Ecological adaptation is of major relevance to speciation and sustainable population management, but the underlying genetic factors are typically hard to study in natural populations due to genetic differentiation caused by natural selection being confounded with genetic drift in subdivided populations. Here, we use whole genome population sequencing of Atlantic and Baltic herring to reveal the underlying genetic architecture at an unprecedented detailed resolution for both adaptation to a new niche environment and timing of reproduction. We identify almost 500 independent loci associated with a recent niche expansion from marine (Atlantic Ocean) to brackish waters , and more than 100 independent loci showing genetic differentiation between spring- and autumn-spawning populations irrespective of geographic origin. Our results show that both coding and non-coding changes contribute to adaptation. Haplotype blocks, often spanning multiple genes and maintained by selection, are associated with genetic differentiation.DOI:http://dx.doi.org/10.7554/eLife.12081.001 The Atlantic herring is one of the most common fish in the world and has been a crucial food resource in northern Europe. One school of herring may comprise billions of fish, but previous studies had only revealed very few genetic differences in herring from different geographic regions. This was unexpected since Atlantic herring is one of the few marine species that can reproduce throughout the brackish Baltic Sea, which can be about a tenth as salty as the Atlantic Ocean.This unexpected finding could be explained in at least two different ways. Firstly, perhaps Atlantic herring are flexible enough to adapt to very different environments  without much genetic change. Secondly, the previous studies only looked at a handful of sites in the Atlantic herring’s genome and so it is possible that genetic differences at other genes control this fish’s adaptation instead.Now, Martinez Barrio, Lamichhaney, Fan, Rafati et al. have sequenced entire genomes from groups of Atlantic herring and revealed hundreds of sites that are associated with adaptation to the Baltic Sea. The analysis also identified a number of genes that control when these fish reproduce by comparing herring that spawn in the autumn with those that spawn in spring. This is important because natural populations must carefully time when they reproduce to maximize the survival of their young.These new findings provide compelling evidence that changes in protein-coding genes and stretches of DNA that regulate the expression of other genes both contribute to adaptation in herrings. The analysis also clearly shows that variants of genes that contribute to adaptation were likely to evolve over time by accumulating multiple sequence changes affecting the same gene. Furthermore, these gene variants essentially form a rich “tool-box” that underlies the Atlantic herring’s adaptation to its environment, and different subpopulations of herring were found to have their own optimal sets of gene variants. For instance, autumn-spawning herring and spring-spawning herring from the Baltic Sea both have gene variants that favor adaptation to low salinity. However, autumn-spawning Baltic herring also share gene variants that favor spawning in the autumn with autumn-spawning herring from the North Sea, but not with spring-spawning Baltic herring.The next step will be to study how the 500 or so genes identified affect adaptation at the molecular level. This will likely involve experiments with other model fish such as zebrafish and sticklebacks. Finally, these new findings can be directly applied to monitor stocks of herring to make herring fisheries more sustainable.DOI:http://dx.doi.org/10.7554/eLife.12081.002 Clupea harengus) is a pelagic fish that occurs in huge schools, up to billions of individuals. The herring fishery has been crucial for food security and economic development in Northern Europe and currently ranks among the five largest fisheries in the world with nearly 2 million tons fish landed annually (FST)-values among herring populations deviated significantly from expectation for selectively neutral loci.The Atlantic herring , and one example of sympatric balancing selection (variation in the timing of reproduction). The results provide a comprehensive list of hundreds of independent loci underlying ecological adaptation and shed light on the relative importance of coding and non-coding variation. The results have important implications for sustainable fishery management, and provide a road map for cost effective high-resolution characterization of genetic diversity in natural populations.gag, pol and env genes . Each pool comprised 47–100 fish and was sequenced to ~30x coverage. Furthermore, 16 fish, eight Baltic and eight Atlantic herring . The estimate was one heterozygous site per 309 bp, giving a nucleotide diversity of 0.32%; no estimate based on the 16 herring sequenced individually deviated significantly from this value and there was no significant difference between Atlantic and Baltic herring. The average decay of linkage disequilibrium between loci was very steep, with average se pairs .-16, Kolmogorov-Smirnov test), due to an excess of rare alleles  and Baltic herring (Clupea harengus membras) were classified as subspecies by th century. They are adapted to strikingly different environments, in particular regarding salinity that ranges from 2–3‰ in the Gulf of Bothnia to 12‰ in Southern Baltic Sea, whereas salinity in Kattegat, Skagerrak, North Sea and Atlantic Ocean is in the range 20‰–35‰ .The Atlantic  by taking into account gaps in the assembly and (iii) by using the Comb-P software , but the most obvious difference concerns salinity. We used the Bayenv 2.0  softwaresalinity . Twenty-salinity .prolactin receptor (PRLR), that is essential for mammalian reproduction but has a central role for osmoregulation in fish (-39). This signal overlaps HCE encoding high choriolytic enzyme. This locus was also identified as one of the most differentiated region in our screen for structural changes (solute carrier family 12 (sodium/chloride transporter) member 3 (SLC12A3) shows strong correlation with salinity  and 69 regions with copy number variation (p<0.001)  ; the higp<0.001) .10.7554/–10   did not deviate markedly from IT F= 0 and in fact to a lesser extent than the spring-spawning population (BÄV) sampled at the same locality (IT F= −0.10 (excess of heterozygotes) for the summer spawners (BÄS) whereas if the sample had constituted an equal mix of spring- and autumn spawners from the same locality (BÄV and BÄH) the expected ITF-value would have been 0.46 (strong heterozygote deficiency). Thus, the data strongly suggest that these summer spawners represent a distinct population rather than admixture. Spawning time may be fine-tuned by the dosage of alleles affecting spawning time. The three populations from Gävle showed nearly identical allele frequencies at loci with strong genetic differentiation between Atlantic Ocean and Baltic Sea  . We then.9x10-6) . Remarka.9x10-6) . This malocality . For inslocality  resultedltic Sea , whereasltic Sea .We used SNP-chip data to construct a haplotype tree based on highly differentiated SNPs in scaffold 190/1420. Two haplotype groups were strongly associated with spring- and autumn spawning ; haploty-120) in this contrast is located within and up to 25 kb upstream of TSHR encoding thyroid-stimulating hormone receptor, which has a central role in this pathway in birds and mammals   is more important than estrogen receptor alpha (encoded by ESR1) for photoperiodic regulation of reproduction.Genetic differences in spawning time are expected to involve photoperiodic regulation of reproduction. Interestingly, our strongest signals   downstreold 1440 , encodesold 1440 . Finally biology . Interes biology . Also, t in mice , consistA common feature for the signatures of selection for adaptation to low salinity and for seasonal reproduction in herring is the presence of haplotype blocks (10–200 kb in size) showing strong differentiation , 4C, desHaving excluded inversions as a major explanation for the long haplotype blocks, two other possible explanations were considered. Haplotype blocks may occur as a consequence of recent fast selective sweeps that leads to hitchhiking of neutral polymorphism in close genetic linkage with causal variants . AlternaGenome-wide analysis combined with strong signatures of selection enabled us to explore the genomic distribution of sequence polymorphisms underlying ecological adaptation. We carried out an enrichment analysis as previously used to identify categories of SNPs showing differentiation between domestic and wild rabbits . We calcNon-synonymous substitutions showed the most striking enrichment in both contrasts and showed a steady increase above dAF=0.15 reaching a two-fold enrichment at dAF>0.50 . This enSNPs located in the 5’untranslated and 3’untranslated regions (UTRs) showed a more consistent enrichment compared to synonymous changes implying that this enrichment is unlikely to be caused entirely by close linkage to coding sequences under selection. Thus, changes in UTRs have contributed to ecological adaptation in the herring, most likely due to their role in regulating mRNA stability and translation efficiency. In this analysis we combined 5’UTR and 3’UTR SNPs to avoid too small classes for the extremely high dAF. However, an analysis based on all SNPs showing a dAF > 0.1 in the Atlantic vs. Baltic contrast and all SNPs showing a dAF > 0.2 for the spring- vs. autumn-spawning contrast demonstrated that both 5’UTR and 3’UTR SNPs are overrepresented at high dAF and the trend is particularly strong for 5’UTR SNPs .The importance of regulatory changes underlying ecological adaptation is evident from the highly significant enrichment of SNPs within 5 kb upstream and downstream of coding sequences . Further-25; -7; Intergenic and intronic SNPs were in general underrepresented among SNPs showing high dAF . For theWe also explored the possibility that loss of function-mutations have contributed to ecological adaptation. We identified a total of 469 nonsense mutations but expect that many of these will be false predictions due to errors in the gene model. Eight predicted nonsense mutations had a dAF higher than 0.20 in one of the contrasts and were further examined. Seven of these were unlikely to be correct annotations since the positions were not annotated as coding in zebrafish, and the remaining one had a dAF of 0.21 but was far from statistical significance. Thus, we conclude that gene inactivation is not a common mechanism for ecological adaptation.We have generated an Atlantic herring genome assembly and used this for a comprehensive analysis of the genetic basis for ecological adaptation. Hundreds of independent loci underlying ecological adaptation were revealed by comparing spring- and autumn-spawners as well as populations adapted to marine and brackish waters. The data show that both coding and non-coding changes contribute to ecological adaptation and we find that haplotype blocks spanning up to hundreds of kb show strong genetic differentiation.The genetic architecture of multifactorial traits and disorders is an important topic in current biology. Genome-wide association studies (GWAS) in humans as well as in livestock have