Clunio (Diptera) have recolonized Northern European shore areas after the last glaciation. In response to local tide conditions they have formed different ecotypes with respect to timing of adult emergence, oviposition behavior and larval habitat. Genomic analysis confirms the recent establishment of these ecotypes, reflected in massive haplotype sharing between ecotypes, irrespective of whether there is ongoing gene flow or geographic isolation. QTL mapping and genome screens reveal patterns of polygenic adaptation from standing genetic variation. Ecotype-associated loci prominently include circadian clock genes, as well as genes affecting sensory perception and nervous system development, hinting to a central role of these processes in ecotype formation. Our data show that adaptive ecotype formation can occur rapidly, with ongoing gene flow and largely based on a re-assortment of existing alleles.Adaptive ecotype formation can be the first step to speciation, but the genetic underpinnings of this process are poorly understood. Marine midges of the genus  Midas cichlids, where a polygenic trait architecture was found to promote rapid and stable sympatric speciation . In adaptation to their habitat, Clunio midges differ in oviposition behavior and reproductive timing, involving both circadian and circalunar clocks.In this study, we examine local adaptation and population divergence in another exceptional biogeographic setting, namely the postglacial evolution of new ecotypes in marine midges of the genus Clunio marinus they have additional ecological relevance  and three laboratory strains of the Baltic ecotype, from Bergen , Sehlendorf  and Ar . We also established a strain of the Atlantic ecotype from Bergen  and used two existing Atlantic ecotype laboratory strains from Helgoland  and Port-en-Bessin . We confirmed the identity of the ecotypes in the laboratory by the absence of a lunar rhythm in the Baltic and Arctic ecotypes  in 50 kb windows  along the genome, followed by topology weighting. There are 105 possible unrooted tree topologies for six populations, and 46,656 possibilities to pick one individual from each population out of the set of 36. For each window along the genome, we assessed the relative support of each of the 105 population tree topologies by all 46,656 combinations of six individuals. We found that tree topologies change rapidly along the chromosomes . The treIn order to test whether this is only incomplete lineage sorting or if there is additional introgression beyond the detected case in the Bergen populations, we calculated the f4 ratio test for the geographically distant Por-1SL and He-1SL, as well as Ar-2AR and Seh-2AR populations. The genome-wide f4 ratio was –0.00062 (standard error 0.00008) and by comparison to coalescent simulations was found to be significantly different from 0, indicating some degree of introgression. However, the signature of introgression was driven by only 775 SNPs, which is only 0.17% of the total dataset. Thus, the largest part of haplotype sharing can be explained by incomplete lineage sorting, typical for such radiation situations.Baltic ecotype into both Bergen populations  for those that are overly differentiated between the six populations after correcting for the neutral covariance structure across population allele frequencies (see mc). The distributions of FST values in all pairwise population comparisons confirmed that genetic differentiation was particularly low in the Ber-1SL vs. Ber-2AR comparison , and even variants with FST ≥ 0.75 are very rare , nucleotide diversity (2r) of the two Bergen populations do not show marked differences along or between chromosomes  and St. Jean-de-Luz  If, as assumed above, adaptation in these populations was indeed based mostly on standing genetic variation, one should find evidence of this in the data structure. We selected highly ecotype-associated SNPs   gene. Among other functions, sNPF-R is involved in coupling adult emergence to the circadian clock cOps1 there is the differentiated locus of matrix metalloprotease 1 (Mmp1), which is known to regulate circadian clock outputs via processing of the neuropeptide pigment dispersing factor (PDF) sox100B is known to affect male gonad development ecdysone-induced protein 93 F is involved in response to hypoxia in flies C. marinus.While GO term analysis gives a broad picture of which processes have many genes affected by ecotype-associated SNPs, this does not necessarily imply that these genes and processes also show the strongest association with ecotype. Additionally, major genes might be missed because they were not assigned GO terms. As a second line of evidence, we therefore selected variants with the highest ecotype-association by increasing the eBP00 to 69 . Additio (cOps1) , the traFinally, we assessed the top 13 strongly ecotype-associated loci for signatures of selective sweeps in genetic diversity and LD . DespiteClunio populations has allowed us to reconstruct a detailed biogeographic history of their different ecotypes. Most surprisingly, the two sympatric ecotypes that were considered different species turned out to be the most closely related populations in the sample, testifying the high power of ecology in generating differential adaptation in the face of gene flow. The data allowed further to infer a polygenic architecture for ecotype formation. We found that while ecotype-associated alleles differ in allele frequency, they are largely shared between ecotypes, which suggests that adaptation primarily involves standing genetic variation from many different loci. A similar re-use of existing regulatory variation has been found in ecotype formation in sticklebacks Heliconius butterflies Heliconius alleles are shared over large evolutionary distances via introgression, Clunio ecotypes diverged recently from a common source, as is illustrated by massive and genome-wide shared polymorphism. Combined with the observation that many genes from the same biological processes have ecotype-associated alleles, this draws a picture of polygenic adaptation, involving many pre-existing alleles with probably small phenotypic effects. Particularly for adaptation in circadian timing this scenario is highly plausible. The ancestral Atlantic ecotype comprises many genetically determined circadian timing types that are adapted to the local tides . Possibly, the SV is so complex that it did not leave an interpretable genomic signal. Alternatively, Clunio’s long history of genome rearrangements  or, emergence during the second 12 hr after sunrise (‘2’) or emergence during every low tide . Lunar phenotypes in this study are either emergence during full moon and new moon low tides (‘SL’ for semi-lunar) or arrhythmic emergence (‘AR’). As a consequence, the Arctic ecotype is ‘tAR’, the Baltic ecotype is ‘2AR’ and the Atlantic ecotype populations in this study are all of timing type ‘1SL’ , Ar (Sweden), Tromsø , and Bergen  during eight field trips in 2017 and 2018 . Field cAtlantic and Arctic ecotype strains were kept in natural seawater diluted 1:1 with deionized water and fed with diatoms (Phaeodactylum tricornutum) and powdered nettles (Urtica sp.). The Baltic ecotype was kept in natural Sea water diluted 1:2 and fed with diatoms and powdered red algae . For entrainment of the lunar rhythm all strains were provided with 12.4 hr tidal cycles of water turbulence  (Laboratory strains were reared under standard conditions min off) .Baltic ecotype eggs are generally found submerged at the bottom of the culture vessel, Atlantic and Arctic ecotype eggs are always found floating on the water surface or on the walls of the culture vessel (for details see next paragraph). Daily emergence times were recorded in 1 hr intervals by direct observation  or with the help of a fraction collector  was selected for QTL mapping. DNA of the two parents and the F2 progeny was extracted with the QuickExtract DNA Extraction Solution  according to manufacturer’s protocol with modifications. Instead of vortexing before incubation, a pestle was used to grind the sample. In order to identify scorable markers for the genetically very similar strains, the two parents of the cross were subject to whole-genome sequencing. Raw reads were processed through our genotyping pipeline (described below), resulting in 656,368 detected variants, 53,097 of which were diagnostic for the grandparents. Based on the list of diagnostic markers, we picked eight evenly spaced regions  on each chromosome for amplicon sequencing . MultiplFor QTL mapping, the degree of rhythmicity of an individual was recorded as the number of days between its emergence day and day 9, which is the peak of emergence in the rhythmic strain (Ber-1SL). The phenotyping has a specific, inevitable shortcoming: Arrhythmic individuals can also emerge during the peak days by chance, so that this small fraction of individuals is phenotyped as rhythmic while actually being arrhythmic. As the phenotype is not normally distributed  it was treated as non-parametric. QTL scans were performed in the R package ‘qtl’ . The genFor each of the seven populations, 24 field caught males  were subject to whole genome sequencing. DNA was extracted from entire individuals with a salting out method  and amplC. marinus. Mapped reads were sorted, indexed, filtered for mapping quality (-q 20) and transformed to BAM format with SAMtools v.1.9 http://picard.sourceforge.net/) Raw sequence reads were trimmed for adapters and base quality using Trimmomatic v.0.38 For the nuclear genome, SNP and insertion-deletion (indel) genotypes were called using GATK v.3.8–0-ge9d806836 Reads mapped to the mitochondrial genome were transformed into mitochondrial haplotypes as described in fluxus-engineering.com).Mitochondrial haplotype networks were calculated using the Median-Joining algorithm vcf2treemix.sh script TreeMix 1.13Nuclear SNP genotypes were converted to PLINK format with VCFtools. SNPs were LD pruned with PLINK v.1.90b4 2) was calculated in VCFtools with ‘--geno-r2’. Local LD was calculated with ‘--ld-window-bp 500’. Preliminary tests showed that local LD decays within a few hundred base pairs , allele frequencies were extracted with VCFtools ‘--freq’ and dxy was estimated from allele frequencies according to Population estimates along the chromosomes were calculated in 100 kb overlapping sliding-windows with 10 kb steps  or 10 kbse pairs . For lonNuclear genome phylogeny was calculated for a random set of six individuals from each population, without Tro-tAR (n=36). We down-sampled to six individuals per population because the following topology weighting algorithm assesses all possible combinations of six individuals (i.e. one per population) in the sample. The number of combinations grows exponentially with the number of individuals and thus leads to immense run-times of the algorithm. For windowed phylogenies, the VCF file was subset into non-overlapping 50 kb windows using VCFtools ‘--from-bp --to-bp’. SNP genotypes were transformed into FASTA alignments of only informative sites with the vcf2phylip.py v.2.3 script weather-atlas.com, accessed 27.04.2020; 16:38), as given in tX statistics and finally assesses the approximate Bayesian p value of association (eBPmc). To obtain a significance threshold for XtX values, the data was randomly subsampled  and re-analyzed with the standard covariate model, as implemented in baypass_utils.R. All analyses we performed in three replicates  and the median is shown.Population-based association between genetic variants (SNPs and Indels) and ecotype, as well as environmental variables  was asse−10 and subject-query alignment coverage of >60%. Conservatively, we only transferred GO terms with ‘non-electronic’ GO evidence from best-hit orthologs restricted to an automatically adjusted per-query taxonomic scope, resulting in 5,393 C. marinus gene models with GO term annotations. Enrichment of ‘Biological Process’ GO terms in the genes associated with ecotype-specific polymorphisms was assessed with the weight01 Fisher’s exact test implemented in topGO Gene annotations to the CLUMA1.0 reference genome http://www.evl.uic.edu/pape/data/WDB/). Circular plots were prepared using the R package ‘circlize’ http://www.phylosoft.org/archaeopteryx) Inkscape. Neighbor Joining trees of the omega statistic distances from BayPass were created with the R package ‘ape’ Figures were prepared in R  This valuable study combines phenotypic analysis, quantitative genetics and population genomics to provide solid evidence for multiple genes underlying adaptive divergence in a marine midge system linked to tidal rhythm. Genes with a plausible role in perceiving and responding to lunar information are among the loci that most highly differentiate populations with distinct behaviors. