Biology subjects

Francois, O.

Publications and source records attributed to Francois, O..

9 recordsLinked to original sources

bioRxiv2026

Genomic forecasting links climate-associated variation to spring phenology in the European wild apple, a crop wild relative of the cultivated apple

Climate-associated genomic variation is often used as a proxy for adaptive potential, yet whether it reflects adaptation with phenotypic consequences, rather than demography alone, is rarely tested in long-lived species. Genomic offsets forecast climate maladaptation from genotype-environment models but are seldom validated against phenotypes. We combined whole-genome resequencing, SNP and transposable element insertion polymorphism (TIP) variation, landscape genomics, genome-wide association, genomic offset, and common-garden phenotyping in the European wild apple (Malus sylvestris Mill.), a crop wild relative with a complex demographic history, to test this. Results: Of 148 resequenced accessions (135 wild, 13 cultivated), 89 wild individuals formed the core dataset. Population structure was associated with Last Glacial Maximum precipitation rather than contemporary climate, and genetic diversity increased from west to east. Genome-environment associations across 9.75 million SNPs identified climate-associated loci enriched in photoperiod-related flowering-time genes, with limited overlap with phenotype-associated loci. SNP-based genomic offset was associated with bud-break timing in the Saclay common garden (marginal R2 = 0.31), with higher offsets corresponding to earlier bud break, but not with growth or ecophysiological traits. This association depended on Austrian and Romanian populations and was clearest in non-admixed wild individuals. Offsets based on TIPs or glacial climate did not predict measured phenotypes. Conclusions: Climate association, phenotypic relevance, and predictive power of genomic variation are separable. In a long-lived tree, genomic offset predicted spring phenology, a key adaptive trait, but this signal varied across provenances and ancestries and was absent for transposable elements. Genomic offsets can therefore forecast potential phenological maladaptation, provided they are validated against phenotypes such as bud break.

ecology↗

bioRxiv2023

A quantitative theory for genomic offset statistics

Genomic offset statistics predict the maladaptation of populations to rapid habitat alteration based on association of genotypes with environmental variation. Despite substantial evidence for empirical validity, genomic offset statistics have well-identified limitations, and lack a theory that would facilitate interpretations of predicted values. Here, we clarified the theoretical relationships between genomic offset statistics and unobserved fitness traits controlled by environmentally selected loci, and proposed a geometric measure to predict fitness after rapid change in local environment. he predictions of our theory were verified in computer simulations and in empirical data on African pearl millet (Cenchrus americanus) obtained from a common garden experiment. Our results proposed a unified perspective on genomic offset statistics, and provided a theoretical foundation necessary when considering their potential application in conservation management in the face of environmental change.

evolutionary biology↗

bioRxiv2021

Theoretical Analysis of Principal Components in an Umbrella Model of Intraspecific Evolution

Principal component analysis (PCA) is one of the most frequently-used approach to describe population structure from multilocus genotype data. Regarding geographic range expansions of modern humans, interpretations of PCA have, however, been questioned, as there is uncertainty about the wave-like patterns that have been observed in principal components. It has indeed been argued that wave-like patterns are mathematical artifacts that arise generally when PCA is applied to data in which genetic differentiation increases with geographic distance. Here, we present an alternative theory for the observation of wave-like patterns in PCA. We study a coalescent model - the umbrella model - for the diffusion of genetic variants. The model is based on genetic drift without any particular geographical structure. In the umbrella model, splits from an ancestral population occur almost continuously in time, giving birth to small daughter populations at a regular pace. Our results provide detailed mathematical descriptions of eigenvalues and eigenvectors for the PCA of sampled genomic sequences under the model. Removing variants uniquely represented in the sample, the PCA eigenvectors are defined as cosine functions of increasing periodicity, reproducing wave-like patterns observed in equilibrium isolation-by-distance models. Including rare variants in the analysis, the eigenvectors corresponding to the largest eigenvalues exhibit complex wave shapes. The accuracy of our predictions is further investigated with coalescent simulations. Our analysis supports the hypothesis that highly structured wave-like patterns could arise from genetic drift only, and may not always be artificial outcomes of spatially structured data. Genomic data related to the peopling of the Americas are reanalyzed in the light of our new theory.

