Artificial selection has proven to be effective at altering the performance of animal production systems. Nevertheless, selection based on assessment of the genetic superiority of candidates is suboptimal as a result of errors in the prediction of genetic merit. Conventional breeding programs may extend phenotypic measurements on selection candidates to include correlated indicator traits, or delay selection decisions well beyond puberty so that phenotypic performance can be observed on progeny or other relatives. Extending the generation interval to increase the accuracy of selection reduces annual rates of gain compared to accurate selection and use of parents of the next generation at the immediate time they reach breeding age. Genomic prediction aims at reducing prediction errors at breeding age by exploiting information on the transmission of chromosome fragments from parents to selection candidates, in conjunction with knowledge on the value of every chromosome fragment. For genomic prediction to influence beef cattle breeding programs and the rate or cost of genetic gains, training analyses must be undertaken, and genomic prediction tools made available for breeders and other industry stakeholders. This paper reviews the nature or kind of studies currently underway, the scope or extent of some of those studies, and comments on the likely predictive value of genomic information for beef cattle improvement. Genetic improvement results from selection of above-average candidates as parents of the next generation. In a competitive market, above-average candidates would be those that improve consumer satisfaction, influencing immediate eating quality, purchase cost, long-term health implications of consumption, care of the environment in the production and processing of the beef; and welfare of the animals. Satisfied consumers demand and pay more for desirable beef, and under perfect competition this will be reflected along the production chain by increased farm-gate prices for cow-calf producers. Seedstock suppliers that sell bulls to cow-calf producers would be expected to respond by developing and implementing breeding programs that provide successive crops of bulls that outperform their predecessors.Inspection of genetic trends, e.g. ,2, showsMolecular-based information has long held promise to improve the prediction of young animals by first using phenotypic markers, second using microsatellite markers, and most recently using ever-increasing densities of single nucleotide polymorphisms (SNP).Phenotypic markers such as blood groups were found to characterize the inheritance of certain chromosomal regions, proving useful for selection if that region contained a major gene responsible for variation in a trait of interest . UnfortuHighly polymorphic microsatellite markers provided new opportunities to find major genes or quantitative trait loci (QTL) that influence important traits . These mFollowing the sequencing of the bovine genome, which led to the discovery of millions of bi-allelic SNP, and the creation of subsets of SNP that can characterize the genome and be multiplexed for cheap and efficient genotyping , moleculThe breeding objective comprises a list of traits that influence the breeding goal, along with their relative emphasis . An ideaThe tools available to the animal breeder to improve consumer satisfaction from beef include: the choice of breed, the choice of mating plan to exploit complementarities and heterosis, and selection for within-breed improvement . The maiThe current focus of the use of genetic markers for genomic prediction is to improve within-breed selection, by increasing the accuracy of existing EBV by the time the selection candidate reaches puberty, or by providing new EBV for attributes that influence the breeding goal but have not been available from conventional performance recording. Other genomic analyses that will not be considered in this review include correct assignment of parents, identification of genetic diseases, detection of signatures of selection, prediction of breed composition of crossbred animals and identification of QTL.Bos indicus and Bos taurus breeds. Reproductive measures have been difficult to evaluate since most breed associations have not used inventory recording systems until relatively recently, so it is impossible to determine if a female not represented as a dam actually calved or not [National cattle evaluations (NCE) in beef cattle began with measures of weight traits, and now include birth, weaning and yearling weights, and to a lesser extent mature weights. Rather than reporting EBV, US breed associations typically report Expected Progeny Differences (EPD), that are one-half the EBV. A summary of the traits for which EPD are typically reported is in Table d or not . Reprodud or not .et al. [The concept of using high-density SNP genotypes to predict genetic merit was popularized by the landmark publication of Meuwissen et al. . Their aet al. , and theet al. . The conet al.  by severet al. . In pracet al. , leadinget al. . In thiset al. . Given tet al. ,23. In tet al. . That apet al. ,26.Whereas microsatellite marker studies have typically failed to identify QTL and subsequently SNP that could apply equally well across a range of breeds, there was hope that the reduced cost and the increased density of multiplexed SNP panels would lead to discoveries that could be exploited across breeds. The reduced cost per genotype for panels of 50,000 or more multiplexed SNP compared to microsatellite markers allows for more animals to be used in analyses, increasing power. In both conventional QTL studies and in genomic prediction, detection of effects relies on an association between the segregating marker genotype and the segregating causal polymorphism. The strength of this association reflects the extent of linkage disequilibrium (LD), which can be represented by the squared correlation between genotypes at two loci. Microsatellite studies exploited linkage relationships to create LD between the flanking sparse markers and a QTL within families, even when the marker was in linkage equilibrium with the QTL from a population perspective. Genomic prediction does not require family structures but takes advantage of the higher density of SNP markers and the fact that physically close loci tend to have higher LD than distant loci. Provided the genome is saturated with SNP markers, any QTL should be near some genotyped SNP and hopefully at least one will be in sufficient LD with the QTL.Research studies of genomic prediction in livestock populations began with the release by Illumina of a high-density bovine panel of some 54,001 SNP markers . In any Training involves statistical analyses that exploit individuals with both high-density genotypes and recorded performance . The amoIndustry populations have advantages for genomic prediction. In the case of elite or widely used industry animals, the individuals included in the training data will be relevant to the commercial population. For AI sires, DNA is readily accessible despite the disparate ownership or physical location of the animals. The principal source of performance information comes as EPD from NCE and is well represented for growth traits, moderately well for ultrasound traits, poorly for behavior, reproduction and longevity traits, and typically with no information on many other traits such as disease resistance or eating quality. Since most recorded animals are purebred, training on crossbred data is seldom an option using NCE data and is limited to those few breed associations that collect crossbred data.production traits: calving ease (direct); birth weight; weaning weight; yearling weight; yearling height; scrotal circumference; maternal traits: maternal calving ease; milk; mature weight; mature height; carcass traits: carcass weight; marbling; rib eye area; fat depth; along with some newly released trait EPD: docility; and heifer pregnancy. The accuracies of EPD on old bulls are limited for some traits. Igenity, a genomic testing service owned by the animal health company Merial, has used the results from the analysis of this Angus population, along with other resource populations, to market a reduced panel comprised of a subset of informative SNP referred to as a 50k-derived product. It is marketed in the US in conjunction with the American Angus Association and costs $65 [A US repository of DNA from over 3,000 Angus bulls born since 1948 was assembled by the University of Missouri . These bosts $65 .The US Meat Animal Research Center (US-MARC) at Clay Center Nebraska has worked with some breed associations to develop a repository of some 2,026 influential or upcoming bulls in 16 of the most prominent beef breeds in the US with EPD from NCE and includes: Angus, Beefmaster, Brahman, Brangus, Braunvieh, Charolais, Chiangus, Gelbvieh, Hereford, Limousin, Maine-Anjou, Red Angus, Salers, Santa Gertrudis, Shorthorn, and Simmental. Initial plans for the use of this repository were to provide genomic predictions of these bulls from training analyses based on a US-MARC crossbred population  and to cThe alternative to training on widely-used sires is to train using phenotypes collected specifically for genomic analyses. This could be achieved using non seedstock field data, but in many cases the mating designs and contemporary group classifications are not entirely adequate for the purpose. Most field data comprise offspring from natural mating, so sires tend to be nested within rather than cross-classified by contemporary groups. In the case of carcass traits, animals tend to have their ownership transferred several times between weaning and harvest, making it difficult to ensure harvest cohorts were managed together throughout their entire lifetime. For reproductive traits, it is difficult to obtain sizeable cohorts of animals for comparison, particularly for phenotypic measurements obtained after first calving, as birth cohorts get subdivided according to sex of calf, age of dam, and whether or not yearlings became pregnant. These problems can be overcome by sourcing animals from large herds and by designing the study prior to the birth of the study animals, which may be several years prior to the collection of phenotypes.The US carcass merit project (CMP) was one such long-term industry-funded semi-structured undertaking initiated