N-glucosylated (N-Glc) peptide antibodies that can be easily detected in Solid-Phase Enzyme-Linked ImmunoSorbent Assays (SP-ELISA) on MS patients’ sera preferentially recognize hyperglucosylated adhesin of non-typeable Haemophilus Influenzae. Since multivalency can be useful for diagnostic purposes to allow an efficient coating in ELISA, we report herein the development of a collection of Multiple N-glucosylated Peptide Epitopes (N-Glc MEPs) to detect anti-N-Glc antibodies in MS. To this aim, a series of N-Glc peptide antigens to be represented in the N-GlcMEPs were tested in competitive ELISA. We confirmed that the epitope recognized by antibodies shall contain at least 5-mer sequences including the fundamental N-Glc moiety. Using a 4-branched dendrimeric lysine scaffold, we selected the N-Glc MEP 24, carrying the minimal epitope Asn(Glc) anchored to a polyethylene glycol-based spacer (PEG) containing a 19-atoms chain, as an efficient multivalent probe to reveal specific and high affinity anti-N-Glc antibodies in MS.Diagnostics of Multiple Sclerosis (MS) are essentially based on the gold standard magnetic resonance imaging. Few alternative simple assays are available to follow up disease activity. Considering that the disease can remain elusive for years, identification of antibodies fluctuating in biological fluids as relevant biomarkers of immune response is a challenge. In previous studies, we reported that anti- Multiple Sclerosis (MS) is the most frequent, chronic, inflammatory, demyelinating, disabling disease of the central nervous system, mainly caused by an autoimmune response to self-antigens in genetically susceptible individuals. MS diagnosis and prognosis are mainly supported by magnetic resonance imaging (MRI) that up to now is considered the gold standard diagnostic technique . To the Covid-19, triggering the coronavirus pandemic era we have been living in 2020, marks the return of the old and familiar, but unfortunately misunderstood, enemy killing more human beings than natural disasters: viruses, bacteria, and parasites killers that our modern world tried to fight mainly by social distancing.Historically, protein antigens isolated from biological material or reproduced by recombinant technologies were used to detect antibodies, but this approach has sometimes turned out as unrealistic. The main limiting factor depends on epitope recognition because of sequence mutations, incorrect folding, lack of post-translational modifications (aberrant versus native), and nonspecific binding.Peptides mimicking the appropriate epitopes can be valuable tools. In fact, peptides that can be synthetically produced as unique molecules, can increase specificity of antibody recognition, eliminating or minimizing potential cross-reactivity with structurally similar fragments in non-relevant proteins ,10,11.Several Enzyme-Linked ImmunoSorbent Assays (ELISA) based on synthetic peptides have been proposed to detect antibodies in different diseases like Acquired ImmunoDeficiency Syndrome (AIDS), Infectious Bronchitis (IB), Severe Acute Respiratory Syndrome (SARS), and Bluetongue (BT) ,13. HoweAn interesting strategy to increase surface binding on the ELISA plate, improving sensitivity, is based on multimeric peptide dendrimers. In particular, Multiple Antigen Peptides (MAPs) are an optimal compromise between short peptide epitopes and recombinant or native antigens. It is widely recognized, that surface antibody binding is increased by multivalent presentation of the antigen. In fact, multivalent interactions can be collectively much stronger than the sum of the corresponding monovalent interactions ,18. TherN-glycosylation has been described as possible PTM involved in an antibody-mediated form of MS hMOG(30–50)  [Haemophilus influenzae (NTHi)  [N(Glc)GS (9). Moreover, considering that a tripeptide sequence is probably not long enough for a good antigen-antibody interaction, the following step in the identification of the epitope was to test the pentapeptides 16–22, including the sequons. At this purpose the two amino acids placed in positions –B2 and –B1 in the CSF114(Glc) sequence were added to allow the spatial hint in the shape of a β-turn, with Asn(Glc) on the tip. The same idea was applied to the sequon N(Glc)AT (15), inserting amino acids in positions 29 and 30 of hMOG(30–50) sequence, originally containing Asn(Glc) at position 31 [As a proof of concept, among the possible combinations of tripeptide sequons NX(T/S), we selected the ones containing X = Gly or Lys. In particular, we synthesized s in MS) , but als, and 9)  togetherition 31 .9–22  antibodies even if with an affinity lower than the synthetic glucosylated 21mer-peptide (2 (11) and Ac-N(Glc)KT-NH2 (12) showed the best inhibitory activity KT-NH2 (19) presented the best inhibitory activity  (IC50 = 0.58 µM) and Ac-KGN(Glc)AT-NH2 (22) (IC50 = 0.60 µM). This result indicates that the IC50 values for N-Glc pentapeptides are closely similar, and no substantial differences were observed among the pentapeptides tested, independently of the sequence.The longer linear sequences N-Glc), and increasing at the same time the efficiency in coating to the ELISA polystyrene plate of short linear peptides, we synthesized different N-Glc MEPs based on a dendrimeric lysine scaffold, having in common Asn(Glc) [In order to mimic a multivalent