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Detlef Weigel as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Polygenic adaptation from standing genetic variation allows rapid ecotype formation" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission. As you will see, both expert reviewers like the topic and the system you have established, but have also significant concerns, mostly about interpretation, but some also regarding technical issues. The companion paper is clearly the stronger paper, but we would like nevertheless invite revision for the current paper as well, as the two papers complement each other.Essential revisions:1) Describe the power of the QTL analysis and clearly describe its limitation. State that it remains unclear how complex the genetic basis of rhythmic versus arrhythmic behavior is, and that a moderately complex genetic basis (vulgo oligogenic) cannot be excluded.2) Be more open to the possibility that some of the apparent differentiation is due to demographic processes instead of adaptation only.3) Be more conservative with the GO enrichment analyses.Reviewer #1 (Recommendations for the authors):The Introduction should set the scene better before moving on to Clunio. Other cases of polygenic adaptation should be mentioned. There should be some discussion of theoretical expectations for the distribution of effect sizes for adaptive traits .The interpretations are generally strong and the core points are interesting and significant for the field – the recent, polygenic adaption from standing variation, the genomic clustering and the identification of candidate pathways are all strong results. Evidence from the QTL analysis is least convincing – see specific comments below. The absence of evidence here is consistent with the results of the genome scan but that is about as far as one can go.Absence of evidence for structural variants is also quite hard to interpret. I agree that the patterns do not obviously fit with a simple interpretation (such as a single polymorphic inversion) but there is remarkably strong LD over long distances. This might be explained by gene flow for the sympatric population pair but it appears to extend to other populations too. That said, I think the authors handle this well, leaving open the possibility that further work will reveal structural variants.Argument for standing variation does seem good – question about maintenance – plausibly suggest LA within the ancestral Atlantic ecotype.The GO enrichment results are very neat. A key strength is knowing the dominant axis of selection and being able to link that to genetic pathways likely to influence the relevant phenotypes (and so avoid 'story-telling'). These points are made in the Discussion but I think some of this could usefully be moved to a revised introduction, perhaps giving more emphasis to insights other than the polygenic nature of adaptation that can come from the unique features of this system.Below I give further comments by line number:15 – Not 'is' but 'can be' since speciation by other routes is possible and also because ecotype formation need not lead to speciation.36 – In addition to these recent references, it would be good to recognise long-standing awareness of the role of polygenic adaptation. See, for example, Le Corre and Kremer 2012 – doi: 10.1111/j.1365-294X.2012.05479.x38 – A polygenic basis can be demonstrated by quantitative genetics methods such as 'regional heritability'. It is currently unclear whether this is included under 'deep genomic and genetic analysis'.Figure 1 – The meaning of zero on the x-axis needs to be explained in the figure caption. The y-axis label (% emergence) seems to conflicts with the legend which says that these are plots of oviposition timings. Bergen is very far from the entrance to the Baltic – somewhere, the ecotype present in the Skagerrak needs to be mentioned.118 – I am not sure why this is described as a contrast. The previous paragraph describes geographic rather than ecotype structure. That is surely confirmed by the observation that sympatric populations of different ecotypes are similar genetically.154-164 – This inference should not be pushed too far since it is based on a single point in what is, presumably, a large area of overlap, perhaps with varying past and present mixes of the two ecotypes. So, the inferences apply to the specific sampled populations but may not generalise to the Atlantic-Baltic interaction at other locations. It also begs the question of whether this is a secondary contact, after an allopatric origin of the Baltic ecotype.190 – How many topologies fit this pattern?210 – Comparison to the SD locus only says that the number of loci for rhythmicity is likely to be >1, and even this conclusion is questionable since sex is a binary phenotype with no environmental variance. Therefore, to conclude from the lack of detectable QTL that 'many' loci underlie rhythmicity is going too far. It may be possible to use simulation to get a better idea of the power of the QTL analysis and so the likely maximum effect size. If the vertical lines in Figure 4 represent marker loci (not specified in the legend) then the number is rather low and distribution rather uneven, perhaps making QTL detection in some parts of the genome difficult (including the important region in the centre of Chr1). I also wonder how good a measure of rhythmicity is provided by the emergence day  for an individual. This seems like a difficult trait to handle since rhythmicity cannot easily be measured for any one individual.280 – It is surprising that the cluster of outliers on Chr1 does not appear as a QTL, especially given the long-range LD in this areaMethods543 – 8 markers per chromosome was on the low side. 'Evenly spaced' here was presumably in physical distance since the marker spacing on the genetic map looks far from even. This also tells us something about the distribution of recombination along the chromosomes, which may be relevant to the cluster of divergent loci on Chr1 but this connection is not made in the manuscript.620 – The rapid decline in LD suggests that window sizes for some analyses (e.g. 100kb for population genetic statistics) was rather large. This could have influenced sweep detection, for example.627 – It is not clear why it was necessary to down-sample to 6 individuals per population here. Using 50kb windows are, again, rather large , but I doubt either will have impacted the results strongly.Figure S4 – Too many decimal places are used for both distance and Fst. The comma separator in the distance should be replaced with a point (or probably the fractions of km simply ignored).Figure S7 – TreeMix also provides a plot of %variance explained, which helps to determine the appropriate number of migration events. This could usefully be given here.Figure S21 – Are numbers above marker lines just their IDs? It might be helpful to indicate their linkage map positions .Reviewer #2 (Recommendations for the authors):Although I am enthusiastic about the promise of the system more generally and the importance of the questions posed, I have significant concerns about the manuscript and its conclusions. I am embedding my suggestions for new analyses with the descriptions of the issues above so that they are readable:1) My primary concern in is in the design and interpretation of the QTL analysis. The QTL approach used here has low power, both due to the sample size and the number of markers used (it looks like ~8 per chromosome). The authors use an analysis of the sex determining locus as a "control" but because of the complete heritability of this trait in most systems it is more of a straw man to me. The authors conclude that the architecture of the trait is polygenic based on this, but we are missing key information to evaluate this. For example, what is the estimated heritability of the trait in this family and under laboratory conditions? I was also wondering if with the number of markers whether the authors might miss certain loci if the recombination rate is high.2) There are some issues with the presentation and interpretation of the population genetic analyses. Many assumptions are made about whether introgression or ILS occurred and there are statements that are not accurate about it being "impossible" to distinguish between these scenarios. There are quite a lot of methods devoted to this problem, see e.g. . Simpler tests like D-statistic and F4 ratio tests would add a lot of interpretability to these results.3) Some of the analyses associated with ecological adaptation that follow on the QTL results struck me as ad hoc and with the potential to lead to spurious results. I am not familiar with the BayPass approach but since it is the approach that explicitly accounts for population structure it seems the one that would be most appropriate for the authors to focus on in a revised manuscript. The use of phylogenetic windows that associate with ecotype is concerning to me as given the level of ILS and gene flow that appears to be present in this system is would be very challenging to distinguish signal from noise. Related to this, in the companion manuscript the authors note that the chromosome 1 region has several inversions. I realize that the samples used in this manuscript are different but was unclear on how they excluded the possibility inversions were present on the chromosome 1 region that is highly differentiated in this analysis. This seems to be the primary region that distinguishes ecotypes in the BayPass analysis and thus is foundational to their conclusion that this signal is polygenic . I also noticed that there were not many QTL markers in this region of chromosome 1 regarding their previous analysis.4) There were issues with the GO analysis that should be addressed. Because the gene universe used for GO enrichment is a subset of the full gene set, GO enrichment results will be biased. This will mostly lead to false positives . The authors should pair this with simulations or other analyses, or temper the strength of their claims. Essential revisions:1) Describe the power of the QTL analysis and clearly describe its limitation. State that it remains unclear how complex the genetic basis of rhythmic versus arrhythmic behavior is, and that a moderately complex genetic basis (vulgo oligogenic) cannot be excluded.We completely rewrote the section on QTL analysis. We now clearly describe its limitations (lines 203-229) and conclude that the phenotype is controlled by “more than one“ locus.2) Be more open to the possibility that some of the apparent differentiation is due to demographic processes instead of adaptation only.We have inserted a statement on the possible role of demographic processes in the Results section (lines 251-254), and also inserted a statement that part of the clustering of divergent alleles may be due to reduced recombination (lines 266-269).3) Be more conservative with the GO enrichment analyses.We maintain that we have been adequately cautious in the GO enrichment analysis. First, we conservatively only annotated orthologous genes with GO terms, avoiding false positives due to annotating duplicated genes  with GO terms from their paralogs. Second, we think that the reviewer who is sceptic about our GO enrichment analysis has not fully appreciated the sophistication of our analysis. While details are given in the response below, briefly, we accounted for the GO term distribution of our reduced gene set when we specified the set of genes with GO terms as the custom background universe and only analyzed ecotype-associated genes with GO terms. The two subsets with GO terms are the same fraction of the respective full