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2020-3-7 Goat Evolution Cheats, Tips & Hints: 5 Tricks Ever Player Should Know. Casual Matt June 15, 2015. Goat Evolution is a very similar game in concept to Cow Evolution, and it’s made by the exact same developer – Tapps. The game is available for both iOS and Android platforms and in terms of mechanics, it pretty much works the same way as Cow.
AbstractThe platypus is an egg-laying mammal which, alongside the echidna, occupies a unique place in the mammalian phylogenetic tree. Despite widespread interest in its unusual biology, little is known about its population structure or recent evolutionary history. To provide new insights into the dispersal and demographic history of this iconic species, we sequenced the genomes of 57 platypuses from across the whole species range in eastern mainland Australia and Tasmania.
Using a highly improved reference genome, we called over 6.7 M SNPs, providing an informative genetic data set for population analyses. Our results show very strong population structure in the platypus, with our sampling locations corresponding to discrete groupings between which there is no evidence for recent gene flow. Genome-wide data allowed us to establish that 28 of the 57 sampled individuals had at least a third-degree relative among other samples from the same river, often taken at different times.
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Taking advantage of a sampled family quartet, we estimated the de novo mutation rate in the platypus at 7.0 × 10 −9/bp/generation (95% CI 4.1 × 10 −9–1.2 × 10 −8/bp/generation). We estimated effective population sizes of ancestral populations and haplotype sharing between current groupings, and found evidence for bottlenecks and long-term population decline in multiple regions, and early divergence between populations in different regions. This study demonstrates the power of whole-genome sequencing for studying natural populations of an evolutionarily important species. , IntroductionNext-generation sequencing technologies have greatly facilitated studies into the diversity and population structure of nonmodel organisms. For example, whole-genome sequencing (WGS) has been applied to investigate demographic history and levels of inbreeding in primates, with implications for conservation (;;;; ). It has also been used to study domesticated species such as pigs , dogs , maize , and bees , to infer the origins of domestication, its effect on effective population size ( N e) and nucleotide diversity, and to identify genes under selection during this process. Some studies have identified signatures of introgression or admixture (; ) between species, which is important to inform inference of past N e.
Others have used WGS data to identify particular genomic regions contributing to evolutionarily important traits, such as beak shape in Darwin’s finches , mate choice in cichlid fish , and migratory behavior in butterflies. Here, we describe a population resequencing study of the platypus ( Ornithorhynchus anatinus), which is one of the largest such studies of nonhuman mammals, and the first for a nonplacental mammal.In addition to laying eggs, platypuses have a unique set of characteristics , including webbed feet, a venomous spur (only in males), and a large bill that contains electroreceptors used for sensing their prey. Their karyotype is 2 n = 52 , and they have five different male-specific chromosomes (named Y chromosomes), and five different chromosomes present in one copy in males and two copies in females (X chromosomes), which form a multivalent chain in male meiosis.Though apparently secure across much of its eastern Australian range, the platypus has the highest conservation priority ranking among mammals when considering phylogenetic distinctiveness.
Given concerns about the impact of climate change , disease and other factors on platypus populations, there is a need to better understand past responses of platypus populations to climate change, and the extent of connectivity across the species range.The first platypus genome assembly (ornAna1) was generated using established whole-genome shotgun methods from a female from the Barnard River in New South Wales (NSW) (see ). This assembly was highly fragmented and did not contain any sequence from the Y chromosomes.
The initial genome paper included only a limited analysis of interindividual variation and population structure based on 57 polymorphic retrotransposon loci. Subsequently, several other studies have investigated diversity and population structure using microsatellites or mitochondrial DNA (mtDNA) (,;; ).