in 1998 that collected carcass data, tenderness and sensory attributes on over 8,200 progeny. Some of the half-sib offspring of more than 70 sires across 13 breeds were DNA sampled. The sires were widely-used AI bulls from various breeds and dams were commercial cows . The datCollecting data for more novel phenotypes requires the deliberate generation of suitable populations. Given the current dominant market position of the Angus breed in the US, it was an obvious candidate for any new studies to expand the scope of traits for genomic prediction. Two large studies have been undertaken, one at Iowa State University to investigate fatty acid and mineral content in beef as possible targets for improving the human healthfulness of beef, and another at Colorado State University to investigate feedlot health. The healthfulness study involved several cohorts representing 2,300 predominately Angus cattle assessed for carcass and meat quality attributes, including tenderness and sensory information, in addition to extensive phenotyping of traits that might influence the human healthfulness of beef. These healthy beef traits include mineral and fatty acid compositions of key muscles . The feeResearch herds with deep phenotyping are also candidates for studies of genomic prediction. The most comprehensive such resource is represented by the US-MARC germplasm evaluation studies, the recent cohorts being known as the Cycle VII and F1-squared populations. In addition to an across-breed training analysis for which single-SNP effects have been published for birth, weaning and yearling weights and their respective gains , this poThe collection of feed intake on large numbers of animals is still problematic from a practical viewpoint, and to date, such data has been limited to measuring relatively small disparate groups of animals during finishing, with findings focused on QTL detection rather than genomic prediction. Other datasets of limited size have been collected on a range of traits, including reproductive performance and tick resistance but have not yet had any findings published from a genomic prediction perspective.Costs for conventional pedigree and performance recording and for NCE have been met by producer funds in the US. Public funds have been used for the development of NCE methodology. Public funds were not immediately available for extensive genotyping of training populations, and neither seedstock breeders nor breed associations had funds to adopt this technology beforehand given the uncertain nature of its value. Fortunately, applications of this approach in beef cattle improvement were considered as business opportunities by commercial companies such as Merial Igenity and Pfizer Animal Genetics to invest in the training phase, presumably with expectations of recouping returns on that investment through future sale of genomic tests. However, this situation has changed industry dynamics, introducing competitive partners into the process of ranking animals, and has increased the proprietary nature of performance information, genotypes and analytical approaches. This is one reason for the dearth of refereed publications on the accuracy of genomic prediction in beef cattle, in contrast to the dairy cattle situation.Confidence in genomic predictions can only be provided by validation in a group of animals that are not included in the training population. Close relationships between animals in training and validation populations tend to lead to better predictive ability than when the groups are more distantly related . AnalysiOne of the few reports on accuracy of genomic predictions in beef cattle analysed deregressed EPD  from NCEOther numerically important breeds tend to have fewer registrations than Angus and it will be difficult to collect comparable sized training populations of AI sires. In contrast to the dairy industry, most bulls are used solely in commercial herds that do not record parentage or individual performance and therefore do not obtain progeny information for training or validation. The American Hereford Association has increased the 50k genotypes provided by US-MARC to develop a training population of 800 animals, but no results have been published yet. The other US breeds have even fewer animals ready for training.Longissimus dorsi, iron concentration of beef could be readily predicted, whereas magnesium, manganese, phosphorus and zinc concentrations appeared to be under less genetic control. For other minerals such as calcium, copper, potassium and sodium, concentrations could not be predicted. Prediction of the fatty acid's concentrations showed similar trends to that of the minerals' concentration. For the predominant even-numbered saturated fatty acids C14:0, C16:0 and C18:0, monounsaturated C18:1 and polyunsaturated C18:2, prediction was good, while for C18:3 and conjugated linoleic acid (CLA) concentrations, predictions were not conclusive. These results look promising to develop tools capable of modifying the concentration of saturated fatty acids, or the relative proportions of saturated and unsaturated fatty acids. For these traits, the challenge will consist in developing a market for beef with modified fatty acid composition.Genomic prediction for beef cattle healthfulness has shown varying levels of predictive ability, as determined by the proportion of variation accounted for by markers . Using sUsing the same dataset as for beef healthfulness, it has been shown that carcass and beef quality traits can be predicted . Hot carReduced SNP panels can be produced either to be highly informative for a particular trait or for several traits by including the most strongly associated SNP, or to be informative for high-density genomic prediction after imputing the high-density panel from a reduced set of evenly spaced SNP with high minor allele frequency . To dateMixture models such as Bayes B and Bayes C  assume tIn the study on beef healthfulness , subsetsBos indicus and Bos taurus) and breeds. Furthermore, many pre-pubertal selection candidates are offspring of natural mating rather than of AI sires. Collectively, these facts increase the genetic distance between the training and target populations.The development of reduced panels for any quantitative trait in breeds other than Angus is currently limited by the lack of training populations. In contrast to the dairy industry, where reduced panels are being used for imputation of 50k markers for genomic prediction , target Prediction across breeds is more problematic because different breeds may exhibit different QTL, dominance or epistasis can occur, and allele frequencies may vary between populations. Linkage disequilibrium (LD) is not very consistent across breeds and therefore training in one beef cattle breed using 50k genotypes will not be very effective to predict a different breed . SimulatRecently released next generation Illumina HD or Affymetrix Bos-1 panels, with more than a 10-fold increase in SNP density beyond the 50k, will allow imputation of missing SNP genotypes in animals already genotyped for 50k panels ,46. It iGenomic prediction across-breed using reduced panels will be inferior to 50k based predictions. A subset of 192 SNP markers was chosen from the US-MARC association analysis for weaning weight reported in  and applBoth predictions from Merial Igenity and Pfizer Animal Genetics are currently used in the American Angus Association (AAA) NCE by including them as correlated traits. The estimated genetic correlations for the Merial Igenity MBV are 0.54 for carcass weight, 0.58 for REA, 0.50 for fat and 0.65 for marbling . CorrespPredictive ability is influenced by effective population size, heritability, and the number of animals in the training data, among other factors ,29. IncrExtension of genomic predictions to the full range of traits that influence consumer satisfaction will further require a focus on the collection of reliable phenotypic information across the broad spectrum of traits. Collecting such information will likely rely on public funding efforts, but even then will be limited by the availability of meaningful phenotypes for some traits. New electronic technologies that facilitate the collection of phenotypes on large cohorts will also be invaluable.Genomic prediction offers accuracies that exceed those of pedigree-based parent average of young selection candidates. The highest accuracies are achieved for offspring of the training population. Accuracies can be equivalent to progeny tests based on up to 10 or so offspring, providing a slightly higher predictive ability than a single phenotypic observation on the individual. These accuracies are not yet sufficiently high to warrant selection in the absence of phenotypic information, particularly as these accuracies tend to erode when assessed in validation populations that are more distant from the training population in terms of the number of meioses separating generations. Accuracies are expected to improve with further research, as the training population grows in terms of numbers of genotyped animals, and density of SNP genotypes per animal.Phenotyping is now the principal limitation in expanding the series of traits beyond those routinely recorded for NCE. In the meantime, applying genomic prediction will influence traits that were easy to record in conventional improvement programs, rather than addressing the traits difficult and costly to measure.Sharing of information among parties to the benefit of industry is still in its infancy, as is the incorporation of MBV into NCE. The latter activity will cause particular challenges for small breed associations which lack the funding or expertise to change their NCE systems. Whereas it had been hoped that genomic prediction would facilitate selection in small breed associations with fewer registered animals, the current need for within-breed training will serve only to increase the technology gap between the breeds and facilitate faster rates of change in those breeds that have a large market share.CMP: (carcass merit project); EBV: ; EPD: (expected progeny difference); LD: (linkage disequilibrium); IMF: (intramuscular fat); MBV: ; NBCEC: ; NCBA: ; NCE: ; PA: (parent average); QTL: (quantitative trait locus); REA: (rib-eye area); SNP: (single nucleotide polymorphism); US-MARC: .The author declares that they have no competing interests.