gene set, suggesting there are no biases in sub-setting. In our GO enrichment analysis, the Fisher’s exact test gives the exact hypergeometric probability under the null hypothesis for observing the degree of enrichment of a GO term given the custom gene universe, accounting for the potential differences in the representation of GO terms within the reduced gene set in comparison to the set of all genes. If there was any bias in the reduced gene universe, it would thereby have been accounted for in our analysis.If the reviewer is concerned with the general stability of GO enrichment results between different subsets of a gene universe, we would like to highlight that Groß et al. have demonstrated that GO enrichment is surprisingly robust to changes in the GO term ontology and annotations (https://academic.oup.com/bioinformatics/article/28/20/2671/204694), further suggesting that using a reduced gene universe should not pose major problems.Reviewer #1 (Recommendations for the authors):The Introduction should set the scene better before moving on to Clunio. Other cases of polygenic adaptation should be mentioned. There should be some discussion of theoretical expectations for the distribution of effect sizes for adaptive traits .We have re-written the introduction and now mention other cases of polygenic adaptation, as well as a discussion of the expected distribution of effect sizes for adaptive traits (lines 35-51).The interpretations are generally strong and the core points are interesting and significant for the field – the recent, polygenic adaption from standing variation, the genomic clustering and the identification of candidate pathways are all strong results.We thank the reviewer fort this positive evaluation of our work.Evidence from the QTL analysis is least convincing – see specific comments below. The absence of evidence here is consistent with the results of the genome scan but that is about as far as one can go.We have re-written this paragraph, and now conclude that we can exclude a single or few strong-effect loci. We also explicitly included a discussion of the weaknesses of the QTL data (lines 203-229). However, we still consider them as relevant support for the overall conclusions.Absence of evidence for structural variants is also quite hard to interpret. I agree that the patterns do not obviously fit with a simple interpretation (such as a single polymorphic inversion) but there is remarkably strong LD over long distances. This might be explained by gene flow for the sympatric population pair but it appears to extend to other populations too. That said, I think the authors handle this well, leaving open the possibility that further work will reveal structural variants.We agree that the evidence for SVs is hard to interpret. As the reviewer states we handled this well, we did not make any changes to the respective paragraphs.Argument for standing variation does seem good – question about maintenance – plausibly suggest LA within the ancestral Atlantic ecotype.We are happy the reviewer appreciates our argument for adaptation from standing genetic variation. We are not quite sure what she/he refers to with “LA”, but assume it is local adaptation. We make the point that standing variation for circadian timing is likely due to local adaptation in the discussion.The GO enrichment results are very neat. A key strength is knowing the dominant axis of selection and being able to link that to genetic pathways likely to influence the relevant phenotypes (and so avoid 'story-telling'). These points are made in the Discussion but I think some of this could usefully be moved to a revised introduction, perhaps giving more emphasis to insights other than the polygenic nature of adaptation that can come from the unique features of this system.We agree with the reviewer that the GO term enrichment results are neat, and hope that this helps to convince the other reviewer and the editor of the strength of the dataset (see detailed argumentation above and below). Indeed, knowing the dominant axes of selection is a particular strength in our system and we now mention this in the introduction (lines 92-94).Below I give further comments by line number:15 – Not 'is' but 'can be' since speciation by other routes is possible and also because ecotype formation need not lead to speciation.We have made the requested change.36 – In addition to these recent references, it would be good to recognise long-standing awareness of the role of polygenic adaptation. See, for example, Le Corre and Kremer 2012 – doi: 10.1111/j.1365-294X.2012.05479.x.We have added the respective citation.38 – A polygenic basis can be demonstrated by quantitative genetics methods such as 'regional heritability'. It is currently unclear whether this is included under 'deep genomic and genetic analysis'.Deep genomic and genetic analysis can also include Regional Heritability Mapping (RHM). However, as the method requires a pedigree and is thus not applicable to our data, we did not explicitly mention it here.Figure 1 – The meaning of zero on the x-axis needs to be explained in the figure caption. The y-axis label (% emergence) seems to conflicts with the legend which says that these are plots of oviposition timings. Bergen is very far from the entrance to the Baltic – somewhere, the ecotype present in the Skagerrak needs to be mentioned.There is no zero label on the x-axis. The zero label is on the y-axis (Emergence %).The y-axis label does not conflict with the legend. The legend mentions “oviposition behavior” as one aspect in which the ecotypes differ. This was not listed in the figure, and may therefore have been misleading. We included “oviposition on exposed substrate” and “oviposition on water surface” in the figure. Otherwise, the legend clearly states that plots B-H are “lunar rhythms of adult emergence”.Unfortunately, we have not sampled and we do not know any Clunio population from the Skagerrak. We have therefore included a comment that the wider overlap of the distribution Baltic and Arctic ecotypes must be subject to further investigation (lines 161-164).118 – I am not sure why this is described as a contrast. The previous paragraph describes geographic rather than ecotype structure. That is surely confirmed by the observation that sympatric populations of different ecotypes are similar genetically.The contrast referred to differentiated vs not differentiated populations. But this was indeed not clear from the context. So we removed the statement on this being a contrast (line 129).154-164 – This inference should not be pushed too far since it is based on a single point in what is, presumably, a large area of overlap, perhaps with varying past and present mixes of the two ecotypes. So, the inferences apply to the specific sampled populations but may not generalise to the Atlantic-Baltic interaction at other locations. It also begs the question of whether this is a secondary contact, after an allopatric origin of the Baltic ecotype.We agree with the reviewer that our conclusions are drawn based on a single point, and we believe we have stated this clearly that we are only referring to Bergen. In order to further clarify this, we have added the statement that further work will be required to see if there is a larger area of overlap, and if so which evolutionary scenarios  apply there (lines 161-164).190 – How many topologies fit this pattern?It is 357 topologies that fit the pattern. We have inserted the information in the text (line 200).210 – Comparison to the SD locus only says that the number of loci for rhythmicity is likely to be >1, and even this conclusion is questionable since sex is a binary phenotype with no environmental variance. Therefore, to conclude from the lack of detectable QTL that 'many' loci underlie rhythmicity is going too far.We agree with the reviewer and have rewritten the entire paragraph, delineating the shortcomings of the QTL mapping approach and concluding that the phenotype is not controlled by a single large-effect locus.It may be possible to use simulation to get a better idea of the power of the QTL analysis and so the likely maximum effect size.While it might be possible to estimate the maximum effect size from simulations, we think that this effort goes beyond the scope of our manuscript and would actually be distracting. We have instead followed the editor’s suggestion and have clearly spelled out the limitations of the QTL analysis and have tempered our claims to say the trait is controlled by “more than one” locus (lines 203-229).If the vertical lines in Figure 4 represent marker loci (not specified in the legend) then the number is rather low and distribution rather uneven, perhaps making QTL detection in some parts of the genome difficult (including the important region in the centre of Chr1).The vertical lines in Figure 4 represent marker loci, and we have added this information to the figure legend. We have also added the statement that the low marker number limits QTL mapping in the respective paragraph (lines 219-220). The uneven distribution of markers may make QTL detection difficult, but not for the important region in the center of Chr1 one, which is covered by the markers with low recombination at the left end of the linkage map. This in turn is important in the light of the reviewers comment below on lines 543-8, and we have therefore added this information to the manuscript (lines 264-269) and the “Figure 4—figure supplement 2”.I also wonder how good a measure of rhythmicity is provided by the emergence day  for an individual. This seems like a difficult trait to handle since rhythmicity cannot easily be measured for any one individual.Rhythmicity is indeed a difficult phenotype and we have now clearly stated its specific problems in both the methods (lines 548-550) and the Results sections (lines 219-224). We can safely assume that individuals emerging outside the peak are arrhythmic. However, arrhythmic individuals which happen to emerge during the peak are scored as rhythmic, leading to a fraction of individuals with false phenotypes. We have come up with various phenotyping procedures, which are not reported here. This included the exclusion of days at the edges of the peak, which are particularly prone to phenotyping error. It also included an expectation-maximization (EM) algorithm that would assign phenotypes to the individuals in the peak based on the probability of being rhythmic and in a way so that the LOD score of the resulting phenotype panel, i.e. the probability of finding a significant QTL, is maximized. This algorithm successfully allowed QTL mapping based on rhythmicity in another case – see our preprint at https://doi.org/10.1101/2022.10.12.511720. In this preprint, the different scoring schemes and the EM algorithm converged to 1-3 detectable QTL for rhythmicity, notably based on smaller mapping families (but more markers) than used here. For the cross reported here, no scoring scheme led to detectable QTL and the EM algorithm did not converge to reproducible QTL. This may underscore that the genetic basis of arrhythmicity in the case we report here may be based on more than a few loci. But it may in part also be attributable to the difficult phenotype. Hence, in order not to over-analyze and over-interpret, we decided to only report the simplest phenotyping scheme and considerably temper our claims to say “more than one locus” contributes to the arrhythmic phenotype.280 – It is surprising that the cluster of outliers on Chr1 does not appear as a QTL, especially given the long-range LD in this areaIt is surprising that the region does not show up as a QTL. This may underscore that other loci are likely to contribute.Methods543 – 8 markers per chromosome was on the low side. 