indicated that most multifactorial traits and disorders are controlled by large number of loci each explaining a tiny fraction of trait variation . Thus, iALX1 haplotype in Darwin’s finches (Drosophila melanogaster populations (An important finding was the presence of large haplotype blocks (10–200 kb in size) showing strong genetic differentiation, standing in sharp contrast to the rapid decay of linkage disequilibrium at selectively neutral sites . Althoug finches . Under t finches  strongly finches . The com finches . The modulations .A long-standing question in evolutionary biology is the relative importance of genetic variation in regulatory and coding sequences. thyroid-stimulating hormone receptor (TSHR), calmodulin and SOX11  captured at Forsmark, east of Uppsala, Sweden on September 21, 2011 was used as the reference individual. Skeletal muscle was isolated, placed in 20% glycerol and stored in -80oC until DNA preparation was performed. DNA extraction was carried out with a standard salt precipitation method without vortexing to generate high molecular weight DNA.A single Baltic herring ; 0.77–0.98 picograms (9 bp/pg)  to a total depth of 127-fold coverage of quality-filtered data . Reads wease 238  resultedease 238 . An addiease 238 , which iicograms  correspo9 bp/pg) .synthetic long reads (SLRs). They were filtered to be at least 1500 bp in size. From the five libraries, we obtained 1.3 million SLRs at an average length of 3.6 kb per read , with 20% longer than 5 kb (We obtained data from Illumina’s synthetic long-read sequencing service (formerly Moleculo) . Illuminhan 5 kb . Since Smapped bases instead. We consider a base of a long read to be mapped if an alignment exists that includes that base. Under this definition, 4.47 of 4.69 Gbp (95.6%) could be aligned to the reference. On this level, we therefore observe a good agreement between reference and long reads.Reads were mapped with BWA-MEM 0.7.10 , with deUnder ideal conditions, each long read would align to the reference in a single alignment that extends from the first to the last base of the read. Not being quite as strict, we considered a read to be fully aligned if there exists a single alignment of the read to the reference that involves at least 95% of the bases in the read. We observed 822,802 (62.68%) fully aligned reads. If we require 99% of the bases to be aligned, 771,192 (58.75%) reads remain. A limitation of this approach of assessing quality is that both the reference and the synthetic long reads are assemblies. An agreement between reference and SLR could therefore mean that both assemblies contain the same error. However, synthetic long reads are assumed to be more reliable since the assembly problem, being restricted to only a fraction of the whole genome, is inherently less complex for SLRs. On the other hand, disagreement between reference and SLR may represent normal variation between alleles, not necessarily an assembly error. Despite their length, SLRs did not improve the assembly since we observed that SLRs typically failed to assemble at the same loci at which the genome assembly contained gaps, preventing these gaps from being spanned and closed. The coverage from SLRs mapped to the assembly was also uneven, with 36.7% of genomic bases not being covered at all .SLRs allowed an improved read-based phasing  of the 3Indels in the reference individual were called separately with Illumina data and with SLR data using FreeBayes (version 0.9.8). The availability of long, high-quality reads makes it possible to call medium-sized indels. This type of indels are too long to be found using short reads, but too short to be found by techniques relying on mate-pair mapping distance so by using long reads we should improve our chances of finding them. Calling variants on the reference individual from only short reads results in 537,186 indels of quality 100 or better. Only 580 of those have a length of 30 bp or more. Feeding the variant caller with the SLRs resulted in 8,372 additional indels of length 30 bp or more .We calculated per base mappability, a measure of base pair uniqueness in the reference sequence, with the GEM library . We tranThe completeness of the assembled genome was evaluated by analysing a set of 248 ultra-conserved eukaryotic genes using hidden Markov models (HMM) as implemented in CEGMA (v2.4) , 2009. 8Liver and kidney tissue were collected from the reference individual and stored in RNAlater. After extraction of RNA, using Qiagen RNeasy Fibrous Tissue Mini Kit, and poly-A selection, strand-specific dUTP libraries were produced. Fragments with an insert size of 200 bp were then sequenced by Illumina Hi-Seq instrument using 101 cycles per run producing ~200 million paired-end reads for each library .We reconstructed the herring transcriptome combining RNAseq data for liver and kidney, from the reference herring, in addition to the previously published transcriptome from skeletal muscle from another Baltic herring individual .ab initio predictions were used to discover 23,336 coding gene models   was applotations . First, otations  and cuffotations , we recootations . In orde package  with defTeleostei taxonomic group . Proteins gathered had to be supported at either the proteomic or transcriptomic level and could not be fragmented. Additionally, we downloaded the Uniprot-Swissprot reference data set (on 2014-05-15)  for a wider taxonomic coverage with high-confidence proteins. The two protein data sets yielded a total of 587,735 non-redundant sequences that provided guidance to the putative structure and CDS phases of annotated loci.We also collected further evidences at the protein level by querying the Uniprot database  for sequTo increase the accuracy of the annotation and annotate repeats, we used the existing reference repeat library included in the RepeatMasker (v4.0.3)  enhancedab initio predictions. This yielded a set of 19,762 gene models and 24,551 mRNAs, subsequently referred to as candidate 4 (rc4). However, evidence-based annotation alone is limited by the available sequence data, potentially returning fragmented structures or missing loci entirely. To prevent this happening, we next performed an ab initio aided re-annotation of the initial gene build. Information from the evidence build was used to curate 1100 non-redundant, high-confidence transcript models  and to train the augustus gene predictor (version 2.7) (We generated different gene builds using the Maker pipeline (version 2.31.6) . We firsion 2.7) .ab initio model, we performed a second pass (re-annotation) and replaced any existing loci where a longer putative CDS could be predicted by the gene finder or filled in gene predictions where sufficient evidence was lacking for the construction of evidence models. This improved annotation was called rc5 and yielded 22,380 genes , adding 2767 novel loci compared to the previous rc4 build. In contrast, 357 loci had been omitted from the re-annotation but were present in the rc4 so we added them back to the rc5, upgrading the gene count to 22,737 . In order to verify the added value of this two-step build strategy, we utilized a quality check referred as Annotation Edit Distance (AED) comparing mRNAs from rc4 and rc5. This metric means to quantify the congruency between a gene annotation and its supporting evidence. The closer an AED score is to 0, the 'better' the annotation is. The comparison of the density distribution of AED scores between gene builds at this step . Outputs from both analyses were parsed using the Annie annotation tool   to retriion tool  to extraab initio predictions applied to our present assembly predicted a total of 23,336 gene models in the herring genome , downloaded from Ensembl 78, with OrthoMCL (version 2.0.9) and used granularity of 1.5 as recommended for the mcl algorithm , and 573 of these gene families were specific only to the herring genome  were constructed for each population pool and the 16 individuals, and 2x100 bp paired-end reads were generated using Illumina HiSeq2000 sequencers. The amount of sequence per pool was targeted to ~30x coverage. These sequences in addition to the data from eight herring populations from our previous study  Table 2Table 2 wcytochrome B sequence using the molecular clock calibration for this sequence from fish (The filtered SNP dataset was used to estimate genetic diversity within and between populations using Plink  and to grom fish . Averagerom fish .n=1000) under either a constant population size model (command: “ms 1000 100000 -s 1”), or a model with an expansion event in the recent past (command. “ms 1000 100000 -s 1 -eN. 1. 35”). Thereafter we randomly sampled 32 chromosomes in order to get a distribution that was comparable to our empirical sample, which contains data from 16 individuals. The sampling procedure was repeated 10 times, allowing for estimation of the variation in each allele frequency bin. However, due to the large number of sampled loci, this was close to zero. The demographic history of the Atlantic herring was explored using the diCal software , we used the string “3+2+2+2+2+2+2+2+2+2+3”, for a total of 11 independent time periods.In order to compare the observed allele frequency distribution with the expected one at genetic equilibrium we performed a simulation study. We first used the coalescence simulation software 'ms'  to genersoftware . This sosoftware . The ext5 SNPs, arbitrarily chosen from parts of the genome that did not show strong signs of selection. A representative population history was then reconstructed by averaging across the analysed regions.We analysed the Baltic and Atlantic populations separately, using phased sequence data from eight individuals (i.e. 16 unique chromosomes) from each population. Due to memory usage constraints, we performed the analysis for a limited number of genomic regions, each containing approximately 102 tests using the allele frequency estimates at each locus to identify genomic regions showing significant allele frequency differences between populations sorted according to these contrasts. In order to control for the inflation in P values at positions with high coverage, we normalised the reference and variant allele read counts at these positions using genome-wide expected coverage. The Bonferroni correction threshold was p=8.2x10-9 and p<1x 10-10 was chosen as the stringent significance threshold. We also estimated pooled heterozygosity as previously described (-20) were clustered as one independent genomic region under selection using the following steps. 1) We rescaled the coordinates by subtracting gaps from SNP positions along each scaffold. 