Gasterosteus aculeatus. By (i) using a meristic trait and (ii) basing our calculations on a large half-sib design experiment involving 2,079 offspring from 84 families, we overcame many of the difficulties faced by earlier FA studies. Both lateral plate number and FA in lateral plates were heritable , even after controlling for marker genotypes linked to EDA (the major locus influencing plate number). Likewise, DA in lateral plates was heritable h2 = 0.23). The additive genetic component of FA in lateral plates makes it a prime candidate for further investigation into the evolutionary implications of FA and the genetic underpinnings of developmental instability. This discovery in an evolutionary model species holds the possibility to invigorate the study of FA from an evolutionary perspective.The estimation of individual fitness and quality are important elements of evolutionary ecological research. Over the past six decades, there has been great interest in using fluctuating asymmetry (FA) to represent individual quality, yet, serious technical problems have hampered efforts to estimate the heritability of FA, which, in turn, has limited progress in the investigation of FA from an evolutionary perspective. Here we estimate the heritability of number of lateral plates, their FA and directional asymmetry (DA) in threespine stickleback,  The estimation of the quality or fitness of individuals is an important part of many biological research projects, especially in evolutionary biology. One relatively easy way that has been proposed to approximate individual quality/fitness is to measure the asymmetry of bilateral characters. While directional asymmetry (a consistent bias towards a given side) and antisymmetry (consistent asymmetry towards a random side) result from normal development, fluctuating asymmetry  is a result of disturbed development The study of FA was initially received with enthusiasm because it appeared to be a useful measure of individual quality in various contexts in evolutionary ecology and conservation biology research et al.Despite the potential usefulness of FA, its overall value in evolutionary ecology has been questioned for a number of reasons. From an evolutionary perspective, the usefulness of FA resides in the possibility that it can predict lifetime reproductive success Drosophila melanogaster have shown responses to selection indicating that FA is heritable Artificial selection experiments in Using simulations, Fuller and Houle a better understanding of the genetic architecture of FA should provide a much-needed perspective for sorting out the sometimes unexpected or contradictory patterns of differences in FA.” In the present manuscript our goal was to attempt to lay a foundation for future work on the investigation of the underlying genetic architecture of FA through the rigorous testing of the heritability of FA in a model trait of a model species.The inability to determine whether there is a genetic basis for FA is unfortunate in that it has prevented progress on understanding the underlying evolutionary processes that affect development and asymmetry. In their review, Leamy and Klingenberg Gasterosteus aculeatus). Apart from being easy to count with little measurement error (see below), lateral plates are important structures in the defense against predatory attacks An ideal trait to study the genetics and evolutionary implications of FA is the number of lateral plates of threespine sticklebacks  gene in our analyses, we were able to address the question to what extent plate number is heritable after the variance due to this major gene influencing plate number Here, our main aim was to estimate the heritability of FA for threespine stickleback lateral plates. We did this by conducting a large scale breeding experiment involving 42 sires and 84 dams, with a total number of 2,079 offspring, and subjecting the data to a rigorous Bayesian analysis. Hence, we were able to circumvent the two main problems faced by most previous analyses of heritability of FA: the small sample size and the large measurement error in the trait. The secondary aims of our research were to estimate the heritability of plate number and DA. The fish in our crosses varied greatly in plate number, and ranged from 30+ plates to a minimum of nine plates on one side of the fish, which correspond to full and partial plated morphs Most fish (63%) had plates at all of the myomeres, and the variation in fish that had some plates missing appeared continuous . About 3a = 5.5, 95% Highest Posterior Density Interval (HPDI): 4.6–6.5): if a Long homozygote myomere has a probability of 0.99 of being plated, substituting genes to a Short homozygote changes the probability of being plated to 0.28; 93% of the genetic variance in Stn380 being additive. In contrast, only 19% of the genetic variance in Stn381 was additive: most of the variance was dominance variance, due to the contrast between genotypes including the 173 allele: the combination with allele 186 gave less plates than either the homozygote or the 173/192 genotypes  . When the heritability of plate number is estimated without taking EDA-linked loci into account a somewhat lower estimate is produced 0.36 (95% HPDI: 0.12–0.62)  . Similar12–0.62) . The abo12–0.62) , with th2h = 0.24 for lateral plate FA is high compared to general estimates of FA heritability stemming from meta-analyses D. melanogaster individuals and estimated 2h bristle count FA to be <0.05. When our results are viewed in light of previous work showing that predation pressure selects for symmetric fish The heritability estimate of The detection of FA heritability over the last half century of research has been surprisingly difficult, yet it appears that we are now just beginning to understand what factors must be taken into account to estimate heritability of FA effectively. In this light, we examine the characteristics of our study that have aided in the detection of FA heritability. The sample size used in our study is large when compared to most other attempts to measure FA heritability and is only exceeded by work with fruit flies The use of a meristic trait, such as presence of lateral plates, to measure FA has some distinct advantages that may aid the accuracy to which heritability can be estimated. Meristic traits are less prone to measurement error than metric traits, because they can simply be counted as present or absent. Meristic traits also allow for a highly accurate assessment of asymmetry if the trait in question can be compared to its homologous pair on the other side of the organism .When FA is estimated using homologous pairs of a meristic trait, a few other important advantages emerge. Repeated measurements of the same trait can be made . Although it is commonly accepted that DA is genetically determined We found a right-side bias in plate number indicating that directional asymmetry (DA) prevails in our study population and is also heritable  modifier genes have been inferred to have a smaller effect EDA has shown a large effect, the populations studied have had individuals with all plates present as well as fish where all posterior plates were missing We found that heritability of plate number was 0.46 and the variance explained by the two marker loci linked to the er e.g., . In caseEDA in our study population. However, this seems unlikely since previous research has shown that the same EDA alleles found by Colosimo et al.EDA having significant additive effects on plate number in a Baltic Sea population of threespine sticklebacks.An important caveat to consider is the possibility that the markers we used are not directly linked to vs environmental causes of FA should be evaluated on a case by case basis, and our results add weight to the idea that the inability to find significant heritabilities for FA may be due to methodological difficulties inherent to the study of FA We have shown that there is significant heritability in the FA of threespine stickleback lateral plates. By identifying a trait with significant FA heritability in a model species, our work should open avenues for more detailed evolutionary and genetic investigations in the role and indicator value of FA. For instance, future work on threespine sticklebacks can begin to identify the underlying genetic architecture of FA, as well as to elucidate the implications of spatially varying predation mediated natural selection on plate FA among different populations. Furthermore, the fact that FA has a genetic basis also has implications for the use of FA in conservation biology as a measure of stress The experiment was approved and conducted under the license (HY 121-06) from the Helsinki University Animal Experimentation Committee.in vitro, which were obtained by gently pressing the abdomen of the ripe females. The fertilised eggs were placed in cylindrical plastic containers with a plastic mesh bottom. The containers were submerged in 10 L plastic tanks with air supply to keep the water saturated with oxygen. Throughout the experiment the water temperature was set to 17 °C and the photoperiod to 12 L : 12 D. Once the eggs hatched, each clutch was divided into two replicate 10 L plastic tanks, and fed daily to excess with Artemia sp. nauplii. After three months, the fish were also fed daily with chopped chironomid larvae.Mature male and gravid female threespine sticklebacks were collected during the breeding season in June 2006 from a Baltic Sea population . A seine net with 6 mm mesh size was used for trapping. The fish were transported to the laboratory in 30 L tanks supplied with a battery-operated air pump, and the crosses were made immediately upon arrival. The crosses were performed using a nested paternal half-sib design, i.e. each male was crossed with two different females (North Carolina I design). In total, 42 males were crossed with 84 females. Males were anaesthetized and killed with an overdose of MS-222 (tricane methanesulphonate) before the testes were extracted and finely chopped in a few drops of water. The sperm solution was used to fertilise eggs Initially 25 sticklebacks were kept in each replicate tank, two months after hatching, to ensure the fish did not suffer stress from abnormally high density, the number of fish was reduced to 15. To accomplish this, all fish were caught in a hand net and then 15 fish were randomly picked, so as to avoid any potential bias in catching fish. Families with less than 15 fish per replicate  were pooled with other families to make the density in each tank 15. The fish from different families were marked with fluorescent elastomers  prior to pooling. The fish were killed after six months, when they had reached ca. 4 cm in standard length and thus completed their lateral plate development The presence or absence of each lateral plate on both sides of each myomere for 2,079 fish was assessed from photographs . We mode2 (Bioline), 20 µM of each dNTP (Finnzymes), 0.32 µM of Idh exon II 37F and Idh exon II 290R primers, as well as 0.25 U of BioTaq polymerase (Bioline). PCRs were conducted with an MBS thermal cycler (Thermo) according to following thermal profile: 94°C for 3 min, 38 cycles of 94°C for 30 s, 56°C for 30 s, 72°C for 1 min and the final extension at 72°C for 5 min. After PCRs, 5 µl of amplicon was run on a 2% Agarose LE (Cambrex) with 1x Loading dye (Fermentas). Fragment sizes were determined against a size standard . Males had two fragments (∼280 bp and 300 bp), females only one (300 bp). Each PCR plate contained certified male DNA as a positive control and a negative control (no sample). In some cases - either due to failed DNA-extraction or poor quality of DNA - the sex-specific region failed to amplify despite the further optimization of used PCR-protocol.Individuals were sexed using DNA obtained from fins. DNA was extracted following Duan and Fuerst EDA gene, EDA–linked microsatellite loci using primers for Stn380 and Stn381 To investigate the effect of a known genetic determiner ), the asymmetry (λ(i)), and a plate effect (π(m)): sE{1,2}, so the asymmetry effect is for s = 2, or the right hand side of the fish. η(i) and λ(i) are both constant over myomeres, so we can interpret the model as a repeated measures model over the myomeres of the fish.Presence of a plate on a myomere (on one side of a fish) was modeled as a Bernoulli trial, so that.tµ(i) , at the individual level:We model three individual-level traits related to platedness: the number of plates, directional asymmetry  and fluctuating asymmetry (FA - i.e. random asymmetry fluctuating around DA). For each of these we use a standard genetic model s(i)) and α(d(i)) are the parents' breeding valuesα(τ(d(i)) and δ(d(i)) are the maternal and dominance effects: these are partially confounded because of the experimental design, so are not interpreted further.εa(i) and εd(i) are the additive and dominance deviations, due to segregation.