'Evenly spaced' here was presumably in physical distance since the marker spacing on the genetic map looks far from even. This also tells us something about the distribution of recombination along the chromosomes, which may be relevant to the cluster of divergent loci on Chr1 but this connection is not made in the manuscript.We have added the information that ‘evenly spaced’ is with respect to physical distance (lines 531-533). We have also revised “Figure 4—figure supplement 2”  to include the comparison between physical distance and genetic map distance. We have included the information that the divergent region on chr1 has low recombination, which may aid the clustering of divergent loci (lines 266-269).620 – The rapid decline in LD suggests that window sizes for some analyses (e.g. 100kb for population genetic statistics) was rather large. This could have influenced sweep detection, for example.In this case our methods section was unfortunately incomplete. Figure 6 (sweep detection) reports 10 kb windows with 1kb steps. We have added the information to the methods (lines 613-615) and the figure legend. Additionally, we have tried 1kb windows for LD and 5 kb windows for pi, but these plots were very noisy, likely due to the limited number of SNPs in such small windows.627 – It is not clear why it was necessary to down-sample to 6 individuals per population here. Using 50kb windows are, again, rather large , but I doubt either will have impacted the results strongly.6 = 46,656 combinations, with the full dataset it would have been 624 = 4.7*1018 combinations. That is why we had to down-sample.We included the explanation that running-times of the topology weighting algorithm increases exponentially with the number of individuals in the methods section (lines 628-632). With six individuals it is 6While 50kb windows are indeed large, we also tried 5kb windows and did not observe any notable differences. See Figure S4 – Too many decimal places are used for both distance and Fst. The comma separator in the distance should be replaced with a point (or probably the fractions of km simply ignored).We have adjusted the figure accordingly.Figure S7 – TreeMix also provides a plot of %variance explained, which helps to determine the appropriate number of migration events. This could usefully be given here.We couldn’t find the respective plotting function in TreeMix. Instead we prepared a plot of the model likelihood for zero to five migration events. The sharp flattening after one migration event suggests one migration event produces the only significant improvement of the model .Figure S21 – Are numbers above marker lines just their IDs? It might be helpful to indicate their linkage map positions .ST . This also allowed us to insert statements about reduced recombination in certain areas of the genome (lines 266-269).Yes, the numbers above markers are their IDs. We have adjusted the figure to show the position of the markers both on the linkage map and the physical map, as well as relative to peaks in FReviewer #2 (Recommendations for the authors):Although I am enthusiastic about the promise of the system more generally and the importance of the questions posed, I have significant concerns about the manuscript and its conclusions. I am embedding my suggestions for new analyses with the descriptions of the issues above so that they are readable:1) My primary concern in is in the design and interpretation of the QTL analysis. The QTL approach used here has low power, both due to the sample size and the number of markers used (it looks like ~8 per chromosome). The authors use an analysis of the sex determining locus as a "control" but because of the complete heritability of this trait in most systems it is more of a straw man to me. The authors conclude that the architecture of the trait is polygenic based on this, but we are missing key information to evaluate this. For example, what is the estimated heritability of the trait in this family and under laboratory conditions? I was also wondering if with the number of markers whether the authors might miss certain loci if the recombination rate is high.We agree that our QTL analysis has limitations in terms of sample size, as well as number and distribution of markers. To fully acknowledge these limitations, we have now spelled them out in the results and methods section. We have also tempered our claims significantly, and now only state that QTL analysis suggests that the trait is controlled by more than one gene (lines 203-229). We have not included an estimate of heritability, as it is not straightforward to calculate it with the data we have at hand. Also, we do not over-emphasize the results of the QTL analysis – we rather acknowledge that it has limitations. Still, its results complement the whole analysis.2) There are some issues with the presentation and interpretation of the population genetic analyses. Many assumptions are made about whether introgression or ILS occurred and there are statements that are not accurate about it being "impossible" to distinguish between these scenarios. There are quite a lot of methods devoted to this problem, see e.g. . Simpler tests like D-statistic and F4 ratio tests would add a lot of interpretability to these results.We added the f4 ratio test to our analysis and found that the genome-wide f4 ratio is significantly different from 0, i.e. there is some introgression. However, the signature of introgression is driven by only 775 SNPs, a small fraction of the total dataset. We have added this information in the manuscript and re-phrased our interpretation accordingly (lines 187-196).3) Some of the analyses associated with ecological adaptation that follow on the QTL results struck me as ad hoc and with the potential to lead to spurious results.It would have been helpful if the reviewer had explicitly stated which analyses struck her/him as ad hoc. We assume it is the analyses referred to below.I am not familiar with the BayPass approach but since it is the approach that explicitly accounts for population structure it seems the one that would be most appropriate for the authors to focus on in a revised manuscript. The use of phylogenetic windows that associate with ecotype is concerning to me as given the level of ILS and gene flow that appears to be present in this system is would be very challenging to distinguish signal from noise.ST outliers. One of the strengths of our analysis is that the three approaches lead to very similar results, e.g. the clustering of ecotype-associated variants on chr1. Hence, we would not like to drop any of the analyses.We present three different approaches to detect genomic regions associated with ecotype: phylogenetic windows, BayPass and FST outliers) in one paragraph each, the entirety of the downstream analyses  is based solely on the BayPass results.We agree with the reviewer that BayPass is indeed the strongest approach. That is why in the manuscript we already focus on the BayPass results: After reporting on all three approaches , as the results are fully consistent with the other analyses.Related to this, in the companion manuscript the authors note that the chromosome 1 region has several inversions. I realize that the samples used in this manuscript are different but was unclear on how they excluded the possibility inversions were present on the chromosome 1 region that is highly differentiated in this analysis. This seems to be the primary region that distinguishes ecotypes in the BayPass analysis and thus is foundational to their conclusion that this signal is polygenic . I also noticed that there were not many QTL markers in this region of chromosome 1 regarding their previous analysis.We also report on putative inversions on chromosome 1 in this manuscript, represented by blocks of observed long-range LD . These putative inversions overlap with the divergent region on chromosome 1 and reduce recombination in that region, as can be seen from long-range LD, as well as the combination of physical markers distances with map length in our revised “Figure 4—figure supplement 2” . However, we cannot find a 1:1 association of these putative inversions with the divergent region (the boundaries differ markedly), nor with ecotype (putative inversions are present in both ecotypes and rather population-specific than ecotype-specific). In a PCA of the divergent region, the individuals do not segregate in any pattern that would be typical for inversion haplotyopes (as is observed in the companion manuscript).-10), but black dots are already significant at p < 0.001 (as is stated in the figure legend). Thus, ecotype associated SNPs are all over the genome, and this is foundational to the conclusion that the signal is polygenic.We would also like to point out that it is not the case that few other regions outside the chromosome 1 cluster are significant in BayPass. In our figure 3 the red dots are only the extreme outliers  to make the connection between the linkage map and physical marker locations.4) There were issues with the GO analysis that should be addressed. Because the gene universe used for GO enrichment is a subset of the full gene set, GO enrichment results will be biased. This will mostly lead to false positives . The authors should pair this with simulations or other analyses, or temper the strength of their claims.We see that our conservative approach to annotate only the subset of orthologous genes with GO terms produces an issue, namely that the genes without GO terms will be missing from the analysis, i.e. there are false negatives. In order to address this problem we have performed a second set of analysis which takes into account all genes with highly divergent SNPs irrespective of whether or not they are annotated with GO terms (lines 350-388). While we found that indeed many divergent genes are not annotated with GO terms, the functions we can assign to these genes by investigation of the relevant literature match the results from GO term analysis very well. This is the best we can do about false negatives.The reviewer suggests there might be false positives in our analysis. We disagree with that for several reasons:First, we think there might be a misunderstanding of the analysis due to insufficient information provided in the methods and we apologize for it. In our topGO analysis, the reduced dataset of 5,393 genes with GO terms was specified as the custom background gene universe. By specifying this reduced gene universe to topGO, with the parameter ‘allGenes’, any potential differences in the background frequencies of certain GO terms between our reduced gene set and the set of all genes will be statistically accounted for by the tool, through incorporation into the null hypothesis. 463 out of 1,400 ecotype-associated gene models were annotated with GO terms and only those were fed into the topGO analysis. The p-value returned by the Fisher’s exact test implemented in topGO calculates exactly the probability of observing at least the same amount of enrichment of a GO term when 463 genes are randomly selected out of the specified gene universe of 5,393 genes. Thus, if there is any bias in GO terms of our custom gene universe, this should have been accounted for. We also note that the fraction of genes with GO terms among the ecotype-associated genes and among the total genes is not significantly different , indicating that both sets of genes are affected in the same way by the lack of GO term annotations.Second, the reviewer asks for simulations to support our claims. If the reviewer is concerned with the ‘Open World Assumption’ that is inherent in Gene Ontology, namely that we have made statements about whether or not enrichment is true without knowing all observations  and would like us to perform simulations to evaluate the impact of using varying subsets of the gene universe on the stability of enrichment results -- Here we can say that Groß et al. have investigated the difference in results of functional analyses using GO annotations in both primates and rodents from different GO versions (https://academic.oup.com/bioinformatics/article/28/20/2671/204694), i.e. different Gene universes and levels of completeness. Their conclusions are that enrichment results are surprisingly stable to changes in ontology and annotation. Hence, we conclude that our reduced gene universe will very likely not cause major issues to the reliability of the conducted enrichment analysis.In order to avoid these possible misunderstandings, we have inserted the respective information in the methods section (lines 670-681).We also want to point out that the other reviewer considers our GO term enrichment results particularly strong.