They reported much stronger differences between rather than within river systems, but found some evidence of migration between rivers that were close together, implying limited overland dispersal. Paper.Number of Samples.Sampling Locations.Genetic Data.90QLD, NSW, VIC, TAS, South Australia57 retrotransposons120Five river systems in NSW, predominantly Hawkesbury and Shoalhaven12 microsatellites28422 river systems across whole platypus rangeHaplotypes of mitochondrial control region and cytochrome b gene75233 river systems across NSW, Victoria, TASThree microsatellites, two mitochondrial haplotypesThis study5712 river systems across QLD, NSW, TAS6.7 million SNPs from WGS data. Paper.Number of Samples.Sampling Locations.Genetic Data.90QLD, NSW, VIC, TAS, South Australia57 retrotransposons120Five river systems in NSW, predominantly Hawkesbury and Shoalhaven12 microsatellites28422 river systems across whole platypus rangeHaplotypes of mitochondrial control region and cytochrome b gene75233 river systems across NSW, Victoria, TASThree microsatellites, two mitochondrial haplotypesThis study5712 river systems across QLD, NSW, TAS6.7 million SNPs from WGS data. Paper.Number of Samples.Sampling Locations.Genetic Data.90QLD, NSW, VIC, TAS, South Australia57 retrotransposons120Five river systems in NSW, predominantly Hawkesbury and Shoalhaven12 microsatellites28422 river systems across whole platypus rangeHaplotypes of mitochondrial control region and cytochrome b gene75233 river systems across NSW, Victoria, TASThree microsatellites, two mitochondrial haplotypesThis study5712 river systems across QLD, NSW, TAS6.7 million SNPs from WGS data. Paper.Number of Samples.Sampling Locations.Genetic Data.90QLD, NSW, VIC, TAS, South Australia57 retrotransposons120Five river systems in NSW, predominantly Hawkesbury and Shoalhaven12 microsatellites28422 river systems across whole platypus rangeHaplotypes of mitochondrial control region and cytochrome b gene75233 river systems across NSW, Victoria, TASThree microsatellites, two mitochondrial haplotypesThis study5712 river systems across QLD, NSW, TAS6.7 million SNPs from WGS data. Map showing our sampling locations within river regions in eastern and southeastern Australia. The lighter lines on the figure are rivers and the darker lines demarcate catchment areas.
The waterways our samples come from are indicated by arrows, with sample sizes in brackets after the river name. The specific catchments these rivers fall into and their corresponding larger drainage division are indicated in small letters after the river name. The text colors correspond to those used in later figures.
The transparent gray region represents the Great Dividing Range (GDR). Note that all samples except those from the Fish River, Gwydir River, and Rifle Creek come from river basins that drain east from the GDR. This map is adapted from one obtained from the Australian Bureau of Meteorology under a CC-BY license. Map showing our sampling locations within river regions in eastern and southeastern Australia. The lighter lines on the figure are rivers and the darker lines demarcate catchment areas. The waterways our samples come from are indicated by arrows, with sample sizes in brackets after the river name. The specific catchments these rivers fall into and their corresponding larger drainage division are indicated in small letters after the river name.
The text colors correspond to those used in later figures. The transparent gray region represents the Great Dividing Range (GDR). Note that all samples except those from the Fish River, Gwydir River, and Rifle Creek come from river basins that drain east from the GDR. This map is adapted from one obtained from the Australian Bureau of Meteorology under a CC-BY license.Each locus has a genealogy, which can be correlated for loci near each other on the same chromosome. At a particular locus, the actual genealogy is the result of chance events in the history of the sample, with the probabilities involved being affected by the demographic history of the population.
Thus, even perfect genealogies from a single locus (such as the mitochondrial control region; ) contain limited information about the demographic history of the population or about the ancestral relationships among different populations. Incomplete information from several loci, such as microsatellites , also contains limited information for the same reasons.
We thus chose to sequence entire genomes of the study individuals to give the widest possible source or information about their ancestral history.We sequenced the genomes of 57 platypuses from Queensland (QLD), New South Wales (NSW), and Tasmania (TAS) (;, online), in order to gain insights into the population genetics of the species. We investigated the differentiation between subpopulations, the relative historical population sizes and structure, and the extent of relatedness between the individuals sampled, which could be informative about the extent of individual platypus dispersal. Corpse party blood drive plot. Results Genome Reassembly and SNP CallingWe sequenced 57 platypus samples at 12–21× coverage, one in duplicate. We used the improved genome assembly ornAna3 for all analyses, and ran standard software to jointly call variants across all samples (PLATYPUS; ). The variant calls were filtered to produce a set of 6.7 M stringently filtered SNPs across 54 autosomal scaffolds comprising 965 Mb of the assembly. Although the sequence coverage was variable across samples, we found that the number of variants called was not affected by the sequence coverage for the individual (, online). Data QualityWe undertook two different approaches to assess the quality of our SNP callset.
