Oncorhynchus mykiss) experimental or commercial lines based on a medium-density single nucleotide polymorphism (SNP) chip and various molecular genetic indicators: fixation index (FST), linkage disequilibrium (LD), effective population size (Ne) and inbreeding coefficient derived from runs of homozygosity (ROH).Selective breeding is a relatively recent practice in aquaculture species compared to terrestrial livestock. Nevertheless, the genetic variability of farmed salmonid lines, which have been selected for several generations, should be assessed. Indeed, a significant decrease in genetic variability due to high selection intensity could have occurred, potentially jeopardizing the long-term genetic progress as well as the adaptive capacities of populations facing change(s) in the environment. Thus, it is important to evaluate the impact of selection practices on genetic diversity to limit future inbreeding. The current study presents an analysis of genetic diversity within and between six French rainbow trout (FST ranging from 0.08 to 0.15). LD declined rapidly over the first 100 kb, but then remained quite high at long distances, leading to low estimates of Ne in the last generation ranging from 24 to 68 depending on the line and methodology considered. These results were consistent with inbreeding estimates that varied from 10.0% in an unselected experimental line to 19.5% in a commercial line, and which are clearly higher than corresponding estimates in ruminants or pigs. In addition, strong variations in LD and inbreeding were observed along the genome that may be due to differences in local rates of recombination or due to key genes that tended to have fixed favorable alleles for domestication or production.Our results showed a moderate level of genetic differentiation between selected lines  contains supplementary material, which is available to authorized users. Oncorhynchus mykiss) is native to the Pacific drainages of North America and also to Kamchatka in Russia. This fish was introduced at the end of the nineteenth century to waters on all continents except Antarctica, for recreational angling and aquaculture purposes. Rainbow trout is one of the main species of fish reared in cold freshwater around the world, particularly in Europe, North America and Chile [Rainbow trout . The Ne of a population is a concept that was developed by Wright [Ne from demographic, pedigree, or molecular data have been proposed , e.g. preferential association of alleles at different loci. The analysis of LD plays a central role in many areas of population genetics, including: the determination of genetic maps, ascertainment of levels of recombination at the population level, and estimation of effective population sizes . Most o al. [Ne  through ilibrium . The LD  by Sved  and modi by Sved , is baseNe, or equivalently, by minimizing the increase in inbreeding across generations. A molecular estimate of the inbreeding coefficient can be based on measuring long stretches of consecutive homozygous genotypes in each individual, the so-called runs of homozygosity . Long hn et al. , 16.In the present study, we used medium-density SNP chips to analyze the genetic diversity within and between six lines of rainbow trout. The objective of this research was to evaluate the impact of selection practices on genetic diversity through basic population indices and individual molecular genetics statistics. Our results may help to evaluate breeding practices in the light of the genetic evolution of farmed fish populations under selection. In addition, it provides commercial breeders a better understanding of the genetic composition of their selected lines and allows them to design and implement effective genomic breeding programs.n), and from the 2016 birth year class (SY) . Individuals genotyped for the current study were from the 2006 birth year class (SY1) line was spawned from a full factorial cross among SYn founder parents (32 dams and 44 sires). The SU line in the current study is the 5th generation of selection for survival and growth on a totally plant-based diet provided from first feeding [In early 2008, the first generation of the Suave  have been based on closed populations that were selected for at least five generations at the time the cohorts were sampled for the study. The samples represented the 5th, 8th, 9th and 10th generation of selection in SB, SC, SA and SD breeding companies, respectively  by optimized within-group mass selection and a 3 to 10% selection pressure according to the PROSPER procedure . To main® Trout Genotyping array [n and SY lines, respectively.The 57,501 SNPs (57K SNP) Axiomng array  was used® procedure on the second genome assembly at the chromosome level Omyk_1.0 [Oncorhynchus mykiss is a diploid species, there is still residual tetraploidy in the 2.2 Gb of the rainbow trout genome due to the fourth round salmonid whole-genome duplication event that occurred approximatively 96 million years ago [Quality control of SNPs was performed in several steps. First, all 57,501 SNP probes were positioned with a BLASTnOmyk_1.0 , 26 and ears ago . In addiears ago . The AmeSecond, we used the Axiom Analysis Suite 2.0 software  to contrFST analysis).Third, regarding the genotypes for the remaining 38,425 SNPs, we performed a final quality control using PLINK v1.9 software  on each Finally, we only retained the SNPs that had a sufficiently high minor allele frequency (MAF) to obtain reasonable results for each of the analyses. For the ROH studies MAF ≥ 1%), 34,077–37,340 SNPs were kept depending on the lines. For LD calculation (MAF ≥ 5%), 31,190–34,723 SNPs were considered depending on the lines  and expected heterozygosity (He) for a population under Hardy–Weinberg equilibrium were derived with the PLINK v1.9 software . Levels stimates , using tstimates . In addistimates  to visuar2 value does not necessitate phasing, it is very similar to but not identical to the r2 estimate derived from haplotype frequencies [To estimate LD, we used the squared correlation based on genotypic allele counts  using the PLINK v1.9 software . This r2quencies .r2 values were calculated for each chromosome and line by considering the following average distances between SNPs: 10 kb with a 20 kb-window; 50 kb and 100 kb with a 50 kb-window; 1, 3, 5, 10 and 30 Mb within a 100-kb window for each distance.Pairwise LD between adjacent SNPs and pairwise LD between all SNPs in a 60-Mb long window were derived for each chromosome and line. Mean Ne:  and (6) of Hill [c per chromosome.To derive mean  of Hill  considerNe was derived by considering the temporal approach [Second, approach  and the approach : \documey is the allele frequency at generation t. Then, y Pollak  and consROH were identified for each fish within all lines using PLINK v1.9 . ROH werROH), number of SNP per ROH (SROH), length of ROH (LROH) and percentage of ROH segment longer or equal to 10 Mb were calculated per individual and line. To identify the genomic regions most commonly associated with ROH, the percentage of individuals with a SNP in a ROH segment was calculated by counting the number of times the SNP was detected in a ROH within the population. This count was plotted against the position of the SNP along the chromosome.Mean number of ROH (NFROH) were calculated as the sum of ROH lengths of an individual divided by the total length of the autosomal genome covered by SNPs. The total size of the autosomal genome covered by SNPs was calculated within each line and chromosome. Genome regions with a gap between two adjacent SNPs larger than 1 Mb (authorized gap to derive a ROH) were deducted from the total size of the genome covered by SNPs. In addition, recent inbreeding (FROH>10Mb) was derived as the sum of the lengths of ROH segments longer than 10 Mb in order to estimate inbreeding occurring within the last three generations  ranged from 33.5 to 35.3% Table . On averFST values ranged from 0.02 (SYn–SU) to 0.15 (SA–SB). All commercial lines were moderately distant from each other with FST ranging from 0.09 to 0.15. The SD line was the commercial line that was genetically the closest to the INRA SY line, whereas the SA and SB were the most distant.Results showed moderate genetic differentiation between lines. Overall, 10% of the total genetic variation is explained by the first two PCA axes Fig. . Except r2 tended to decrease with increasing distance between pairs of SNPs in all the populations studied, the most rapid decline being over the first 100 kb  and the largest number (90) of ROH belonged to the SYn and SA lines, respectively. The average size of ROH varied from 3.84 Mb for SYn to 5.38 Mb for SB. The proportion of long ROH (≥ 10 Mb) varied from 7% for SYn to nearly 14% for SB.Statistics concerning the average number and size of ROH segments per individual according to the lines are reported in Table S3  ranged from 2.9 for SYn to 7.9% for SB [Fig. FROH>10Mb varied from 0 to 22.9% for an individual in SY  was observed in the unselected SY line in five generations. All selected lines had higher average inbreeding levels than the unselected SY line, the highest levels for total (> 16%) and recent (> 5%) inbreeding being found for the SA, SB and SC lines that are selected based on pedigree information since seven, four and six generations, respectively.Average FROH at the chromosome level  was observed for SA on Omy23 and the lowest value (4%) for SYn on Omy3. Average FROH is sometimes relatively close from one line to another , whereas variation in FROH is much larger for Omy23, from 7% for SY to 30% for SA.Regarding vel Fig. , the meaFST: 0–0.05 for little genetic differentiation, 