Selection of livestock based on their robustness or sensitivity to environmental variation could help improve the efficiency of production systems, particularly in the light of climate change. Genetic variation in robustness arises from genotype-by-environment (G × E) interactions, with genotypes performing differently when animals are raised in contrasted environments. Understanding the nature of this genetic variation is essential to implement strategies to improve robustness. In this study, our aim was to explore the genetics of robustness in Australian sheep to different growth environments using linear reaction norm models (RNM), with post-weaning weight records of 22,513 lambs and 60 k single nucleotide polymorphisms (SNPs). The use of scale-corrected genomic estimated breeding values (GEBV) for the slope to account for scale-type G × E interactions was also investigated.Additive genetic variance was observed for the slope of the RNM, with genetic correlations between low- and high-growth environments indicating substantial re-ranking of genotypes (0.44–0.49). The genetic variance increased from low- to high-growth environments. The heritability of post-weaning body weight ranged from 0.28 to 0.39. The genetic correlation between intercept and slope of the reaction norm for post-weaning body weight was low to moderate when based on the estimated (co)variance components but was much higher when based on back-solved SNP effects. An initial analysis suggested that a region on chromosome 11 affected both the intercept and the slope, but when the GEBV for the slope were conditioned on the GEBV for the intercept to remove the effect of scale-type G × E interactions on SNP effects for robustness, a single genomic region on chromosome 7 was found to be associated with robustness. This region included genes previously associated with growth traits and disease susceptibility in livestock.This study shows a significant genetic variation in the slope of RNM that could be used for selecting for increased robustness of sheep. Both scale-type and rank-type G × E interactions contributed to variation in the slope. The correction for scale effects of GEBV for the slope should be considered when analysing robustness using RNM. Overall, robustness appears to be a highly polygenic trait.The online version contains supplementary material available at 10.1186/s12711-022-00734-6. Genotype-by-environment (G × E) interactions occur when the expression of the genes carried by an individual changes depending on the environment in which it is raised. This means that the best animal for one environment might not necessarily be the best one in another environment. One approach to this problem is to design breeding programs for specific geographical environments . HoweverThe robustness of an animal cannot be directly measured, since it is typically raised in a single location at a given time point. However, robustness can be inferred for a genotype using a reaction norm model (RNM) that includes genetic relationships between relatives that have been raised in different environments. In a RNM, the estimated breeding value (EBV) of each animal is modelled as a function of a continuous environmental descriptor using random regression. A linear function is typically used as it is easier to interpret than higher order polynomials . This reMore recently, RNM have been extended to include genomic information in the form of single nucleotide polymorphisms (SNPs) . The useThe correlation between the genetic variance in the intercept and that in the slope has often been limited to a discussion as a correlation between overall performance and sensitivity . Beyond The objectives of this study were to: (1) estimate the genetic variation of robustness in Australian sheep between different growth environments using linear reaction norms models fitted by random regression; and (2) compare the use of direct and scale-corrected GEBV for the slope in a genome-wide association study, using post-weaning weight (PWWT) as the response variable.In this study, data from 34,584 sheep recorded in the Australian Sheep CRC Information Nucleus Flock (INF) and the Meat and Livestock Australia Resource Flock (RF) were used. The INF animals were located at eight sites across southern Australia between 2007 and 2011. Each year, approximately 100 sires from the two main sites  were mated via artificial insemination, each one with 900 ewes. For the six remaining sites, 450 ewes were mated annually, with at least 50% of the Kirby/Katanning sires. Then from 2012 onwards, the program was changed: it used the RF and only the Armidale and Katanning sites were kept under a similar mating structure, but with about 150 sires mated at both sites each year. The management practices at each site reflected the typical practices used for raising lambs on pasture in each of the respective geographical areas. Supplemental feed was occasionally provided in years with periods of drought to help maintain the body weight of animals.The population consisted of multiple breeds. Merino, Maternal (such as Border Leicester) or Terminal (such as Poll Dorset) sires were mated to either Merino or first cross Maternal-Merino dams. Only the Merino breed had pure-bred representatives, while the remaining breeds were represented as crosses. Overall, 39 different breed groups were available, referred to as genetic groups hereafter. A summary of the breed composition of the data is in Additional file Phenotypic records for post-weaning growth rate (PWGR) and post-weaning weight (PWWT) were used. First, PWGR was used to estimate the environmental descriptor used in the RNM. The PWGR of each lamb was calculated as the difference between weaning and post-weaning weights, recorded at approximately 96 64–120) and 238 (120–329) days of age, respectively, which was divided by the number of days between measurements and expressed in g/day. PWWT was then used as the dependent variable for the RNM analysis. The distribution of PWGR and PWWT as well as the other descriptive statistics after filtering are available in Additional file 0 and 238Since explicit environmental information was not available for the each of the sites, the best linear unbiased estimation (BLUE) of the contemporary group effect for PWGR was used to estimate the environmental descriptor. Contemporary groups (CG) were formed as a cohort of site ., PWWT) .Lambs that had a growth period shorter than 40 days, a PWGR more than 4 standard deviations (SD) from the mean PWGR or that were born as quintuplets or reared as quadruplets were not included in the analysis. Only CG with a minimum of 15 animals were considered. After filtering, 33,773 individuals from 249 CG were available to estimate the CG effects. The model for obtaining the BLUE of CG effects was a regular pedigree-based animal model:Only the CG effects for genotyped animals  were extracted. Each animal was assigned the BLUE of their CG as their environmental descriptor, which was then standardised to have a mean of 0 and variance of 1, forming the EG used in the RNM.This subset of genotyped animals was re-filtered to ensure that the animals had a PWWT within 4 SD from the mean PWWT, originated from CG of at least 15 other animals, or had at least one half-sib. After filtering, 22,513 animals from 1582 sires and 11,576 dams across 206 CG were available for RNM analysis.The CG effects ranged from − 2.15 SD (− 72.4 g/day) to 2.06 SD (69.1 g/day) respectively, with an overall range of 141.5 g/day. On average, sires had progeny in environments with PWWT differing by 59 g/day. A small number of the sires (6%) had progeny in only one environment, but these were still included since their genomic data can link them to other animals across environments. The distribution of records and mean PWWT along the EG is plotted in Additional file –15), had call rates lower than 90% and Gen-Cal scores lower than 0.6. When the correlation between the genotype sample of an animal and that of another animal was higher than 0.98, they were removed, as this indicated an error in matching ID. Following quality control, the Beagle 5.1 software  = 6.63). These findings suggest that correcting for scale effects on the GEBV for the slope can greatly influence the genomic regions detected and the interpretation of GWA analyses for robustness.The direct GEBV for the slope have been used in GWA studies to identify SNPs associated with robustness , 35, 36.Interestingly, the correlation between the SNP effects for the intercept and scale-corrected slope in both models was not exactly zero. However, the genetic regression used for the scale-corrected slope was based on the estimated genetic (co)variance components, rather than the (co)variance of the GEBV used to back-solve the SNP effects. These (co)variances are not identical, as the genetic (co)variance components estimate the variance of the true breeding values  to inform the GEBV of the other trait (the slope) through the covariance. This sharing of information can result in GEBV for the two traits having a higher correlation than the estimated genetic correlation . It is cSLC24A1, DENND4A and RAB11A genes are associated with both average daily gain in Nellore cattle [MEGF11 is associated with feed efficiency in cattle and pigs [SLC24A1 is involved in respiratory and metabolic alkalosis in chickens under heat stress, and speculated that it may have a driving role in stabilizing the acid–base balance in the blood. SLC24A1 has also been shown to be downregulated in cows with subclinical mastitis [MEGF11 is associated with somatic cell count in cattle [IGDCC4 and IGDCC3 genes encode antibodies [One genomic region on chromosome 7 was significantly associated with the robustness of PWWT to different growth environments. Several genes identified in this region are associated with body weight and composition-related traits in livestock. The e cattle  and backe cattle . Similarand pigs , 41, andand pigs . Intriguand pigs  showed tmastitis , while Mn cattle . In additibodies , which aThe identification of these genes improves our understanding of the biology of robustness. However, the value of this information for increasing the accuracy of prediction of robustness is likely negligible, since the effect of these genes appears to be very small. Implementing selection for robustness based on GBLUP should efficiently capture these polygenic effects without the need to understand the underlying biology .While the results of this study demonstrate the importance of considering the impact of scale-type G × E interactions when analysing robustness, the impact of the scale-correction in affecting selection decisions and genetic progress in breeding programs is less clear. A genotype which ranks highly across environments for traits such as body weight is evidently more valuable than a genotype which only ranks highly in specific environments. Simulations that explore selection for robustness under various scenarios of G × E interactions are needed to further understand the impact of the scale-correction, as well as the value of selecting for robustness in breeding programs.This study demonstrates the existence of important G × E interactions for PWWT of sheep in different growth environments and highlights the possibility of selecting sheep based on their robustness. Correction of slope breeding values using a genetic regression is suggested to account for scale-type G × E interactions on estimates of robustness. The genetic architecture of robustness in sheep appears to be highly polygenic.Additional file 1: Table S1. Breed composition of the dataset used to estimate contemporary group effects and reaction norm models.Additional file 2: Table S2. Descriptive statistics for post-weaning growth rate (PWGR) and post-weaning weight (PWWT).Additional file 3: Figure S1. Distribution of animals and mean post-weaning weight across the environmental gradient.Additional file 4: Table S3. Approximate number of reference animals used for imputation for each SNP panel.Additional file 5: Figure S2. Residual variance along the environmental gradient for the RNM-HET.Additional file 6: Table S4. Comparisons of additive genetic variance (Table S4a), heritability (Table S4b) and genetic correlations (Table S4c) between the three growth environments .Additional file 7: Figure S3. Manhattan plots for the intercept in the RNM-HOM and RNM-HET.Additional file 8: Figure S4. Correlation between SNP effects for scale-corrected slope from the RNM-HOM and RNM-HET.