presentation of the minimal but fundamental epitope Asn(Asn(Glc) . N-Glc MEPs 23–26 bearing spacers of different length: β-Alanine and two different polyethylene glycol-based spacers (PEG), containing 9- and 19-atoms chain, respectively. The following antigens were selected: (i) the β-turn N-glucosylated pentapeptide ERN(Glc)GH (12) and (ii) the simple minimal N-glucosylated asparagine epitope Asn(Glc). The spacers were introduced to avoid steric hindrance possibly hampering antibody binding. The N-Glc MEP structures are reported in Starting from a lysine tetrameric core, we synthesized five N-Glc MEPs were synthesized starting from Fmoc4-Lys2-Lys-β-Ala-Wang resin, according to the general procedure described in details in the 4)-OH building block was synthesized according to the method developed by Paolini et al. [N-Glc MEPs were acetylated on the N-terminal function. All the N-Glc MEPs were purified by semi-preparative RP-HPLC, and characterized by RP-HPLC and ESI-MS , and compared to 14 healthy blood donors as controls  analysis was employed to perform an accurate comparative investigation of the performances of the different lculated . SelecteN-Glc MEP 25 and 26 presented the best Area Under the Curve (AUC) values , observing the data distribution we assume that their ability to identify specific MS antibodies is lower compared with N-Glc MEPs 23 and 24. This is justified with the increased means of the antibody titers in controls compared to MS patients in both N-Glc MEP 25 and 26, which apparently improved AUC and p values, but are due to non-specific interactions. In fact, the specificities calculated for N-Glc MEPs 25 and 26 are sensibly decreased compared with CSF114(Glc) . Considering the sensitivity values, once the specificity is fixed in 92.31%, the N-Glc MEP 24 presented the best performance identifying as positive the 35.29% of the MS patients.Despite N-Glc MEP 24 ability (containing only N-Glc moieties) to recognize specific MS antibodies in patients’ sera. At this purpose, N-Glc MEP 24 was tested against an increased number of MS patients’ sera (81) and healthy controls’ (30) sera, in parallel, to the glucopeptide CSF114(Glc) as reference. Data distribution of IgG and IgM antibody titers identified by SP-ELISA are reported in Consequently, we decided to deepen on p value < 0.001, two-tailed test) for CSF114(Glc), both for IgM and IgG-type antibodies. Slightly different results were observed in the case of N-Glc MEP 24. In fact, the differences among groups were statistically significant when IgM-isotype antibodies were detected . On the other hand, the IgGs against the multivalent N-Glc MEP 24 showed no level of significance . In our opinion, the IgG antibody response as detected by N-Glc MEP 24 appears to be less specific because it detects a “noise” level in the control group, forcing a decreased specificity to maintain sensibility when selecting the corresponding cut-off.The non-parametric Mann–Whitney test was applied to evaluate significant differences between MS patients’ and controls’ groups. The results showed significant differences ; the Spearman’s correlation coefficients  were r = 0.7507 and 0.7424 for IgG and IgM, respectively  antibodies significantly correlated with the ones against the ectively C,D.N-Glc MEP 24 in a competitive ELISA. In a set of three MS positive sera tested in parallel, the multivalent N-Glc MEP 24 inhibited the binding of antibodies to the glycopeptide CSF114(Glc), giving rise to contrasting inhibition curves among the different representative sera employed (N-Glc MEP 24 was lower than CSF114(Glc) , whereas serum MS2 exhibited superimposable affinity (IC50 = 6.373 × 10−8 M and 6.088 × 10−8 M respectively). Moreover, in MS3 serum IC50 was lower for N-Glc MEP 24 compared to CSF114(Glc) . This finding indicates that the N-Glc MEP 24 shares similar epitopes, all including the Asn(N-Glc) residue. In particular, its antibody affinity can be slightly different among the MS patients, probably because of the differential innate and adaptive immune responses typical of each subject.Then, we investigated the absolute antibody affinity of employed . Data ofemployed  showed tN-glucosylation is part of a relevant epitope that was identified by the structure-based designed β-turn 21-mer glucopeptide CSF114(Glc). This synthetic tool was instrumental for the discovery of antibodies in an MS patients’ population preferentially recognizing the hyperglucosylated bacterial adhesin of non-typeable Haemophilus influenzae. With the idea in mind that multivalent presentation of glucosylated asparagine residues may occur in a variety of native antigens, as in the case of citrullination in rheumatoid arthritis [N-Glc MEP 24, based on a lysine-dendritic scaffold (relatively simple to be produced), carrying four copies of the minimal glucosylated epitope Asn(Glc) anchored to a PEG-based spacer containing a 19-atoms chain [N-Glc MEP 24 displays an interesting ability to identify in SP-ELISA both IgG and IgM antibodies in a large number of MS patients’ sera (81) compared to controls (30), with a sensitivity of 35% (95% CI: 14.21–61.67) and a specificity of 92.31 (95% CI: 63.97–99.81). This result is particularly relevant, since it offers the possibility to detect with the same tool both IgMs and IgGs in MS, thus increasing the diagnostic and prognostic value of