2) We combined strongly correlated SNPs using the Comb-p software (-20).We classified populations based on their adaptations to different environmental variables, marine (Atlantic Ocean) vs. brackish  waters, and different spawning seasons (spring vs. autumn). We then performed contingency χescribed  in 5 kb software  and requ2 test by running 100,000 MCMC iterations. Bayenv2 reports two statistics: i) Bayes’ factor (BF) and ii) Pearson correlation (r). We defined the significance threshold of these two statistics based on their distribution; log10 (BF)≥4 and |r|≥0.8.Bayenv2  was usedIn order to identify the loci showing the most consistent allele frequency differences between autumn and spring spawning herring we used a generalized linear mixed model analysis  , which t-6 and cut-off of p<1x10-10 was chosen as the stringent significance threshold.glmer(y ~ Group+(1|populations), family=binomial, nAGQ = 10, data=input_file), where, Group = autumn or spring spawning population, Population = specific population, Family = error distribution used in the model, nAGQ = the number of points per axis for evaluating the adaptive Gauss-Hermite approximation to the log-likelihood, Default = 1, that corresponds to the Laplace approximation. As the differences in allele frequencies between the populations within the groups were small in our dataset, we did not use the Laplace approximation, but used a greater nAGQ (i.e. 10) for more thorough likelihood maximization procedure. The Bonferroni correction threshold was p=4.9x102 tests.SnpEff (v.3.4)  was usedWe extracted depth of coverage for all populations using GATK:DepthOfCoverage , after fM-value|>0.6 resulted in the detection of 707 loci of which 491 had mappability above 0.5. The size of the structural changes ranged from 1–26 kb (We compared populations for two contrasts: 1) Atlantic vs. Baltic and 2) spring vs. autumn spawning. For the Atlantic-Baltic contrast we scanned 878,278 windows of which 79,809 windows were excluded due to low depth across all populations. 3,780 windows showed significant difference with a p<0.001. Stringent filtering based on p<0.001 and an | 1–26 kb . In the  1–26 kb .We searched for inversions using mate-pair (2x100 bp) Nextera libraries (Illumina) with an average insert size of 3–4 kb generated from four individuals: two Atlantic  and two Baltic  sequenced to 3X coverage using Illumina HiSeq2000, After trimming and filtering low quality reads, we aligned the reads on herring genome by BWA-MEM . We usedWe designed an Affymetrix custom genotyping array with 72,560 SNPs, tiling the best strand for each SNP. Due to the fact that A/T and G/C SNPs require twice the features on the array that other markers require, the array layout covered 82,569 probe pair sets. These covered the majority of SNPs showing significant genetic differentiation together with the best 2000 monomorphic nucleotide sites to validate the individual plate/sample performance . We submitted 36-mer nucleotide sequences around target SNPs to the manufacturer. SNPs flanked by other sequence polymorphisms were avoided as well as regions containing repetitive or low mappability sequences, Finally, we deprioritized A/T and C/G SNPs, as they take twice as much room on the array. Array experiments were performed by the Array and Analysis Facility, SciLifeLab  according to standard protocol .Genotype calling was performed using the Affymetrix Power Tools  followed by SNP-Polisher (version 1.4.0), according to the Best Practice Analysis Workflow described in Axiom Genotyping Solution Data Analysis Guide . The DQC threshold was set to 0.95 according to the manufacturer´s instructions, based on previous data from herring arrays. Only the samples passing a Sample Call Rate (CR)>97% were retained after first genotyping round and were subjected to genotyping step 2. Twelve samples were excluded when using both DQC (n=2) and CR (n=10) filtering. At this point, all plates run were considered to be high quality plates. We utilized SNPolisher, an R package, for the purpose of genotyping the remaining samples. SNPolisher generates the different groups of clusters, AA, AB, BB and off target variant (OTV), with corresponding cluster plots for each SNP and evaluate quality. Thereby, this post-process analysis identifies the best probeset and assign to each one of six categories . It also calculates QC metrics for each SNP, classifies SNPs into categories, changes dubious SNP calls, and tests for intensity shifts between batches. We took only converted polymorphic SNPs  into account for further analyses. Genotypes at these positions are referred to as high quality genotypes.This SNP array was used to genotype 360 individual samples, 30 random fish from 12 of the populations included in pooled sequencing. A total of 348 samples retained enough quality to be used for downstream analysis. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, including Nick Barton and Magnus Nordborg, who is a member of our Board of Reviewing Editors. The evaluation was overseen by Diethard Tautz as the Senior Editor. Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that this manuscript cannot be considered further for publication in eLife. However, we would consider a completely reworked resubmission which addresses the concerns of the reviewers, and in particular goes much further in distinguishing between causal from non-causal alleles.Thank you for submitting your work entitled "The genetic basis for ecological adaptation revealed by genome sequencing of the Atlantic herring" for consideration by The consensus opinion of the reviewers is that this is a really interesting study in an excellent study system, but that the analysis is insufficient and several of the conclusions not supported. In particular, the reviewers were not convinced that you can actually identify the causal sites, and several conclusions rest on this. However, exploring the limits of what may be concluded is also of great interest, especially given the quality of the data.In essence, the analysis does not do the data justice.Reviewer #1:This is a substantial piece of work addressing an important question using a beautiful and promising system. It is certainly the case that identifying loci that show evidence having been under selection is much easier in gigantic population with little population structure, as appears to be the case for herring.Unfortunately, the paper suffers from several conceptual/logical flaws and I do not think the major conclusions are supported.The confusion begins in the first sentence of the abstract, where it is stated that "Ecological adaptation is of major relevance…, but the underlying genetic factors are typically hard to study in natural populations due to confounding between population structure and signatures of selection".It is indeed true that population structure makes it hard to identify signatures of selection, but who says that genes involved in ecological adaptation will exhibit such signatures? Generally, such signals arise as a consequence of strong selection on individual genes. For a quantitative trait, is not clear that there will be any such genes, and is also not clear that any such genes found will explain much of the variation. Furthermore, there is no phenotype in the present study, nor any heritability. Thus the comparison with human height (Discussion section) does not make sense. You have no idea how much of the variation for fitness your SNPs explain. Indeed it is formally possibly that all your SNPs are associated with defense against a parasite with a life-cycle that depends on salinity. I obviously don't believe this, but the point remains that in order to discuss the genetic architecture of adaptation, there needs to be genetics in the study. For example, a reciprocal transplant study that quantified the fitness effects of the identified loci.Speaking of associations, I don't understand how the threshold for calling frequency differences significant was set . This isIt is also stated that the presence of haplotype blocks was striking. Why? Presumably these are simply the consequence of selection? Are they longer than you would expect? Why? What is the evidence that they contain multiple causal sites as opposed to being the result of linkage drag?Regardless of why they are there, the existence of these blocks mandate extreme caution when trying to decide which sites are causal. It is by now clear that local haplotype structure, especially when coupled with allelic heterogeneity causes large number of local spurious associations, and that, as a consequence, the most strongly associated SNP is NOT likely to be the causal one: it is likely to be an intermediate-frequency SNP that happens to tag the underlying variants well…A consequence of this is that most of the conclusions about what kinds of SNPs do what  are not supported. You find few significantly associated non-synonymous substitutions, but this could simply be because they are all rare, and can only be found by proxy.It does make sense to compare different kinds of regions, as you do, but everything has to be controlled for allele frequency.On a more population genetics level, I was struck by your low nucleotide diversity coupled with a rapid decay of linkage disequilibrium. Unless mutation- and recombination rates are very different from other organisms, they make no sense. My guess is that your nucleotide diversity is underestimated because you are detecting heterozygotes in single individuals, and that your rate of linkage disequilibrium decay is overestimated because of bad SNPs.Third paragraph subsection “Genetic basis underlying timing of reproduction”: This something not right about this argument. Why would rare SNPs affect nucleotide diversity one way or another?Subsection “Genomic distribution of causal variants”: "Some of these will be false positives due to close linkage to true positives but many will be true positives" I discuss this above, but it would be important to realise that, without assumptions about the meaning of "some" and "many", it is not possible to conclude any of what you conclude further down.Same section: "Enrichment" compared to what?Reviewer #2:eLife. However, the interpretation of the results seems quite naive: more detailed and rigorous argument is needed to be convincing.This paper uses whole-genome sequencing from 20 population pools, supported by SNP genotyping of ~360 individuals, to identify variants associated with differences between Baltic and Atlantic, and between autumn and spring spawning herring. This is an impressive set of data, and as the authors argue, the recent divergence, large population size and low differentiation makes herring exceptionally well suited to identifying genes responsible for adaptation. Thus, the study could in principle be a very nice contribution to SNP and structural variants associated with divergence are clustered into haplotype blocks that may span multiple genes, and up to 200kb. It seems to be assumed that all  of the variants within each block are causal, on the grounds that within the Atlantic population, LD decays very rapidly, over a hundred bp or so. This would be very interesting, implying that complex adaptive alleles build up as a result of successive substitutions in the same region. However, careful arguments are needed to exclude the more obvious alternative, that selection has raised specific haplotypes to high frequency. Suppose that the original population was indeed at linkage equilibrium. Under selection in a new environment, many alleles might increase at the same locus – either from standing variation or from new mutations. Such "soft sweeps" might not be detected in these data. However, at a subset of loci, one or a few haplotypes might increase, and their size would reflect the time since adaptation – presumably a few thousand generations, corresponding to perhaps 10-100kb. Even if two or three successive causal alleles were involved, the great majority of SNPs would still be neutral. Seeing LE within the base population does not address this issue; and the ascertainment