0.05–0.15 for moderate genetic differentiation, 0.15–0.25 for large genetic differentiation, and above 0.25 for very large genetic differentiation, respectively.Most of our rainbow trout populations were moderately differentiated according to the qualitative guidelines proposed by Wright  for the FST < 0.03) was observed between the SU and SY experimental lines, which is reasonable given that the SU selected line was derived from the SYn line. The SD commercial line was also genetically close to the SY INRA lines, partly due to the fact that about 50% of the male founders of the SD line came from the INRA sex-reversed males, 25 years ago. Although the other commercial lines are more distant from the INRA line than the SD line  and expected (He) heterozygosity. We believe that these parameters should be derived without setting a threshold MAF value in order to keep all SNPs in the analysis and avoid any bias. In this way, we estimated an Ho of about 34% (± 1%); however, most of studies in the literature give results for a subset of SNPs with MAF above 5%. We also calculated Ho under this MAF threshold and found values close to 37% in all lines, which are very similar to the values estimated in pig lines  or cattlIn our study, we observed a slight heterozygosity excess (Ho > He) with a large set of neutral loci for all lines. This observation may indicate a recent (< 100 generations) bottleneck  that is r2 values reported for dairy cattle breeds [The LD at short distances between markers (≤ 100 kb) is very similar to e breeds  and slige breeds ; howevere breeds , 54. At e breeds .r2 using long distance windows (5 Mb) were observed between the French rainbow trout lines and an American commercial line of rainbow trout [r2 among chromosomes  was estimated to range from 0.09 to 0.18 [Ne, not only by decreasing the mean reproductive success but also by increasing the variance in reproductive success among breeding parents, whereas there was no reduction in Ne found in fish breeds for a single generation in a local hatchery [In the ideal scenario, population genetics theory recommends keeping equal numbers of males and females and maintaining a constant population size over time. However in most livestock breeding programs, it is often impossible to maintain a 1:1 sex ratio, which greatly affects the oodstock . This laoodstock ), which oodstock  found in to 0.18 , 0.17 to to 0.18 , and a 0 to 0.18 , with a  species . Fish brNe values less than 100 are observed for many livestock populations. In our study, the impact of intense selection can be quantified by comparing the evolution of Ne between the SU and SY lines, but also by taking into account that Ne is affected by the smaller number of broodstock used for SU than for SY breeding. Based on LD values, Ne at Ne based on allele frequency variation, 66 for the SU line and 216 for the SY line.Intense directional selection of relatively few animals often results in a skewed genetic contribution and may explain why Ne and FROH. Values for total FROH and recent inbreeding were almost unchanged, but Ne estimates were very sensitive to the absence of Omy5 and Omy20 although r2 values were averaged per chromosome before deriving Ne. Removing these chromosomes increases our estimates of Ne across all lines, on average by 40% at generation Ne remained small for all the lines compared to census sizes that were close to 200 for all commercial lines, 140 for the SY line and 80 for the SU line. Regardless of the line considered in the study (selected or not), we observed that in general Ne tended to decrease in the last 10 generations, which may be explained by drift and SRS phenomena. It should also be noted that in the three selected lines  for which a mating optimization procedure to limit inbreeding was set up in the last 10 years by SYSAAF, Ne stabilized (SC) or even slightly increased (SA and SB) in the last three generations. This suggests that some mating optimization protocol (genomic-based pedigree) should be similarly introduced to limit the inbreeding increases in the SD, SU and SY lines.Due to the existence of two major inversions  on Omy5 mentclass2pt{minimNe due to selection, our results underscored the difficulty in acquiring accurate estimates for this parameter, since estimates based on the same molecular information can be doubled or tripled depending on the evolutionary model considered [Ne are obtained using molecular rather than genealogical information [In addition to quantifying the important decreases in nsidered . It is wormation , at leasormation . In mostormation . In addiormation  and Foruormation  reportedNe from genomic population data rely on the Wright–Fisher model’s assumptions of low fecundity non-skewed offspring distributions. Although proven to be robust to violations of most of these assumptions, these methods drastically failed to approximate the genealogies of species with high SRS, whereby few individuals contribute most of the offspring to the next generation [Most methods applied to infer neration . As statneration , the devNe assumes that the value remains constant across the genome. However, Ne varies across the genome, such that some regions have an increased loss of diversity compared with others [Ne is expected to vary across the genome as a consequence of genetic hitchhiking due to selection [Ne, leading to lower levels of genetic diversity and reduced effectiveness of selection. We could have derived Ne per chromosome based on chromosome-specific LD estimates; however, we preferred to study the heterogeneity of genetic diversity throughout the genome using ROH because Ne estimates have been proven unreliable for fish populations (see previous section), and quantifying the absolute level of inbreeding  in our rainbow trout lines was per se an objective of the study. ROH can be used to directly estimate inbreeding depression [The majority of metrics to estimate h others , 75. Theelection  and negaelection ). The acpression  and can pression .FROH varied between 12 and nearly 20%, estimates were a bit lower (~ 11%) for the INRA unselected line. These results are consistent with our Ne estimates for three to ten generations ago: the SY and SD lines had the largest Ne and the lowest FROH whereas SB had the smallest Ne and the highest FROH . Estimates varied from 3 to 9% in dairy cattle breeds [While empirical information on genome-wide diversity in model species and livestock has been collected, we still lack a clear picture for farmed fish of the heterogeneity of genetic diversity across the genome. As far as we know, our study is the first to calculate ROH and estimate e breeds , 38, 39,e breeds , 85 and e breeds , 86, 87.Ne. In spite of the existence of mating plans for selected lines with known pedigree , recent inbreeding is relatively high in these lines (5–8%). Nevertheless, we may be able to manage their genetic diversity along the entire genome. Because no ROH fragments are fixed within the populations  and has increased by 1% in six generations , probably due to the very low marker density in these areas. Hotspot areas for ROH were observed for each chromosome . However, this information could also be used to manage the rate at which inbreeding builds up in the genome of livestock populations. Characterizing and efficiently managing inbreeding levels is a major goal to ensure that populations can adapt to future breeding goals while maintaining genetic diversity and avoiding the accumulation of detrimental effects associated with inbreeding. Identifying the main regions of the genome that contribute to inbreeding depression may help to manage inbreeding not only at the individual level but directly at the genome level.FST values as those reported across ruminant breeds or pig lines. Within each line, effective population size seems rather small and inbreeding levels are higher than in terrestrial livestock selected populations. This may be explained by founder effects, sweepstakes reproductive success, and intense selection in some lines. The impact of these significant levels of inbreeding on rainbow trout performance should be quantified in order to assess potential inbreeding depression phenomena and risks for future genetic gains. The levels of molecular inbreeding derived from the identification of homozygous genomic segments could also be used to directly identify the regions that are responsible for inbreeding depression along the genome. We could then expect a more efficient purging allowing for higher relatedness between selected individuals without inbreeding drawbacks in breeding programs.Our findings provide the breeding companies a better understanding of the genetic diversity in their rainbow trout lines in order to implement efficient breeding programs. The availability of a genome-wide SNP chip allowed us to characterize genetic diversity between and within lines. Lines are moderately differentiated across commercially developed trout lines, with similar Additional file 1.Table S1: Number of SNPs per minimum allele frequency (MAF) category in each line. Table S2: Average r2 ± SD between SNPs according to different distances. Table S3: Sensitivity analysis of ROH estimates to MAF and maximal distance gap between two SNPs and derived inbreeding coefficient. Table S4: Estimates of effective population size (standard errors in brackets) for each line with or without Omy5 and Omy20.Additional file 2.Figure S1: Line mean linkage disequilibrium at 50 kb for each chromosome. Figure S2: Proportion of individuals per line with a SNP in a ROH along the genome. Figure S3: Proportion of individuals per line with a SNP in a ROH along chromosome 10.