In the first approach, two separate DNA samples from a single individual were sequenced. These were processed in identical fashion to all the other sequence data, with the processing blind to the fact that they were duplicates. After processing and SNP calling, we then compared the genotypes in the two samples from this individual. The rate of discordant genotypes between the two duplicate samples was very low (2.20 × 10 − 3 per SNP; 1.62 × 10 − 5/bp;, online); because an error in either duplicate could lead to discordant genotypes, this would lead to an estimated error rate of 1.10 × 10 − 3 per SNP and 8.1 × 10 − 6/bp.
During our analyses, we also discovered we had sampled a family quartet of two parents and two offspring (, online). This allowed us to use a second approach to assess the quality of our SNP callset by using the rate of Mendelian errors. We found a Mendelian error rate of 1.10 × 10 − 3 per SNP. In some configurations, an error in any one of the four genotypes would result in a Mendelian error; in others, an error would not be detected.
Both approaches suggest an error rate of order 0.001 per genotype, suggesting that the data set used in the analyses is of high quality. Our genotype error rate compares favorably with previous work in mountain gorillas despite our lower sequencing coverage.Sequencing a quartet also allowed us to estimate the switch error rate of haplotypes inferred in the new reference genome compared with the original assembly.
Switch errors are changes in the pattern of inheritance in the two offspring, either due to errors or real recombination events. We found that the switch error rate was reduced by nearly 80% for ornAna3 compared with ornAna1 (supplementary section S1 and table S5, online), indicating the new assembly contains far fewer errors. We conclude that the improved reference genome and stringent filters we have used mean that our data set is of high quality. RelativesUnlike earlier studies, our genome-scale data allowed us to look for relatives among our samples. Using the KING algorithm as implemented in VCFtools , we identified many pairs of relatives (, online). In addition to a first-degree relative pair, we had intentionally sequenced (a father–daughter pair from Taronga Zoo) we found we had sampled the aforementioned quartet from the Shoalhaven River, as well as the quartet mother’s sister.
Additionally, there were 26 pairs of second- or third-degree relatives, in all cases from the same river or creek, or closely connected waterways, involving 28 of our 57 samples. For the analyses in this paper, except where otherwise noted, we used a set of 43 unrelated samples, indicated in, online. De Novo Mutation RateWe identified putative de novo mutations in the two offspring in the family quartet using the Bayesian filter incorporated into PLATYPUS, and further filtered them to remove any putative de novo mutation which was seen in any other sample. This gave us a total of 12 de novo mutations in the quartet, 6 in each offspring, and we estimated the de novo mutation rate at 7 × 10 − 9/bp/generation (95% CI 4.1 × 10 − 9–1.2 × 10 − 8/bp/generation). See supplementary section S3, online, for more details. Dispersion and InbreedingOf the quartet individuals, the mother, her sister, and her two offspring were sampled in a pool at a junction ∼2 km downstream of the point where the father was found, which was in Jerrabattgulla Creek, a small tributary of the Shoalhaven River. Although the male offspring was first captured as a juvenile, because of difficulties in determining the age of adult platypuses, it is not possible to tell whether the two offspring were born as dizygotic twins or at different times, which would indicate that their parents mated in 1 year.Given that we found so many instances of close relative pairs within the same river, it was natural to ask whether inbreeding is common in platypus.