εe(i) is the residual/within-individual environmental deviationβ(b(i)) is the block effect (each block was one 10 liter tank), for an individual i grown in block b(i). This is constrainedξ(x(i)) is the sex effect. This is constrained so that the sum of the effects is zero.β(b(i)) and ξ(x(i)) were each constrained so that so that the sum of their effects were zero. The other parameters are modeled as random effects, with variances given by the standard quantitative genetic model for the additive (AddV), maternal (MatV), dominance (DomV) and within-family environment (EnvWV) respectively:γ380 and γ381 were the effects of the EDA locus, was modeled through the joint effects of the two genetic markers, with genotypes g380(i) and g381(i).The plate effect is.γ380(1) and γ380(3)) having effects −a and +a (for Short and Long respectively), and the heterozygote (γ380(2)) having an effect d. The additive genetic variance due to this locus was then calculated as AddV(380)  = 2p380(1−p380)(a+(1−2p380)d)2, and the dominance variance is DomV(380)  = 4p3802(1−p380)2(a+(1−2p380)d)2, where p380 is the frequency of the Long allele.Stn380 had five alleles, of which three were rare , so thisγ381(j) for j = 1,…,6) were estimated in total, each with frequency p381(i). The genotypic effects were assumed to be normally distributed, i.e. γ381(i) ∼ N. From this the average deviation of each genotype from the mean was calculated, and hence the breeding value, as the average effect of that gene in its offspring. The additive variance is then the weighted (by the frequency of the genotype) mean squared breeding value. The dominance effect for each genotype was calculated as the difference between the genotypic value and the breeding value of the genotype, and the dominance variance then calculated as the weighted average squared dominance effect (see Appendix for details). The additive genetic variance due to Stn381, AddV(381), is thus the mean squared breeding value, and the dominance genetic variance, DomV(381), is the mean squared deviation from the breeding value The Stn381 locus had three alleles , and allψ is the mean directional asymmetry, μ2(i) and μ3(i) are the DA and FA terms (see above), and S denotes the random direction of the FA effect: it can take values -1 or 1, with probability 0.5.The model above defines the asymmetry effect as zero for the “left” side of the fish, and was estimated for the “right” side. Both a directional effect and fluctuating effect were modeled: the direction of the fluctuating effect had to be allowed to fluctuate between observations in an individual. The asymmetry effect was thus:sp. Similarly, 6.6% and 6.8% of the Stn380 and Stn381 genotype data, respectively. were missing. These missing values were imputed assuming they had been drawn from a Dirichlet distribution The fixed effect sex had to be modeled because 12% of the individuals could not be sexed. It was assumed to be a Bernoulli random variable with probability ν(σ) is a wrapped t distribution  with ν “degrees of freedom” and scale σ. This is only weakly informative for the variance components in the range of likely values. The other priors were:The model was fitted with a Bayesian approach. The priors were chosen to be vague. For the variance components, wrapped t-distribution priors were placed on the variances β(b)′s, βA(b)′s, ξ(x)′s and ξA(x)′s each sum to zero). Where Dirch(α) is a Dirichlet distribution with α being a vector of the same length as p381: here length 6, and we set every element of α to 1.+AddV(381) and dV = DomV(380)+DomV(381). The definition of heritability used above avoids the problem that the means and variance of the trait may differ Depending on our exact focus, we can calculate heritability in different ways. Swain (1987) recommended that for models of meristic traits (such as plate number) our attention should focus on the liability level, i.e. the trait of interest is the 'propensity' to be plated (here this is the log odds of any myomere being plated on one side). If we take this approach, we can calculate heritability on the liability scale:M = 60). The variance of this, Var(Pl), is the sum of the variances in the individual plate effects. We can calculate p from the model, evaluating it at the mean over the population. This is exp(π(m))/1+exp(π(m))) for the left hand side of the fish, and exp(π(m)+ψ(s))/1+exp(π(m)+ψ(s))) for the right hand side.We can calculate an approximate heritability for the trait on the trait scale (i.e. probability) by calculating the binomial sampling variance at the trait mean. For lateral plates, the trait is the number of plates divided by the maximum number of plates , and pV and ηV are the variances on the p and η scales.The other variance components then need to be transformed from the liability scale  onto the same scale. We can make this transformation (approximately) with the delta method, i.e.q = p)+p). The variance in FA is thus:For DA and FA, the trait of interest is the proportion of myomeres that are asymmetric. The probability that a myomere is asymmetric is Note that we are summing over myomeres, as it is pairs of plates that are asymmetric.


In this paper, a modified expanded graphite composite electrode based on natural zeolitic volcanic tuff modified with silver (EG-Ag-Z-Epoxy) was developed. Cyclic voltammetry measurements revealed a reasonably fast electron transfer and a good stability of the electrode in 0.1 M NaOH supporting electrolyte. This modified electrode exhibited moderate electrocatalytic effect towards urea oxidation, allowing its determination in aqueous solution. The linear dependence of the current versus urea concentration was reached using square-wave voltammetry in the concentrations range of urea between 0.2 to 1.4 mM, with a relatively low limit of detection of 0.05 mM. A moderate enhancement of electroanalytical sensitivity for the determination of urea at EG-Ag-Z-Epoxy electrode was reached by applying a chemical preconcentration step prior to voltammetric/amperometric quantification. BecauSeveral analytical procedures have been developed for the analysis of urea in aqueous samples. Most of these investigations on the determination of urea have been based on measuring the changes of ammonia enzymatically released during the hydrolysis of urea. There are several analytical procedure based on the optical methods , 8 and eZeolite-modified electrodes (ZMEs) have been drawing attention as chemically modified electrodes (CMEs) because of synergistic combination of zeolite features with electrochemical interfaces -12. ZME In the present study, an expanded graphite-Ag-modified zeolite-epoxy composite (EG-Ag-Z-Epoxy) electrode was prepared from Romanian natural zeolitic volcanic tuff modified with silver, and electrochemically characterized for urea detection.The catalytic electrochemical oxidation of urea on (EG-Ag-Z-Epoxy) electrode was investigated. The kinetic parametrs for the heterogeneous electron transfer at EG-Ag-Z-Epoxy electrode (the rate constant and the transfer coefficient) were estimated using Laviron's treatment . The ele2.−1).The prepared EG-Ag-Z-Epoxy composite electrode showed good mechanical strength and electrical conductivity 2]− complex species , to the electroformation of Ag2O (the second peak), and to the formation of AgO (the third peak) − complex species and the electroformation of Ag2O are influenced by increasing urea concentration. This may be the result of the competitive adsorption between ammonium and hydroxyl ion or a change in mechanism. The both peaks results from the formation of a soluble complex between ammonium and silver species.Differential pulsed voltammetric experiments show that the peaks corresponding to the electroformation of soluble [Ag(OH)2]− complex species was not evidenced.The similar results were obtained also by using and square-wave voltammetry, and the voltammograms in relation with urea concentrations are presented in Batch amperometric measurements at constant applied potential (+0.55 V vs. SCE) prove that EG-Ag-Z-Epoxy composite electrode works well as amperometric sensor for urea determination. A typical example of amperometic response to successive adding of 0.4 mM urea is presented in K′M, and maxI were determined by analysis of the slope and intercept of the plot of the reciprocals of the steady-state current recorded at 50 seconds versus urea concentration  of 3.2 % showed the good reproducibility of the electrode. A recovery test was also performed by analyzing three parallel tap water samples, which contain 60.07 mg·dm−3 urea. This test was run in 0.1 M NaOH as supporting electrolyte and a recovery of 98 % was found.The equations of the linear dependencies of the current vs. urea concentration, with corresponding characteristic analytical parameters, working conditions and techniques applied in various situations are gathered in B/b , where S3.−1. Discs with a surface area of 19.63 mm2 were embedded in polyethylene and electrical contacts were made using a copper wire. Prior to use, the working electrode was gradually cleaned, first polished with abrasive paper, then polished with (0.3-0.1 µm) alumina powder in aqueous suspension on a pad and rinsing with distilled water. A Scanning Electron Microscope (Philips CM30T) was used to observe the working electrode surface.The expanded graphite-Ag-zeolite-epoxy (EG-Z-Ag-Epoxy) composite electrode was obtained from two-component epoxy resin  mixed with conductive expanded graphite (EG) fillers powder  and silver modified zeolite (clinoptilolite). The ratio between the components was chosen to reach 20 weight percent (w/w) content of expanded graphite, 40 weight percent (w/w) content of silver-modified zeolite. Ag-modified zeolite with a content of 0.008 mg Ag /g zeolite was prepared using natural zeolite from Mirsid, Romania, with 68% wt. clinoptilolite as we previously described . It was −1, a pulse modification of 50 mV in amplitude and 50 ms in duration at intervals of 200 ms. Also, SWV is a pulse technique and the parameters applied was the frequency of 10 Hz, amplitude of 0.45 mV, and the scan rate of 50 mV·s−1.The electrochemical behaviour/performance of the electrode was studied by cyclic voltammetry (CV), linear-scan voltammetry (LSV), square-wave voltammetry (SWV), differential pulse voltammetry (DPV) and chronoamperometry (CA). Subsequently, an electrochemical pre-treatment by three repetitive cyclings between −0.5 V to 1.25 V vs. SCE in 0.1 M NaOH supporting electrolyte was performed. All measurements were carried out using an Autolab potentiostat/galvanostat PGSTAT 302  controlled with GPES 4.9 software and a three-electrode cell, with a saturated calomel electrode as reference electrode, a platinum counter electrode and the composite working electrode. DPV is a pulse technique, dependent on applied parameters, i.e., a scan rate of 25 mV·sAccumulation was performed at open circuit potential (OCP) in the analyte solution, the electrode was then removed and rinsed with distilled water, and immersed in the detection cell for recording the voltammetric curve. Regeneration was achieved by mechanical polishing and above-mentioned electrochemical pretreatment.4.An EG-Ag-Z-Epoxy composite electrode prepared from Romanian natural zeolitic volcanic tuff showed good mechanical strength and electrical conductivity. A random distribution and orientation of expanded graphite particles and Ag-zeolite particles in epoxy matrix was shown by SEM.2O. The moderate apparent charge transfer rate across the modified electrode interface within the potential range of two types of silver oxides should be correlated with particular structural characteristics of Ag-modified zeolite and suggests a charge transfer mechanism partially based on surface-mediated electron transfer [This composite electrode displayed a complex behaviour in 0.1 M NaOH supporting electrolyte with several electrochemically distinct species, involving the redox couple of AgO and Agtransfer .2O towards urea oxidation allowing its determination in aqueous solution. The results obtained by all techniques used, i.e., CV, LSV, DPV, SWV, CA supposed that the mechanism of urea determination by EG-Ag-Z-Epoxy composite electrode in 0.1 M NaOH was the result of continuously remove of the surface layers of Ag2O in the presence of ammonium as a product of catalyzed hydrolysis of urea with formation of soluble complex of diammine silver (Ag(NH3)2+).This modified electrode exhibited an electrocatalytic effect via redox couple of AgO and Ag−1 function of the technique used.The electronalytical sensitivity for the determination of urea at EG-Ag-Z-Epoxy composite electrode ranged from 0.030 to 0.065 mA·mMA moderate enhancement of electroanalytical sensitivity for the determination of urea at EG-Ag-Z-Epoxy composite electrode was reached by applying a chemical preconcentration step prior to voltammetric/amperometric quantification.Taking into account that the application of this sensor in the electroanalysis of real water/wastewater samples or clinical diagnosis, our future research will be focused on to evaluate the influence of urea-associated chemicals as potential interfering agents.