the antigen thanks to multivalent presentation of the minimal epitope [Multiple sclerosis diagnosis is still very challenging, relying on clinical and radiological criteria and in the absence of “better explanations” , the devrthritis , we focurthritis ,40. Therms chain . In prevms chain , sensiblN-glucosylated Multivalent Epitope Peptides for the detection of anti-N(Glc) antibodies in Multiple Sclerosis. After a preliminary screening of several short linear peptide epitopes and multiple epitope peptides, the promising results suggest that multivalent interaction can be useful in designing SP-ELISA to detect antibodies to aberrant N-glucosylation in Multiple Sclerosis for both diagnostic and prognostic purposes. Indeed, the multivalent N-Glc MEP 24 ligand, carrying the minimal glucosylated epitope Asn (Glc) anchored to the 19-atoms PEG-based spacer, can be an efficient probe to reveal both IgM and IgG autoantibodies as disease biomarkers.The present study reported the development of new


Across a wide range of studies, researchers often conclude that the home environment and children’s outcomes are causally linked. In contrast, behavioral genetic studies show that parents influence their children by providing them with both environment and genes, meaning the environment that parents provide should not be considered in the absence of genetic influences, because that can lead to erroneous conclusions on causation. This article seeks to provide behavioral scientists with a synopsis of numerous methods to estimate the direct effect of the environment, controlling for the potential of genetic confounding. Ideally, using genetically sensitive designs can fully disentangle this genetic confound, but these require specialized samples. In the near future, researchers will likely have access to measured DNA variants (summarized in a polygenic scores), which could serve as a partial genetic control, but that is currently not an option that is ideal or widely available. We also propose a work around for when genetically sensitive data are not readily available: the Familial Control Method. In this method, one measures the same trait in the parents as the child, and the parents’ trait is then used as a covariate . When these options are all not possible, we plead with our colleagues to clearly mention genetic confound as a limitation, and to be cautious with any environmental causal statements which could lead to unnecessary parent blaming. This study used a very rich and well-powered dataset, and found a correlation between the number of books in adolescents’ homes and literacy performance in adulthood. They conclude that “growing up with home libraries boosts adult skills”, inferring a causal connection1. This is depicted in Fig. Most parents spend hours fretting over decisions about the environment they provide to their children. The scientific literature mirrors this idea. Across a wide range of studies from many psychological domains, researchers often conclude that the environment parents provide and children’s outcomes are causally linked, through environmental transmission see Box . For exa3. Likely more surprising to hear for most is that genetic influences are often seen on measures of the “environment”, suggesting that the contexts surrounding children are partly under genetic control4. For example, a meta-analysis found cumulative support for genetic influences on the parenting children received5. This idea, that there is genetic influence on exposure to environments, is called a gene–environment correlation. A gene–environment correlation describes the process by which a person’s genotype influences their exposure to the environment6. It is certainly not the case that genes are doing this directly, but instead genotypes matter for aspects of our personality, behaviors and cognitions, which then influence how we interact with our environment and how others interact with us7. This concept of an individual purposely and dynamically interacting with their surrounding environment is not limited to behavioral genetics; similar processes have been described in other literatures, for example person-centered interactions8 and the Selective Optimization with Compensation9.Decades of work from behavioral genetics show that children’s traits are influenced by both genetic and environmental effects6. First, a “passive gene–environment correlation” describes the association between the genotype a child inherits from their parents and the environment the child is raised in. Another way to think of it is that genes are a third variable which influence both the rearing environment a child receives as well as the child’s own traits, via genetic transmission from parents to child. This means it is not possible to draw causal conclusions between the rearing environment and children’s traits. For example, home environments have been found to be less chaotic for children with high effortful control, with results indicating that the same genes in parents which contribute to the levels of structure in their home  are also transmitted to their children and contribute to effortful control10. Second, an “evocative gene–environment correlation” is when a person’s genetically influenced trait elicits, or evokes, a specific response from others in the environment. For example, it has been found that a person’s