We examined long runs of homozygosity (LROHs) to investigate levels of inbreeding in the samples., online, shows F ROH, the estimated fraction of the analyzed genome that is in LROHs (see Materials and Methods). The Carnarvon sample stands out, with F ROH estimated at 24.4%, but several other samples have F ROH higher than 10% (N745 and N711 from NQLD, N730 from the Gwydir River, N746 from the Broken River, N724 from the Barnard River, and N710 from Tasmania).The length of homozygous segments depends on the recombination rate and number of generations since the most recent common ancestor of the two haplotypes.
![Platypus Evolution Final Evolution Platypus Evolution Final Evolution](https://vignette.wikia.nocookie.net/platypusevolution/images/a/a9/Land.jpg/revision/latest?cb=20160117235449)
Since we do not know the fine-scale recombination rate in platypus, and estimation of segment length is complicated by the fragmented nature of the assembly, which may lead to ROHs being truncated artificially, for example, by scaffold ends (, online), interpretation of the observed distribution of segment lengths is challenging. However, online, shows that the samples clearly fall into two groups: the north QLD (NQLD), central QLD (CQLD), and Gwydir samples (group 1) all have more ROHs than the other NSW and Tasmanian samples (group 2), but these have lower mean length than in some of the group 2 samples, and both groups contain samples with high overall F ROH. This undoubtedly reflects differences in demographic history. In the case of the Carnarvon sample (N753), it is difficult to disentangle true inbreeding from low historical N e, since we only have one sample from this location, but the overall F ROH could be consistent with a mating between first-degree relatives. However, since N724 appears to be an outlier among the Barnard River samples, it may be that this individual is derived from a mating of individuals as closely related as second-degree.
Thus, we cannot rule out the possibility of close inbreeding in wild platypus populations. Population StructureWe first ran a Principal Component Analysis (PCA) to summarize the genetic variation, using the stringent SNP set after filtering based on minor allele frequency and missingness. Shows the first two principal components (PCs). The first PC separates the Tasmanian from the mainland samples and accounts for 41.6% of the variation, and the second separates the mainland samples on a north–south axis and accounts for 22.2% of the variation. If we prune the SNPs based on linkage disequilibrium (LD), the first two PCs are exchanged and account for 27.6% and 23.7% of the variation (data not shown).
Principal component analysis on 43 unrelated samples. The first two principal components (PC1 and PC2) are shown, with the proportion of variance accounted for by each indicated in parentheses. Each unfilled circle in the plot represents an individual, with the colors of the circles corresponding to the sampling location. Circles are plotted at the value of the first two principal components for that individual.In order to explore differences between regions, we divided the samples into groups based on the PCA results and the samples’ known geographical proximity to one another (see, online). The Barnard River group has 11 individuals, and is combined with the Gwydir River individual to form the north NSW group. The Shoalhaven River group has 12 individuals, and is combined with 2 individuals from the Wingecarribee River and one individual from the Fish River to form the central NSW group. Similarly, we grouped samples within each of Tasmania ( N = 5) and NQLD ( N = 7).
The three samples from central QLD form their own group based on PCA and geography, and have been excluded from the following analyses due to low sample numbers.The groups show different levels of nucleotide diversity, π (average number of nucleotide differences between individuals per site), ranging from 4.73 × 10 − 4 in the north QLD samples to ∼1.02 × 10 − 3 in central NSW (CNSW). A large proportion of the SNPs segregating in each region are only polymorphic in that region (, online): 34.5% of those in north QLD, 33.5% in north NSW (NNSW), 37.1% in central NSW and 72.1% in Tasmania (after downsampling to consider the same number of samples per region). Note.—North NSW is Barnard + Gwydir, and Central NSW is Shoalhaven + Wingecarribee + Fish Rivers.
Central QLD has only three samples and is excluded from this analysis.There was high F ST between the different regional groupings, with the highest value between Tasmania and north QLD (0.677) and the lowest between the Wingecarribee and Barnard groupings (0.077). The F ST values were slightly higher when we did not prune the SNPs based on local LD, since this unpruned SNP set retained many fixed differences between sampling locations. There were a large number of fixed differences between both the Tasmanian and north QLD samples and the reference individual (, online): 10% of the ∼6.7 million SNPs segregating in the 57 samples were fixed for the alternate allele in the five unrelated Tasmanian samples, and 7.3% were fixed in five randomly sampled unrelated north QLD samples. Note.—The black numbers above the diagonal are calculated using SNPs before LD pruning, and the blue ones below the diagonal are calculated after LD pruning (see Materials and Methods).We applied STRUCTURE , a Bayesian model-based clustering algorithm, to identify subgroupings within our sampling location groups and assign the samples to them without using any prior information.