bias means that only more or less hard sweeps can be detected.Paragraph six subsection “Genetic adaptation to a new niche environment”: There needs to be a proper test of whether it is surprising that significant variants cover genes in certain functional classes – I have no idea how many genes may be involved in osmoregulation, for example. This is especially an issue because haplotype blocks may cover multiple genes. A proper permutation test is needed here.Subsection “Genomic distribution of causal variants”: The section on genomic distribution of causal variants was confused. It should be possible to make a rigorous statistical estimate of the fraction of markers that are likely to be causal from the extent of enrichment, but this needs to be done carefully.Reviewer #3:This is a comprehensive and nice study and well-written paper, with possible implications for inference on ecological adaptation of species other than the Atlantic herring.My major comment is that I was surprised by how the paper was written with respect to the literature. After reading Lamichhaney et al. 2012 from the same group it is clear that the current study is a kind of (nice) follow-up from that. Yet the authors only refer to Lamichhaney et al. 2012 once in the Introduction and once in the Results and not at all in the Discussion. In my opinion, the authors should be upfront of what they found in L2012, how this study goes well beyond that, and how the current results confirms or contradicts previously reported results.My quick reading of Lamichhaney et al. 2012 is that the authors reported: (i) low level population differentiation, (ii) evidence for local adaptation , (iii) evidence for selection on haplotype blocks, (iv) candidate genes under natural selection  and (v) evidence of natural selection of reproduction (spawning season).[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]eLife. Your article has been reviewed by three peer reviewers, including Nick Barton and Magnus Nordborg, who is a member of our Board of Reviewing Editors, and the evaluation has been overseen Diethard Tautz as the Senior Editor.Thank you for submitting your work entitled "The genetic basis for ecological adaptation of the Atlantic herring revealed by genome sequencing" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:As emphasized in the decision letter encouraging you to resubmit this manuscript, the reviewers unanimously agree that this is one of the best data sets in existence for addressing questions about the architecture of local adaptation. The resubmitted version is greatly improved, but is still somewhat frustrating in that it was felt that much more could be done with the data. However, it was recognized that this would take the analysis well beyond the current state of the art, and that it would not be constructive to demand this. We actually do not yet have the intellectual framework for thinking about these kinds of data. It says on page 5 that "the results provide a comprehensive and detailed view on the genetic architecture underlying ecological adaptation". So what is the answer? How far can we go without reciprocal transplant studies?Better then to publish, and be clear about what has and has not been demonstrated.Essential revisions:The distinction between causal and non-causal SNPs needs to be more clearly spelled out. The paper identifies a very large number of SNPs that differ significantly between groups. These tend to be clustered on the genome, and also enriched for certain functional types. Some clusters may arise by chance, whilst others may include one or more causal alleles, which tend to be in candidate loci. The enrichment analysis shows the clearest evidence for selection, but it seems to us that this could be consistent with multiple causal alleles at ~ 20 loci, rather than the "thousands" of SNPs cited in the abstract. It should be possible to estimate the minimum number of causal loci consistent with the observed clustering and enrichment, at least roughly.The existence of long haplotypes distinguishing the different population is emphasized, but it is not clear whether these are in any sense unusual given the low effective population (surprisingly low given the vast census sizes in this species). It should be possible to use standard coalescent simulation to at least test whether the observed haplotype lengths are long compared to neutral expectations under the estimated demography and a range of plausible values for the recombination rate. [Editors’ note: the author responses to the first round of peer review follow.]The consensus opinion of the reviewers is that this is a really interesting study in an excellent study system, but that the analysis is insufficient and several of the conclusions not supported. In particular, the reviewers were not convinced that you can actually identify the causal sites, and several conclusions rest on this. However, exploring the limits of what may be concluded is also of great interest, especially given the quality of the data. In essence, the analysis does not do the data justice.First we would like to thank the reviewers for their constructive criticism of our paper that has clearly helped us to improve the paper. The most important change in the revised version is that we have included a much improved analysis of the genomic distribution of causal changes and this shows that we had underestimated the role of changes in the coding sequence based on the previous analysis.Reviewer #1:This is a substantial piece of work addressing an important question using a beautiful and promising system. It is certainly the case that identifying loci that show evidence having been under selection is much easier in gigantic population with little population structure, as appears to be the case for herring. Unfortunately, the paper suffers from several conceptual/logical flaws and I do not think the major conclusions are supported. The confusion begins in the first sentence of the abstract, where it is stated that "Ecological adaptation is of major relevance…, but the underlying genetic factors are typically hard to study in natural populations due to confounding between population structure and signatures of selection". It is indeed true that population structure makes it hard to identify signatures of selection, but who says that genes involved in ecological adaptation will exhibit such signatures? Generally, such signals arise as a consequence of strong selection on individual genes. For a quantitative trait, is not clear that there will be any such genes, and is also not clear that any such genes found will explain much of the variation.We have changed the sentence in the Abstract to “…due to confounding between genetic differentiation caused by natural selection and by genetic drift in subdivided populations” to make a more specific statement. Furthermore, we think this is a major merit of the paper that it demonstrates that very convincing evidence of natural selection can be detected in this system.Furthermore, there is no phenotype in the present study, nor any heritability. Thus the comparison with human height (Discussion section) does not make sense. You have no idea how much of the variation for fitness your SNPs explain. Indeed it is formally possibly that all your SNPs are associated with defense against a parasite with a life-cycle that depends on salinity. I obviously don't believe this, but the point remains that in order to discuss the genetic architecture of adaptation, there needs to be genetics in the study. For example, a reciprocal transplant study that quantified the fitness effects of the identified loci.We disagree with the statement that there is no phenotype or genetics in this study. We have sampled fish from the same locality spawning in May, July and September and these were phenotypically classified as spring-, summer- and autumn-spawners. Our sequence analysis revealed highly significant allele frequency differences at hundreds of loci between these groups. This is a genetic result! However, the reviewer is 100% correct that we cannot estimate heritabilities since we do not have genotype-phenotype relationships for individual fish. We have therefore revised the Discussion and now explain that we cannot determine how much of the phenotypic variation these loci control.Speaking of associations, I don't understand how the threshold for calling frequency differences significant was set . This is-20 to 10x10-10, but this did not have any major effects on the main conclusions in this study. We still keep the Q-Q plot as We have decided to rather use a Bonferroni correction since that is a conservative threshold in a study like this. This means that the significance threshold has changed from p=10x10It is also stated that the presence of haplotype blocks was striking. Why? Presumably these are simply the consequence of selection? Are they longer than you would expect? Why? What is the evidence that they contain multiple causal sites as opposed to being the result of linkage drag?We agree that we cannot exclude the possibility that linkage drag is an important explanation for haplotype blocks so we have modified the text in the Discussion. However, we do think that the new data showing the presence of multiple non-synonymous substitutions in many of the genes showing the strongest genetic differentiation support the hypothesis of haplotype evolution involving several causal changes within the haplotype blocks.Regardless of why they are there, the existence of these blocks mandate extreme caution when trying to decide which sites are causal. It is by now clear that local haplotype structure, especially when coupled with allelic heterogeneity causes large number of local spurious associations, and that, as a consequence, the most strongly associated SNP is NOT likely to be the causal one: it is likely to be an intermediate-frequency SNP that happens to tag the underlying variants well… A consequence of this is that most of the conclusions about what kinds of SNPs do what  are not supported. You find few significantly associated non-synonymous substitutions, but this could simply be because they are all rare, and can only be found by proxy. It does make sense to compare different kinds of regions, as you do, but everything has to be controlled for allele frequency.This section has been completely revised and the text rewritten. In fact our new improved analysis gives a different and more correct picture about what types of genetic changes are underlying ecological adaptation in herring.On a more population genetics level, I was struck by your low nucleotide diversity coupled with a rapid decay of linkage disequilibrium. Unless mutation- and recombination rates are very different from other organisms, they make no sense. My guess is that your nucleotide diversity is underestimated because you are detecting heterozygotes in single individuals, and that your rate of linkage disequilibrium decay is overestimated because of bad SNPs.The nucleotide diversity is calculated in both the ~70x coverage reference individual as well as the 16 ~10x coverage individual samples with very consistent results, indicating that the diversity is not substantially underestimated due to lack of power to detect heterozygous sites. Also, while perhaps low in some contexts, the observed value is higher than for