We present a new statistical method to identify genes in which one or more variants influence quantitative traits. We use the Genetic Analysis Workshop 17 (GAW17) data set of unrelated individuals as a test of the method on the raw GAW17 phenotypes and on residuals after fitting linear models to individual-based covariates. By performing appropriate randomization tests, we found many significant results for a proportion of the genes that contain variants that directly contribute to disease but that have an increased type I error for analyses of raw phenotypes. Power calculations show that our methods have the ability to reliably identify a subset of the loci contributing to disease. When we applied our method to derived phenotypes, we removed many false positives, giving appropriate type I error rates at little cost to power. The correlation between genome-wide heterozygosity and the value of the trait Q1 appears to drive much of the type I error in this data set. Multilocus approaches to associations between variants and traits are likely to be advantageous when rare single-nucleotide polymorphisms (SNPs), which have an undetectable effect on a trait when considered singly, can explain a large proportion of the genetic variance at a locus when they are taken together -3. InvesSevon et al.  developeFor these analyses we use the unrelated individuals genotype data from Genetic Analysis Workshop 17 (GAW17); these data consist of 697 individuals genotyped at 22,487 sites. The data generation is detailed by Almasy et al. [Loadings from principal components analysis (PCA) were calculated for allele counts for all genotype data using standard R functions. Plots of loadings are shown in Figure To incorporate suspected relations between Q1, Q2, Q4, and the other explanatory variables , we calculate two additional derived phenotypes for each of Q1, Q2, and Q4. We construct the first residual phenotype by fitting the linear model:ε ~ N, using backwards stepwise selection on the explanatory variables and using the Bayesian information criterion [where riterion  to decidk sites the leaves contain BMGs of length k. This procedure is illustrated in Figure Consider a set of BMGs for a gene  as strings of 0’s and 1’s. Put all the individuals at the root of a tree. Now consider the variant positions in that gene one position at a time from left to right or using some other ordering. For the first position, all those samples with a 0 at the position are put on the left branch of the root, and all those with a 1 are put on the right branch. The two leaves of this tree now contain BMGs of length one 1. Now step through all the other variable positions for each leaf. If there is any variation at the current position among the individuals at a leaf, the leaf is split in two, with all individuals with a 0 on the left branch and all individuals with a 1 on the right branch. Repeat for all the variable sites at the gene. After disjoint here for a set of nodes in which none of the nodes is the ancestor of another. For further details see Sevon et al. [Obtaining a tree test statistic is a two-stage process. First, we require values of partial test statistics defined on the leaves and internal nodes of our tree. A variety of test statistics are available, but we are restricted to those that depend only on values of a trait at a node and its descendants. Using information at ancestral nodes or on nodes on other branches of the tree  is not possible within this framework. We use the term n et al. .z score:Although many partial test statistics are possible, we take a simple one, the ijx, j = 1, …, in, are the values of the trait at node i and s are the sample mean and standard deviation, respectively, of the trait over all individuals. Two trees with the respective z scores at the nodes are shown in Figure where kS, is defined as the maximum value of:The QTLTree test statistic over the whole tree, m disjoint nodes where m ≤ k, and f is some function. For our analyses, we take:where the summation is over jS, j = 1, …, k − 1. Typically for our tests, we take k = 10.that is, kS is not available. Because the GAW17 data were sampled from different populations, a straightforward randomization was not appropriate and individuals were randomized within populations; that is, the traits were randomly relabeled within each population so that the mean and standard deviation of the traits stayed the same within populations across replicate permutations. The seven different populations used were Luhya, Yoruba, Japanese, Denver Chinese, Han Chinese, CEPH (European-descended residents of Utah), and Tuscan.The null distribution of 6 randomized replicates per gene for the phenotype data set. We perform power calculations over multiple data sets using 105 replicate simulations over all 200 data sets. One hundred thousand replicate permutations for all 3,205 genes of a single data set typically takes about 60 minutes, and these calculations are easy to perform in parallel, making them feasible for whole-genome data. The R package QTLTree [http://www.staff.ncl.ac.uk/i.j.wilson).We calculate statistical significance with 10 QTLTree  is availThe backwards model selection results in three additional derived phenotypes: Q1A, the residuals after fitting Age and Smoke; Q1B, the residuals after fitting Age + Smoke + PC1 + PC4; and Q4A, the standardized residuals after fitting a linear model with predictors Age + Sex + Smoke to Q4. No nonconstant terms were kept in models for Q2, and no extra PC terms were kept for Q4.p < 5 × 10−5). No improvement is seen after correcting for Age and Smoke. Only the derived trait Q1B behaves well. For Q2 there are no false positives after strict Bonferroni correction. Quantile-quantile plots of p-values from QTLTree tests of Q1, Q2, Q1B, and Q4 are shown in Figure p-values of phenotype Q4 follow their expectations well, whereas those for Q1 approach acceptability only when using phenotype Q1B, corrected using PC loadings. Results from phenotype Q2 show some deviation from the expected at low p-values, but these do not seem to be due to true associations from results in Table Table Power calculation results are summarized in Figure The methods in QTLTree described here are a quick way to collapse the information contained in genotypes within a gene into a form that allows quick calculation of an optimum set of SNPs or combination of SNPs. Within the R environment, the method can also be used to interactively explore the sets of SNPs that may be affecting a quantitative trait. The methods seem to be able to detect an appreciable proportion of genes underlying variation in phenotypes, although the large number of detected loci that do not contribute to variation using the raw trait data is worrying, because within-population randomization should account for any simple differences between populations. Because different levels of structure exist within the data and because gene-environment-region interactions are possible , we attempted a further level of correction. Using derived phenotypes after regressing on correlated phenotypes and PC loadings improved the type I error rates while not reducing the power for realistic false-positive rates.Plots of PC loadings produced some unusual results that looked different from those from SNP arrays . These ar2, p = 5.6 × 10−9), and the correlation for Q2 is r2 = 0.14 (p = 2 × 10−4). This may explain some of the false positives. Although such problems are unlikely to arise for real data, they emphasize the difficulties that may crop up in future studies using next-generation sequencing technologies if case and control subjects are not treated in the same way and if genotyping and variant calling are not performed blind to disease status.The strong false-positive signals with the raw data lead us to ask, can the difference in the number of variable sites between individuals explain the inflated errors in Q1? To test this for phenotypes Q1 and Q2, we created data sets with just the SNPs that affected disease and all non-disease-causing SNPs. The correlation across individuals between average heterozygosity for SNPs affecting Q1 and average heterozygosity for SNPs not affecting Q1 is 0.22 (Pearson The authors declare that there are no competing interests.IJW wrote the computer code, carried out statistical analyses and drafted the manuscript. RAJH and DTH assisted with statistical analyses and helped to draft the manuscript. MSK conceived the statistical methodology. All authors read and approved the final manuscript.