Messor arenarius and a non-described Messor sp.) in fitness-related behaviors, pertaining to foraging performance, nest maintenance, and aggression. We linked these behaviors to the colonial genetic diversity, by genotyping workers, using six and five microsatellite markers for M. arenarius and M. sp., respectively. Correlations of genetic diversity with colony performance and aggression level contrasted between the two species. In M. arenarius, genetic diversity was correlated with foraging performance and nest maintenance but not with the overall aggression level, while in M. sp., genetic diversity was correlated with the overall aggression level, but not with foraging performance or nest maintenance. The two species exhibited similar specific aggression levels, with higher aggression shown towards heterospecifics and lower towards non-nestmate conspecifics and nestmates. However, M. sp. workers displayed a tendency to interact for longer with heterospecifics than did M. arenarius. We speculate that the different foraging strategies, group vs. individual foraging, and possibly also the different mating systems, contribute to the differences found in behavior between the two species.There is accumulating evidence that genetic diversity improves the behavioral performance and consequently the fitness in groups of social animals. We examined the behavioral performance of colonies of two co-occurring, congeneric harvester ant species ( Similarly, groups of related individuals often benefit from within-group diversity, because heterogeneous groups possess a larger pool of skills, which better conduce to contending with fluctuating conditions than those of homogenous groups3. Such benefits can be reflected in improved survival, foraging, and parental care compared to more homogenous groups6 (but see8 for a more complex effect).There has been a growing interest in recent years in the effect of within-population diversity on performance. Elevated population diversity enables specialization and exploitation of a broader range of resources and reduces intraspecific competition10. This higher efficiency has led to elevated productivity11 and enhanced survival15. The mechanism behind this could be that of diversification of the workers’ response thresholds and optimizing task allocation according to colony needs18. More diverse social insect colonies also forage more efficiently: some ant colonies forage over a longer part of the day19, and honey bee colonies exchange more recruitment signals20 and collect more pollen21.In social insects, the effect of within-colony diversity on colony efficiency has been studied mostly in honeybees. Genetic diversity in honeybees contributes to the colony efficiency in performing important tasks, such as nursing and removing carcasses25. Colony diversity may also positively affect nest maintenance activities, such as nest reconstruction in ants (as implied in27) and temperature regulation in bee colonies29. Similarly, in colonies in which queens mate more than once, some studies have shown a link between patrilines and tendencies to perform specific tasks, plausibly resulting in a more efficient division of labor31. Nevertheless, the positive link between within-colony diversity and colony performance is not a general rule and other studies have found no such correlation34.Genetic or phenotypic diversity in ants has been positively correlated with colony growth, success in various tasks, and parasite resistance36. The failure to do so could lead either to losing nestmates seeking to re-enter the nest or allowing potentially harmful ants to enter the nest . Within-colony genetic diversity is expected to decrease aggression towards non-nestmates due to the colony’s low ability to discriminate between nestmates and non-nestmates, because high genetic diversity, existing in many polygynous colonies for instance, makes it harder to form a unique template that does not overlap with those of other colonies41. However, more recent studies have indicated that the production of a shared communal odor  in polygynous colonies does not necessarily compromise the colony’s ability to separate between nestmates and non-nestmates, and a unique colony odor is nonetheless produced43. Plausibly, in such colonies, this could affect aggression levels towards non-nestmates.The contribution of high genetic diversity to colony performance might be at the cost of increased difficulty in recognizing nestmates. Colonies of social insects must distinguish between nestmates and non-nestmates44. Workers also increase aggression according to the increasing level of threat expected from each encountered opponent, with the highest aggression directed, for example, towards slave-making ants that seek to steal the colony pupae45.In general, attacking heterospecifics is perhaps less common than attacking conspecifics, because the latter are perceived as the greatest competitors. However, in ants, there is evidence that aggression against heterogeneric workers is higher than that against congeneric ones, which is in turn higher than that against nestmatesMessor arenarius and Messor sp. Although the latter had previously been considered to be young colonies of a different species, M. ebeninus, our genetic screening has since revealed it to be a new species, still to be described . Our first and main goal was to study the correlation between within-colony genetic diversity of the two Messor species and foraging performance, nest maintenance, and overall colony aggression. The first two aspects were examined in the field, while the latter was quantified under laboratory conditions, by challenging focal workers with different opponents. Our second goal was to study the specific aggression responses in each species and to determine whether aggression is higher towards heterospecifics than towards conspecifics and nestmates.Here, we studied two harvester ant species: For our first goal, we predicted that within-colony genetic diversity would be positively correlated with foraging and nest maintenance performance. Genetically diverse colonies are assumed to produce a more specialized and thus more efficient worker force. In contrast, we predicted that within-colony genetic diversity would be negatively correlated with overall colony aggression level. Regarding our second goal, we predicted that in both species, the specific aggression level would increase with phylogenetic distance and would be the highest against heterospecifics, followed by conspecifics, and the lowest against nestmates.M. sp. and M. arenarius are abundant. We first assessed the foraging and nest maintenance performances in the field  over two consecutive days, in order to obtain sufficient data . Lastly, 103 and 183 randomly sampled workers  were stored in absolute ethanol for subsequent genetic analyses in order to determine the genetic colonial diversity and the number of patrilines among the worker force. In all experiments conducted, to ensure the colonies were distinct, they were sampled at 45.6 m ± 36.3; mean ± 1 SD; range  distance from other conspecific colonies, and were chosen for sampling if they featured only one entrance; we determined there was no second entrance to the nest within a radius of 1.5 meters around the main entrance. For the aggression assays and genetic analysis, workers were collected from the colony entrance. Each sampled colony was then placed in an individually marked and closed plastic container. Air and ground temperatures and relative humidity  were measured on each working day . M. sp., the currently undescribed new species, was observed in the field for its foraging activity and measured for body characteristics. We report our findings here for the first time.The experiments were conducted between December 2015 and May 2016 in the Tel Baruch sand dunes (1.5 × 0.5 km area) on the Mediterranean coast in north-west Tel Aviv , where M. sp., stable foraging trails were observed and measured using a 50 m measuring tape. Foragers on the trail were counted twice, 10 and 20 min after placing a Petri dish with 2 g of millet seeds 30 cm from the colony entrance. The foraging trail’s fragments of three colonies were photographed as an additional control over possible forager counting error by the observers. For the mostly individual forager, M. arenarius, foraging trails were not located and foragers were therefore counted twice, in a radius of 1 m around the colony entrance, 25 and 50 min after placing a Petri dish with 2 g of millet seeds 30 cm from the colony entrance. In both species, forager count was performed to estimate foraging intensity and colony size (see Results). Finally, foragers of both species (n = 10) were sampled from the colony entrance in absolute ethanol for morphological identification by a myrmecologist taxonomy expert.Six colonies per species were observed for their foraging activity in the study area. For the group forager 49. The non-native millet seeds are favored by both studied species . M. arenarius and M. sp. were allowed to forage for 50 and 20 min, respectively, before the plates were collected. The test was longer for M. arenarius, because of their potentially slower depletion of food patches49. The remaining seeds in all Petri dishes were weighed to the nearest 0.01 mg to estimate foraging performance. Since it was impossible to observe all colonies due to the simultaneous test, tracking trails (a 50 × 100 cm trail that was cleared around each tested colony in the field) were photographed before and after the experiment, to control for tracks of other animals that might have consumed the seeds, as also done in49. In only two cases did we spot tracks of birds (probably crows) and we excluded these from the analysis.Three 9 cm Petri dishes, each with 2 g of millet seeds, were placed concomitantly at increasing distances, 30, 60, and 90 cm from the colony entrance Fig. . We usedM. sp. workers were weighed and their head width measured . Workers were dried at 60°c for 48 hours and then weighed individually to the nearest 0.01 mg. Heads were photographed with a digital camera (Axiocam ICC5) connected to a stereomicroscope  and measured via ImageJ software50. M. arenarius body mass and head width were already known from a previous study51.54.The nest entrance of each colony was completely blocked using wooden toothpicks . The behavior of the focal ant was then recorded for 3 minutes using the JWatcher software , while observing its behavior through a Microsoft LifeCam Cinema HD camera. The focal ant was color-marked with white non-toxic paint, in order to track its behavior. The behavior of the observed focal ant was scored from 0–4 (based on55): antennation (0), mandibular threat (1), a threat run (2), short