Biology subjects

Gaggiotti, O. E.

Publications and source records attributed to Gaggiotti, O. E..

4 recordsLinked to original sources

bioRxiv2021

Information-based summary statistics for spatial genetic structure inference

O_LIInference of spatial patterns of genetic structure often relies on parameter estimation and model evaluation using a set of summary statistics (SS) that summarise the information present in the data. An important subset of these SS is best described as diversity indices, which are based on information theory principles that can be classified as belonging to three different families encompassing a spectrum of information measures, qH. These include the richness family of order q = 0, ArSS; the Shannon family of order q = 1, HSS; and the heterozygosity family of order q = 2, HeSS. Although commonly used by ecologists, the Shannon family has been rather neglected by population geneticists and evolutionary biologists. However, recent population genetic studies have advocated their use, yet the power of these SS for spatial structure discrimination has not been systematically assessed. C_LIO_LIIn this study, we performed a comprehensive assessment of the three families of SS, as well as a fourth family consisting of SS belonging to the Shannon family but expressed in terms of Hill numbers [Formula], for spatial structure inference using simulated microsatellites data under typical spatial scenarios. To give an unbiased evaluation, we used three machine learning methods, Kernel Local Fisher discriminant analysis (KLFDA), random forest classification (RFC), and deep neural network (DL), to test the performance of different SS to discriminate between spatial scenarios, and then identified the most informative metrics for discriminatory power. C_LIO_LIResults showed that the SS family of order q = 1 expressed in terms of Hill numbers, [Formula], outperformed the other two families (Ar SS, He SS) as well as the untransformed Shannon entropy (H SS) family. Jaccard dissimilarity (J) and its Mantels r showed the highest discriminatory power to discriminate all spatial scenarios, followed by Shannon differentiation {Delta}D and its Mantels r. C_LIO_LIInformation-based summary statistics, especially the diversity of order q = 1 and Shannon differentiation measures, can increase the power of spatial structure inference. In addition, different sets of SS provide complementary power for discriminating between spatial scenarios. C_LI

genetics↗

bioRxiv2021

Deciphering signatures of natural selection via deep learning

Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. We propose a deep learning-based framework, DeepGenomeScan, that can detect signatures of local adaptation. We demonstrate that DeepGenomeScan outperformed PCA and RDA-based genome scans in identifying loci underlying quantitative traits subject to complex spatial patterns of selection. Noticeably, DeepGenomeScan increases statistical power by up to 47.25% under non-linear environmental selection patterns. We applied DeepGenomeScan to a European human genetic dataset and identified some well-known genes under selection and a substantial number of clinically important genes that were not identified using existing methods.

genomics↗

bioRxiv2021

Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC) significantly improves the accuracy of predicting geographic origin of individuals

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect geographic patterns from genetic data is principal components analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised nonlinear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC significantly improved the population separability compared with PCA and DAPC. The application to POPRES and CONVERGE datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals, and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.

genomics↗

bioRxiv2020

Selection on ancestral genetic variation fuels parallel ecotype formation in bottlenose dolphins

What are the mechanisms that allow species to extend their ranges and adapt to the novel environmental conditions they find in the newly available habitat? The study of parallel adaptation of pairs of populations to similar environments can provide great insights into this question. Here, we test for parallel evolution driven by niche specialization in a highly social marine mammal, the common bottlenose dolphin, Tursiops truncatus, and investigate the origins of the genetic variation driving local adaptation. Coastal ecotypes of common bottlenose dolphins have recurrently emerged in multiple regions of the world from pelagic ecotype populations, when novel habitat became available. Analyzing the whole genomes of 57 individuals using comparative population genomics approaches, we found that coastal ecotype evolution was relatively independent between the Atlantic and Pacific, but related between different regions within the Atlantic. We show that parallel adaptation to coastal habitat was facilitated by repeated selection on ancient alleles present as standing genetic variation in the pelagic populations. Genes under parallel adaptation to coastal habitats have roles in cognitive abilities and feeding. Therefore, parallel adaptation in long-lived social species may be driven by a combination of ecological opportunities, selection acting on ancient variants, and stable behavioural transmission of ecological specialisations. Tried and tested genetic variation that has been subject to repeated bouts of selection, may promote linked adaptive variants with minimal pleiotropic effects, thereby facilitating their persistence at low frequency in source populations and enabling parallel evolution.

evolutionary biology↗