genes are associated with being rated as “more likeable” by others, meaning how others perceive you as a social partner, and then likely interact with you, is influenced by your genes11. Third, an “active gene–environment correlation” describes the association of a person’s genetically influenced traits and the environments they select. For example, the genetically influenced personality trait of socialization, measured in childhood, was associated with exposure to risky environments related to substance abuse in adolescence, in that children with low socialization were exposed to more risky environments12. All three have the potential to cloud the true combination of genetic and environmental influences transmitted between parents and children , but it is theorized that passive gene–environment correlations have a greater effect in childhood13, and as such passive gene–environment correlations are the focus of our review.Specifically, there are three types of gene–environment correlations that can result in genetic confounds14. The authors attribute helping with math homework as the causal environmental factor, concluding that parents with high math anxiety should not help with their children’s math homework. This causal connection could exist, but equally parents with math anxiety also pass on genetic  risks related to both lower math cognition and higher math anxiety15. Because this genetic transmission was not controlled for, causal claims and associated parenting advice are not justified.To give an example of how (passive) gene–environment correlations can result in genetic confounding in studies focused on the rearing environment, a high impact finding reported that parents with higher math anxiety have children with higher math anxiety, solely due to the home environment16. Again, this study focused on the environmental transmission from parents to offspring without accounting for the transmission of genes related to smoking behaviors and risk-taking behaviors17. Furthermore, the authors conclude that smoking cessation interventions in adults can reduce smoking in subsequent generations. Parent-centered interventions might help to reduce adolescent smoking, but what is overlooked is that children carry their own genetic risks for smoking, and direct intervention with the adolescents18 could more strongly influence their smoking behaviors.Another example, this time from the medical literature, examined the intergenerational transmission of smoking behavior from parents to adolescents, concluding that “the attitudes, beliefs, and behaviors toward [adolescent] cigarette use are learned through [parent] modeling”13, and nearly 30 years ago Plomin and Bergeman19 addressed the prevalence of genetic confounding by illustrating that genetic influences are found on most if not all environmental measures. Since then, several reviews have pointed to multiple examples from parental warmth to alcohol use to depression where causal pathways from parent behavior to child outcomes are reported, without accounting for genetic confounding22. These reviews have called for researchers to use caution with causal statements, and to address genetic confounding in their limitations. Further, they have asked for journal editors and reviewers to be better watch-dogs in this endeavor; to insist that manuscripts adhere to these standards. However, based on our experience listening to conference presentations and reading press releases and newspaper articles, we believe these guidelines are not yet being met.We are certainly not the first to point out this familial transmission confound within the ecological literature. Indeed, nearly 40 years ago, Scarr and McCartney proposed that “the human experience and its effects on development depend primarily on the evolved nature of the human genome”We believe a reason why these previous reviews have not successfully changed minds and methods is because they have not given actionable correlational design solutions to researchers outside of behavior genetics. Therefore, when faced with not doing the work or publishing work with only a potential genetic confound, researchers have chosen the latter. Therefore, in the following section we will give many possible solutions, from genetically sensitive designs to design solutions that work in lieu of genetically sensitive data, and finally, a renewed call for changes in reporting standards.Box 1 A glossary of some key termsThe designs that we discuss below present a not all-encompassing but global overview of genetically sensitive designs and polygenic-scores (PGS) designs, and include a genetic-proxy control design , which we recommend when genetically sensitive data are not available, as well as several other proxy control designs. These designs vary in how well they disentangle the genetic confound and in how challenging they are in terms of obtaining and analyzing the data.23. Both types of twins share some parts of their environment such as their home, school, and neighborhood , and experience some aspects of their environments separately from each other such as peer groups, hobbies, or illness . By comparing the average correlation between the two twins in a twin pair on a trait for monozygotic versus dizygotic twins, variance can be partitioned into additive genetic influences or heritability, shared environmental