Incorporating family data in genetic association studies has become increasingly appreciated, especially for its potential value in testing rare variants. We introduce here a variance-component based association test that can test multiple common or rare variants jointly using both family and unrelated samples.The proposed approach implemented in our R package aggregates or collapses the information across a region based on genetic similarity instead of genotype scores, which avoids the power loss when the effects are in different directions or have different association strengths. The method is also able to effectively leverage the LD information in a region and it can produce a test statistic with an adaptively estimated number of degrees of freedom. Our method can readily allow for the adjustment of non-genetic contributions to the familial similarity, as well as multiple covariates.We demonstrate through simulations that the proposed method achieves good performance in terms of Type I error control and statistical power. The method is implemented in the R package “fassoc”, which provides a useful tool for data analysis and exploration. With the availability of cost-effective next generation sequencing platforms, one hot topic in the field is the analysis of low frequency and rare variants, which are believed to play an important role in the etiology of common complex diseases and may explain a portion of the missing heritability ,2. HowevIn this work, we present an R package that implements a variance-component (VC) based association test that can test multiple common or rare variants jointly using both family and unrelated samples. The VC or linear mixed model (LMM) based approach aggregates or collapses the information across a region based on genetic similarity instead of genotype effects, which avoids the power loss when the effects are in different directions. A comparison study of binary traits  has alsoOur method can readily allow adjusting for a non-genetic contribution to the familial similarity , as well as multiple covariates such as principal components of population structure. We compare the performance of the proposed method across a range of simulation scenarios with a fixed-effect or sum test based on Feasible Generalized Least Squares (FGLS). We also investigate the factors that influence power for testing rare variants. In this paper, we also show the connection between our method and kernel machine based methods ,14, whicq subjects in the sample studied, including both family and unrelated individuals; and suppose for all individuals there is one gene or genetic region genotyped or sequenced that contains n variant sites or SNPs. For the ith individual, yi denotes the observed quantitative trait value; Xi =  ' denotes an m × 1 vector of covariates ; Si =  ' denotes an n × 1 genotype score vector for the n SNPs or variants in the region, coded 0, 1, or 2 , reflecting the number of copies of the minor allele; and Zi =  ' denotes a standardized genotype vector with the ij-th element fj is the minor allele frequency of the jth SNP or variant site.Assume there are y = Xβ + Wμ + ϵ  with ith row is Xi’, β is a vector of coefficients (fixed effects) for covariates X, μ is a vector of causal variant effects with y = Xβ + δ + ϵ, where δ is a vector representing random effects of all SNPs, with jk-th element is The setup of our model is similar to the linear mixed model recently proposed to estimate the genetic variance explained by genome-wide SNPs ,16, in wTo estimate and test the variance expressed by a gene or a genomic region using both family and unrelated data, intuitively one can extend the above model byy can be partitioned into components attributable to the SNPs in the studied region, polygenic and residual variances:where γ is a vector of the random effect of SNPs in the studied region distrtibuted S = ZZ '/n, and y = Xβ + g + δ + α + ϵ  with jk-th element being 1 if the j-th and k-th individuals belong to the same family and 0 otherwise. Note that, by adding a variance component common to siblings, it is also easy to allow for the fact that siblings resemble each other more than do parents and their offspring, whether due to dominant effects or common environment.where i records the i-th individual’s haplotype pair via a given scoring rule , or via extending the logistic kernel machine method [Many extensions are possible for improved implementation of the proposed model and testing procedure. This method can be easily extended to incorporate nonlinear and interaction effects. As discussed previously, our method can be considered as a special case in the framework of the kernel machine method. Interaction and nonlinear effects among markers can be further included in the model through specifying a valid kernel function or similarity metric. Also, more flexible weights may be incorporated into the kernel function according to allele frequencies or other prior information. Although a normally distributed trait was assumed throughout this study, the derived score statistic is also appropriate for non-normal traits . For bine method . When thWe propose a multi-marker VC-based association test using both family and unrelated data. A fast score test has been built on the ML and REML framework, in which only the parameters in the null model need to be estimated. Owing to the non-block-diagonal structure of the genotype-based similarity matrix, the score statistic derived has a different form from that based on the typical VC model for linkage analysis. We demonstrate through simulations that the proposed method achieves good performance in terms of Type I error control and statistical power. The method is implemented in the R package “fassoc”. We believe that “fassoc” will be a useful tool to complement existing software for family-based association studies.Project name: fassoc packagehttps://r-forge.r-project.org/R/?group_id=1379Project home page: Operating system(s): Linux, Mac OS X, WindowsProgramming language: ROther requirements: R (≥2.15.1)License: GNU GPLAny restrictions to use by non-academics: none except those posed by the licenseThe authors declare that they have no competing interests.XW and NJM participated in the design of the study and implementation of the method. XW drafted the manuscript. XZ and RCE participated in the conception and design of the study and in editing the manuscript. All authors read and approved the final manuscript.Additional simulation results and software.Click here for file


We found consistent positive correlations of broad-range as well as specific SLE-associated IgG with aTreg frequencies within unaffected relatives, but not patients or unrelated controls. Our interpretation: unaffected relatives with shared genetic factors compensated pathogenic effects by aTregs engaged in parallel with the individual autoantibody production. To study this further, we applied a novel analytic approach named coreferentiality that tests the indirect relatedness of parameters in respect to multivariate phenotype data. Results show that independently of their direct correlation, aTreg frequencies and specific SLE-associated IgG were likely functionally related in unaffected relatives: they significantly parallelled each other in their relations to broad-range immunoblot autoantibody profiles. In unaffected relatives, we also found coreferential effects of genetic variation in the loci encoding IL-2 and CD25. A model of CD25 functional genetic effects constructed by coreferentiality maximization suggests that IL-2-CD25 interaction, likely stimulating aTregs in unaffected relatives, had an opposed effect in SLE patients, presumably triggering primarily T-effector cells in this group. Coreferentiality modeling as we do it here could also be useful in other contexts, particularly to explore combined functional genetic effects.In human systemic lupus erythematosus (SLE), diverse autoantibodies accumulate over years before disease manifestation. Unaffected relatives of SLE patients frequently share a sustained production of autoantibodies with indiscriminable specificity, usually without ever acquiring the disease. We studied relations of IgG autoantibody profiles and peripheral blood activated regulatory T-cells (aTregs), represented by CD4 IgG autoantibodies are the diagnostic hallmark of Systemic Lupus Erythematosus (SLE). They show particularly high affinity for nucleic acid (dsDNA) as well as for nuclear and cytoplasmic proteins that physically interact with nucleic acids, although individual reactivity patterns are highly heterogeneous. Autoantibody generation, in part originating from inappropriately processed self-antigens + regulatory T-cells (Treg), which have raised extraordinary interest since it was demonstrated that their deficiency, depletion or alteration results in lymphoproliferative and autoimmune disorders in many contexts. Activated CD4+Foxp3+CD25bright Tregs (aTreg), recently characterized in humans + effector T-cells is essential for maintenance and proliferation of Foxp3+CD25bright Tregs +Foxp3+ cells with low or absent surface CD25 Effector T-cells undergo regulation by Foxp3Given the uncharacterized heterogeneity of autoantibody patterns in unaffected relatives IL2 and IL2RA (CD25) loci. In our unaffected relatives, we detected significant coreferentiality between genetic variants in these two loci in respect to immunoblot autoantibody profiles. Subsequently modeling the functional effect of IL2RA genetic variation and relating it to aTreg frequencies, it turned out that IL-2-CD25 interaction was indeed likely to trigger aTregs in the unaffected relatives, while in SLE patients IL-2/CD25-mediated effects were rather opposed to aTreg effects, supporting the notion that IL-2 triggered primarily T-effector cells in manifest SLE. More generally, we argue that the coreferentiality method has the power to model combined functional genetic effects, which may be very useful in many respects.We report the novel finding that various IgG autoantibody measures were consistently positively correlated with aTreg frequencies in unaffected relatives. We interpret these correlations as the result of a mechanism of efficient compensatory T-cell regulation, with a likely role of IL-2, that breaks down in manifest SLE. Coreferentiality analysis, allowing to test the functional relatedness of two parameters not in terms of direct correlation but in respect to multivariate reference data, was used to indirectly examine IL-2-mediated effects in this context through an analysis of genetic variation in the We assessed quantitatively standardized immunoblot profiles of plasma IgG autoantibody reactivity from 128 SLE patients, 215 unaffected relatives and 140 healthy control subjects , anti-SSA (SpearmanR = +0.24 [P = 0.007]), anti-Sm (SpearmanR = +0.40 [P = 8E-6]), and anti-nRNP (SpearmanR = +0.31 [P = 0.0006]), but not IgM anti-dsDNA , we parallelly stained peripheral blood mononuclear cells (PBMC), freshly isolated together with the plasma, for flow-cytometric analysis in order to quantify active regulatory T-cells (aTregs). Results from these experiments have been published earlier in the context of a genetic study rion see . We partti-dsDNA , Table 2ti-dsDNA . A diffemedian,weightedR = +0.26; PermP<0.001) but insignificant for unaffected relatives , while IgG anti-Ro60/SSA rather behaved like aTreg frequencies and showed significant positive median correlations only in the relatives  and also in the control group .For comparison, we analyzed analogous group-wise correlations of SLE-associated specific IgG with the same panel of immunoblot reactivities. In contrast to those of the aTreg frequencies, correlations calculated for IgG anti-Sm were very strong in the patients group  respectively related to a standardized