other species of fish See .Regarding the genotype quality and LD decay, we observe strong concordance between SNPs called from sequencing and SNP chip , and see Supplementary Third paragraph subsection “Genetic basis underlying timing of reproduction” This something not right about this argument. Why would rare SNPs affect nucleotide diversity one way or another?The text has been revised. The message is that spring-spawning herring possess a larger number of variable sites but the average heterozygosity per site is lower.Subsection “Genomic distribution of causal variants”: "Some of these will be false positives due to close linkage to true positives but many will be true positives" I discuss this above, but it would be important to realise that, without assumptions about the meaning of "some" and "many", it is not possible to conclude any of what you conclude further down.This entire section has been revised and rewritten.Same section: "Enrichment" compared to what?The section has been rewritten and a new Reviewer #2:This paper uses whole-genome sequencing from 20 population pools, supported by SNP genotyping of ~360 individuals, to identify variants associated with differences between Baltic and Atlantic, and between autumn and spring spawning herring. This is an impressive set of data, and as the authors argue, the recent divergence, large population size and low differentiation makes herring exceptionally well suited to identifying genes responsible for adaptation. Thus, the study could in principle be a very nice contribution to eLife. However, the interpretation of the results seems quite naive: more detailed and rigorous argument is needed to be convincing. SNP and structural variants associated with divergence are clustered into haplotype blocks that may span multiple genes, and up to 200kb. It seems to be assumed that all  of the variants within each block are causal, on the grounds that within the Atlantic population, LD decays very rapidly, over a hundred bp or so. This would be very interesting, implying that complex adaptive alleles build up as a result of successive substitutions in the same region. However, careful arguments are needed to exclude the more obvious alternative, that selection has raised specific haplotypes to high frequency. Suppose that the original population was indeed at linkage equilibrium. Under selection in a new environment, many alleles might increase at the same locus – either from standing variation or from new mutations. Such "soft sweeps" might not be detected in these data. However, at a subset of loci, one or a few haplotypes might increase, and their size would reflect the time since adaptation – presumably a few thousand generations, corresponding to perhaps 10-100kb. Even if two or three successive causal alleles were involved, the great majority of SNPs would still be neutral. Seeing LE within the base population does not address this issue; and the ascertainment bias means that only more or less hard sweeps can be detected.For sure, we do not think that all variants in a haplotype block is causal, in fact we assume that the majority of highly differentiated SNPs are linked to causal SNPs. However, we think it is unlikely that these fairly large haplotype blocks under selection are due to single causal variants but we agree that it is difficult to exclude this possibility and we have therefore softened the discussion of this issue.Paragraph six subsection “Genetic adaptation to a new niche environment”: There needs to be a proper test of whether it is surprising that significant variants cover genes in certain functional classes – I have no idea how many genes may be involved in osmoregulation, for example. This is especially an issue because haplotype blocks may cover multiple genes. A proper permutation test is needed here.TSHR, SOX11, CALM and ESRB loci associated with spawning time which all have an established role in reproductive biology.We have attempted to carry out proper GO analysis to address this but our conclusion is that the GO annotations of fish genes are too poor to make this a powerful analysis. However, we find it encouraging that several of the top hits make perfect sense, such as the prolactin receptor and high choriolytic enzyme genes associated with adaptation to Baltic Sea – Atlantic Ocean that differs dramatically as regards salinity, and the Subsection “Genomic distribution of causal variants”: The section on genomic distribution of causal variants was confused. It should be possible to make a rigorous statistical estimate of the fraction of markers that are likely to be causal from the extent of enrichment, but this needs to be done carefully.This section has been completely revised and the text rewritten.Reviewer #3:This is a comprehensive and nice study and well-written paper, with possible implications for inference on ecological adaptation of species other than the Atlantic herring. My major comment is that I was surprised by how the paper was written with respect to the literature. After reading Lamichhaney et al. 2012 from the same group it is clear that the current study is a kind of (nice) follow-up from that. Yet the authors only refer to Lamichhaney et al. 2012 once in the Introduction and once in the Results and not at all in the Discussion. In my opinion, the authors should be upfront of what they found in Lamichhaney et al. 2012, how this study goes well beyond that, and how the current results confirms or contradicts previously reported results. My quick reading of Lamichhaney et al. 2012, is that the authors reported: (i) low level population differentiation, (ii) evidence for local adaptation , (iii) evidence for selection on haplotype blocks, (iv) candidate genes under natural selection  and (v) evidence of natural selection of reproduction (spawning season).We have revised the Introduction and now cite our previous work more extensively in the Introduction.[Editors’ note: the author responses to the re-review follow.]Summary: As emphasized in the decision letter encouraging you to resubmit this manuscript, the reviewers unanimously agree that this is one of the best data sets in existence for addressing questions about the architecture of local adaptation. The resubmitted version is greatly improved, but is still somewhat frustrating in that it was felt that much more could be done with the data. However, it was recognized that this would take the analysis well beyond the current state of the art, and that it would not be constructive to demand this. We actually do not yet have the intellectual framework for thinking about these kinds of data. It says on page 5 that "the results provide a comprehensive and detailed view on the genetic architecture underlying ecological adaptation". So what is the answer? How far can we go without reciprocal transplant studies?We agree that we really do not know how much of the genetic variance our loci control so we have changed this sentence as follows:“The results provide a comprehensive list of hundreds of independent loci underlying ecological adaptation and shed light on the relative importance of coding and non-coding variation.”Better then to publish, and be clear about what has and has not been demonstrated. Essential revisions: The distinction between causal and non-causal SNPs needs to be more clearly spelled out. The paper identifies a very large number of SNPs that differ significantly between groups. These tend to be clustered on the genome, and also enriched for certain functional types. Some clusters may arise by chance, whilst others may include one or more causal alleles, which tend to be in candidate loci. The enrichment analysis shows the clearest evidence for selection, but it seems to us that this could be consistent with multiple causal alleles at ~ 20 loci, rather than the "thousands" of SNPs cited in the abstract. It should be possible to estimate the minimum number of causal loci consistent with the observed clustering and enrichment, at least roughly.-20 to consider a region to make sure that the number of false positives is very low if any. We took into account gaps in the assembly, we looked at the correlation between SNP P-values within regions and we required a distance of at least 20 kb without highly significant SNPs to close a region. Using these criteria we identified 472 independent loci associated with the Atlantic-Baltic contrast and 125 independent loci distinguishing autumn- and spring spawners. It is clear from In the current version of the paper we provide conservative estimates of the minimum number of independent loci detected in this study. We requested a statistical support of at least 1x10The existence of long haplotypes distinguishing the different population is emphasized, but it is not clear whether these are in any sense unusual given the low effective population (surprisingly low given the vast census sizes in this species). It should be possible to use standard coalescent simulation to at least test whether the observed haplotype lengths are long compared to neutral expectations under the estimated demography and a range of plausible values for the recombination rate.We think the most important question to explore is if the large haplotype blocks are caused by strong, fairly recent selective sweeps or the evolution of haplotypes harbouring multiple causal alleles. We took advantage of the fact that these two models give entirely different predictions as regards the nucleotide diversity within the differentiated regions within populations. The selective sweep/hitchhiking model is expected to lead to a loss of nucleotide diversity whereas the haplotype evolution model predicts that nucleotide diversity in the differentiated regions can be as high or even higher than in the rest of the genome if these haplotype blocks have been maintained by balancing selection. Our analysis presented in the revised section “Adaptive haplotype blocks are maintained by selection” and in the new