influences, and non-shared environmental influences . In contrast, resemblance between adopted children and their adoptive parents is fully due to the environment that the parents have provided. Another way to examine the rearing environment while partially controlling for genetic confounding is to use non-twin biological siblings within a family27. Because biological siblings, like dizygotic twins, share half of their genes, sibling resemblance on a trait suggests the influence of genetic factors along with some shared environmental influences. However, non-twin siblings can differ on several aspects of the rearing environment such as family size or parental health and age at birth, therefore, any dissimilarity between siblings can help to determine the influences of these non-shared aspects of the rearing environments on a given trait. Sibling designs have also recently incorporated the use of genome-wide PGS which strengthen their ability to control for confounding by disentangling direct genetic influences from gene–environment correlations29. PGS designs are discussed in more detail, below.Other genetically sensitive designs that can address the direct effect of the rearing environment, after accounting for genetic confounding, are adoption studies, within-family sibling studies, and twin-family studies21. Put simply, consider a mother who has an identical twin sister. The mother’s son shares half of his genetic variants with his mother, but also with his aunt. If for a given trait he resembles his mother as much as his aunt, this suggests that the resemblance is fully due to shared genes. Conversely, if he is more like his mother than aunt, this demonstrates that the resemblance between mother and son is at least partially due to the environment provided by his mother  answer related but different questions: the first on “what is”, so causality in the natural situation, and the second on “what could be”, so causality due to intervening34.In sum, genetically sensitive designs can assess whether the rearing environment is influencing children’s outcomes, outside of genetic confounds. Although they are observational and hence cannot establish causality, or the absence thereof, they can strongly infer causality above and beyond the majority of typical observational studies. An important point to make is if a genetically sensitive study suggests no direct causality of the rearing environment on children’s outcomes, it does not imply that intervening is pointless. Successful parenting interventions are able to experimentally induce changes in parents’ skills or behaviors, which then causally improve child outcomes. Thus, observational studies, such as all the genetically sensitive designs described here, and experimental studies , TEDS (http://www.teds.ac.uk/researchers), NLSY kinship links (http://nlsy-links.github.io/NlsyLinks/), Netherlands Twin Register (http://tweelingenregister.vu.nl/research), and others are available online or via application. In addition, there is a data sharing culture in the behavioral genetics community, and most will likely share when asked. We suggest that researchers consider using these resources to better test their research questions.Returning to genetically sensitive designs, the disadvantage is that they require access to such data, which are challenging to collect and analyze. We note for a reader interested in using twin data to better answer their questions about the direct role of the rearing environment, twin datasets are increasingly becoming publically available. For example, TwinLife (N > 1 million) has identified 1271 genetic variants associated with educational attainment35. Each of them has a tiny effect, but these tiny effects can be summed in a PGS. The PGS, calculated for all (unrelated) individuals in an independent sample, explains 12% of the variance in educational attainment. Note that twin studies estimate the heritability of educational attainment at 40%36, so the PGS currently captures less than one-third of this; the remainder is the “missing heritability”37.A new avenue to study intertwined genetic and environmental effects employs genome-wide PGS. This method relies on genome-wide association studies (GWASs) which pinpoint genetic variants ) that are linked to a trait Fig. . The mos38 bring PGS into the classical twin design. By doing so, one can estimate the gene–environment correlation, rather than assume it is absent. Second, Lee et al.35 and Selzam et al.29 found that for cognitive traits, the predictive power of a PGS within a family was about 50% lower than across unrelated individuals. The attenuation of the PGS’ predictive power within families suggests that passive gene–environment correlations (as captured by the PGS) contribute to children’s cognitive development. As a third example, both Kong et al.39 and Bates et al.40 separately proposed the same design incorporating parental and offspring PGS to disentangle environmental transmission from genetic transmission . In both cases, the researchers split the genetic variants of the parents in half—those that the parent had and had not transmitted to the offspring—and calculated for each half the PGS for educational attainment . Amazingly, what the researchers found was both sets of parent PGS predicted adult offspring’s educational attainment. The predictive value