CoChat
Sign Up
Cited by 233Genetic and phenotypic traits in livestockGenetic Mapping and Diversity in Plants and AnimalsGenetic diversity and population structure

The DOI matches its Crossref record; 79 of the 79 references it cites were found in a registry or an index.

Classic Selective Sweeps Revealed by Massive Sequencing in Cattle

Open access
Qanbari, Saber, Pausch, Hubert, Jansen, Sandra, Somel, Mehmet, Strom, Tim M., Fries, Ruedi, Nielsen, Rasmus, Simianer, Henner·2014·PLoS Genetics
DOI 10.1371/journal.pgen.1004148PMC PMC3937232
Open this paper in CoChat — free accountOpen PDF

Abstract

Human driven selection during domestication and subsequent breed formation has likely left detectable signatures within the genome of modern cattle. The elucidation of these signatures of selection is of interest from the perspective of evolutionary biology, and for identifying domestication-related genes that ultimately may help to further genetically improve this economically important animal. To this end, we employed a panel of more than 15 million autosomal SNPs identified from re-sequencing of 43 Fleckvieh animals. We mainly applied two somewhat complementary statistics, the integrated Haplotype Homozygosity Score (iHS) reflecting primarily ongoing selection, and the Composite of Likelihood Ratio (CLR) having the most power to detect completed selection after fixation of the advantageous allele. We find 106 candidate selection regions, many of which are harboring genes related to phenotypes relevant in domestication, such as coat coloring pattern, neurobehavioral functioning and sensory perception including KIT, MITF, MC1R, NRG4, Erbb4, TMEM132D and TAS2R16, among others. To further investigate the relationship between genes with signatures of selection and genes identified in QTL mapping studies, we use a sample of 3062 animals to perform four genome-wide association analyses using appearance traits, body size and somatic cell count. We show that regions associated with coat coloring significantly (P<0.0001) overlap with the candidate selection regions, suggesting that the selection signals we identify are associated with traits known to be affected by selection during domestication. Results also provide further evidence regarding the complexity of the genetics underlying coat coloring in cattle. This study illustrates the potential of population genetic approaches for identifying genomic regions affecting domestication-related phenotypes and further helps to identify specific regions targeted by selection during speciation, domestication and breed formation of cattle. We also show that Linkage Disequilibrium (LD) decays in cattle at a much faster rate than previously thought.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1371/journal.pgen.1004148