Dic2) and a recent durum elite cultivar (Silur). Six thousand sequence baits were designed to target Dic2 vs. Silur polymorphisms discovered in a previous RNAseq study. These baits were exposed to genomic DNA of the RIL population. Eighty percent of the targeted SNPs were recovered, 65% of which were of high quality and coverage. The final high density genetic map consisted of more than 3,000 markers, whose genetic and physical mapping were consistent with those obtained with large arrays.Targeted sequence capture is a promising technology which helps reduce costs for sequencing and genotyping numerous genomic regions in large sets of individuals. Bait sequences are designed to capture specific alleles previously discovered in parents or reference populations. We studied a set of 135 RILs originating from a cross between an emmer cultivar ( Triticum spp.) is one of the most widely grown food grain crops in the world and provides about a fifth of the calories consumed by humans . Durum wheat [Triticum turgidum (L.) subsp. turgidum (L.) convar. durum (Desf.)] accounts for about 10% of the total global wheat production . It is a minor crop compared to bread wheat, which is the focus of considerable efforts with regard to breeding new high performance cultivars, genomic investigations and resource developments. In this respect, new omics resources developed under the umbrella of the International Wheat Genome Sequencing Initiative (IWGSC) represents a major input for all Triticeae genomic approaches [Triticum turgidum subsp. durum) is a modern representative of a group of allotetraploid subspecies (Triticum turgidum subsp.) that were domesticated from the wild T. turgidum dicoccoides [Triticum aestivum L.), which arose via spontaneous interspecific hybridization between a domesticated T. turgidum spp. form  and the wild diploid Ae. tauschii  [Wheat  .As durum wheat and bread wheat share two closely related genomes, molecular tools and basic research are mostly focused on bread wheat and are secondarily used for durum wheat breeding . Moleculhttp://wheatgenomics.plantpath.ksu.edu), most polymorphisms were of bread wheat origin [T. turgidum subsp. Lastly, this technology is remarkably cost efficient per data point for projects requiring a high number of polymorphisms, such as GWAS studies, but it remains expensive for genetic mapping, diversity surveys or genomic prediction programs when interesting SNPs have been identified. Medium throughput technology built on parental specific polymorphism may represent a valuable alternative to high throughput microarrays for several applications.Single nuclear polymorphisms (SNPs) in coding and noncoding sequences have recently become favorable markers for building high-density genetic maps thanks for their high abundance in the genome ,21. Rapit origin . Nevertht origin . Moreovede novo genotyping of large populations at low cost [Array uses fluorescent technology, which also limits data interpretation, especially in a polyploid context with closely related sequences, like homeologous genes, where intralocus heterozygous states can be easily confused with interlocus divergence. Genotyping by sequencing thus appears to be a promising alternative and could be used even for low cost . A firstlow cost , Genotyplow cost  or doubllow cost  target tlow cost .Sequencing transcribed portions of the genome using RNA extracted from standardized tissues (RNAseq) is also a good alternative since the transcribed gene-coding regions represent only one to two percent of the whole genome . GenotypWhole exome capture ,33 was sad hoc genome reduction technique, notably in cases where polymorphisms have already been identified in a set of parental lines. Baits are synthesized from a documented set of SNPs and hybridized to genomic DNA for capturing relevant homologous fragments that are subsequently sequenced. Baits would also capture fragments highly homeologous and/or paralogous to the targeted sequence. The resulting reads could be nevertheless assigned properly and could permit the disclosure of untargeted SNPs.In this case, reducing the number of captures to a small set of targeted polymorphisms is an efficient http://www.kbioscience.co.uk), which are attractive, cost effective and easy to use markers for breeders in routine breeding programs [Specific capture ensures that the population will be polymorphic at most targeted positions, once a given sequencing error level is accepted. Deep sequencing of relatively few targets (~5000) should also ensure a good coverage of those targeted loci, thus reducing the risk of missing data. This technique appears to be especially well adapted for the construction of dense genetic maps since the bait design can maximize the number of targeted contigs, as sequencing will reveal all SNPs in the contig area for which the bait has been designed. Targeting specific loci permits spreading of future markers along chromosomes, thus maximizing the detection of recombination events, which is a desirable feature for genetic map building. The density of the bait design can be very easily adapted in regions of interest (e.g. higher density nearby known QTL). Finally, sequence knowledge around an SNP eases the design of competitive allele-specific PCR markers (KASPar)  on the bread wheat reference [Genotyping by sequencing data has been successfully used to build a high density genetic map comparable to recently published maps using the 90K iSelect array. Marker positions are very consistent with those provided by the International Wheat Genome Sequencing Consortium  used in this study consisted of 135 F6 recombinant inbreed lines (RILs) derived from a cross between an emmer wheat accession (Triticum turgidum ssp dicoccum) named Dic2 and the durum elite Silur variety.The de novo assembly of the transcriptome (DWr) composed of 80,691 contigs was available from a past study [http://bioweb.supagro.inra.fr/homeoSplitter) was applied on this de novo assembly to unravel homeologous copies that were initially erroneously merged in a single chimeric contig [A durum wheat st study   was used here for comparison purposes [Transcripts of the bread wheat chromosome survey sequence for cv. Chinese Spring (BWr) generated by IWGSC and provided by the Ensembl database  and preprocessed with Cutadapt [http://broadinstitute.github.io/picard/) to remove PCR and optical duplicates [Dic2 and Silur resp.) were then mapped on BWr using the same protocol, thus allowing us to map 3 and 2 million reads, respectively. Reads2SNP [Fis value = 0.8). Genotypes called with less than 10 reads or a read2SNP probability of below 99% were considered as missing data. Polymorphism was considered reliable only when both parents were homozygous, leading to 17,543 SNPs on DWr, and 1,356 additional on BWr .Different bait design strategies were used. SNPs were targeted using two or four baits. As the first 3,625 SNPs were located in exons longer than 200 bp, four baits were designed for each: two centered on the targeted SNP, and two on its 5’ and 3’ flanking regions, hence targeting a total of 200 bp around the SNP (type 1). A second set of 508 SNPs also located in exons longer than 200 bp were targeted by two shifted baits only (type 2). The 2107 remaining SNPs were located in exons between 120 and 200 bp and targeted by two aligned baits (Type 3) . For eacBriefly, for each member of the DS population, total DNA was extracted from fresh young leaves with the Chemagic DNA Plant Kit (Perkin Elmer). Library preparation for multiplexed individuals was done according to the protocol published by Rohland et al. . EnrichmCaptured DNA was sequenced on an Illumina HiSeq3000, which gave paired reads of about 150 bp each. Reads were preprocessed and cleaned according to the protocol used for initial polymorphism detection. Mapping and genotype calling were done using whole DWr and targeted BWr contigs.Read2SNP identified new SNPs in loci homeologous or paralogous to the targeted one. A threshold of four reads per individual was used to call a genotype for these untargeted SNPs. These SNPs were likely in low expressed genes and were thus not detected in the preliminary transcriptomic analysis of the DS population parents. They accounted for the bonus SNP dataset (bonus SNP) that complemented the ER-SNP.For targeted SNPs, as we were confident that polymorphism did exist, genotype calling was accepted with as few as two reads per individual. Recovered SNPs constituted the expected recovered SNP dataset (ER-SNP). Finally, all SNPs were filtered according to three criteria so as to keep only highly reliable SNPs: i) a low number of heterozygotes controlled by an Fis value above 0.8, as 1.5% heterozygosity was expected after six successive selfing generations ii) at least 100 out of the 135 available RILs genotyped, and iii) balanced frequencies with a minimum expected heterozygosity  of 0.34mrkmerges function of Carthagene [build, annealing, greedy and flips algorithms proposed by Carthagene. Markers of LGs attributed to the same chromosome were pooled for this final step.All ER-clean-SNPs were used for the genetic map. Only one bonus SNP per contig on non-targeted DWr contigs was kept to avoid redundancy of genetic markers known to have strong DLs. The rthagene  was usedIn a second step, 249 additional SNPs for which the number of genotyped RILs was between 50 and 99 were positioned. The method is described in the To confirm the quality of the map, marker assignment to LGs were compared with putative assignment coming from BWr. Their orders and genetic positions on the genetic map were compared with putative physical and genetic positions in BWr. Spearman’s rank correlation coefficients were calculated on a per-chromosome basis.For each chromosome, a third order polynomial regression was fitted between genetic and putative physical positions. This allowed us to identify SNPs whose predicted physical positions and mapped positions on DS were not consistent. Markers outside the 95% confidence interval of the model—although kept for further analysis—were tagged as outliers.A mean of 2.8 million  reads per sample was obtained. The cleaning step resulted in 2.5 million usable reads per sample on average , with an average of 0.3 million orphan reads.Read mapping revealed a high degree of on-target enrichment efficiency for all accessions. Indeed, on average, 86% of the reads were mapped on DWr, which denoted a low degree of hybridization between baits and off-target DNA.However, a high number of reads were accumulated on some microsatellite-like regions within contigs that were not carrying baits and 20 DWr contigs accounted for more than 42% of the total number of mapped reads. This phenomenon was due to the presence of microsatellite-like regions nearby the SNP targeted in these contigs, thus capturing thousands of small reads containing repetitive elements.This means that some of our baits may have captured genomic fragments carrying microsatellites, even though none of our baits could have been considered as a “true” microsatellite. Close inspection of our baits revealed that very few of them (96) were carrying a 7-mer of dinucleotide repeated element or a 5-mer of trinucleotide repeated element.Direct capture of long stretches of repetitive DNA such as microsatellite patterns by the baits seemed unlikely. The fact that we did not detect any other strongly repeated sequence in our reads suggests that the corresponding fragments were indeed captured primarily by our baits but successively amplified by a daisy-chain effect on non-targeted microsatellite capture. The daisy-chaining principle was first introduced in a cross hybridization context that used a standard adapter at the ends of each fragment of the genomic library. Blocking oligonucleotides must be used during the hybridization phase of the adapters to avoid these unexpected technical captures and reduce the adapter size ,46. We bThis huge presence of repetitive elements did not prevent capture of the targeted SNPs. Indeed, only 6% of the targeted positions were not covered at all. The median number of reads received per targeted contig per individual was 11.4.Dic2 and Silur  of them belonged to targeted contigs and were thus discarded to avoid redundancy due to high linkage disequilibrium. The remaining 1,410 SNPs belonged to contigs homeologous or paralogous to the targeted ones e.g. . As for Three bait designs were tested. As expected, loci targeted using four baits (type 1) were about twofold more sequenced than those targeted using only two baits (bait types 2 and 3), which was consistent with the stoichiometric expectations. This variation in the average locus coverage, i.e. 8.53 reads (bait design 2), 9.02 (bait design 3) and 16.54 (bait design 