biting with the mandibles (3), abdomen curling and spraying or severely clinging with the mandibles to an organ (4). See Supplementary Material, Figure Ai and ti are the aggression score and duration of each interaction, and T is the total interaction time . We performed 3–12 replicates for each encounter type, the average of which was defined as the aggression index . This assay enabled us to obtain two measurements: the aggression index and the total interaction time (the denominator of the equation). For M. arenarius, 12 colonies participated in the encounter types of nestmates, heterospecifics and conspecifics . For M. sp., 9 colonies participated in the encounter types of nestmates and heterospecifics, and 8 colonies participated in the conspecific encounter type . See Table Workers of both species were randomly collected from the focal colonies and from the nearest conspecific and heterospecific colonies in the field. Each colony fragment (36 workers sampled) was placed in a round artificial ventilated nest (11 cm in diameter), lined with moist plaster to ensure an optimal level of humidity. Aggression assays were performed under a controlled setting in the laboratory  after six hours of adjustment and up to 24 h post field collection. We placed two workers in a 6 cm Petri dish lined with filter paper to prevent odor transfer between replicates. The ants were first allowed to acclimate, secluded in two glass tubes in the Petri dish for 30 sec. Encounters comprised the focal ant species and one of the three tested opponents . Following 3 min centrifugation at 20,000 g, 75 μL of the supernatant were stored at 4 °C. Genotyping was achieved using five and six statistically-independent microsatellite, for M. sp. and M. arenarius respectively, that had been previously developed for other Messor species58. For the current studied species, microsatellite loci were specifically developed under different PCR conditions . We thus compared the genotypic frequencies of all nests sampled using a log-likelihood (G) based test of differentiation, from GENEPOP ON THE WEB59.DNA was extracted from ant legs by incubation for 120 min in 100 μl of 5% Chelex at 85 °C60. The genetic regression  between workers of the two species was estimated from 12 worker genotypes (on average) per colony , from 15 and 9 colonies of M. arenarius and M. sp. respectively, using the algorithm of Queller and Goodnight61 implemented in the COANCESTRY software v1.062 (http://www.zsl.org/science/software/coancestry). Since the assessed loci showed satisfactory levels of polymorphism, the number of genotyped workers was sufficient to detect differences in relatedness between colonies. For M. arenarius, the queen and patriline genotypes were inferred from the worker genotypes; each worker was assigned to a given patriline with the maximum-likelihood method implemented in the software COLONY 1.163. Additionally, we calculated the probability of non-detection of an additional male carrying the exact same genotype at all loci studied, using Boomsma and Ratniek’s64 equation:fij is the frequency of the allele i at the locus j. For M. sp., there were no workers that shared at least one allele at every locus with an inferred queen (see Results) and we were therefore unable to determine the colony patriline number. Workers that could not be unambiguously assigned to the mother queen or to one of the patrilines in each colony (n = 3 from 2 colonies), due to failed PCR amplification or because they shared no allele with the colony queen, were excluded from the analyses. Overall, out of 286 sampled, 284 workers were successfully assigned to their respective colony queen and one of the queen’s respective mates  inversely proportional to the genetic correlation between workers, and (2) proportional to the number of matings per queen for each colonyM. arenarius only, see Results); (3) foraging performance: the average of collected seeds (g); (4) nest maintenance: the proportion of obstacles (toothpicks) removed from the colony entrance; (5) colony overall aggression: the sum of the aggression indices for the three encounter types ; (6) colony aggression index separately towards nestmates, conspecifics, and heterospecifics; and (7) colony total interaction time separately with nestmates, conspecifics, and heterospecifics. The above aggression and interaction parameters (5–7) were obtained using equation number 1.We calculated the following response variables per species and colony: (1) the genetic relatedness, using Queller and Goodnight’s relatedness index; (2) number of patrilines per colony (for M. sp.), due to deviations from a normal distribution. In addition, linear regressions were performed using the number of patrilines per colony as the explanatory variable and the above variables 1–5 were the response variables . For the nest maintenance proportion variable, we examined whether it correlated with within-colony relatedness (in both species) or patriline number (only in M. arenarius) using logistic regressions, and we corrected for over dispersion using the quasibinomial option in R (similar to66).To achieve our first goal, we regressed, for each species, the following response variables with within-colony relatedness: (1) foraging performance, (2) colony overall aggression level, (3) colony aggression level towards heterospecifics, (4) colony aggression level towards conspecifics, and (5) colony aggression level towards nestmates. All variables were tested for normal distribution. Variables 3–5 above were square-root transformed  as the explanatory variable, and two response variables: (1) the aggression index, and (2) the total interaction time. Both latter variables were averaged per colony. For each species, data were tested for normality and homogeneity of variance. Following, the aggression index variable was square-root transformed due to its deviation from a normal distribution.M. sp., 1–3 foraging trails per colony were observed, presenting a combined length of 1749.5 ± 547.8 cm; mean ± 1 SD; range , n = 6. All similar reports in the Results section hereafter indicate the mean ± 1 SD. This species initiates foraging on clear vegetation-free trails and seems to follow a pheromone trail; similar to M. ebeninus , although another study suggests that M. arenarius colonies consist in up to 5,000 workers, but no measurement methods are provided72.For M. arenarius, and 0.289 ± 0.255 g; range  for M. sp. The mean ± 1 SD mass of one millet seed is 5.6 ± 0.8 mg (n = 100) and, accordingly, the total number of seeds taken by the ants was estimated at 120 ± 127.3 g; range  for M. arenarius and 51.3 ± 45.2 g; range  for M. sp. Moreover, since each ant can carry only one seed at a time, the number of seeds taken is equivalent to the number of trips made.Foraging performance, expressed as the average of seeds collected from the three plates, within the given time period, was 0.608 ± 0.671 g; range  for M. sp. workers was 1.072 ± 0.466 mg; range  and head width was 1.286 ± 0.199 mm; range . M. arenarius dry body mass was 42 ± 15.45 mg and head width 3.13 ± 0.58 mm .The dry body mass of M. arenarius increased with decreasing relatedness, i.e., with increasing genetic heterogeneity . In M. sp., relatedness was not correlated with foraging performance .Foraging performance in M. arenarius and M. sp., respectively.Nest maintenance, measured as the proportion of obstacles removed from the nest entrance, was 29%  and 12%  (mean and range) for M. arenarius, nest maintenance increased with decreasing relatedness, similar to foraging performance .In M. arenarius overall aggression level was not correlated with within-colony genetic relatedness or patriline number . Specific aggression levels were also not correlated with relatedness in M. arenarius . The same pattern held true for the patriline number . In contrast, in M. sp., overall aggression decreased with increasing within-colony genetic relatedness , but correlations with the other specific aggression levels were not significant . All correlations of specific aggression levels with relatedness for both species are presented in the Supplementary Material  All colonies sampled were distinct genetic entities, i.e., distinct colonies . (2) At least one maternal allele at every locus was not shared by the workers, suggesting the occurrence of more than one reproductive queen. Consequently, we could not unequivocally infer patrilines. The apparent occurrence of multiple reproductives in this species resulted in relatively low relatedness indices among workers , ranging from 0.067 to 0.451 . In contrast, in all of the M. arenarius colonies, a single queen was inferred. Moreover, the queens were facultatively polyandrous and had mated with 1 to 3 males . This result was robust and unlikely to have been biased to the possibility of non-detection of an additional male carrying the same alleles at all loci, as this probability is low for all males inferred . This mating system resulted in higher relatedness indices in M. arenarius, varying from 0.182 to 0.891 , compared to M. sp. .The genetic analysis for M. arenarius maintained their nests better and foraged more extensively, there was no such link in M. sp. Second, the overall colony aggression of M. sp. was positively correlated with its genetic diversity – diverse colonies were more aggressive – but there was no such link in M. arenarius. However, we predicted that overall colony aggression level would be negatively correlated with within-colony genetic diversity, in contrary to our findings, and we later offer several suggestions to explain this result. We also raise several possible explanations for the differences detected between the two species, while acknowledging that the comparative method may be a complementary tool when tackling this question. For example, we speculate that the two species’ different foraging strategies may explain our results: the genetic component may be more influential in affecting individual foraging performance in M. arenarius. Differently, the genetic component may be more influential in affecting aggression in the group forager, M. sp., an inherently more aggressive species. In addition, we predicted that the specific aggression level would increase with phylogenetic distance and would be the highest against heterospecifics, followed by conspecifics, and the lowest against nestmates. Both species confirmed this prediction and demonstrated higher aggression towards heterospecifics and lower aggression towards conspecifics and nestmates. Lastly, M. arenarius spent less time interacting with heterospecifics compared to conspecifics and nestmates, while M. sp. showed a tendency towards the opposite pattern.Within-colony genetic diversity often contributes