evolutionary biology↗

bioRxiv2020

A Spectral Theory for Wright's Inbreeding Coefficients and Related Quantities

Wrights inbreeding coefficient, FST, is a fundamental measure in population genetics. Assuming a predefined population subdivision, this statistic is classically used to evaluate population structure at a given genomic locus. With large numbers of loci, unsupervised approaches such as principal component analysis (PCA) have, however, become prominent in recent analyses of population structure. In this study, we describe the relationships between Wrights inbreeding coefficients and PCA for a model of K discrete populations. Our theory provides an equivalent definition of FST based on the decomposition of the genotype matrix into between and within-population matrices. The average value of Wrights FST over all loci included in the genotype matrix can be obtained from the PCA of the between-population matrix. Assuming that a separation condition is fulfilled and for reasonably large data sets, this value of FST approximates the proportion of genetic variation explained by the first (K - 1) principal components accurately. The new definition of FST is useful for computing inbreeding coefficients from surrogate genotypes, for example, obtained after correction of experimental artifacts or after removing adaptive genetic variation associated with environmental variables. The relationships between inbreeding coefficients and the spectrum of the genotype matrix not only allow interpretations of PCA results in terms of population genetic concepts but extend those concepts to population genetic analyses accounting for temporal, geographical and environmental contexts. Authors summaryPrincipal component analysis (PCA) is the most-frequently used approach to describe population genetic structure from large population genomic data sets. In this study, we show that PCA not only estimates ancestries of sampled individuals, but also computes the average value of Wrights inbreeding coefficient over the loci included in the genotype matrix. Our result shows that inbreeding coefficients and PCA eigenvalues provide equivalent descriptions of population structure. As a consequence, PCA extends the definition of this coefficient beyond the framework of allelic frequencies. We give examples on how FST can be computed from ancient DNA samples for which genotypes are corrected for coverage, and in an ecological genomic example where a proportion of genetic variation is explained by environmental variables.

evolutionary biology↗

bioRxiv2020

LEA 3: Factor models in population genetics and ecological genomics with R

A major objective of evolutionary biology is to understand the processes by which organisms have adapted to various environments, and to predict the response of organisms to new or future conditions. The availability of large genomic and environmental data sets provides an opportunity to address those questions, and the R package LEA has been introduced to facilitate population and ecological genomic analyses in this context. By using latent factor models, the program computes ancestry coefficients from population genetic data, and performs genotype-environment association analyses with correction for unobserved confounding variables. In this study, we present new functionalities of LEA, which include imputation of missing genotypes, fast algorithms for latent factor mixed models using multivariate predictors for genotype-environment association studies, population differentiation tests for admixed or continuous populations, and estimation of genetic offset based on climate models. The new functionalities are implemented in version 3.0 and higher releases of the package. Using simulated and real data sets, our study provides evaluations and examples of applications, outlining important practical considerations when analyzing ecological genomic data in R.

evolutionary biology↗

bioRxiv2020

Sparse latent factor regression models for genome-wide and epigenome-wide association studies

Association of phenotypes or exposures with genomic and epigenomic data faces important statistical challenges. One of these challenges is to account for variation due to unobserved confounding factors, such as individual ancestry or cell-type composition in tissues. This issue can be addressed with penalized latent factor regression models, where penalties are introduced to cope with high dimension in the data. If a relatively small proportion of genomic or epigenomic markers correlate with the variable of interest, sparsity penalties may help to capture the relevant associations, but the improvement over non-sparse approaches has not been fully evaluated yet. Here, we present least-squares algorithms that jointly estimate effect sizes and confounding factors in sparse latent factor regression models. In simulated data, sparse latent factor regression models generally achieved higher statistical performance than other sparse methods, including the least absolute shrinkage and selection operator (LASSO) and a Bayesian sparse linear mixed model (BSLMM). In generative model simulations, statistical performance was slightly lower (while being comparable) to non-sparse methods, but in simulations based on empirical data, sparse latent factor regression models were more robust to departure from the model than the non-sparse approaches. We applied sparse latent factor regression models to a genome-wide association study of a flowering trait for the plant Arabidopsis thaliana and to an epigenome-wide association study of smoking status in pregnant women. For both applications, sparse latent factor regression models facilitated the estimation of non-null effect sizes while overcoming multiple testing issues. The results were not only consistent with previous discoveries, but they also pinpointed new genes with functional annotations relevant to each application.

