Dietary Variation and Evolution of Gene Copy Number among Dog Breeds

Prolonged human interactions and artificial selection have influenced the genotypic and phenotypic diversity among dog breeds. Because humans and dogs occupy diverse habitats, ecological contexts have likely contributed to breed-specific positive selection. Prior to the advent of modern dog-feeding practices, there was likely substantial variation in dietary landscapes among disparate dog breeds. As such, we investigated one type of genetic variant, copy number variation, in three metabolic genes: glucokinase regulatory protein (GCKR), phytanol-CoA 2-hydroxylase (PHYH), and pancreatic α-amylase 2B (AMY2B). These genes code for proteins that are responsible for metabolizing dietary products that originate from distinctly different food types: sugar, meat, and starch, respectively. After surveying copy number variation among dogs with diverse dietary histories, we found no correlation between diet and positive selection in either GCKR or PHYH. Although it has been previously demonstrated that dogs experienced a copy number increase in AMY2B relative to wolves during or after the dog domestication process, we demonstrate that positive selection continued to act on amylase copy number in dog breeds that consumed starch-rich diets in time periods after domestication. Furthermore, we found that introgression with wolves is not responsible for deterioration of positive selection on AMY2B among diverse dog breeds. Together, this supports the hypothesis that the amylase copy number expansion is found universally in dogs

Disentangling Immediate Adaptive Introgression from Selection on Standing Introgressed Variation in Humans.

Recent studies have reported evidence suggesting that portions of contemporary human genomes introgressed from archaic hominin populations went to high frequencies due to positive selection. However, no study to date has specifically addressed the postintrogression population dynamics of these putative cases of adaptive introgression. Here, for the first time, we specifically define cases of immediate adaptive introgression (iAI) in which archaic haplotypes rose to high frequencies in humans as a result of a selective sweep that occurred shortly after the introgression event. We define these cases as distinct from instances of selection on standing introgressed variation (SI), in which an introgressed haplotype initially segregated neutrally and subsequently underwent positive selection. Using a geographically diverse data set, we report novel cases of selection on introgressed variation in living humans and shortlist among these cases those whose selective sweeps are more consistent with having been the product of iAI rather than SI. Many of these novel inferred iAI haplotypes have potential biological relevance, including three that contain immune-related genes in West Siberians, South Asians, and West Eurasians. Overall, our results suggest that iAI may not represent the full picture of positive selection on archaically introgressed haplotypes in humans and that more work needs to be done to analyze the role of SI in the archaic introgression landscape of living humans

Genomic analyses inform on migration events during the peopling of Eurasia

High-coverage whole-genome sequence studies have so far focused\ud on a limited number1 of geographically restricted populations2–5,\ud or been targeted at specific diseases, such as cancer6. Nevertheless,\ud the availability of high-resolution genomic data has led to the\ud development of new methodologies for inferring population\ud history7–9 and refuelled the debate on the mutation rate in humans10.\ud Here we present the Estonian Biocentre Human Genome Diversity\ud Panel (EGDP), a dataset of 483 high-coverage human genomes\ud from 148 populations worldwide, including 379 new genomes from\ud 125 populations, which we group into diversity and selection\ud sets. We analyse this dataset to refine estimates of continent-wide\ud patterns of heterozygosity, long- and short-distance gene flow, archaic\ud admixture, and changes in effective population size through time as\ud well as for signals of positive or balancing selection. We find a genetic\ud signature in present-day Papuans that suggests that at least 2% of\ud their genome originates from an early and largely extinct expansion\ud of anatomically modern humans (AMHs) out of Africa. Together\ud with evidence from the western Asian fossil record11, and admixture\ud between AMHs and Neanderthals predating the main Eurasian\ud expansion12, our results contribute to the mounting evidence for\ud the presence of AMHs out of Africa earlier than 75,000 years ago

Genomic analyses inform on migration events during the peopling of Eurasia.