Skeletal deformities and malocclusions being heterogeneous traits, affect populations worldwide, resulting in compromised esthetics and function and reduced quality of life. Skeletal Class III prevalence is the least common of all angle malocclusion classes, with a frequency of 7.2%, while Class II prevalence is approximately 27% on average, varying in different countries and between ethnic groups. Orthodontic malocclusions and skeletal deformities have multiple etiologies, often affected and underlined by environmental, genetic and social aspects. Here, we have conducted a comprehensive search throughout the published data until the time of writing this review for already reported quantitative trait loci (QTL) and genes associated with the development of skeletal deformation-associated phenotypes in different mouse models. Our search has found 72 significant QTL associated with the size of the mandible, the character, shape, centroid size and facial shape in mouse models. We propose that using the collaborative cross (CC), a highly diverse mouse reference genetic population, may offer a novel venue for identifying genetic factors as a cause for skeletal deformations, which may help to better understand Class III malocclusion-associated phenotype development in mice, which can be subsequently translated to humans. We suggest that by performing a genome-wide association study (GWAS), an epigenetics-wide association study (EWAS), RNAseq analysis, integrating GWAS and expression quantitative trait loci (eQTL), micro and small RNA, and long noncoding RNA analysis in tissues associated with skeletal deformation and Class III malocclusion characterization/phenotypes, including mandibular basic bone, gum, and jaw, in the CC mouse population, we expect to better identify genetic factors and better understand the development of this disease. The intricate morphology of the facial skeleton plays a variety of important tasks. It provides crucial anatomy for oral communication, eating, and facial expression as well as housing and protecting the majority of sensory organs and the forebrain. Variation in the biological processes that regulate facial growth is what causes structured differences (covariance) in facial shape and between facial form and size ,2. ExamiSkeletal patterns and malocclusions are heterogeneous conditions affecting populations worldwide, resulting in compromised esthetics and speech function and reduced quality of life . The WorThe prevalence of class III malocclusion varies between different populations and races. Prevalence among Asians is high, while it is low among Caucasians ,12,13,14The purpose of this report is to evaluate genetic associations between candidate craniofacial genes and skeletal variations in patients with skeletal malocclusions in mouse models. This study characterizes phenotypes of the craniofacial skeleton in mice with malocclusion and generates genetic data on craniofacial genes/loci to identify phenotype-genotype interactions. Several previous research results suggested genetic pathways that play a role in the anteroposterior and vertical skeletal variation observed in patients with malocclusion and in the type of skeletal malocclusion. Future studies should also consider examining soft-tissue differences to uncover the genetic etiology of skeletal and soft-tissue variations in patients with malocclusion. Identifying the genetic influence on these phenotypes will improve our understanding of the molecular regulation of postnatal facial growth and inform clinical practitioners to improve treatment efficacy for patients with malocclusion. Furthermore, for a better understanding of the complexity and heterogeneity of human class III malocclusion, developing a new class III malocclusion mouse model would suffice. To determine the effects of genetic variation on class III malocclusion signaling networks and phenotypic variability, we reviewed all the reported studies in the mouse model for investigating the genetic basis of mandibular size and prognathism, which we believe will help improve the understanding the human skeletal Class III malocclusion.In recent years, it has been widely recognized that both genetic and environmental factors contribute to class III malocclusion development ,20. VariIdentifying these genetic factors and understanding their mechanisms would aid in the diagnosis, prediagnosing, predicting and treatments. Single-nucleotide polymorphisms (SNPs) and the haplotypes defined by shared SNPs can be genotyped to determine normal and variable craniofacial phenotypes . StudiesRecently, five genome-wide nonparametric linkage analyses and one genome-wide association study (GWAS) for identifying susceptibility loci associated with mandibular prognathism and skeletal Class III phenotype showed conflicting results in humans ,20,21. HThe epigenetic regulation is a reversible, heritable change in chromatin structures that control genes’ turning on and off. The proposed epigenetic mechanisms are via DNA methylation, RNA interference, histone modifications, or a combination of these processes . In eukaControlled and standardized investigations of the genetics of complex human diseases are challenging because of the cumulative effect and interactions of numerous genes and environmental factors. Despite major discoveries of genetic risk factors for some diseases, the specific genes involved in susceptibility to most complex diseases and the mechanisms translating genetic effects into disease susceptibility are commonly unknown. The mouse model is a powerful tool for dissecting the genetic bases of complex phenotypes/diseases . Once the genes associated and underlined a given phenotype are identified in the mouse, these results subsequently can be translated to humans using comparative and orthologue analysis.In a previous study by Dohmoto et al. , it was Mandibular morphology analysis has long been used for tribe identification by discriminant analysis . In suchDohmoto et al.  focused http://www.informatics.jax.org/ accessed on 25 January 2023) was searched for candidate genes according to their position at approximately 60cM of chromosome 10 and between 13cM and 16cM of chromosome 11, identifying more than 10 genes as candidates for the mandibular size revealed in chromosomes 10 and 11. It is interesting that a candidate gene near the QTL for mandibular size on chromosome 11 is Otx1 (orthodentices), a gene closely related to Otx2. Mouse embryos homozygous for a knockout allele of Otx2 display a striking phenotype, in which the entire brain is absent rostrally to rhombomere 3 [The SM/J allele on chromosome 10 for QTL is associated with a large lower jaw. The size of the mandible was not only determined by genes located in chromosomes 10 and 11, as there were also some effects that were weak in other chromosomes. The Mouse Genome Database . Interestingly, Snai3 null mice show no significant abnormalities, suggesting that Snail genes are complementary in their biological functions [It is possible that the phenotypes represented in PC1 and PC4 are more complex in their genetic etiology than those captured in PC2 and PC3 and are associated with a greater number of genetic variants, each with a small effect. If this is the case, large-sample GWAS may be a more effective approach to identify loci for such phenotypes, since there is little evidence that they are associated with the candidate genes and loci evaluated. The main results suggest that SNAI3 is associated with craniofacial variations from highly concave to convex profiles, while TWIST1 is related to variations from short to long mandibular bodies. SNAI3 is a member of the SNAIL family of zinc finger transcription factors important at epithelial to mesenchymal junctions that contribute to the formation of the mesoderm and neural crest . Snai3 iunctions . Snai2 aunctions . The neuunctions , indicatunctions . Therefounctions . Mutatiounctions , a condiunctions . Twist1+unctions . Given tunctions . Given tunctions  and in cunctions . WhetherM. musculus and wild M. domesticus mice, respectively) and five classical inbred lines . The final population of CC mice has a significant genetic variety due in part to this phylogenetic variation. The map growth in the CC population is around four times greater, which increases the proportionate accuracy of the QTL map position. Since all genetic traits are homozygous due to their inbred origin, each QTL’s genetic variance is increased. Additionally, environmental sources of variance can be reduced by phenotyping numerous individuals in each line. When compared to typical F2 mapping populations, the set of RILs’ effective mapping power is multiplied many times over in this way.Many limitations in studies of human populations can be addressed in mice, which share human susceptibility to many pathogens and environmental circumstances. Once the genes associated and underlining a given trait are identified in the mouse, they subsequently can be identified in humans using comparative and orthologue analysis. However, standard laboratory mouse lines have low genetic diversity and thus limited usefulness for the study of genetic variation in complex traits. To remedy this, the Collaborative Cross (CC), a new genetically highly diverse set of recombinant inbred mouse strains, was designed. With a focus on features important to human health, the collaborative cross CC strains were created as a new tool for high-resolution mapping and identification of the genetic elements underlying complex phenotypes. The creation of the mouse CC genetic reference population (GRP) was motivated by the need to model genetic variety. The wide panel of recombinant inbred (RI) strains that make up this special GRP resource were specifically developed for complicated trait analysis ,58,59,60The genomes of the eight CC founder strains are introduced into a single CC lineage with a well-planned breeding schedule. After more than 20 generations of brother-sister mating, following the introduction of genomes from the parental founding strains at the G2-F1 stage, it was expected that approximately 99% homozygosity would be achieved with an approximately equivalent contribution from the eight founding strains ,59,60,61A new class III malocclusion model should be created, taking into consideration the diversity of the human population, in order to better understand the hereditary elements that may be implicated in the beginning, progression, and establishment of skeletal class III malocclusion. The first thorough review of the molecular basis of complex features has been provided by systems genetic analysis, which is helpful for pinpointing the genes, signaling pathways, and networks that underlie