bioinformatics↗

bioRxiv2020

Genome wide association study pinpoints key agronomic QTLs in African rice Oryza glaberrima

BackgroundAfrican rice, Oryza glaberrima, is an invaluable resource for rice cultivation and for the improvement of biotic and abiotic resistance properties. Since its domestication in the inner Niger delta ca. 2500 years BP, African rice has colonized a variety of ecologically and climatically diverse regions. However, little is known about the genetic basis of quantitative traits and adaptive variation of agricultural interest for this species. ResultsUsing a reference set of 163 fully re-sequenced accessions, we report the results of a Genome Wide Association Study carried out for African rice. We investigated a diverse panel of traits, including flowering date, panicle architecture and resistance to Rice yellow mottle virus. For this, we devised a pipeline using complementary statistical association methods. First, using flowering time as a target trait, we demonstrated that we could successfully retrieve known genes from the rice flowering pathway, and identified new genomic regions that would deserve more study. Then we applied our pipeline to panicle- and resistance-related traits, highlighting some interesting QTLs and candidate genes (including Rymv1 for resistance and SP1, Ghd7.1, APO1 and OsMADS1 for panicle architecture). Lastly, using a high-resolution climate database, we performed an association analysis based on climatic variables, searching for genomic regions that might be involved in adaptation to climatic variations. ConclusionOur results collectively provide insights into the extent to which adaptive variation is governed by sequence diversity within the O. glaberrima genome, paving the way for in-depth studies of the genetic basis of traits of interest that might be useful to the rice breeding community.

plant biology↗

bioRxiv2019

Inference of population genetic structure from temporal samples of DNA

The recent years have seen a growing number of studies investigating evolutionary questions using ancient DNA techniques and temporal samples of DNA. To address these questions, one of the most frequently-used algorithm is based on principal component analysis (PCA). When PCA is applied to temporal samples, the sample dates are, however, ignored during analysis, which could lead to some misinterpretations of the results. Here we introduce a new factor analysis (FA) method for which individual scores are corrected for the effect of allele frequency drift through time. Based on a diffusion approximation, our approach approximates allele frequency drift in a random mating population by a Brownian process. Exact solutions for estimates of corrected factors are obtained, and a fast estimation algorithm is presented. We compared data representations obtained from the FA method with PCA and with PC projections in simulations of divergence and admixture scenarios. Then we applied FA with correction for temporal drift to study the evolution of hepatitis C virus in a patient infected by multiple strains, and to describe the population structure of ancient European samples.

evolutionary biology↗

bioRxiv2018

LFMM 2.0: Latent factor models for confounder adjustment in genome and epigenome-wide association studies

MotivationGenome-wide, epigenome-wide and gene-environment association studies are plagued with the problems of confounding and causality. Although those problems have received considerable attention in each application field, no consensus have emerged on which approaches are the most appropriate to solve this problem. Current methods use approximate heuristics for estimating confounders, and often ignore correlation between confounders and primary variables, resulting in suboptimal power and precision.\n\nResultsIn this study, we developed a least-squares estimation theory of confounder estimation using latent factor models, providing a unique framework for several categories of genomic data. Based on statistical learning methods, the proposed algorithms are fast and efficient, and can be proven to provide optimal solutions mathematically. In simulations, the algorithms outperformed commonly used methods based on principal components and surrogate variable analysis. In analysis of methylation profiles and genotypic data, they provided new insights on the molecular basis of diseases and adaptation of humans to their environment.\n\nAvailability and implementationSoftware is available in the R package lfmm at https://bcm-uga.github.io/lfmm/.

bioinformatics↗