multivariate dataset representative for self-reactive immune repertoires, e.g., our 130 quantitatively standardized immunoblot IgG reactivities. A test criterion for specific relatedness between the two parameters could then be formulated as follows: were they correlated with the same elements inside the reference system in the same way, i.e., did they behave We first wanted to know if particular repertoire substructures were functionally related to aTregs. Were proteins originating from the three different antigen sources  equally involved in our hypothesized feedback regulation or not ? The coreferentiality criterion was appropriate to answer this, formulated as follows: would the same among the 130 immunoblot reactivities that correlated most positively with aTreg frequencies also correlate most positively with the diversity, i.e., the recognized band number, of IgG reactive toward each given antigen source, and vice versa ? Coreferentiality with aTreg frequencies was tested accordingly, separately within the groups of SLE patients, unaffected relatives and unrelated healthy controls. Weighted first-order correlations were used in order to correct for unequal representations of families. While no significant coreferentiality was found for any of our three band number counts within patients or unrelated control subjects, it was highly significant within the unaffected relatives group, but exclusively for the diversity of IgG to HEp2-cytoplasmic proteins. Analogously, we asked whether aTreg frequencies were coreferential with SLE-associated specific IgG autoantibodies. Once again, we found no significance within SLE patients or the unrelated control group, while log-transformed IgG anti-dsDNA, anti-SSA, and anti-Sm measures showed significant coreferentiality within the unaffected relatives , Fig. 6.bright aTregs were compensatorily expanded in unaffected relatives and exerted a regulatory feedback function, we could readily expect that this effect depended on IL-2-CD25 interaction. To test if a functional context of IL-2, CD25 and aTreg effects in our unaffected relatives was related to a particular immune repertoire structure, we chose an approach based on the obvious fact that IL-2 and CD25 are closely functionally related. Therefore, if the coreferentiality approach was valid and sufficiently powerful, we could expect to detect coreferentiality. Thus, would the respective functionalities of IL-2 and CD25 be coreferential in respect to our multivariate reference system of autoreactive IgGs, indicating that IL-2-CD25 interaction influenced the self-reactive IgG repertoire in a defined manner ? And if so, was this effect related to T-cell regulation ? Unfortunately, IL-2 itself largely escapes meaningful measurement due to its local effect and short life span. Also the physiologic functionality of CD25 in lymphatic organs is not clearly reflected by measures in conditions where they are feasible, namely the use of peripheral blood cells. In contrast, genetic variation can easily and accurately be assessed, and does not vary with measurement conditions. Polymorphisms with likely functional effects have indeed been described for both the loci IL2 and IL2RA encoding CD25 (see discussion). Since genotypes are furthermore individually invariant and independent of phenotypic effects, coreferentiality between genetic effects could be expected to be particularly robust. Hence, we first aimed to find out whether IL2 genetic variation behaved coreferentially in respect to IgG repertoires with genetic variation in the IL2RA locus, independently of aTregs.Next we wanted to apply the coreferentiality approach to interpret the role of a molecular mechanism with evident relevance for T-cell regulation and likely for SLE pathology: the interaction of IL-2 with CD25. If our above interpretation was correct and CD25IL2 locus, two SNPs have been most studied: rs2069762, located within the promoter region and associated to differences in IL-2 production by cultured cells rs2069763, synonymous-coding in the first exon, the minor allele of which was recently found protective against SLE in a Chinese population IL2 locus and the 5′ and 3′ flanking regions  although located 5′ and 3′ of the coding region, respectively. We further found that for rs11575812, rs2069762 and rs2069763, each respective minor allele very likely represented an independent locus-spanning haplotype (see discussion), which is compatible with the fact that rs11575812 and the strongly linked rs1479924 are located at opposite sides of the locus. To cover the much larger IL2RA locus, we typed 25 SNPs .In the relatively small  25 SNPs , includiIL2 and IL2RA loci indeed parallelled each other in the unaffected relatives in their relations to self-reactive repertoires, together with the evidence for strong immune regulation in this group, prompted us to use coreferentiality as a modeling tool. Since transcriptional regulation of IL-2 is known to be complex and specifically affected by SLE IL2RA locus. We did this by maximizing the coreferentiality between two functions representing linear combinations of (a) the three principal IL2 SNPs  and (b) five representative IL2RA SNPs  IL2 and (b) IL2RA SNP scores:The finding that functional effects of genetic variation in the SNPs see . The optrs11575812]+0.03[rs2069762]−0.47[rs2069763];−0.88[rs791589]+0.15[rs706778]+0.51[rs7073236]−0.77[rs10795791]+0.14[rs11594656].−0.33[P = 0.04. Thus, the derived model was likely non-random and meaningful, and we proceeded to analyze its properties.In order to test whether the observed degree of optimized coreferentiality was meaningful or could also be achieved accidentally, we optimized 50 further models with the same method but using random data permutations instead of our real data . In this null distribution of models, only two out of 50 gave maximized coreferentialities reaching the value of 0.77 found for the true data, corresponding to an empirical significance of IL2 and IL2RA genetic effects according to this optimization, we calculated both model scores for all typed unaffected relatives from functions (a) and (b), respectively. IL2 and IL2RA model scores were not significantly correlated with each other , excluding a trivial interpretation of the achieved coreferentiality by an artificial score correlation. Next, we wanted to know whether the resulting IL2RA model was compatible with an effect related to T-cell regulation as it was suggested above. Cytometric aTreg frequencies, which had not been used to derive the IL2-IL2RA effects model, could in fact now be used to test this: correlated with self-reactive IgG and coreferential with repertoire substructures as shown above, were they also coreferential with the independently modeled IL2RA genetic effect in the unaffected relatives group ? Indeed, we found a significant coreferentiality of CR = +0.39  of the model score with specific IgG measures or the IgG band number detected to HEp2-cytoplasmic proteins remained insignificant. In contrast, it was significant with the IgG band number toward HEp2-nuclear proteins  and was therefore not followed. However, while the transcriptional control of IL-2 is known to be altered in SLE pathology IL2RA model optimized for the unaffected relatives, it should be applicable also beyond that group. Indeed, model scores calculated with function (b) from genotypes of patients or unrelated controls showed the same principal relatedness with the dominant IL2 SNP rs11575812: coreferentiality (CR = −0.66 for the unaffected relatives) was equally signed and reached statistical significance when calculated within patients . It was also equally signed in the control group (CR = −0.11) although not significant. Next, addressing the relation of our IL2RA model with TCR-dependent cell activation, it was not coreferential with HLA-DR types in SLE patients as it had been in the unaffected relatives . We also found significant positive coreferentialities with IgG anti-Sm and anti-nRNP in the control group. If our IL2RA model was generally representative for IL2RA functional genetic effects and functionally related to CD3ζ effects, we further expected that an IL2RA genetic effects model independently optimized for CD247-IL2RA coreferentiality would be close to that derived by IL2-IL2RA coreferentiality optimization. Indeed, coreferentiality optimization within the unrelated controls, using the same five IL2RA SNPs as above and three CD247 SNPs, led to an IL2RA model with almost identical coefficients, and scores that were more than 90% correlated with those of the initial IL2-IL2RA model in all three subject groups , inversely signed in respect to that found for the unaffected relatives  surface CD25, does not consist of functionally active Tregs bright aTregs efficiently excludes this population, thus can be said to use CD25 as the Treg activity marker as which the most recent literature characterizes it. At the same time our aTreg gate enriched Foxp3+ cells sufficiently and to similar extents in the three groups studied, as confirmatory Foxp3/CD25 stainings show .We did not stain for Foxp3 in this study since intracellular anti-Foxp3 staining, now regularly used to characterize Tregs, was not available when the experiments were performed. Neither were auxiliary Treg markers such as CD127 known. However, the way we have studied aTregs appears accurate also in respect to current knowledge, particularly since recent literature makes it more and more likely that CD25 (gated with a high threshold) is a more important marker for active Tregs than Foxp3: it has been well demonstrated that a relevant subset of peripheral human CD4show see . ParticuIL2RA locus. The properties of the derived and empirically validated model suggested that (i) the IgG autoreactive repertoire was indeed significantly influenced by IL-2-CD25 interaction in the unaffected relatives, and (ii) aTreg frequency in this group was related to a similar substructure in the autoantibody repertoire as was the modeled IL-2-CD25-mediated genetic effect: both were coreferential, thus likely functionally related. Applied to SLE patients, the same IL2RA genetic-effects model surprisingly showed an inverse coreferentiality with aTreg frequency, indicating that the modeled IL-2-mediated effects were functionally opposed to aTreg effects. This is most easily explainable by the notion that IL-2, in the condition of manifest SLE, favored T-effectors rather than aTregs.IL-2 is commonly regarded as the most important maintenance factor for Tregs IL2RA genetic variation in this group, although related to the aTreg effects on autoantibodies, primarily concerned an autoantibody repertoire that was clearly dependent on HLA-DR types, focused on nuclear rather than cytoplasmic targets, and showed no evident relation to SLE-associated specific IgG. Unaffected relatives' aTreg frequencies, however, were related to an autoantibody profile with no evident HLA-DR dependency, focused on cytoplasmic targets and also strongly coreferential with the presence of SLE-associated specific IgG. This suggests that the hypothesized compensatory mechanism that lead to the initially observed positive correlations between autoantibodies and aTreg frequency was not only driven by the IL-2-mediated repertoire effects, but by an additional factor that was related to the recognition of SLE-specific antigens and less HLA-dependent. In summary, it appears plausible that strong IL-2-mediated effects principally favor aTregs in immune reactions including natural self antigens but without an apriori relation to SLE autoantigens, and that the efficiency of this mechanism is a condition for SLE compensation in genetically susceptible but healthy individuals. Nevertheless, specific aTregs recognizing particular epitopes and expanded or induced