1), had little impact on the proportion of ER-SNPs (ranging from 69.7 to 70.1%), but significantly impacted the average number of available genotypes per SNP . The probability of an SNP passing the quality filter was thus impacted by the number of baits designed on one SNP: ~0.4 for two baits per SNP vs ~0.5 for four baits per SNP.Dic2 and Silur alleles were unknown, and 1,624 unique positions were found by the Carthagene mrkmerges function. Only one SNP was kept per unique position for mapping and all markers at that position received the same genetic position  because position . Twenty-Among the set of markers, 2,608 (68.5%) had a good blast hit on BWr and thus had a putative assignment to wheat chromosomes. Each LG was attributed to the chromosome that was attributed to most of its markers. The resulting putative assignments are provided in Many markers were available for each chromosome, ranging from 144 for chromosome 7B to 403 for chromosome 2A, with an average of 266. This high density genetic map has inter-marker distances ranging from 0.5 to 1.38 cM/marker  and an average distance of 0.8 cM between two successive chromosome markers. However, some gaps are still present in the map, especially within chromosome 5A which lacks markers at its distal part, with a huge gap of 15.5 cM. The biggest gap per chromosome is 15 cM long on average.On average, more than 80% of the markers within any LG shared a common putative assignment on BWr .Arrays are currently considered as the most powerful and practical way to genotype a population, but GBS on whole exomes may also become a medium throughput genotyping tool, even for complex species like wheat . Once deT. dicoccum , including the bonus-SNP dataset. Roughly comparing the number of SNPs was not sufficient here. As the baits were deliberately designed to sample existing polymorphisms in a maximum of different contigs, it enhanced the mapping accuracy by maximizing the chance of detecting recombination at distant loci. This was clearly noted when comparing the number of unique mapping positions between our map and the 10 durum maps from the 90K iSelect array. The latter maps had between 580 (for elite x elite) and 2,056 unique positions (for emmer x elite), i.e. between 9 and 35% of the total number of markers , while oGenotyping by capture provides information on the surrounding genomic context of the targeted SNP markers. Indeed, baits are derived from known contigs, so the surrounding sequence is known for lengths of at least 120 or 200 bp. These surrounding sequences can be highly useful for designing ready to use genome-specific KASPAR markers for breeding.Confusion between homeo-genomes is a recurrent problem for polyploid species when the divergence between genomes A and B is low. On a targeted position, it may be impossible to unravel the two genomes once captured ,38,50, lClearly, a bait can capture untargeted homologous genomic sequences . Ideally, this complexity should be addressed by using a complete reference for mapping the reads. Indeed, if the reference contains all existing homologous sequences, then the reads will likely be properly mapped, SNPs adequately identified and genotypes accurately called. In our case, as durum wheat is not sequenced, and as the bread wheat sequence is not fully complete, slightly divergent from durum and contains copy number variations among individuals , we prefExon capture is efficient for deciphering gene complexity in wheat  but stilhttp://www.mycroarray.com/) for a particular program, allowing the capture of 576 (12×48) genomic libraries in our current conditions. The cost of these individual libraries, already optimized [Our entire protocol is based on the possibility of buying bait batches  will help assemble a database of tested useful baits targeting 10,000 SNPs located in as many contigs. This set could be used to score very large populations for few SNPs by selecting only the most relevant baits for the task, hence allowing drastic increases in multiplexing. It could also be used for targeting 10,000 or more SNPs on few individuals.Genotyping by capture of targeted loci enabled us to obtain a dense high quality genetic map of 3,729 markers containing 1,624 unique positions.Our map has almost as many markers as individual maps obtained by the 90K iSelect array , but produrum x dicoccoides cross when markers of 4A/5A/7B translocations were removed [turgidum ancestor [The putative assignment of markers on BWr was highly consistent with LG formation. However, some markers were highly similar to a BWr chromosome while being in strong LD with many markers assigned to a different BWr chromosome. Such chromosome assignment swaps occurred between loci of chromosomes 7A-4A and 4B-5A . The lon removed . Here we removed , such trancestor . Durum aAs the 3B chromosome of BWr is fully sequenced, the physical positions are much more precise, which permits accurate estimation of the recombination rate for this chromosome. The recombination pattern in DS was very similar to that observed by Maccaferri et al. in durum wheat . First, T. dicoccum parent. The capture efficiency could also be improved by targeting more loci and taking care concerning the bait area in order to avoid the presence of microsatellite repetitive elements.Our study demonstrates the feasibility of a genotyping by capture approach for complex polyploid species such as durum wheat, and confirmed that it is a reliable strategy for genotyping whole populations for thousands of SNPs. The set of baits required to genotype this population is provided with a real effort made to localize the proposed markers. These resources will likely be useful for other mapping populations including a www.breedwheat.fr/). As whole exome capture is developing, preliminary sequencing of a set of interesting parents followed by the development of specific baits would help to rapidly identify recombinants in large population sets or to map efficiently interconnected mapping populations.Genotyping by capture could be used to easily target durum elite specific polymorphisms. Nevertheless it is still hard to predict if seeking polymorphism in coding sequence (using RNAseq as has been done here or in the 90K iSelect array) will be sufficient to get polymorphism in area deeply depressed by the successive bottlenecks experienced by elite durum ,57. In tS1 FigThe 14 durum wheat chromosomes are shown on the same plot, with the putative physical position of SNPs on the X-axis (bp) and the genetic position on the Y-axis (cM). It allows checking for markers having distinct genetic and putative attributions.(PDF)Click here for additional data file.S1 Filede novo assembly obtained from [The fasta file of the ned from .(GZ)Click here for additional data file.S2 FileDescription of the exact protocol used for the capture step.(DOC)Click here for additional data file.S3 FileThis file explains how we estimated the genetic positions of SNPs that were genotyped for fewer than 100 individuals but more than 50.(DOCX)Click here for additional data file.S4 FileThe genetic map is provided here. Markers names follow the nomenclature A@pos, with A being the contig name in the DWr  and pos (CSV)Click here for additional data file.S5 FileThe set of baits used for this study is provided. Baits suspected to capture microsatellite-rich regions were removed. Bait names are as follows: A@pos|B|C|D|E with:A: contig name in the DWr pos: position of the polymorphism previously detected in the RNA-seq experiment between dic2 and silur in the contig A.B: parental allelic status of the bait (Dic2 or Silur)C: position of the SNP in the baitD: type of bait E: origin of the SNP (GZ)Click here for additional data file.S1 TableThe characteristics of the 25 linkage groups (LG) are presented. For each LG, contigs containing markers are distributed on the 14 A+B chromosomes of the IWGSC reference according to their best blast score . For each LG, the percentage of markers with a consistent genetic and putative assignments on the same chromosome are given, as well as the percentage of markers distributed on homeologs and different chromosomes.(XLSX)Click here for additional data file.S2 TableThirty markers showed inconsistency between the genetic and putative assignments to chromosomes. Their names, barley and IWGSC assignments (bp) and DS genetic positions (cM) are reported. Assignments were obtained by blast on barley  and IWGS(XLSX)Click here for additional data file.S3 TableAmong the populations used to build the consensus durum wheat map published by Maccaferri et al. , 10 have(XLSX)Click here for additional data file.


Body weight (BW) is an important trait for meat production in sheep. Although over the past few years, numerous quantitative trait loci (QTL) have been detected for production traits in cattle, few QTL studies have been reported for sheep, with even fewer on meat production traits. Our objective was to perform a genome-wide association study (GWAS) with the medium-density Illumina Ovine SNP50 BeadChip to identify genomic regions and corresponding haplotypes associated with BW in Australian Merino sheep.hsphase; to estimate SNP haplotype effects, linkage disequilibrium blocks were identified in the detected QTL region.A total of 1781 Australian Merino sheep were genotyped using the medium-density Illumina Ovine SNP50 BeadChip. Among the 53 862 single nucleotide polymorphisms (SNPs) on this array, 48 640 were used to perform a GWAS using a linear mixed model approach. Genotypes were phased with Ovis aries) chromosome 6 (OAR6) between 36.15 and 38.56 Mb, included 13 significant SNPs that were associated with BW; the most significant SNP was OAR6_41936490.1 (P = 2.37 × 10−16) at 37.69 Mb with an allele substitution effect of 2.12 kg, which corresponds to 0.248 phenotypic standard deviations for BW. The region that surrounds this association signal on OAR6 contains three genes: leucine aminopeptidase 3 (LAP3), which is involved in the processing of the oxytocin precursor; NCAPG non-SMC condensin I complex, subunit G (NCAPG), which is associated with foetal growth and carcass size in cattle; and ligand dependent nuclear receptor corepressor-like (LCORL), which is associated with height in humans and cattle.Thirty-nine SNPs were associated with BW at a Bonferroni-corrected genome-wide significance threshold of 1 %. One region on sheep  contains supplementary material, which is available to authorized users. Bos taurus) BTA6, 8, 11, 14, 24 and 25 [pleiomorphic adenoma gene 1 (PLAG1), coiled-coil-helix-coiled-coil-helix domain containing 7 (CHCHD7), proenkephalin (PENK), V-Mos Moloney murine sarcoma viral oncogene homolog (MOS), V-Yes-1 Yamaguchi sarcoma viral related oncogene homolog (LYN) and trimethylguanosine synthase 1 (TGS1). The same QTL region on BTA14 has also been shown to affect birth weight and size in zebu cattle (Bos primigenius indicus) [In sheep, body weight (BW) is an important economic trait for meat production. Currently, genome-wide association studies (GWAS) are applied to identify candidate genes for many quantitative traits, not only in sheep, but also in many other species –6. Sever4 and 25 –12. For 4 and 25  and Seto4 and 25  mapped a4 and 25 : pleiomoindicus) . In addiindicus)  detectedindicus) .Ovies aries) chromosome 21 (OAR21) [Although many GWAS have identified important candidate genes in different