Shows the results from the admixture model in STRUCTURE, in which individuals are allowed to have membership in more than one of the K subgroupings, or clusters. We emphasize that inference of membership in multiple clusters does not necessarily mean that an individual has recent admixture; rather, these clusters represent putative ancestral populations which may have contributed to modern-day populations. Population structure inferred from 43 unrelated individuals using STRUCTURE. Each individual is represented by a vertical bar partitioned into K colored segments that represent the individual’s estimated membership fractions in K clusters.
Ten STRUCTURE runs at each K produced very similar results, and so the run with the highest likelihood is shown. The Broken River and Carnarvon samples are labeled as central QLD and the Running River sample as north QLD, even though these samples did not form part of large clusters on the PCA and were excluded from the groupings used in tables 2 and 3 and supplementary figures S5 and S6, online. Population structure inferred from 43 unrelated individuals using STRUCTURE. Each individual is represented by a vertical bar partitioned into K colored segments that represent the individual’s estimated membership fractions in K clusters. Ten STRUCTURE runs at each K produced very similar results, and so the run with the highest likelihood is shown.
The Broken River and Carnarvon samples are labeled as central QLD and the Running River sample as north QLD, even though these samples did not form part of large clusters on the PCA and were excluded from the groupings used in tables 2 and 3 and supplementary figures S5 and S6, online.For K = 2, the clusters are anchored by north QLD and Tasmania, with the central QLD and NSW groups inferred to be a mixture of these two. The next cluster at K = 3 corresponds to these central QLD and NSW individuals, and at K = 4, the north NSW (Barnard and Gwydir) individuals are delineated, along with individuals from the Fish and Wingecarribee Rivers. The additional cluster at K = 5 contributes the majority of the ancestry of the Carnarvon and Broken River samples, as well as a small amount of the ancestry of the samples from the Running River (from the Burdekin river system, like the Broken River individuals), and from the NSW rivers. Coancestry matrix from 43 unrelated individuals using FineSTRUCTURE. Each row represents one of the sampled individuals, with the colors along the row for a particular individual representing the number of pieces of their genome for which each other individual shares most recent common ancestry with them.
The tree shows the clusters inferred by FineSTRUCTURE from the coancestry matrix. The groupings on the x-axis are as in.The deepest branch on the tree separates the Tasmanian samples from the mainland, and the coancestry matrix shows little evidence of the sharing of most recent common ancestors between Tasmania and the mainland, implying a largely distinct population history for the Tasmanian samples, at least over the timescales during which they share recent ancestry with each other.
The next branching splits the mainland samples into Queensland and New South Wales clusters, with a further split separating central NSW (including the Shoalhaven, Wingecarribee, and Fish River samples) and north NSW (including the Gwydir River and Barnard samples). However, the Wingecarribee and Fish River samples show more haplotype sharing with the north NSW samples than with the Shoalhaven samples, supporting the evidence from STRUCTURE and PCA that these samples fall between the two larger clusters.Fine-scale population structure is also evident within some river systems, with the samples from Shoalhaven and Barnard rivers subdivided into smaller population clusters. By contrast, the five samples from the Dirran River in north QLD form a single cluster.