to the colony overall performance. Our results, however, are more complex, and differ between the two studied ant species. First, we predicted that within-colony genetic diversity would be positively correlated with foraging and nest maintenance performance. While the more genetically diverse colonies of 75 . In ants, colony genetic diversity effects have been suggested in only a few species, mostly those featuring highly polyandrous queens31, but see26, which was not the case here. In the present study, foraging performance positively correlated with genetic diversity in M. arenarius but not in M. sp. These contrasting results could perhaps be explained by the different foraging strategies of the two species. Individually-foraging species are probably more affected by genetic variance within colonies, because each worker makes decisions independently. In contrast, group foragers recruit and forage on a trail and are heavily influenced by pheromones that could mask differences in individual behavior. Supporting our suggestion, other studies that have focused on species that forage either individually or sometimes individually, have also demonstrated a positive correlation between inter-worker genetic variance and behavioral diversity27. Similarly, phenotypic divergence contributes to fitness in social arthropods that forage individually76. Alternatively, the different link found between genetic diversity and performance in the two species, might be explained by their different mating systems (see the discussion of “Genetic architecture of colonies” below). Importantly, we note that the interspecific differences probably derive from many traits. A rigorous way by which to test these suggestions would be to employ the comparative method and study either related species or populations of the same species that differ in their foraging strategy and to quantify their within-colony genetic relatedness. In short, only the application of many interspecific comparisons will be able to assess the generality of an evolutionary phenomenon.Many studies have reported the positive effects of colony diversity on foraging in honeybees29. A few studies have suggested the positive contribution of genetic diversity to nest construction27, but its ongoing maintenance is more frequently performed, and no less important. The nest entrance is the deposition site of colony-specific pheromones, enabling unrestricted access in and out of the colony, and it is the location where interactions with potential invaders usually take place80. A positive link between colony genetic diversity and nest maintenance was found here only for M. arenarius, similar to its link with foraging performance. The reason for this could be the association between foraging and nest maintenance behavior. After the nest entrance in another harvester ant species (Pogonomyrmex barbatus) was blocked, foragers encountered difficulty in leaving the nest, leading to a switch of tasks from foraging to nest maintenance53. This suggests that the foragers are also those involved in nest maintenance, and that more efficient foraging implies more efficient nest maintenance.We are not familiar with any study demonstrating how genetic diversity affects nest maintenance, other than a study that found a positive effect of genetic diversity on the efficiency of colony thermoregulation in honeybeeset al.81, for instance, showed that genetically cloned ant workers vary in their phenotype, expressed as sucrose response threshold. This might be explained by learning, developmental fluctuations, or epigenetics. Since we collected the colonies randomly, we assumed that the colonies represented the “average value” of the common phenotype. However, the best way to test our first goal  could have been by controlled laboratory experiments using an ant species that produces genetic clones. We could thereby have separated between the genetic and phenotypic components and their effect on behavior. Second, colony size was unknown. In the studied species, excavating complete colonies and rearing them under laboratory conditions has proven to be almost impossible. Indeed, all past studies involving M. arenarius and similar Messor species were also performed in the field89. One method by which to estimate colony size in field studies is the mark-release-recapture method92. However, if only a small proportion of colony workers are outdoor-foragers, and most of the population resides underground, the ‘equal catchability’ assumption of this method93 cannot be applied to ants. Consequently, we estimated colony size according to the number of foragers . According to our findings, the two species differed in their colony size, which could have affected performance. Larger colonies could have foraged more intensively and removed more obstacles compared to smaller ones. In the two studied species, colony size overlaps with foraging strategy; the group forager has larger colonies than the individual forager . This could offer an alternative explanation for the obtained differences between the species, at least in the foraging and nest maintenance tasks.Our field experiments experienced several limitations. First, we ignored the possible effects of phenotypic plasticity. Hasegawa 94. Indirect evidence for this comes from studies of invasive ant species: many such species demonstrate low genetic diversity and very low aggression among colonies, leading to the creation of “super-colonies”95. Specifically, genetic diversity was positively correlated here, with the overall aggression level in M. sp. but not in M. arenarius. As a group forager, M. sp. is inherently aggressive due to its recruitment behavior. Therefore, the genetic component might have a stronger impact in this species and perhaps this is why we found this correlation in the group forager but not in the individual forager. Although the individual forager M. arenarius is larger51 and quite abundant48, it might lose in direct conflicts with group-foraging ants that can more successfully exploit and dominate food resources. Similarly, in a parallel system, the group forager M. ebeninus better exploits and dominates food patches compared to M. arenarius47. Similar to our finding for M. arenarius, other studies too did not detect any correlation between genetic diversity and aggression96.We found that genetic diversity positively correlated with aggression level, in contrast to our prediction. Low genetic diversity could lead to the absence of cues on the cuticle or to a high resemblance among the profiles of different colonies that make separate colonies resemble as a single large colony, and thereby result in low aggression against non-nestmates99. Although odor effects were not tested here, we speculate that aggression levels might have been derived from odor-based recognition cues, as commonly seen in ant research. From an ecological point of view, aggression should be higher towards conspecifics and not heterospecifics, as the former are perceived as the ultimate competitors. However, in these two harvester ant species that presumably share their ecological niche, at least in regard to resources collected and foraging territory72 , it is reasonable to assume that higher aggression would be directed towards heterospecifics. In support of this, in addition to being more aggressive towards heterospecifics, it seems that in M. sp. the correlation between relatedness and overall aggression level was based mainly on the aggression towards heterospecifics , reflected in a shorter interaction time. We observed this behavior in the field as well. These two species frequently interact in the field, as they have overlapping foraging territories . Consequently, although the two species differ in body size, we do not believe that size had an impact on their tendency to interact. In general, body size may affect the collection of different load sizes of food items103; although another study in Israel compared between M. arenarius and M. ebeninus, a species similar in size to M. sp., and found that they collect similar load sizes72.The different interaction levels between the two species could have resulted from a behavior observed only in M. arenarius and colony performance or aggression level. This was despite our expectation that, in a monogynous and polyandrous mating system, the number of queen mates would directly affect the genetic diversity among workers (the more patrilines – the more diverse workers). It is nevertheless possible that there is a skew in paternity, meaning that some drones sire more offspring than others. Perhaps such a skew is more influential than the number of patrilines in the colony. In some species of honeybees, for instance, siring success is biased towards the first or last drones mating with the queen105 but see106, and thus the actual number of the queen’s mates matters less. Nonetheless, we still found a trend of a positive correlation between patriline number and nest maintenance in M. arenarius, in line with the positive correlation between genetic diversity and nest maintenance in this species.Surprisingly, we did not find a correlation between the number of patrilines in M. arenarius, is a rather common feature in ants and has evolved in 13 genera107. It appears adaptive but nonetheless costly. For instance, large sperm storage reduces immunity against pathogens during colony founding108 and increases potential disease transmission from drones to queens110. However, this potential cost could be outweighed by the benefit of multiple patrilines that generate a specialized worker force in the colonies13. Regarding the occurrence of more than a single queen in M. sp. (polygyny), the benefits and costs of increasing genetic diversity probably hold true here as well, and are possibly even more extreme than in polyandrous colonies, due to the elevated genetic diversity. As noted, multiple mating is costly111, and therefore either polygyny or polyandry is sufficient to gain the benefits of diversity in ants (but see112). In support of this, the rate of polyandry has been shown to be lower in polygynous ant species than in monogynous ones113.Queen polyandry, found only in M. arenarius compared to M. sp.; thus, M. sp. colonies are more diverse than M. arenarius colonies. The link between genetic diversity and performance might not be strictly linear. At lower levels of diversity (such as in the case of M. arenarius), performance may be linearly linked, and the effect of diversity on performance is detectable. However, at higher levels of diversity (such as in the case of M. sp.), it may level-off, making the effect of diversity on performance harder to detect.The distinct mating systems of the two species might also have contributed to the differences found in the link between genetic diversity and performance . The mean relatedness index is higher in M. arenarius and M. sp. differ in their foraging strategy, colony size, body size, and mating system. We detected inter-specific differences in the link between within-colony genetic diversity and the foraging and nest maintenance performances, and aggression level. As a short take-home message, we suggest that the different foraging strategies may explain our results: innate genetic structure might have a higher impact on performance in an individually decision-making forager, such as M. arenarius; whereas innate genetic structure may exert a greater influence on aggressive behavior in a group forager, such as M. sp. Finally, the different mating systems may explain our findings, but only regarding foraging and nest maintenance performances, not regarding aggression level. It could be that genetic diversity is more linked to these performances of M. arenarius, because this species is generally less diverse than M. sp.In summary, the co-occurring Supplementary InformationDataset


