetic forces other than
mutation, including selection, drift, and recombination. All these
factors play a role in shaping the relative contribution of genes to the
adaptation of a population and also in the degree of parallelism when
different populations are compared that evolve in the same environment,
and could consequently be considered as replicates. Experimental
evolution is one possible approach to investigate the genomic responses
related to adaptation and to measure the degree of parallelism between
populations faced with a controlled environmental constraint and has
been successfully applied in Drosophila(Graves et al., 2017;
Griffin et al., 2017) and Escherichia coli(Tenaillon et al.,
2012). Alternatively, in biological situations (like epidemics) that are
difficult to reproduce in the laboratory, adaptive architecture can be
investigated in natural systems comprising multiple populations that
evolve independently in similar environments
(Barghi et al., 2020).
The adaptive architecture concept proposed by Barghi et al. 2020
provides a unified framework to understand how pathogens adapt to plant
genetic resistance which is more and more used in agriculture to control
diseases as an alternative to applying chemicals. Two categories of
resistance have been described in the literature: qualitative and
quantitative resistance. Qualitative resistance is often based on
‘effector-triggered immunity’ (ETI), in which major genes confer almost
complete protection after recognition of effectors produced by certain
pathogen genotypes referred to as avirulent genotypes (Cowger & Brown,
2019). Qualitative
resistance is usually not durable because the high specificity of the
host-pathogen interactions exerts strong selective pressure on pathogen
populations and can lead to rapid selection and fixation of a beneficial
mutation (Parlevliet, 2002; Zhong et al.,
2017), a process
corresponding to the selective sweep concept described above. The
genetic basis of quantitative resistance may rely on only a small number
of QTLs but can be also polygenic, i.e. involve a large number of QTLs
(Cowger & Brown,
2019). Diverse
mechanisms can be involved and quantitative resistance is generally
considered as the most durable (Pilet-Nayel et al.,
2017). However,
following changes in quantitative traits of pathogenicity (also referred
to as aggressiveness (Lannou,
2012), many examples
of erosion of quantitative resistance have recently been reported
(reviewed in Pilet-Nayel et al. 2017, Cowger &
Brown, 2019). In
contrast to quantitative resistance of plants, only a few studies have
provided information on the genetic basis of quantitative pathogenicity
in pathogens. A complex genetic architecture of fungal quantitative
pathogenicity was found in a comprehensive QTL mapping analysis of the
wheat pathogen Zymoseptoria tritici supported by genome wide
association studies (GWAS) of a global sample of isolates (Hartmann et
al., 2017; Dutta et al.,
2021). However,
description of the adaptive architecture on one particular host requires
comparison of several fungal populations which can have notable
differences on standing genetic variation and population size (McDonald
& Linde, 2002).
The ascomycete fungus Pseudocercospora fijiensis , which is
responsible for black streak disease (BLSD) of banana, is an interesting
pathogen model to describe adaptive architecture to quantitative plant
resistance. BLSD is the most damaging foliar pathogens of banana
worldwide (Guzmán et
al., 2019). The BLSD pandemic started around 1960 in South-East
Asia/Oceania. In 1972, the disease was detected for the first time in
Latin America, in Honduras, and spread rapidly throughout the region
(Carlier et al.,
2021a). The Fundación Hondureña de Investigación Agrícola (FHIA)
produced several quantitatively resistant hybrids that were used in Cuba
in the 1990s and 2000s and have been used in the Dominican Republic
since 2005. However, after five to 10 years of cultivation, in both
countries, erosion of resistance was reported in FHIA 18 and FHIA 21
cultivars in the field
(Pérez Miranda et al.,
2006; Guzmán et al., 2019). Local adaptation of P. fijiensispopulations explaining the erosion of resistance of FHIA hybrids in the
two countries was demonstrated in cross-inoculation experiments
(Dumartinet et al.,
2019). An even more recent study based on pool sequencing (Pool-Seq)
supported the existence of convergent adaptation in both resistant and
susceptible cultivars in less than 10 genomic regions, suggesting
oligogenic architecture underlies this adaptation
(Carlier et al.,
2021b). However, other genomic regions that did not converge were
detected across the populations analyzed and neither redundancy nor
phenotype-genotype relationship was tackled in that study.
The aim of the present work was thus to characterize the adaptive
architecture underlying the quantitative resistance adaptation ofP. fijiensis . To this end, we analyzed a large number of P.
fijiensis samples from susceptible and resistant cultivars in Cuba
using a paired population sampling design. We first used a genome scan
based on pool-sequencing data to detect host selection footprints in key
genomic regions. Isolates from one location characterized for one trait
of pathogenicity (the diseased leaf area) were individually sequenced to
perform GWAS and to investigate correlations between the phenotype and
the genotype in candidate genomic regions. We then combined all these
data to compare adaptive architecture between populations.