Genes mirror geography within Europe

By John Novembre, T. Johnson, K. Bryc, Zoltán Kutalik, A. Boyko, A. Auton, Amit R. Indap, K. King, Sven Bergmann, Matthew R. Nelson and 2 more. Nature, 2008

1,451 citations recorded by the source corpus.

Subfields: Genetics

View Paper: https://www.semanticscholar.org/paper/2828e44b9dee6057455323dbf43376838970094d

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In Genetics

Citations in the corpus

Contrastive Principal Component Analysis (2017, background): “…is often used to visualize the relationship between ethnic populations based on ge-netic variants, because projecting the genetic variants into two dimensions often produces “maps” that offer striking visualizations of geographic and historic trends (Cavalli-Sforza, 1998; Novembre et al., 2008).”
Visualizing spatial population structure with estimated effective migration surfaces (2014, methodology): “We used a focal subset of 197,146 autosomal SNPs and 1,379 individuals analyzed in a previous publication [20], with the individual IDs and marker list available from https: //github.com/NovembreLab/Novembre_etal_2008_misc .”
Genome-wide association studies in diverse populations (2010, background): “Although large population-genetic studies have detected subtle geographic gradients in allele frequencies across the European continen”
Maximum Likelihood Estimation of Biological Relatedness from Low Coverage Sequencing Data (2015, neutral): “004 [31] and Indian ethnicities 0.”
Variance component model to account for sample structure in genome-wide association studies (2010, neutral): “Some principal components may represent broad differences across individuals within a given data set, effectively capturing a few major axes of population structure, but it is unclear how to interpret the rest of the principal components as surrogates of sample structur”
Quality Control Procedures for Genome Wide Association Studies (2011, neutral): “31 common in populations of European ancestry (Novembre et al., 2008), which are sources of stratiﬁcation that will often appear in the top principal components.”
The Genetic Ancestry of African Americans, Latinos, and European Americans across the United States (2015, neutral): “27,63,65 If such regions were to drive artifacts of spurious ancestry, we would expect that segments of local ancestry would probably occur around these regions, rather than in a uniform distribution across the genome.”
Reconstructing Native American Population History (2012, background): “78, 629-644 4 Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals.”
Applications of next generation sequencing in molecular ecology of non-model organisms (2011, background): “Massively increasing the number of markers will enable researchers not only to get better precision in population genetic studies (Novembre et al., 2008), QTL and linkage disequilibrium (LD) mapping projects (Slate et al., 2009) and kinship assignments (Santure et al., 2010), but also to pursue…”
An integrated metagenomics pipeline for strain profiling reveals novel patterns of bacterial transmission and biogeography (2016, methodology): “To explore how this variation correlated with geography and sampling depth, we conducted a principal component analysis (PCA) of gene content for each bacterial species, as has been done to study the geographic structure of human populations using polymorphism data (Novembre et al. 2008).”
Robust Computation of Linear Models by Convex Relaxation (2012, methodology): “Population stratification Low-dimensional models of single nucleotide polymorphism (SNP) data have been used to show that the genotype of an individual is correlated with her geographical ancestry [57].”
Multi-Population Classical HLA Type Imputation (2013, background): “The figure displays samples from HLARES_ALL 1/3 stratified by the samples’ first two principal components (it is well-known that PCA can be used to control for population stratification [24] and is informative of relatedness [25]).”
Fast Principal Component Analysis of Large-Scale Genome-Wide Data (2014, background): “The top principal components (PCs) of SNP data have been shown to map well to geographic distances between human populations [1,3], thus capturing the coarse-grain allelic variation between these groups.”