Gadus morhua) is a species of great ecological and economical importance in the Baltic Sea. Here, two genetically differentiated stocks, the western and the eastern Baltic cod, display substantial mechanical mixing, hampering our understanding of cod ecology and impeding stock assessments and management. Based on whole-genome re-sequencing data from reference samples obtained from the study area, we designed two different panels of Single Nucleotide Polymorphisms markers (SNPs), which take into account the exceptional genome architecture of cod. A minimum panel of 20 diagnostic SNPs and an extended panel  were developed and validated to distinguish unambiguously between the western and the eastern Baltic cod stocks and to enable studies of local adaptation to the specific environment in the Baltic Sea, respectively. We tested both panels on cod sampled from the southern Baltic Sea (n = 603) caught in 2015 and 2016. Genotyping results showed that catches from the mixing zone in the Arkona Sea, were composed of similar proportions of individuals of the western and the eastern stock. Catches from adjacent areas to the east, the Bornholm Basin and Gdańsk Deep, were exclusively composed of eastern Baltic cod, whereas catches from adjacent western areas (Belt Sea and Öresund) were composed of western Baltic cod. Interestingly, the two Baltic cod stocks showed strong genetic differences at loci associated with life-history trait candidate genes, highlighting the species’ potential for ecological adaptation even at small geographical scales. The minimum and the extended panel of SNP markers presented in this study provide powerful tools for future applications in research and fisheries management to further illuminate the mixing dynamics of cod in the Baltic Sea and to better understand Baltic cod ecology.Atlantic cod ( In fisheries, management units are often not equivalent to biological populations or stock units. This is of particular concern when stocks mix and mixed catches hamper accurate stock assessments and sustainable fisheries management . UnderstMultilocus assignment tests are widely used to identify the genetic origin of an individual. In populations with pronounced genetic structure, a relatively low number of polymorphic markers is sufficient to determine the origin, whereas in more weakly structured populations the number of markers required to assign unknown samples with confidence increases remarkably fast . AlthougModern genetic traceability methods for marine fish apply genome-wide approaches to identify small panels of SNPs with high assignment power , represeGadus morhua) is a commercially important fish species throughout the North Atlantic Ocean and adjacent waters, including the brackish waters of the Baltic Sea. Baltic cod have developed specific physiological and life-history traits to cope with the unique environmental challenges of the Baltic Sea, such as hypoxic and hypo-osmotic conditions [Atlantic cod . Whereanditions ). The stnditions ,13. The nditions . Thus, tnditions .Previous molecular approaches for discriminating between western and eastern Baltic cod made use of various marker types. While allozyme markers failed to reveal genetic differentiation among populations , the appHere, we present the so far smallest existing set of highly diagnostic SNP markers based on low coverage whole-genome re-sequencing data, to unambiguously distinguish between individual western and eastern Baltic cod. We tested the usefulness of this new minimum panel to assign individuals to their respective population of origin by genotyping cod individuals comprehensively sampled from the southern Baltic over two successive years with specific focus on samples collected in the mixing area of the two Baltic cod populations, the Arkona Sea. It enables the discrimination of the two Baltic cod stocks at a high confidence level and with minimum effort. In addition, we tested the efficiency of an extended panel that also includes biologically informative markers. These included sex discriminating SNPs , SNPs loWe genotyped 603 cod individuals from the southern Baltic Sea  based on 10,000 permutations with the package PEGAS v0.1.0 [MMOD v1.3.3 [For each locus, observed heterozygosity, expected heterozygosity and R v3.4.1  and the R v3.4.1 . ClassicS v0.1.0 . Tests wS v0.1.0  and leveD v1.3.3 . For locD v1.3.3  and sequD v1.3.3 .FST) between six sampling locations were calculated for the minimum and the extended panel using the package HIERFSTAT v0.04 [FST-values to test for signs of temporal genetic variation. Since we found no significant genetic differentiation for samples from SD 26 between years, they were pooled for further analyses.Pairwise fixation indices  was two populations, thus 10 replicates assuming k = 2 were run to assign individuals to one of the two populations without use of prior information as to their geographic origin. The admixture model was run to estimate individual admixture proportions, where the estimated proportion of an individual’s genotype (q) is allowed to be a mixture of both parental populations. Each run consisted of a burnin period of 10,000 followed by 100,000 Markov chain Monte Carlo (MCMC) steps. To average cluster memberships CLUMPP v1.1.1 was used applying the full-search algorithm [Individual assignment of Baltic cod was performed on the minimum and the extended panel based on a Bayesian clustering algorithm implemented in STRUCTURE v2.3.4 . The numlgorithm .We also used a second, computationally faster approach to assign individuals to their population of origin based on Principal Component Analysis (PCA) implemented in the EIGENSOFT v5 software (smartpca) . Here, tFST-value was 0.36 for the minimum panel, and FST-values for 18 out of 20 loci were significantly different from zero , whereas aquaporin and zona pellucida were least differentiated among the six samples .To determine the usefulness of the two assays containing diagnostic and biologically informative SNPs to discriminate between Baltic cod populations, we genotyped individuals obtained from across the entire southern Baltic Sea. Population-wise analysis only revealed systematic deviation from HWE for samples from the mixing area SD 24 (S6 Table). At individual loci, the global maximum rom zero . FST-valrom zero . SNPs loFST = 0.12 for both panels). The mean pairwise FST-values based on the minimum and the extended panel were 0.39 and 0.35  were assigned to both stocks: 41.6% were assigned to the western Baltic cod stock, while 58.4% were assigned to the eastern Baltic cod stock. Individual assignments to the western or the eastern Baltic cod stock were identical, irrespective of which panel was used. In total, 554 out of 603 genotyped individuals (92%) could be assigned unambiguously to one of the two Baltic cod stocks  to gThe use of our markers confirmed the co-occurrence of both western and eastern Baltic cod stocks in the Arkona Sea ,19, but Results of a multi-year otolith analysis of stock mixing suggested that eastern Baltic cod have been abundant in the Arkona Basin during the last two decades with a substantial increase from 30% to around 70% in the late 2000s . NeverthWe recorded one spawning female eastern Baltic cod in the western Baltic (SD 22), which is consistent with observations that have been made also in previous studies ,44. SuchA moderate number of individuals could not unequivocally be determined as originating from the western or the eastern Baltic cod population. Methodological inaccuracies cannot be fully avoided due to factors hampering the determination of the different source populations. In our case, most of these samples that failed an assignment were characterized by high levels of missing data across all loci. The non-assigned samples originated from the total study area with a slight overrepresentation of the ones caught in SD 24 (26 out of 49). But compared to the total number of sampled individuals per ICES subdivision, only 7% of all samples caught in SD 24 failed an assignment, but 24% of all samples from SD 25. However, some of the individuals that failed to be assigned might be hybrids of mixed ancestry. There is an ongoing debate on the possibility of hybridization between western and eastern Baltic cod ,19,40, bIn the Baltic Sea, the steep gradient from high salinity waters in the Kattegat to regions of lower salinity in the eastern- and northernmost parts limits the spatial distribution of marine organisms, and in the case of the Baltic cod, successful reproduction –48. The In Atlantic cod, four large chromosomal inversions  have been identified to discriminate between cod populations throughout its geographical distribution, i.e. dominating the observed genomic divergence by large allele frequency shifts ,23,55, wThe application of the new panels introduced here in genetic monitoring and in studies of the genetic signatures of local adaptation, holds strong potential to improve the sustainable management of western and eastern Baltic cod populations and to enhance our understanding of the evolutionary importance of genes related to local adaptation.S1 FileSupplementary information on whole-genome re-sequencing reference Atlantic cod samples used for variant identification. Details on variant calling and design of the minimum and extended SNP-panels are given.(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 TableSNPs selected for differentiating between KBI and BOR are commented.(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 TableLoci significantly deviating from Hardy-Weinberg-Equilibrium are indicated in bold.(XLSX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.