Cites

79 of 79 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 Comparison of the site frequency spectra from resequencing of 43 German Fleckvieh animals. SFS is represented for non-synonymous (Non_syn), synonymous (Syn) and inter-genic polymorphic (Int_genic) variants.
Figure 1 Comparison of the site frequency spectra from resequencing of 43 German Fleckvieh animals. SFS is represented for non-synonymous (Non_syn), synonymous (Syn) and inter-genic polymorphic (Int_genic) variants.CC BY · PubMed Central
Figure 2 A schematic representation of LD plotted as a function of distance. The decay of LD estimated from bovine SNP arrays of 50K and 700K (n = 1,293) are compared with the sequence data (n = 43). The inner plot displays a higher resolution of LD in pair-wise distances of <250 Kb from sequence data of which r2 values are down-sampled from all pairwise estimates (for more details see Material and Methods).
Figure 2 A schematic representation of LD plotted as a function of distance. The decay of LD estimated from bovine SNP arrays of 50K and 700K (n = 1,293) are compared with the sequence data (n = 43). The inner plot displays a higher resolution of LD in pair-wise distances of <250 Kb from sequence data of which r2 values are down-sampled from all pairwise estimates (for more details see Material and Methods).CC BY · PubMed Central
Figure 3 Genome-wide visualization of selection candidates (top 1% signals) localized by |iHS| (A) and CLR (B) metrics. Each dot represents a non-overlapping window of 40 Kb along BTA1 to BTA29. Panels C and D show a high resolution illustration of the candidate regions for the KIT/KDR and MITF genes, respectively on BTA6 and BTA22. |iHS| is plotted in overlapping windows of 40 Kb in steps of 5 Kb, and a grid size of 5 Kb was chosen for the CLR statistic. Finally, Panel E is a haplotype bifurcation plot of the KIT/KDR genes.
Figure 3 Genome-wide visualization of selection candidates (top 1% signals) localized by |iHS| (A) and CLR (B) metrics. Each dot represents a non-overlapping window of 40 Kb along BTA1 to BTA29. Panels C and D show a high resolution illustration of the candidate regions for the KIT/KDR and MITF genes, respectively on BTA6 and BTA22. |iHS| is plotted in overlapping windows of 40 Kb in steps of 5 Kb, and a grid size of 5 Kb was chosen for the CLR statistic. Finally, Panel E is a haplotype bifurcation plot of the KIT/KDR genes.CC BY · PubMed Central
Figure 4 Fleckvieh animals with different coat coloring phenotypes. (A) without spot, (B) spotted and (C) red head. The figures were kindly supplied by BAYERN-GENETIK GmbH (http://www.fleckvieh.de).
Figure 4 Fleckvieh animals with different coat coloring phenotypes. (A) without spot, (B) spotted and (C) red head. The figures were kindly supplied by BAYERN-GENETIK GmbH (http://www.fleckvieh.de).CC BY · PubMed Central
Figure 5 The visualization of the signals revealed by association analyses for coat coloring traits. GWAS are presented for the proportion of daughters with red head (A1) and the proportion of daughters without spotting (B1) based on 15,182,131 imputed variants in 3062 Fleckvieh animals. In (B1) the largest effects emerge from eight SNPs summarized in Table 2 with MITF (P = 2.65e-58) and KIT/KDR (P = 2.46e-44) at the top. Together, KIT/KDR and MITF explained 36.25% of the residual variance of the trait in the studied population. A2 and B2 are the corresponding quantile-quantile plots. Shown in blue is the quantile-quantile plot resulting from removal of all SNPs in the region of significant genes listed in Table 2, for both traits. The shaded area is the 95% concentration band under the null hypothesis of no association. Panels C and D are detailed overviews of the associated regions on BTA6 and BTA22, respectively. Variants in the promoter (defined to encompass 1,000 bp upstream of the transcription start), in the untranslated regions (UTR) and in the amino-acid coding region are highlighted with different color. The red triangles indicate the genomic positions of KIT and MITF genes.
Figure 5 The visualization of the signals revealed by association analyses for coat coloring traits. GWAS are presented for the proportion of daughters with red head (A1) and the proportion of daughters without spotting (B1) based on 15,182,131 imputed variants in 3062 Fleckvieh animals. In (B1) the largest effects emerge from eight SNPs summarized in Table 2 with MITF (P = 2.65e-58) and KIT/KDR (P = 2.46e-44) at the top. Together, KIT/KDR and MITF explained 36.25% of the residual variance of the trait in the studied population. A2 and B2 are the corresponding quantile-quantile plots. Shown in blue is the quantile-quantile plot resulting from removal of all SNPs in the region of significant genes listed in Table 2, for both traits. The shaded area is the 95% concentration band under the null hypothesis of no association. Panels C and D are detailed overviews of the associated regions on BTA6 and BTA22, respectively. Variants in the promoter (defined to encompass 1,000 bp upstream of the transcription start), in the untranslated regions (UTR) and in the amino-acid coding region are highlighted with different color. The red triangles indicate the genomic positions of KIT and MITF genes.CC BY · PubMed Central
Figure 6 The visualization of the signals revealed by association analyses for SCC trait. Manhattan plot presents the association of 15'182'131 imputed SNPs with the “somatic cell count” trait in 3602 Fleckvieh animals (A). Panel B represents corresponding quantile-quantile plot. Shown in blue is the quantile-quantile plot resulting from excluding SNPs in the region of significant genes. The shaded area represents 95% concentration band under the null hypothesis of no association. Panel C displays the associated region on BTA3 with a higher resolution. Variants in the promoter (defined to encompass 1,000 bp upstream of the transcription start), in the untranslated regions (UTR) and in the amino-acid coding region are highlighted with different color. Panel D provides a multi species alignment of DC-STAMP domain containing 1 encoded by DCST1 gene.
Figure 6 The visualization of the signals revealed by association analyses for SCC trait. Manhattan plot presents the association of 15'182'131 imputed SNPs with the “somatic cell count” trait in 3602 Fleckvieh animals (A). Panel B represents corresponding quantile-quantile plot. Shown in blue is the quantile-quantile plot resulting from excluding SNPs in the region of significant genes. The shaded area represents 95% concentration band under the null hypothesis of no association. Panel C displays the associated region on BTA3 with a higher resolution. Variants in the promoter (defined to encompass 1,000 bp upstream of the transcription start), in the untranslated regions (UTR) and in the amino-acid coding region are highlighted with different color. Panel D provides a multi species alignment of DC-STAMP domain containing 1 encoded by DCST1 gene.CC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 79 references

Cited by

Sorted by citations

See all 233

Related

Metadata

Authors
Qanbari, S., Pausch, H., Jansen, S., Somel, M., Strom, T. M., Fries, R., Nielsen, R., & Simianer, H.
Affiliations
  • University of Göttingen (DE)
  • Technical University of Munich (DE)
  • University of California, Berkeley (US)
  • Helmholtz Munich (DE)
Corresponding author
Qanbari, Saber
ORCID
Venue
PLoS Genetics
Publisher
Public Library of Science (PLoS)
Published
2014-02-27
Volume
10
Issue
2
Pages
e1004148
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1004148
PMC
PMC3937232
OpenAlex
W2047669864
Semantic Scholar
b97615af7d0e16d1c4ecfdba9de4183b17efa05b
Cited by
233
References
79
Citations by year
2026: 8 · 2025: 12 · 2024: 12 · 2023: 13 · 2022: 16 · 2021: 26 · 2020: 24 · 2019: 20 · 2018: 20 · 2017: 17 · 2016: 23 · 2015: 32 · 2014: 10
FWCI
17.90
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
  • GoeScholar The Publication Server of the Georg-August-Universität Göttingen (Georg-August-Universität Göttingen) (submittedVersion, cc-by)
  • OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
  • Site cant be reached (submittedVersion, cc-by)
  • GRO (submittedVersion, cc-by)
  • Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
  • ETH Zürich Research Collection (cc-by)
Funders
  • Deutsche Forschungsgemeinschaft
  • Bundesministerium für Bildung und Forschung (FKZ 0315528, 0315528)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic diversity and population structure
Keywords
cattle domestication, selective sweeps, Simmental cattle, coat color inheritance, KIT, MITF, MC1R, ErbB4, TAS2R16, linkage disequilibrium decay, genome-wide association study, body size, somatic cell count, breed formation
MeSH terms
Animals, Animals, Domestic, Breeding, Cattle, Genetics, Population, Haplotypes, Humans, Selection, Genetic, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Genome-Wide Association Study

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review