prevalent disorders. In order to examine the associations of various skeletal Class III malocclusion phenotypes, data on cellular, molecular, and clinical aspects are then merged. The regulatory genomic areas are implicated in phenotypic variance in both in vitro and in vivo monitored traits when SNP genotype data from each CC lineage are combined. The potential for parallel in vitro/in vivo screening in such an experimental design, as well as the advancements in high-throughput assessment technologies and computational techniques, may help us comprehend how the interaction of various genetic alterations affects the onset and severity of class III malocclusion. Then, human systems can be used to apply verified gene-gene interactions and/or gene-environment networks to inform risk for class III malocclusion prevention or pharmaceutical targets. Systems genetics will most likely be able to comprehend both the biology and the severity of the disease by comprehending the mechanisms of the genetic loci (QTL and genes) identified in the genome-wide association study (GWAS) that contribute to susceptibility to skeletal class III malocclusion diseases.Previously, it was shown that integrating GWAS and expression quantitative trait loci (eQTL) data is a powerful analytical framework to predict potential susceptibility genes for complex diseases . TherefoRegulatory RNAs are now extensively investigated on the molecular basis regarding various diseases. Still, to our knowledge, hardly any study is available regarding the status of these molecules in skeletal class III malocclusion. Here, we suggest that these regulatory RNAs will be of pivotal importance studying this condition and will lead to a better understanding of the molecular basis of this disease.The workflow diagram for the generation of system genetic datasets from the highly diverse CC population and their susceptibility to developing skeletal class III malocclusion is represented in It is thought that the genetic influence on skeletal class II and III malocclusion susceptibility cannot be fully understood using only methods intended to pinpoint the primary impacts of specific genotypes/alleles in humans. To better understand the complexity and heterogeneity of human class II and III malocclusion, it is important to develop a new class II and III malocclusion mouse model. The proposal to perform a systems genetic analysis using CC GRP mice to characterize the impact of genetic variation on class II and III malocclusion signaling networks and phenotypic diversity is a promising starting point.


Breeders use breeding goals to guide genetic gain in a population in a desired direction. Breeding goals consist of economically interesting traits, in which each trait receives an economic value. For example, to increase the size of a piglet litter, breeders use a breeding goal that includes the trait “number of live piglets in a litter” for a specific day after birth. While the litter size is selected using the trait “number of live piglets,” it is composed of two traits: “total number born” and “number of dead piglets.” The current study used simulations to illustrate that selection for litter size could be improved by selecting for the latter two traits rather than the former. This approach corrects for the fact that these two traits are genetically related to each other, but they also have genetic differences. Further, splitting one trait into two traits allows breeders to focus on the specific elements of a trait. For example, organic pig breeders could select for better piglet welfare by splitting “number of live piglets” into two traits, giving a negative economic value to the number of dead piglets.Selection for the number of living pigs on day 11 (L11) aims to reduce piglet mortality and increase litter size simultaneously. This approach could be sub-optimal, especially for organic pig breeding. This study evaluated the effect of selecting for a trait by separating it into two traits. Genetic parameters for L11, the total number born (TNB), and the number of dead piglets at day 11 (D11) were estimated using data obtained from an organic pig population in Denmark. Based on these estimates, two alternative breeding schemes were simulated. Specifically, selection was made using: (1) a breeding goal with L11 only versus (2) a breeding goal with TNB and D11. Different weightings for TNB and D11 were tested. The simulations showed that selection using the first breeding scheme (L11) produced lower annual genetic gain (0.201) compared to the second . A sensitivity analysis showed that the second scheme performed better because it exploited differences in heritability, and accounted for genetic correlations between the two traits. When the second breeding scheme placed more emphasis on D11, D11 declined, whereas genetic gain for L11 remained high (0.190). In conclusion, selection for L11 could be optimized by separating it into two correlated traits with different heritability, reducing piglet mortality and enhancing L11. The focus of the pig industry on increased litter size and improved growth rate has noticeably increased litter size, but has also increased the average number of dead piglets per litter, termed “piglet mortality” . Piglet Breeding for reduced piglet mortality could be achieved by selecting for sows that are more responsive, potentially reducing the chances of crushing; however, heritability estimates for sow behavior are low ,7. AlterYet, concerns existregarding using an approach with a combined trait for both piglet mortality and litter size. Using one trait that selects for litter size and fewer dead piglets overlooks the facts that that these traits are correlated, have different heritability, and are not economically equivalent. There is a genetic correlation between the total number born and the number of dead piglets in a litter; yet, their heritability is not the same . FurtherThis study aimed to evaluate how genetic gain for a combined trait could be improved by separating it into two traits. Specifically, we separated the “number of living piglets” into “total number born” and “number of dead piglets”.Three sow traits were simulated: the number of living piglets at day 11 after birth (L11), the total number born, including live and stillborn piglets (TNB), and the number of dead piglets at 11 days after birth (D11). The trait L11 = TNB − D11 is similar to the recent combined survival and litter size trait that is used in pig breeding programs in Denmark, namely, the number of living piglets at day 5 (L5) . For orgData on TNB and D11 were used to estimate the parameters used in the simulations. Data were collected from March 2018 to October 2021 at an organic multiplier farm in Denmark, named Bovbjerg Økologi A/S. On the farm, after the sows were inseminated, they were kept in sow groups containing sows of the same breed/combination. Close to parturition, sows were then transferred to separate huts in a sow field, with individual fields containing 30 to 76 sows. Each sow had a fenced field that included a hut, a feeding and watering station, and a mud pool. Sows in the same sow field typically gave birth within a three-week period. All piglets born to sows in the same field were weaned at the same time. Sows gave birth inside the hut, which was provided with a low fence (“fender”) to keep piglets inside during the first 10 days, whereas the sow was able to go outdoors freely. The trait TNB was recorded immediately after birth by counting all born piglets . The trait D11 was recorded by counting living piglets on the day the fender was removed  and subtracting this number from TNB.Litter size records were recorded for 1235 sows, which had a total of 3977 litters. Of all sows, 781 were Landrace (LLLL) and 466 were Yorkshire boar x LLLL sow (YYLL). Of all litters, 253 were of the breed LLLL, 1935 YYLL, and 1822 Duroc boar x YYLL sow (DDYL). For all litters, both TNB and D11 were recorded. IDsow for the direct genetic effect of the sow, PEsow for the permanent environmental effect of the sow, and e for the residual effect. These random effects were assumed to be independent and followed normal distributions. IDsow~N, where σa2 is the variance of IDsow and A is a relationship matrix based on pedigree information. PEsow~N, where σPE2 is the variance of PEsow and I is an identity matrix. e~N, where σe2 is the variance of e and I is an identity matrix.Parameters were estimated using DMU software . The par2TNB is the phenotypic or genetic variance of TNB, σ2D11 is the phenotypic or genetic variance of D11, and σTNB,D11 is the phenotypic or genetic covariance between TNB and D11.Based on the estimates for TNB and D11, phenotypic and genetic variance and covariance components for L11 were derived as:This study aimed to improve L11 in organic pig populations by adjusting the breeding scheme. In organic pig populations, males are selected from external conventional populations. Females are selected on organic nucleus/multiplier farms . TherefoL11, vTNB, and vD11 are the economic values (EVs) for L11, TNB, and D11, respectively. EV was not based on real economic inputs; rather, weights were given to traits in the breeding goal. The two-trait breeding goal fixed vTNB to 1, but used a value for vD11 that ranged from −5 to 0 using steps of 0.05. In addition, one two-trait breeding goal was added, in which the EV for TNB was equal to 0 and D11 was equal to 1. In total, 102 breeding schemes that used a two-trait breeding goal were simulated.In the simulated breeding schemes, a breeding goal (BG) was used to select dams based on their estimated breeding value (EBV) for the aggregate genotype. This aggregate genotype was based on the additive genetic value of an individual for the trait(s) in the breeding goal. Two breeding goals were tested: (1) a single-trait breeding goal and (2) a two-trait breeding goal.The single-trait breeding goal was similar to the trait that, until recently, was used in pig breeding programs in Denmark ,17. Two-Simulations were performed with ADAM software , and breFor the simulation of a breeding scheme, individuals were placed in age groups based on their age in time steps, where each time step represented approximately six months. Hence, males or females in age group two were two time steps old (~1 year), and females in age group five were five time steps old (~2.5 years). Males and females were available for reproduction in age group two and two to five, respectively.Breeding schemes were simulated for 30 time steps. Each time step, 30 males were randomly selected from the second age group and mated with 150 females. These 150 females originated from age groups two to five, and were truncation-selected based