New genetic testing technologies such as microarrays and whole exome sequencing mean the diagnostic potential for a child with a development disorder is greatly increased over traditional testing techniques.  With this increased potential comes increased expectations from families and professionals about the answers a diagnosis will provide.  However, limitations remain and a proportion of individuals will continue to remain undiagnosed.  In addition, some individuals will receive novel or very rare diagnoses about which very little is known in terms of prognosis and effective treatments.  In this paper, I present an argument for why these families would benefit from additional Genetic Counsellor support and how Clinical Genetics services in the UK could provide this support.  I acknowledge that resources are limited, but as demands on services increase and interactions with families become shorter, I argue that this kind of service should be prioritised, for the benefit of these families. Projects such as the Deciphering Developmental Disorders (DDD) study, and the 100,000 Genomes project, have used whole exome or genome sequencing to pinpoint disease causing mutations  but the most common phenotype is developmental delay (DD) or intellectual disability (ID)  which arTraditionally, those fulfilling the clinical features of a recognised syndrome may be diagnosed on clinical examination alone. As technology has advanced, other diagnostic tools have become available, such as metabolic studies, EEG, CT/MRI imaging, cytogenetic studies , and single-gene targeted testing (‘Molecular karyotyping’ or ‘array Comparative Genomic Hybridisation’ (array CGH) is a technique that came into clinical practice across the UK during the first decade of the 21st century  study was a UK-based study sequencing the exomes of children with undiagnosed developmental disorders . Exomes are the coding regions of our genes, and account for about 1% of all our genetic material . An AmerIt is natural for parents to hope for a healthy child. The point when a developmental disorder may become apparent can range from a prenatal scan, to birth, to later on as the child grows. This can be a sudden or a gradual realisation . Although this should be based on need regardless of diagnosis, this has been reported to be easier when a diagnosis is known  varies between regional Genetics Services in the UK and is sometimes being carried out by family history coordinators or by postal questionnaires. Therefore the opportunity for pre-test counselling by a specialist in Genetics is reducing. With the vast amount of information that can be generated from genomic testing, I see the real value of the Genetic Counsellor’s role being in the post-testing period, where the emotional and familial context can be explored  could be offered. The aims would be to minimise psychological distress, increase personal control and either i) help them cope with an uncertain future in the absence of a diagnosis, or ii) readjust to having a diagnosis for their child, including coping with the uncertainties this brings if this is for a very rare or novel condition. Whereas all families may benefit from extra support, I feel some families would benefit in particular, as I describe below. Once all available investigations have been exhausted, or if results are not expected for a very long time (e.g. via a research study), patients could be considered for the post-test Genetic Counsellor clinic. Indicators for additional support may be:The family have lower levels of perceived control/increased levels of uncertaintyThe family have a lack of social supportThe affected person has a novel or very rare diagnosis about which little information is knownThe affected person remains undiagnosedAssessing perceived control. Increasing personal control over the situation aids coping and adaption  to refer the family for the post-test Genetic Counsellor clinic. Alternatively a questionnaire could be included with a post-clinic letter, asking questions around perceived control, optimism and support, to be returned should the family wish to engage with further support.The objectives of the session could be:Elicit and address concernsDiscuss strategies for increasing perceived controlSignpost to other sources of supportEliciting and Addressing concerns. For families of children with a developmental disorder, it may be that some of their concerns are hindering their ability to adapt to their child’s condition  (Discussing strategies for increasing perceived control. For the questions that cannot be answered (e.g. prognosis or recurrence risk) families will continue to have a degree of uncertainty, which may perpetuate a feeling of lacking control. AsPractical-focussed strategies, such as information gathering, may give an increased perception of control  has a useful booklet called ‘After diagnosis: What happens next? The early years’.agnosis . If a Gewww.undiagnosed.org.uk) has been formed specifically for families of children without a diagnosis.Support groups can provide peer-support for families in a similar situation. In the UK, the support group ‘Syndromes Without A Name (SWAN) UK’ , and Unique (www.rarechromo.org). Unique was originally set up for families affected by rare chromosomal abnormalities. However, they have now broadened their spectrum to include families affected by very rare autosomal dominant single gene disorders, where a more specific support group does not yet exist , third sector support groups, virtual/online/interactive resources, others, to best support families who are considered to be in need. A strong focus on health and social outcomes would have a priority. Perhaps, something a well designed research study could investigate? It would also be helpful to understand what hard-pressed parents (the ones who rarely engage in research!) actually want, what are their priorities. Addressing their social circumstances by other means, through education  and social care (more immediate) might improve their self-perception and sense of control over these difficult matters, and, vitally, their children's developmental outcomes. A few minor comments: 1.  A formal definition of ID in the intro  would be valuable. 2.  Diagnostic yields achieved 50-70% WITH (not UNTIL) the advent of arrays and sequencing. 3.  "...often results in an extended period of coping and adaptation...", but surely the coping is life long? Suggest  say "protracted period for acceptance and adaptation".How would this be formally and equitably assessed? Is the LOT-R validated for perceived control? It was designed more to assess optimism vs pessimism, is this a valid surrogate of personal control?The family have lower levels of perceived control/increased levels of uncertainty -Designing a fair and equitable disability oriented social deprivation risk assessment tool would be enormously challenging, history shows.The family have a lack of social support -very difficult to equitably delineate this. Rare does not equate with complexity of care, extent of disability, impact on family. Consider the huge variation in presentation children diagnosed with the same CNV syndrome.The affected person has a novel or very rare diagnosis about which little information is known -Should always be in follow-up and/or a research study.The affected person remains undiagnosed. - 4. Generally, try not to cite the same reference multiple times, ie Lipinski 2006, Madeo 2012, Middleton 2015. Use other sources. 'Referral criteria':I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Title Too long and tentative, be succinct and give a definite message to attract the reader.Aim Needs a clearer definition of the aims. Is it to define the challenges faced by parents of children with DDs, diagnosed or otherwise, as well as the how well genetic counsellors address these issues? Key aim is a justification for extending genetic counsellor’s role in the post genomic diagnosis period, make this very clear early on. Needs to consider and compere alternative models of family support.Structure Somewhat confused, needs clearly define and describe each topic and refrain from commentary therein, rather to have clearly subtitled commentary paragraphs where appropriate, either as a standalone section or at the end of each topic, and be consistent.Introduction Unnecessary detail on definitions of DD and ID, some of which is inaccurate. Should think about the audience who may not be familiar with jargon such as exome sequencing. It would help to describe typical referral pathways to clinical genetics, most referral for DD/ID are from paediatric neurology services who frequently request array tests and return results prior to visiting genetics. How will this change, if at all, as genome sequencing is commissioned into the NHS. IS there a different perspective in the US, Europe, elsewhere? The description of diagnostic measures and yields is misleading. The focus ought, to be, briefly, on the yield  of current methods in use in UK services (or wherever the author feels this is relevant). Clinical microarray was originally commissioned in around 2007 (NHS England), ie it’s not so modern. Then should describe potential impacts of integrating exome/genome sequencing with increased genomic diagnoses (around 70%) and how this will impact current GC practice.  Needs a paragraph on the psychosocial imperatives of a genetic diagnosis/non-diagnosis and the role of GCs (or others) in counselling for this. Following this present a concluding paragraph outlining the commentary’s aims and objectives – and why there is, arguably, any need to change current practice.Genetic Counsellors and Genetic Counselling Genetic counsellors are not the sole providers of genetic counselling . The commentary should be cognisant of the role of nurse specialists who provide counselling, ie that genetic counselling is not the exclusive the domain of trained GCs. Since developments in genome technology are inevitably influencing developments in service design and delivery, in the UK and other countries, the author should consider their impact on GC roles in supporting families post-diagnosis - as pre-test counselling becomes increasingly obsolete as has been happening for several years due primarily to technological developments and their superior diagnostic accuracy. Argue whether any change in service delivery might be justifiable.Support The author should recognise and comment that a majority, probably vast majority, of families engage support from places other than clinical genetics, especially over the medium-long term. Genetics services role in supporting families is circumscribed and predominantly short-term. The nature of support is vaguely described, what exactly is the support given in a genetic counselling encounter (by a genetic counsellor or other practitioner) which would facilitate improved adjustment and where is the justification to develop a specialist post-test/post-genomic diagnosis support service as distinct from routine follow-up. A significant Would this be a genetic counsellor led service? How would this differ from current open-door policy where parents are able to access follow-up contact with the clinician/practitioner. The author could consider how signposting and information content and provision could be improved, particularly how to ensure support group contact is increased, training for rare disease support groups to tackle complexities associated with single gene diagnoses for DD/ID after exome/genome sequencing, as