species, there are comparatively few QTL studies for sheep. To date, 801 ovine QTL are curated in the Sheep QTLdb , among w (OAR21) .In this study, our aims were (1) to perform a GWAS to detect significant SNPs that are associated with BW in sheep by using data from 1781 Australian Merino sheep genotyped with the Illumina Ovine SNP50 BeadChip and (2) to explore the genomic regions around these SNPs for candidate genes.Samples for genotyping were collected under approval number 344 AEC12-049 of the University of New England Animal Ethics Committee.The data used in this study consisted of phenotypic records from 1781 half-sib family groups of Merino sheep from the Australian Sheep CRC Information Nucleus flock, which included 1088 males and 693 females sired by 111 sires. The maximum and minimum numbers of sheep per sire group were 49 and 1, respectively. Post-weaning weight, which is an early life body weight measurement, was measured at an average age of 287.5 days, with a minimum of 148 days and a maximum of 431 days. Body weights ranged from 17.5 to 67.2 kg, with an average of 39.7 kg, and were normally distributed.snpQC and SNPs were filtered as follows: SNPs were removed if they had a call rate less than 95 %, a GenCall score less than 0.6, a minor allele frequency lower than 0.01 and if their heterozygosity rate was outside the interval of the mean ± 3 × standard deviations (mean heterozygosity rate = 0.37 and s.d. = 0.13). SNPs that departed from Hardy-Weinberg equilibrium  or had no assigned genomic location or were located on the sex chromosomes were also excluded from the analyses. Missing genotypes were imputed using fast PHASE [All animals were genotyped using the Illumina Ovine SNP50 BeadChip , which includes 53 862 SNPs. Quality control was performed with the R program st PHASE .A GWAS was performed with a linear mixed effects model using ASREML . The foly is a vector of individual body weights, X is a design matrix for fixed effects (as described above) and SNP genotypes fitted as covariate, and b is a vector of fixed and SNP effects. Z is a matrix that allocates records to sire, g is a vector of sire effects, and ε is the residual. After fitting the data in model . To evaluate if estimates were overinflated, we calculated the genomic inflation factor λ using the GenABEL R package [where, in model , analysi package .The percentage of genetic variance explained by each significant SNP was calculated according to the following formula:pi and qi are the allele frequencies for the ith SNP, ai is the estimated additive effect of the ith SNP on BW, and σg2 is the estimated genetic variance. ASReml [h2) and genetic variance for BW using the same data and model as Eq. where . ASReml  was usedEstimation of the genetic variance attributable to each individual chromosome was done using the GCTA  softwarehsphase algorithm [The lgorithm  was usedlgorithm . FinallyLinkage disequilibrium (LD) between SNPs within the region between 36.15 and 38.56 Mb on OAR6 was calculated as |D’|, using Haploview (V4.2 ). LD bloyj is the residual phenotypic value for the jth individual after adjusting for the fixed effects  of the ith haplotype for the jth individual, with t the number of haplotypes segregating in the population for that region; Hi is the effect of the ith haplotype; Sj is the random sire effect, and eij is the random residual effect.where ects Eq. ; μ is thSNP) less than 0.95, and 273 SNPs that departed from the Hardy-Weinberg equilibrium were removed. No individual was excluded because of a call rate (CRIND) lower than 0.7, but 38 individuals were excluded because of missing phenotypes. After quality control, 1743 individuals and 48 640 SNPs were retained for analysis.From the initial set of 53 862 SNPs, 1449 (2.69 %) non-autosomal SNPs, 1662 SNPs with a minor allele frequency lower than 0.01, 1838 SNPs with a call rate  plot revealed a large deviation from the distribution under the null hypothesis, which indicated a strong association between this region and BW  were detected for 39 SNPs [See Additional file P = 2.36 × 10−16), which is located at 37.69 Mb. The other three SNPs on this chromosome were s17946.1 (P = 7.97 × 10−14) at 37.1 Mb, OAR6_41877997.1 (P = 2.49 × 10−12) at 37.64 Mb, and OAR6_41003295.1 (P = 2.4 × 10−11) at 36.81 Mb. Apart from this region on OAR6, the most significant SNP was on OAR3, i.e. OAR3_128968872.1 (P = 2.16 × 11−16) at 120.9 Mb. Four SNPs were detected on OAR14, at 23.5, 32.4, 52.2 and 57.9 Mb. The remaining significant SNPs were spread over various chromosomes [See Additional file p-value < 0.001).The GWAS identified one major region with a strong association with BW between 36.15 and 38.56 Mb on OAR6 , while the second most significant SNP was on its border , the region between 36.15 and 38.56 Mb on OAR6 contains 14 genes. These include NCAPG non-SMC condensin I complex, subunit G (NCAPG), which is associated with foetal growth and carcass size in cattle [ligand dependent nuclear receptor corepressor-like (LCORL), which is associated with height in humans and cattle [contig tracks in Fig. NCAPG and LCORL and the syntenic regions on BTA6, HSA4 (HSA for Homo sapiens chromosome) and SSC8 (SSC for Sus scrofa chromosome). When considering a larger 2.41 Mb region  of the total additive genetic variance. The estimated heritability (h2) for BW was equal to 0.63.In our analysis, we calculated the proportion of additive genetic variance explained by each SNP for a Bonferroni adjusted OAR6_41936490.1; P = 2.36 × 10−16) had an allele substitution effect of 2.12 kg, while the second most significant SNP  had an effect of 2.13 kg, corresponding respectively to 24.82 and 29.5 % of the phenotypic standard deviation for BW . |D’| values between SNPs in this block ranged from 0.97 to 1.00  effect on sheep BW. Haplotypes AGGT and GATC showed significant but opposite effects, while haplotype GATT had a marginal effect on BW , and less than 0.006 for a block of 278 kb .A haplotype analysis of the region between 36.15 and 38.56 Mb on OAR6 was carried out and SNPs that were in high LD within this region were grouped together in haplotype blocks. Using the criteria specified by Gabriel et al. , a haploBW Table . By visuATP-binding cassette sub-family G member 2 (ABCG2), polycystin-2 (PKD2), leucine aminopeptidase 3 (LAP3), NCAPG non-SMC condensin I complex, subunit G (NCAPG), and ligand dependent nuclear receptor corepressor-like (LCORL). Chromosomal locations and known functions of these genes are in Table ABCG2 gene was previously reported to affect milk yield and composition in dairy cattle [LAP3 gene encodes leucine aminopeptidase, which is associated with milk production traits in cattle [LCORL gene encodes a ligand dependent nuclear corepressor-like transcription factor and polymorphisms in this gene are associated with skeletal frame size and adult height (http://www.ncbi.nlm.nih.gov/). In cattle, a non-synonymous but chemically conserved variant in the NCAPG gene has been identified as a potential causative variant for body frame size [NCAPG and LCORL genes have been reported to be associated with several body size traits [In this study, we performed a GWAS on Australian Merino sheep by genotyping data using a medium-density chip that included around 50 000 SNPs. The analysis identified 13 SNPs that spanned a 2.41 Mb region on OAR6 that were significantly associated (P < 0.001) with BW. Biologically relevant genes in this region are y cattle . The LAPn cattle . In humaame size : height,ame size . SimilarNCAPG between 37 362 103 and 37 333 949 bp and LCORL between 37 362 163 and 37 488 824 bp. Results are in agreement with several previous studies on different species, including sheep, cattle, horse and chicken. In a study on Scottish Blackface lambs, Riggio et al. [NCAPG gene . The NCAPG c.1326T > G variant is present in various bovine breeds and has been associated with birth weight [NCAPG-LCORL locus is located on ECA3  and a QTL located just upstream of the LCORL gene was found to be associated with height at the withers [Kv channel-interacting protein 4 (KCNIP4), G protein-coupled receptor 125 (GPR125) and glucosidase, beta, acid 3 (GBA3). Recently, a region containing the chicken orthologous KCNIP4 and GPR125 genes was identified in the region between 71.6 and 80.2 Mb on GGA4  and was shown to be associated with BW and average daily weight gain from 6 to 12 weeks [We used LD to identify haplotype blocks associated with BW. In the region between 36.15 and 38.56 Mb on OAR6, a haplotype block of four SNPs was identified that had a significant effect on BW in sheep. This haplotype block ranged from 37 254 883 to 37 359 421 bp and contained two functional candidate genes: o et al.  reportedo et al.  detectedh weight , withersh weight . In Japah weight  also ideNCPAG-LCORL region on OAR6 and syntenic regions in other mammalian species showed a high level of sequence similarity , family with sequence similarity 184, member B (FAM184B), DDB1 and CUL4 associated factor 16 (DCAF16), NCAPG and LCORL genes are all expressed in these tissues and associated with permissive chromatin marks . Moreover, it has been demonstrated that the expression of NCAPG and LCORL in bovine adipose and muscle tissues is associated with feed intake and average daily gain [NCAPG and LCORL.Several candidate genes are present in the 2.41 Mb region on OAR6, although some are unlikely due to the lack of direct syntenic relationships with the human genome. It is likely that body size traits associated with this region are associated with genes that are expressed in affected tissues such as skeletal muscle and to a lesser extent adipose tissue, both of which have interactive functions. Gene expression data for adipose tissue depots and skeletal muscle from ovine late gestation fetuses and lambs indicate that ily gain . By combNCAPG is involved in the condensation and stabilization of chromosomes during meiosis and mitosis. A non-synonymous mutation I442M (c.1326T > G) in the NCAPG gene is strongly associated with fetal growth and carcass traits in cattle [NCAPG variant with arginine metabolism [NCAPG gene is LCORL, which encodes a ligand-dependent nuclear receptor corepressor-like protein. Interestingly, in most mammals the amino-terminal region of this protein contains a poly-alanine tract, part of which is encoded by a pure trinucleotide repeat. More generally, this type of repeats is known to be susceptible to polymorphic variation [LCORL. The first exon of the ovine LCORL has not yet been annotated and it should be investigated for polymorphisms. Sequencing is currently in progress to identify the causal mutation.n cattle , althougn cattle . Two stutabolism , 44. Argtabolism , 46. Cloariation , which oariation –50. IndeNCAPG and LCORL are likely candidate genes for BW. The region that harbors the NCPAG and LCORL genes on OAR6 is highly conserved among mammalian species and multiple body size traits in various species have been associated with this syntenic region, which may reflect that the underlying biological mechanisms share a common ancestry. These findings should facilitate the discovery of causal variants for BW and contribute to marker-assisted selection.In summary, our GWAS identified 39 SNPs associated with body weight in sheep and we found a major QTL region that spans a 2.41 Mb region on OAR6. Within this region, the genes