The single sample from the Carnarvon River (N753), while showing the greatest level with the other central QLD sample from the Broken River, also shows more sharing with the samples from NSW and less with the samples from north QLD than would be expected based on geography, as they are closer to the north QLD rivers than those in NSW. By contrast, the Broken River samples show much greater sharing with the north QLD samples than the Barnard River samples, as expected. We hypothesize that this may be due to ancestral admixture between the Broken and Carnarvon rivers, and subsequent admixture between the north QLD samples and the Broken River samples only.We further investigated demographic history using the pairwise sequentially Markovian coalescent (PSMC) method of. PSMC examines how the local density of heterozygous sites changes along the genome, reflecting chromosomal segments of constant time to the most recent common ancestor ( T MRCA), separated by recombination events. Knowing the coalescence rate in a particular epoch allows estimation of N e at that time. As has become clear from applications in other contexts (;; ), this comparison of the two chromosomes within a diploid genome offers an extremely powerful tool for inferring historical effective population size. The power of this approach lies in the fact that there are many thousands of “replicate” segments within a single diploid genome, and these collectively provide precise estimates of historical population size, except for the very recent past and the distant past.shows estimates of the effective population size, N e, from each sample at a series of time intervals.
The scaling on the X-axis of this plot depends on the generation time, g, and the scaling of both the X- and Y-axes depend on the mutation rate μ. Little is known about these two parameters for the platypus, but changing them will affect the estimates for all samples equally, by simply linearly rescaling the axes (, online). We will thus focus primarily on conclusions based on relative differences between PSMC estimates as these do not depend on assumptions about g and μ. For scaling the axes in, we used g = 10 years, following, which is consistent with the known observations that platypus can live up to 20 years in the wild and that both sexes can reproduce from the age of 2 years, although first breeding in some females can be later than this age (; ). In, we used a mutation rate of 7 × 10 − 9/bp/year, the de novo mutation rate we estimated from our own data using the quartet.
Note that, in the time-scaling used in, the method is not informative more recently than ∼10,000 years ago, or further into the past than ∼1–2 My. Historical effective population sizes inferred using PSMC.
Each line represents a single individual with lines colored according to sampling location. Trajectories were scaled using g = 10 and μ = 7 × 10 −9. Effective population size was truncated at 60,000.
Samples from a similar sampling location show very similar trajectories.Individuals from the same river show strikingly similar trajectories in, supporting the precision of the relative N e estimates and giving us confidence that we are measuring real features of population history. Bootstrapping performed according to the method in shows similar trajectories over 100 replicates for each sample (, online) with the exception of very recent and very distant time points (more recent than 5–10,000 years and older than 1 My), as expected.
The highly congruent trajectories within a river system suggest that, looking backward in time, the ancestors of these samples were probably part of the same population within the timeframe accessible to the method. Although samples from the same population would be expected to show the same N e trajectory, having the same N e trajectory does not necessarily mean that samples are from the same population. However, having different N e trajectories around a certain time in the past is difficult to reconcile with the individuals’ ancestors coming from the same ancestral population at that time.One striking feature of is that there are clearly four distinct groups of samples (all NSW, central QLD, north QLD, and Tasmania, respectively) with the ancestors of each group clearly having separate population histories until well into the past. It is ∼1 My (in the time-scaling of ) before NSW, north QLD, and Tasmania begin to share ancestral history, and perhaps 300,000 years until the central QLD and NSW samples might share ancestral history. This implies that there has been extensive population structure in platypus samples across Australia over a long time period.A second feature of is that all N e trajectories take their lowest values at the most recent time points for which the method is informative.
This is consistent with a decline in platypus numbers across Australia over the time period accessible to the method. In the case of the north QLD samples, the N e level becomes extremely low and remains so, and corresponds to a marked population bottleneck (∼10,000 years ago in this time-scaling).
This is consistent with the low nucleotide diversity observed in these populations, and with these samples clustering as a single homogenous population in the FineSTRUCTURE results. The central QLD population may well also have been affected by a recent bottleneck and shows very low N e during this period.
DiscussionWe have described the first population-scale whole-genome sequencing study of the platypus. The analyses presented here provide insights into the population structure and levels of diversity in this species not previously possible with microsatellite markers or mtDNA. Provides details of the source and extent of genetic variation used in this and previous studies of platypus demography and population structure.Our whole-genome data allowed us to estimate relatedness between individuals, and we found that more than half of our samples had a least a third-degree relative among the other individuals sampled from the same river. The quartet samples were all collected within a small distance of each other over a relatively short timeframe (.
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