on their estimated breeding value (EBV) for traits in the breeding goal. Females had eight surviving offspring with phenotypes that had a 1:1 sex ratio. Approximately 600 female piglets and 600 male piglets were born each time step.In each time step, sires and dams were selected using the following action steps. First, from the 600 newborn males, 30 sires were randomly selected. The remaining males were culled. This step modelled, in a simplified way, the use of sires from the conventional system, without controlling the selection of sires. Second, from the 600 newborn female piglets, 240 individuals were truncation-selected based on their EBVs to form age group one. This step mimicked the pre-selection of gilts for potential reproduction. Third, females were culled at random, mimicking the death or selling of dams. After random culling, 220 females remained in age group two, 190 in age group three, 140 in age group four, and 100 in age group five, totaling 650 females. All of these females had phenotypic records for TNB, D11 and L11. Each female had up to four repeated records. In step four, of these 650 females, 150 dams were truncation-selected based on their EBVs to reproduce offspring. At the end of each time step, all males from age group two and all females from age group five were culled.A sensitivity analysis was performed to test which factors contributed to differences in genetic gain for L11, when the single-trait breeding goal with only L11 was used or the two-trait breeding goal with TNB and D11 was used.t is the annual genetic gain for trait t , and BVt,i is the average breeding value for trait t among the offspring born in time step i. To calculate the annual genetic gain, we assumed two time steps per year, hence the multiplication by 2 to indicate the genetic trend per year.Preparation of input files for ADAM and DMU, and analyses of ADAM simulation output files were completed in R . For eact-test was used to test the significance of differences in the annual genetic gain for L11 between single-trait and two-trait selection.For the sensitivity analysis, a Variance components for L11, TNB, and D11 are presented in Our study shows that genetic gain could be maximized by separating a combined trait into two traits. We demonstrated this by comparing a single-trait breeding goal based on L11 to a two-trait breeding goal with equal weights for TNB and D11. This two-trait breeding goal yielded significantly more genetic gain for L11 compared to using the breeding goal with only L11 . Of noteDifferences in genetic gain for breeding schemes using different breeding goals  were cauUsing a multi-trait breeding goal allows for the economic value of traits to be adjusted according to the production system, country, population, and changing economic conditions . Hence, The current study used piglet mortality in organic pigs as an example because of its relevance in organic production. Selection for fewer dead piglets per litter reflects the aims for improved welfare in organic farming ,24. FurtCurrent organic pig breeding in Denmark depends on semen from male pigs bred in the conventional system. Consequently, genetic gain in organic pigs would follow the genetic trend of the conventional pig population . ConventIn the case of L11, a two-trait breeding goal would give breeders the option to include welfare directly in the breeding goal , and empIn conclusion, selection for a given trait could be optimized by separating it into two correlated traits with different heritability. Specifically, splitting L11 into TNB and D11 allowed for increased litter size, while reducing the number of dead piglets in a litter. This result is promising for organic pig breeding programs, which could improve piglet welfare without hindering improvements in litter size.


However, genetic analysis on raw genotypes can lead to spurious or unreliable results due to defective single nucleotide polymorphism assays or wrong genotype interpretation. Here, we introduce stuart, an open-source R package, which analyzes raw genotyping data to filter single nucleotide polymorphism markers based on informativeness, Mendelian inheritance pattern, and consistency with parental genotypes. The functions of this package provide a curation pipeline and formatting adequate for genetic analysis with the R/qtl package. stuart is available with detailed documentation from  P = 0.05 threshold  and backcrosses (N2), in which the inheritance of the trait is compared with the genotypes at multiple genetic markers encompassing the genome map. Variations of a quantitative trait are controlled by one or more quantitative trait loci (QTLs). A QTL is defined as a marker at which individuals carrying different genotypes show different average trait values. QTL mapping searches for QTLs by testing the association between trait values and genotypes at markers spanning the genome map. The statistical significance of the association is expressed as logarithm of the odds (LOD) score which is calculated for each genotyped marker and, at intermediates positions, for pseudomarkers created by interval mapping, generating an LOD score curve . The curhreshold . QTL mapWith genome sequencing, single nucleotide polymorphisms (SNPs) have become the standard across species for their very high frequency, low cost, and high-throughput analysis using various genotyping platforms. In mice, several generations of Mouse Universal Genotyping Arrays (MUGA) have been developed, the most recent being GigaMUGA . However, this control is performed once genotypes have been imported and involves multiple steps of manual curation. To provide a more automated process of data curation before genetic analysis, we have developed stuart, an R package that implements a pipeline for automatic filtering and curation of SNP genotyping data from 2-generation crosses based on simple rules. This package formats raw SNP allele calls from Illumina files into genotypes ready for importation in R/qtl. Using 3 intercross datasets, we illustrate the consequences of inconsistent genotypes on the estimated marker map and QTL mapping, and how the curation achieved by each function in stuart leads to trustable results.Several tools exist for quality control of SNP genotyping arrays, including Illumina's GenomeStudio. R packages such as argyle  analyze https://gitlab.pasteur.fr/mouselab/stuart/ and can be installed with devtools .stuart is a tidyverse (Ifnar1 KO) F2 mice (dataset 1). The analysis of 2 other data sets, 94 (C57BL/6J-Ifnar1 KO X 129S2/SvPas-Ifnar1 KO) F2 mice (dataset 2) and 89 (C57BL/6NCrl X CC021/Unc) F2 mice (dataset 3) is presented as P = 0.05 for significant association, P = 0.1 and P = 0.63 for the suggestive association. All figures were designed with ggplot2 , which measures the genetic distance between 2 markers, is estimated in a cross by analyzing the proportion of recombinants between adjacent markers in all individuals. The map of markers calculated from the cross data should be consistent with their known positions. The R/qtl est.map and plotMap functions produce a graphical comparison of the 2 maps , a and bP = 0.05 significance threshold was estimated at 19.4 : 2 were located at a marker with non-Mendelian allelic proportions and 2 were located at 1–3 pseudomarkers adjacent to a marker with non-Mendelian proportions  observed in second-generation individuals (SGIs) to genotypes encoded according to the parental alleles may also create genotype errors. Reference SNP alleles established for many mouse strains may be used to infer the SGI genotypes. However, we recommend genotyping individuals of the parental strains used in the cross since they could differ from the reference panel. In our example dataset, the 2 parental strains used in the cross showed allelic differences with their reference panel counterpart at 200 markers.Although each of stuart’s functions can be called independently, we present a logical analysis workflow appropriate for 2-generation crosses. Genetic mapping requires both genotype and phenotype data. Required formats and instructions are detailed in the vignette , each with successive versions differing by multiple SNP markers. If parental and SGI data were produced on different versions, the marker lists must be compared to retain only common SNPs. This is achieved by the mark_match function.Converting alleles into genotypes requires that SGI segregate for the 2 parental alleles, and that each allele is found only in one parent. The aim of the mark_allele function is to control consistency of allele's origin at multiple levels.First, this function excludes markers with missing data in both parents. If allele data are available for only one parent and this allele is also found in SGI, the other allele present in SGI will be assigned to the parent with missing allele. However, this imputation is not error-free since we have observed, in rare occasions, markers which alleles were identical in the parental strains but were polymorphic in the SGI . The mark_prop function filters markers based either on a minimum proportion of each genotype or on the statistically significant departure from the expected proportions , 10 , and 15  which were not visible using noncurated data due to very high LOD score thresholds.Being very simple to use and efficient at curating genotyping errors, stuart will facilitate the use of genotyping arrays for genetic mapping purposes in 2-generation crosses, bridging the gap between raw allele data produced by SNP platforms and genetic analysis software. Moreover, its functions can be used independently to analyze inbred strains genotypes. For example, geno_strain creates a genotype consensus between 2 or more individuals of the same strain suitable for further inspection, which can be useful when genotyping or regenotyping a strain of interest. Comparing genotyping results of an inbred strain after several generations of breeding with mark_allele will readily identify variants that have emerged or been selected over time. Likewise, this function will help identifying genetic variants between substrains.https://gitlab.pasteur.fr/mouselab/stuart/.The source code of the stuart package and the code used for the figures of this article are publicly available from jkac219_Supplementary_Material_LegendsClick here for additional data file.jkac219_Supplementary_Table_1Click here for additional data file.jkac219_Supplementary_Figure_1Click here for additional data file.jkac219_Supplementary_Figure_2Click here for additional data file.jkac219_Supplementary_Figure_3Click here for additional data file.jkac219_Supplementary_Figure_4Click here for additional data file.jkac219_Supplementary_Figure_5Click here for additional data file.