• Canada
• Research dataclose
• Other research productsclose
• Open Access close
• European Commission close
• GR close

Understanding the causes and consequences of population phenotypic divergence is a central goal in ecology and evolution. Phenotypic divergence among populations can result from genetic divergence, phenotypic plasticity or a combination of the two. However, few studies have deciphered these mechanisms for populations geographically close and connected by gene flow, especially in the case of personality traits. In this study, we used a common garden experiment to explore the genetic basis of the phenotypic divergence observed between two blue tit (Cyanistes caeruleus) populations inhabiting contrasting habitats separated by 25 km, for two personality traits (exploration speed and handling aggression), one physiological trait (heart rate during restraint) and two morphological traits (tarsus length and body mass). Blue tit nestlings were removed from their population and raised in a common garden for up to five years. We then compared adult phenotypes between the two populations, as well as trait-specific Qst and Fst . Our results revealed differences between populations similar to those found in the wild, suggesting a genetic divergence for all traits. Qst - Fst comparisons revealed that the traits divergences likely result from dissimilar selection patterns rather than from genetic drift. Our study is one of the first to report a Qst - Fst comparison for personality traits and adds to the growing body of evidence that population genetic divergence is possible at a small scale for a variety of traits including behavioural traits. Data filesArchive.zip

In this video we observe the double-wing featured shown in figure 5 of the manuscript. The transmission of the waveguide increases for a liquid's RI of 1.602 matching the glass's. while on the other side, with a liquid of 1.604 matching the effective RI of the guided mode we observe a decrease in the transmission.

Simulation of a black-hole binary system evolved by the SpEC code.

This file contains code to model adaptive trade-offs across a species' range, and to produce Figs. 2 and 3.

Additional file 1: Data S1. Participant characteristics. S1a: Characteristics of individuals with CSF Aβ1-42 and tau measurements available; S1b: Characteristics of individuals with CSF proteomic data. Data S2. Protein annotation and statistics of group comparisons of protein levels. Data S3a. Full list of GO biological processes associated with proteins that differ according to group and clinical stage. Data S3b. SynGO enriched synaptic cellular components and biological processes that differ according to group. Data S4a. Estimated marginal means of AD GWAS-based polygenic risk scores in controls, AD individuals with increased t-tau and AD individuals with normal t-tau. Data S4b. Top 1000 SNPS from GWAS on AD individuals with increased t-tau and normal t-tau in pooled ADNI and EMIF-AD MBD cohorts. Data S4c. Difference in MAGMA gene score between AD individuals with increased t-tau and normal t-tau based on t-tau GWAS in pooled ADNI and EMIF-AD MBD cohorts. Data S4d. Difference in GO biological process MAGMA geneset score between AD individuals with increased t-tau and normal t-tau based on t-tau GWAS in pooled ADNI and EMIF-AD MBD cohorts. Data S5a. Correlation between genetic risk score and CSF protein level in individuals with abnormal Aβ1-42. Data S5b. Association of the number of GMNC rs9877502-A risk alleles and number of APOE-e4 alleles with CSF protein concentrations in a linear model in individuals with AD. Data S5c. GO-BP processes enriched for proteins that have a positive or negative association with the number of rs9877502-A risk alleles in an additive model. Data S6. Annual change in imaging measures.

Table S3. Cis acting methylation quantitative trait loci in the 40 genes highlighted in the EWAS. (XLS 31 kb)

Genotype data and related phenotype data for Ovarian Cancer Association Consortium project investigating common variantion in GTPAse genes and ovarian cancer risk

Simulation of a black-hole binary system evolved by the SpEC code.

Additional file 2: Dataset S1. Longitudinal microbial composition (relative abundance) in viremic controllers and non-controllers analyzed by MetaPhlAn2.0. Dataset S2. Raw bacterial protein counts at baseline in viremic controllers and non-controllers analysed using Scaffold Q+ software v4.9.0. Dataset S3. Raw gene counts at baseline in viremic controllers and non-controllers estimated using RSEM v1.3.0. Dataset S4. Soluble factors (normalized protein expression units) from the Olink inflammation panel at baseline in viremic controllers and non-controllers. Dataset S5. List of transcripts significantly correlated with the Bacteroidales:Clostridiales ratio adjusted for multiple comparisons (spearman’s rho and corresponding q-value<0.05 are reported). Dataset S6. Spearman’s correlation (rho=0.5) between viral reservoir (CA HIV-1 RNA and HIV-1 DNA), bacterial species (Bacteroidales and Clostrdiales), transcripts correlated with the ratio Bacteroidales / Clostridiales and differentially abundant microbial proteins (p<0.025). Dataset S7. Benjamini–Hochberg multiple hypothesis correction for unadjusted p-values ≤ 0.05 (metagenomics and metaproteomics datasets).

Authors: Stoev et al. 2013 Data type: genomic The archive contains the following data: 1) fasta-Alignment as the basis for all analyses (.FASTA), 2) mega-file for the calculation of the genetic distances and the NJ tree (.MDSX), 3) NJ-tree in Newick format (.NWK), 4) graph of the TCS Software for the Statistical Parsimony method (.GRAPH) File: E_cavernicolus.rar