with 100k genomes. Arguably, well informed peer/third sector support is more accessible, immediate and broader in relevance than highly specialised genetic counsellors/clinical specialists and perhaps better placed to facilitate longer term ‘community’ support, family acceptance and adjustment. Psychological support for families with children with severe/life limiting illness, more generally, isn’t dependent on the disease specialist being trained to provide such additional ‘support/care’. Arguably this applies to aspects of rare disorders care ad management. Moreover, given the extent and complexity of mental health risks, impacts and needs of children with DD/ID, combined with the psychological and social struggles of their parents/carers, who experience high levels of anxiety, depression and trauma (as the author acknowledges), are there alternative models of ‘support’ which are more rigorous and wide-ranging than conventional genetics follow-up, for which mental health and voluntary sector providers would be better placed to provide? A clear definition of the purpose, content and contextual relevance of ‘support’ needs to be given, as forms the basis of the authors justification for a significant change in service.Conclusion This is a thought provoking commentary on a subject of increasing importance and at a time when genomic medicine’s relevance and promise to improve lives is subject to intense scrutiny, presented at a time when certain aspects clinical genetics services are experiencing perceived existential threats. Despite its shortcomings, need for structural correction and lack of coherent justification for what would be a significant change to current service provision which is counter to the evolving emphasis of genetic counselling training programmes, this kind of commentary is welcome and strongly supported. A revised version would be very welcome.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Comments by Sue Kenwrick, Principal Genetic Counsellor The author sets out some of the potential implications for families with a child affected by a developmental disorder who are undergoing broad genetic testing such as array CGH or exome analysis.  She proposes an increased role for genetic counsellors (GCs) in helping families adapt to genetic test results and proposes a model where GCs hold clinics prior to genetic testing to help manage patient expectations and to identify individual family needs. The background overview of the impact of having a child with a developmental disorder, the search for a diagnosis and research into factors that help or hinder adjustment is thought provoking and well structured.  The author should also mention large projects doing whole genome as well as exome analysis for completion . She sets out well the complex needs of families in these situations and the need to pay attention to emotional needs (counselling) as well as educational needs (teaching) of parents going through testing.  In particular, she highlights the need for management of expectations surrounding a test and a need to signpost/refer parents to additional resources/support groups that may further facilitate adjustment and coping.  Clearly, these families would benefit from incorporation of more of the ‘counselling’ aspect of their consultations in order to optimise their adjustment. However, there is more than one way this could be achieved.  Personally I think the idea of introducing a ‘preconsultation’ with a GC prior to broad genetic testing by another clinician (as suggested in the authors model) is a retrograde step for the profession.  I think this would be a great strain on limited GC availability at a time when we are taking steps to deal with increase in referrals by doing more genetic counselling by telephone.  For many genetic services models already exist for pregathering of information by family history questionnaire for certain conditions and some of this is done by coordinators that are not trained in genetic counselling.  This non-GC workforce would not be equipped to ask about family coping strategies or perceived control as seems to be implied in the authors model .after a ‘genomic’ result to facilitate understanding and assess the families further needs and this could be an open offer or case by case.  Additional training is something that might be required for some providers.  Another part of the solution could be multimedia resources that facilitate the process of testing and follow-up .  There is no doubt, however, that, as the author points out, GCs are skilled at managing the psychosocial aspects of genetic counselling and there will be a need to increase the workforce as more complex genetic investigations and results are provided. While it is valid and laudable to highlight GC input and support for families with a new diagnosis or uncertain result, I don’t think this shift in service provision provides the answer.  Rather, any provider  offering a genetic investigation should be equipped to discuss managing expectations and support the family by signposting to resources or offering additional consultations, or referrals as required.  Depending on the centre, It is already common for GCs to see families For readers outside clinical genetics services, I think the author should distinguish between the terms genetic counselling and genetic counsellor, as this can be confusing for those in other specialties.  Genetic counselling is done by different types of provider .  Genetic counsellors, however, are a body of allied health professionals from science or nursing background, ‘usually’ not MDs, who are trained in genetic counselling. Overall, as this is an opinion piece and raises interesting issues surrounding broad genetic testing. I approve publication with reservation based on a limited exploration or potential solutions and significant reservation about the model proposed.Comments by Vicki Wiles, Consultant Genetic Counsellor  This review and opinion piece by a Genetic Counsellor from the Wales Genetic Counselling service raises interesting questions about the use of Genetic Counselling resources. The support and counselling issues for families with children with disorders of developmental are well described and the summary of research papers supports the argument that Genetic Counsellors are well trained in counselling skills, with an understanding of genetic concepts and testing and are therefore equipped to help families with children with undiagnosed disorders. I would agree that there is often a need for support that a GC can provide but this support might also be provided by the family Health Visitor, a paediatric Clinical Nurse Specialist or the Consultant Geneticist working with the family. I would have liked to see more discussion on how genetic counselling varies across England and the devolved Countries, with an acknowledgement that some services have continued to do pre-clinic work sometimes including home visits, with longer term involvement, while other services have moved to more autonomous working for Genetic Counsellors focussing more on genetic testing where there is known diagnosis in the family. Funding and workforce differences in England have influenced the type of work that genetic counsellor’s focus on and I would have welcomed reference to this. The article would benefit from a broader health resources overview to set the author’s views in context including the financial side to her argument, which might well underpin her views. As genomics moves more into mainstream medicine there will be many, possibly conflicting, demands on genetic counsellor’s time with patients and so this article is timely and merits publication to stimulate debate.We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Thank you for giving me the opportunity to review this manuscript. I think that this article highlights several important aspects. New genomic technologies will add to the complexity of genetic disorders. Both pre-test and post-test counselling are of great importance to understand what information the new technologies can bring as well of course the limitations of the tests and results. I do also think as the writer states that patients as well as relatives might need even more support related to the results . It is thus important to be able to help and support the patients in that situation and as the author states in a citation; receiving the result can be not the end but the start of the journey. Genetic counsellors’ roles might also change and develop in this genomic area. So as a subject I think this is an important paper. However, I have some major issues regarding this paper, which need major revisions prior to possible indexing:Title I think the title is long and not very clear. I think it needs to be shortened, given a more focused topic.Aim For me the aim (focus) of the manuscript is not completely clear. What I can see there are two different focuses: 1) What support do these parents need regarding acceptance and coping 2) How can genetic counsellors take part in this area I think the manuscript should benefit from either focus on one of those aims OR to clarify that there are 2 aims/focuses.Structure The manuscript should benefit from changing the structure of the text. I think it is a little unstructured moving back and forward between things.Introduction I think the introduction is too long and to detailed on the different definitions on ID. It is not necessary for the aims of the manuscript. I would also change the order of the subtitles of the introduction. After the first part on the definition and prevalence I think it would be more relevant of having the section on "Diagnostic Tools". I think that this part also can be shortened. Then I think that the section on parental responses and support needs to be put together as one section. There is a bit of overlap between them. Check for overlaps. I do think the section on the different coping issues is very good and relevant. At the end of the left column page 3 I would change the focus from "how it will affect the role of genetic counsellors....." to how it would affect the need for support and genetic counselling and if so, how genetic counsellors can participate in this area of support.Role of genetic counsellors I think it is a very interesting approach that GCs can play a role here. However it lacks discussions on what unique skills GCs have to take this role. What is included in GC education? The author talks in a sentence about that GCs need to be more prepared to do this and that the current knowledge is not sufficient and that further education might be needed. I would like to see a development of the discussion on this and references to studies on the education and role of genetic counsellors.Example I do believe that it is good to show things by examples and I think the citations from parents is a good ways of showing parents thoughts and experiences. However, for me the example of childhood overgrowth stands a little on it is own and does not add very much here. I recommend to take that out.Conclusion I think the conclusion could be clearer and should benefit from clarifying the aim/aims of the study. In summary, I think it is an interesting paper within an interesting area, but which needs major revision to be indexed.We have read this submission. We believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Thank you very much for reviewing my article. You make some valuable points and I will look to revise the article and incorporate them.