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K. A. and K. H. acknowledge funding from the Uehara Memorial
Foundation, the Takeda Science Foundation, the Mitsubishi Foundation,
Core Research for Evolutionary Medical Science and Technology,
and Leading Advanced Projects for Medical Innovation, a program of
the Japan Agency for Medical Research and Development. C.L. and
R.J.X. acknowledge funding from the U. S. NIH (grants DK043351 and
DK92405), the Helmsley Charitable Trust, and the Crohn’s & Colitis
Foundation. We thank P. Wilmes, J. Baginska, M. Wakazaki, O. Ohara,
and Y. Arakawa for their technical support and P. Burrows for helpful
comments. M.H., H. M., and Y.K. conceived the research and performed
initial experiments; K.H. planned experiments, analyzed data, and
wrote the manuscript together with K.A., C.L., and R.J.X.; K.A., T.K.,
S. N., Y.K., I. M., K. Y., E. W., T. T., and C.A. T performed gnotobiotic studies,
immunological analyses, and bacterial cultures; C. L., D.G., R.C.J., J.S.,
R.J.X., E.E., W.S., H.S.S., and M.H. performed bacterial sequence and
microbiome analyses; M.S., K. T., and S.N. performed EM analyses;
H. Y. provided clinical samples; and K.C., J.K.K., and S.A.R. provided
essential materials and contributed to data discussions. J.K.K. is a
recipient of Public Health Service grant R37 HL079142. K.H. is a
scientific advisory board member of Vedanta Biosciences. All data and
code to understand and assess the conclusions of this research
are available in the main text, the supplementary materials, and the
indicated repositories. Sequences of the genome of Klebsiella spp.
and the 16S rRNA sequence data set are deposited in the DNA Data
Bank of Japan under accession numbers PRJDB5883-5886 and
PRJDB5967, respectively. The raw and processed RNA-seq data are
deposited in the National Center for Biotechnology Information’s
Gene Expression Omnibus under accession number GSE23056.
Klebsiella strains are available under a material transfer
agreement with Keio University.
Materials and Methods
Figs. S1 to S12
Tables S1 to S4
2 May 2017; accepted 7 September 2017
Recent natural selection causes
adaptive evolution of an avian
Mirte Bosse,1,2 Lewis G. Spurgin,3,4 Veronika N. Laine,1 Ella F. Cole,3 Josh A. Firth,3
Phillip Gienapp,1 Andrew G. Gosler,3 Keith McMahon,3 Jocelyn Poissant,5,6
Irene Verhagen,1 Martien A. M. Groenen,2 Kees van Oers,1 Ben C. Sheldon,3
Marcel E. Visser,1,2 Jon Slate5†
We used extensive data from a long-term study of great tits (Parus major) in the United Kingdom
and Netherlands to better understand how genetic signatures of selection translate into
variation in fitness and phenotypes. We found that genomic regions under differential selection
contained candidate genes for bill morphology and used genetic architecture analyses to
confirm that these genes, especially the collagen gene COL4A5, explained variation in bill length.
COL4A5 variation was associated with reproductive success, which, combined with
spatiotemporal patterns of bill length, suggested ongoing selection for longer bills in the
United Kingdom. Last, bill length and COL4A5 variation were associated with usage of feeders,
suggesting that longer bills may have evolved in the United Kingdom as a response to
To demonstrate evolutionary adaptation in wild populations, we must identify pheno- types under selection, understand the ge- netic basis of those phenotypes along with effects on fitness, and identify potential drivers of selection. The best-known demonstrations
of genes underlying evolution through natural
selection usually involve strong selection (“hard
sweeps”) on genetic variants that may be recently
derived, with a major effect on variation in preselected phenotypes (1–3). However, most quantitative phenotypes are polygenic (4), and for these
traits, selection is likely to act on many preexisting
genetic variants of small effect (5). Detecting so-called polygenic selection is challenging because
selection acts on multiple loci simultaneously,
and selection coefficients are likely to be small
(6). Most attempts to detect polygenic selection
have focused on gene sets rather than individual
loci (7). Furthermore, even if population genomic
analyses identify genes under selection, these analyses are rarely combined with detailed ecological
and behavioral data (8–10), and as a result, linking
all three components of the genotype-phenotype-fitness continuum remains a challenge. In this
study, we combined fine-scale ecological and genomic data to study adaptive evolution in the great
tit (Parus major), a widespread and abundant
passerine bird and well-known ecological model
system (11) with excellent genomic resources (12).
To do so, we analyzed genomic variation within
and among three long-term study populations
from the United Kingdom (Wytham, n = 949 birds)
and the Netherlands (Oosterhout, n = 254 birds
and Veluwe, n = 1812 birds) (Fig. 1A).
After filtering (supplementary materials, materials and methods), our data set comprised
2322 great tits typed at 485,122 single-nucleotide
polymorphisms (SNPs). Levels of genetic diversity
were high, and linkage disequilibrium (LD) decayed rapidly within all three sample sites (fig. S1).
Admixture and principal component analyses (PCA)
both suggest that genetic structure is low (Fig. 1).
These findings demonstrate a large effective population size and confirm high levels of gene flow
in the species (12, 13), making the long-term study
populations well suited to studying evolutionary
To identify loci under divergent selection between the UK and Dutch populations, we ran a
genome-wide association study (GWAS) using the
first eigenvector from the PCA as a “phenotype”
(EigenGWAS) (14). We identified highly significant
outlier regions of the genome likely to be under
divergent selection (Fig. 2A and fig. S2), which
were supported by fixation index (FST) analyses
(fig. S3). The majority of these outlier regions contained candidate genes (such as COL4A5, SIX2,
TRPS1, and NELL1) involved in skeletal development and morphogenesis (Fig. 2, A to C; table S1;
and database S1). Genes associated with the ontology term “palate development” [Gene Ontology
(GO) 0060021; genes ALX4, BMPR1A, SATB2,
INHBA, and GLI3] were more significantly overrepresented than any other GO term (
Bonferroni-corrected P = 2.9 × 10−5) (Fig. 2C and database S1).
The strongest single-marker signal was found at
the LRRIQ1 gene (table S1 and database S1), where
there was evidence of selection in Wytham, but
not Veluwe (fig. S4). LRRIQ1 is one of four genes
1Department of Animal Ecology, Netherlands Institute of
Ecology (NIOO-KNAW), Wageningen, Netherlands. 2Wageningen
University and Research–Animal Breeding and Genomics,
Netherlands. 3Edward Grey Institute, Department of Zoology,
University of Oxford, UK. 4School of Biological Sciences, University
of East Anglia, Norwich Research Park, UK. 5Department of Animal
and Plant Sciences, University of Sheffield, UK. 6Centre for
Ecology and Conservation, College of Life and Environmental
Sciences, University of Exeter, Penryn, UK.
*These authors contributed equally to this work. †Corresponding
author. Email: email@example.com