in SLE autoantigen-specific immune reactions that also lead to the presence of SLE-type autoantibodies, may well control the potentially pathogenic effects of these reactions. SLE could become clinically manifest when this specific compensatory regulation breaks down. In the compensated state, IL-2 reduction or dysregulation might favor the loss of the specific aTregs. In their absence, however, IL-2 could directly trigger pathogenic effector functions. This could make potential IL-2 therapies a double-edged sword. It would, however, generally encourage efforts to develop new therapies stabilizing or even restoring the compensated state by supporting or expanding Tregs with particularly relevant specificities.This explanation is consistent with the known transcriptional IL-2 repression in SLE, which, however, was so far rather seen associated with altered effector cell activation. In any case it appears to be part of a systemic T-cell pathology that includes aspects of both hypo- and hyperactivity, i.e., more rapid but dysregulated and less sustainable responses IL2 polymorphisms, the strongest genetic effect in our model was attributed to the 3′ SNP rs11575812, which was in almost complete linkage with the far 5′ rs1479924. To our knowledge, this linkage group has not been described or considered in the context of association studies so far. However, it showed a remarkable property that extends earlier observations. Thus, an earlier study of the two traditionally typed SNPs rs2069762  and rs2069763  had already noted that the haplotype consisting of the combined respective minor alleles was inexistent rs11575812 together with these two SNPs, we can confirm and extend this observation: none of our 448 completely typed individuals had more than two minor alleles of the three SNPs combined, while 79% had exactly two. This indicates that in fact no haplotype with more than one minor allele existed, and that therefore each respective minor allele in these three polymorphisms represented an independent locus-spanning haplotype, an interpretation that also explains the strong linkage between rs11575812 and the distant rs1479924. In this context, the fact that the haplotype defined by the rs11575812 variant, which according to our model had the strongest functional effect, was not considered in the earlier studies of genotype-function relationships rs2069762 minor G allele resulted in increased IL-2 secretion by anti-CD3/CD28-stimulated T-cells, thus could be ascribed a relatively positive effect on IL-2 functionality. Our unaffected-relatives IL2 model ascribes negative functional effects to the respective minor alleles of both rs11575812 and rs2069763, but no effect to that of rs2069762. In the context of the interpretation of all three minor alleles as independent haplotypes, this is consistent with the previously described relatively positive effect of rs2069762 G. These haplotype structures may have particular functional relevance since IL-2 is monoallelically expressed in single human T-cells IL2 is also since long suspected for association with various disease phenotypes, but it has to be taken into account that linkage in the IL2 locus (including the discussed haplotype structure) extends to the neighboring IL21 gene and therefore association does not necessarily reflect IL-2-mediated effects. Association of the IL2-IL21 linkage region is in fact established with autoimmune diseases such as type-1 diabetes rs2069762 had shown the strongest trend rs2069763 with SLE was reported for a Chinese population IL2RA locus has an evident functional relevance, clearly demonstrated by its association with rheumatoid arthritis IL2RA model function were located . Two had in fact the highest impacts in the resulting model . One of the two IL2RA-region SNPs suspected to be causal rs11594656, actually had a high coreferentiality with rs11575812 and was included in our model function, but turned out to have little impact in the resulting model . The study was approved by the Ethics Committee of the Portuguese Lupus Patients Association, and written informed consent was obtained from all participants. Collections took place 2001–2004. For analysis, blood donors and unrelated spouses were cumulated in the group of unrelated healthy control subjects. Age and gender distributions are shown in Vacutainer CPT tubes containing citrate (Becton Dickinson) and centrifuged. Plasma supernatants were aliquoted and stored at −20°C. Total protein concentrations were determined by the Biuret method in an automated system . Quantitative ELISAs were performed as described p-nitrophenyl phosphate (Sigma-Aldrich) and 405 nm absorbance measured by an ELISA plate reader (BioRad). Quantification was obtained by fitting ODs to a standard curve present in duplicates on each plate, consisting of 12 serial dilutions of positive control sera (ImmunoVision). One unit of reactivity was defined as the equivalent of the highest control serum concentration, previously chosen to be below saturation level.Peripheral blood was collected in 7,5 mL th percentile of all samples assayed on the same membrane (after verification that no visible reactivity band was recognized distinguishably from background by >75% of the samples), and normalized between membranes by division by the average standardized density of all sections of the two reactivity standard replicates present on each membrane. Given the random distribution of samples over the membranes, this standardization could be tested for possible artefacts by membrane-wise comparison, and no significant difference was detected between the respective 24 membranes blotted with the same extract .Quantitative immunoblot was essentially carried out according to Quantitative immunoblot assays in this study were adjusted for equal total plasma protein and not for total IgG as it was done in earlier studies with this method that had aimed to characterize naturally autoreactive repertoires and accordingly favored a proportional representation. Since, in contrast, this study aimed at representing and analyzing absolute levels of specific IgG as conventional serology does, total plasma protein levels were used for adjustment, which largely represent plasma albumin and not the individual variations in total circulating IgG.6 cells were fixed in 2% paraformaldehyde, washed twice in PBS containing 2% fetal calf serum, and incubated for 20 min at 4°C with the optimal dilution of each conjugated anti-human mAb . Cells were washed again and fluorescence intensity measured using a FACScalibur flow cytometer and the CellQuest™ software. As a quantitative readout of aTregs, without Foxp3 staining that was not available when this study was done, but in consistency with their recent characterization bright within CD4+ cells were measured as follows. First, in order to specify the upper limit of CD25 staining in non-CD25-expressing cells for each sample, the ninety-ninth percentile of PE/Cy5 fluorescence intensity within CD4−CD45RO− cells was determined. CD4+CD25bright aTregs in the same sample were then quantified as those CD4+ cells that exceeded this value at least three-fold  were isolated using Vacutainer CPT™ tubes (Becton Dickinson). 1×10ree-fold .+ cells in all three groups: 81±8% (SLE patients), 79±11% (relatives) and 74±13% (controls). Particularly, the SLE patients did not show any indication for increased contamination of gated CD25bright by Foxp3− cells, which can be seen in the individual cytograms of our 15 confirmatory stainings  with CD4 and CD25 surface staining. The evaluation of this combination in 15 SLE patients, 45 unaffected relatives and 54 blood donor controls confirmed that the described aTreg gate  regularltainings .IL2 SNPs rs2069762 and rs2069763, some samples that did not call were also typed by RFLP as described P>0.01), indicated in Genomic DNA was extracted by standard methods. SNP genotyping was performed on the Sequenom™ platform using multiplexed amplification followed by mass-spectrometric product separation. For the P values below 0.05 were considered significant. Group comparisons were performed by the distribution-independent Mann-Whitney or Kruskal-Wallis tests. Correlations were in principle described and tested for significance by distribution-independent Spearman rank correlation coefficients. Group-wise median correlations with immunoblot reactivities  and specially written procedures (Panama-Blot) for density quantification, migration scale adjustment, section definition and standardization of immunoblot data, as well as for statistics and coreferentiality calculations. Genotype scores for SNPs were defined as the number of minor alleles per individual . For statistical testing, tivities , coreferCR between pairs of test parameters were assessed and tested, resulting in a permutation P-value PermP, as described CR. Group-wise median correlations were also tested analogously, with the median R as test criterion. Bystander coreferentiality, i.e., the possibility that coreferentiality was generated by direct correlation between the test parameters with only one of them being related to the reference data Coreferentiality coefficients i indicating the SNPs considered, and <Score> their respective genotype scores. Coefficients ic were varied in order to maximize coreferentiality, using the optimization algorithm ‘Amoeba’ adapted from IL2 locus, we had good evidence that variant alleles of rs11575812, rs2069762, and rs2069763 excluded each other in the same haplotype (see discussion), and that their possible funcional effects were consequently independent. Accordingly, we modeled the IL2 genetic effect as the linear combination of these three genotype scores. The effect of IL2RA genetic variation was modeled by selecting, from total 25 SNPs typed  showing absolute coreferentialities above 0.5 with the dominant IL2 SNP rs11575812, disconsidering a sixth SNP (rs7072793) that also met this criterion but was in almost complete LD with rs10795791. To obtain model scores for all subjects, including those not used in the model optimization, for secondary calculations, respective functions with the optimized model coefficients were applied to the individual genotype scores. For this purpose, some missing individual genotypes, not calling in Sequenom typing, were imputed using the software fastPhase™. In order to model CD247 genetic effects for secondary model comparison, among a total of 24 SNPs typed, we selected three  that had no evident linkage between each other but covered the 5′ region of the locus where many single SNPs had high coreferentiality in the control group with the unaffected-relatives optimized IL2RA model score.Modeling was based on coreferentiality maximization either between a parameter and a function, or between two functions, each function representing the linear combination of several SNP genotype scores obtained from a respective locus. Functions had the form Ps typed , five Click here for additional data file.Figure S2Linkage disequilibrium map of the IL22 values. locus according to our data (produced by HaploView). Annotations indicate pairwise R(PDF)Click here for additional data file.Figure S3Cytograms of confirmatory combined Foxp3/CD25 stainings of 15 individual SLE patients. This figure is included to demonstrate that possibly activated and CD25-expressing Foxp3−conventional T-helper cells do not significantly contaminate the cells in our CD25bright aTreg gate (R4) defined as described in the + within CD25bright cells. The uppest left cytogram, where Foxp3−CD25+ are most clearly present, shows that this presence does not involve a reduced proportion of Foxp3+ cells in the aTregs that we analyze.(PDF)Click here for additional data file.Table S1Typed IL2, IL2RA and CD247 SNPs.(PDF)Click here for additional data file.