High-coverage whole-genome sequence studies have so far focused on a limited number of geographically restricted populations, or been targeted at specific diseases, such as cancer. Nevertheless, the availability of high-resolution genomic data has led to the development of new methodologies for inferring population history and refuelled the debate on the mutation rate in humans. Here we present the Estonian Biocentre Human Genome Diversity Panel (EGDP), a dataset of 483 high-coverage human genomes from 148 populations worldwide, including 379 new genomes from 125 populations, which we group into diversity and selection sets. We analyse this dataset to refine estimates of continent-wide patterns of heterozygosity, long- and short-distance gene flow, archaic admixture, and changes in effective population size through time as well as for signals of positive or balancing selection. We find a genetic signature in present-day Papuans that suggests that at least 2% of their genome originates from an early and largely extinct expansion of anatomically modern humans (AMHs) out of Africa. Together with evidence from the western Asian fossil record, and admixture between AMHs and Neanderthals predating the main Eurasian expansion, our results contribute to the mounting evidence for the presence of AMHs out of Africa earlier than 75,000 years ago.Support was provided by: Estonian Research Infrastructure Roadmap grant no 3.2.0304.11-0312; Australian Research Council Discovery grants (DP110102635 and DP140101405) (D.M.L., M.W. and E.W.); Danish National Research Foundation; the Lundbeck Foundation and KU2016 (E.W.); ERC Starting Investigator grant (FP7 - 261213) (T.K.); Estonian Research Council grant PUT766 (G.C. and M.K.); EU European Regional Development Fund through the Centre of Excellence in Genomics to Estonian Biocentre (R.V.; M.Me. and A.Me.), and Centre of Excellence for Genomics and Translational Medicine Project No. 2014-2020.4.01.15-0012 to EGC of UT (A.Me.) and EBC (M.Me.); Estonian Institutional Research grant IUT24-1 (L.S., M.J., A.K., B.Y., K.T., C.B.M., Le.S., H.Sa., S.L., D.M.B., E.M., R.V., G.H., M.K., G.C., T.K. and M.Me.) and IUT20-60 (A.Me.); French Ministry of Foreign and European Affairs and French ANR grant number ANR-14-CE31-0013-01 (F.-X.R.); Gates Cambridge Trust Funding (E.J.); ICG SB RAS (No. VI.58.1.1) (D.V.L.); Leverhulme Programme grant no. RP2011-R-045 (A.B.M., P.G. and M.G.T.); Ministry of Education and Science of Russia; Project 6.656.2014/K (S.A.F.); NEFREX grant funded by the European Union (People Marie Curie Actions; International Research Staff Exchange Scheme; call FP7-PEOPLE-2012-IRSES-number 318979) (M.Me., G.H. and M.K.); NIH grants 5DP1ES022577 05, 1R01DK104339-01, and 1R01GM113657-01 (S.Tis.); Russian Foundation for Basic Research (grant N 14-06-00180a) (M.G.); Russian Foundation for Basic Research; grant 16-04-00890 (O.B. and E.B); Russian Science Foundation grant 14-14-00827 (O.B.); The Russian Foundation for Basic Research (14-04-00725-a), The Russian Humanitarian Scientific Foundation (13-11-02014) and the Program of the Basic Research of the RAS Presidium “Biological diversity” (E.K.K.); Wellcome Trust and Royal Society grant WT104125AIA & the Bristol Advanced Computing Research Centre (http://www.bris.ac.uk/acrc/) (D.J.L.); Wellcome Trust grant 098051 (Q.A.; C.T.-S. and Y.X.); Wellcome Trust Senior Research Fellowship grant 100719/Z/12/Z (M.G.T.); Young Explorers Grant from the National Geographic Society (8900-11) (C.A.E.); ERC Consolidator Grant 647787 ‘LocalAdaptatio’ (A.Ma.); Program of the RAS Presidium “Basic research for the development of the Russian Arctic” (B.M.); Russian Foundation for Basic Research grant 16-06-00303 (E.B.); a Rutherford Fellowship (RDF-10-MAU-001) from the Royal Society of New Zealand (M.P.C.)

Results from analysis of aCGH data.

<p><b>a</b>) Relationship between Shar Pei and Alaskan Malamute mean log₂ ratios for 157 copy number variable sites in the dog genome. <i>AMY2B</i> mean log₂ ratio is plotted in red, and deviates significantly from the null distribution. <b>b</b>) Histogram of residuals from linear regression performed on mean log₂ ratios from aCGH data in the Alaskan Malamute and Shar Pei. Value of residuals appears on the x-axis while density, or proportion of a specific residual occurring, falls on the y-axis. The residual for mean log₂ ratio in <i>AMY2B</i> is indicated in red and falls 5.86 standard deviations away from the mean (0±.2).</p

Cladogram depicting the relationship between the dog breeds evaluated in this study and diploid <i>AMY2B</i> copy number.

<p>Cladogram relations were determined from SNP data and was adapted from vonHoldt et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148899#pone.0148899.ref012" target="_blank">12</a>].</p

Distribution of dog-wolf difference of derived allele frequency (DAF).

<p>(<b>a</b>) There is no significant difference between the distribution of DAF genome-wide on chromosome 6 (Welch Two Sample t-test, p-value = 0.5867.) (<b>b</b>) Ancestry Informative SNPs (aiSNPs) are defined as those with an absolute DAF greater than two standard deviations from the genome-wide mean. Threshold are greater than 0.557 or less than -0.579.</p

Absolute latitude and starch intake.

<p><b>a</b>) <i>AMY2B</i> CNV regressed by absolute latitude at breed location of origin for 252 dogs. When absolute latitude is used as a proxy for starch intake, absolute latitude does not predict <i>AMY2B</i> copy number. Variation increases at 40° latitude. <b>b</b>) Tukey boxplot of <i>AMY2B</i> copy number in dog breeds originating from below 40° latitude and above 40° latitude. Variation in <i>AMY2B</i> copy number increases at higher latitudes.</p

Diet and <i>AMY2B</i> copy number variation.

<p><b>a</b>) Density plot of ddPCR diploid <i>AMY2B</i> copy number for dogs that traditionally consumed high-starch diets and low-starch diets. Density reflects frequency with which a given diploid copy number appears in each population. <b>b</b>) Tukey boxplot of diploid <i>AMY2B</i> copy number for dogs that traditionally consumed high-starch diets and low-starch diets. <b>c</b>) Tukey boxplot of diploid <i>AMY2B</i> copy number for specific dog breeds that traditionally consumed high-starch diets and low-starch diets.</p