of the transmitted PGS was unsurprising, as this captures directly transmitted genetic effects. But the predictive value of the non-transmitted PGS was not certain. If non-transmitted PGS influence children’s traits, this effect must be environmental, likely acting through rearing behaviors that affect the child’s development. Kong et al.39 aptly coined this genetic effect through the rearing environment “genetic nurturing”. Belsky et al.42 did a similar analysis but with an updated PGS score. Interestingly, when this design is expanded to include grandparents, there is little evidence for genetic nurturing from the grandparent generation43. Fourth, Wertz et al.44 incorporated both PGS of mothers and children, as well as direct measures of parenting. They showed that mothers’ cognitive stimulation explained the relation of the maternal non-transmitted PGS to child educational attainment. This indicated that there is a direct environmental transmission of parenting on children’s outcomes, unconfounded by correlated genetic transmission. Finally, de Zeeuw et al.45 and Willoughby et al.46 both used the full genetic-nurturing design (employing DNA of children and both parents) and found (thereby replicated) genetic-nurturing effects on adults’ educational attainment. Crucially, for outcomes in childhood, academic achievement and ADHD-symptoms, Zeeuw et al.45 only found direct genetic effects; no genetic nurturing. They concluded that a large contributor to why the rearing environment predicts child outcomes may well be intergenerational transmission of genetic effects.As we speak, novel methods are being designed to disentangle nature and nurture that draw on PGS. Below, we list some examples of recent developments. First, Dolan et al.ent Fig. . The res48. Another concern is that new work is indicting that a PGS is not a measure of only genetic variance. Instead, it likely represents not only causal genetic effects, but genetic ancestry, assortative mating, gene x environment interactions, direct environmental influences , and environmental confounds from, for example, SES50. Therefore, a measure of genes  can predict trait variance via environmental routes. This parallels our earlier notion that a measure of the environment can predict trait variance via genetic routes.At the moment, measured genetic variants only explain small proportions of variance and the papers mentioned above may be seen by behavioral researchers as only a proof-of-principle. Nevertheless, these exciting developments will gain in strength when increasingly larger GWASs of all sorts of traits yield more refined PGS. By this we mean that PGSs will begin to explain more and more portions of the variance in outcomes we are interested in, with the hope that eventually they will reach the theoretical upper limit of SNP heritability. Even then, using them as a genetic control  will continue to underestimate the total genetic effects we are looking to control. PGSs account for only one type of genetic effect, namely common variants. There is increasing evidence that traits such as educational attainment are influenced by not only common variants, but also rare variants51. We predict that in the not-so-far future, using simply and cheaply collected genotypic information will become a regular part of the behavioral researchers’ data collection protocol, especially as the predictive validity of the PGSs increases. This means that PGSs will allow researchers to partly control for genetic confounds in their models. We foresee that it will be easier to use PGSs than rely on genetically sensitive designs.In summary, at the moment the PGS is not a perfect “genetic control”, as it does not account for all of the genetic effects and also accounts for other effects, including the very environment we are interested in. But, we believe that next to no control, using a PGS as a statistical control is still better. Costs of genotyping are falling and the number of cohorts with genotype data is growing53. Our colleagues can measure the same trait in both the parents as the child, and use the paternal and maternal traits as covariates. We advise to assess the traits in both parents , because the child shares only 50% of their genes with one parent, but all of their genes with both parents. Hence, both parents are needed to best tag the child’s genetic liability. The parental traits, included as two covariates, then serve as a proxy for the familial transmission, including genetic transmission. In doing so, you have a proxy control for the familial effect. Hence, we term this method the Familial Control Method.The designs discussed above are the current gold-standards. However, as these types of samples discussed above are not currently easy to collect and analyze, we propose here a useful work around55 , like neurological traits, are more likely to be transmitted just genetically compared to traits with large shared-environment influences, like social values. Results of meta-analyses of twin studies on a very large number of traits can be found in Polderman et al.2 and the accompanying webtool (http://match.ctglab.nl/).We advise researchers who are interested in applying the Familial Control Method to search first in the literature for adoption and twin-family studies. Such studies with the outcome trait of interest  assessed in both the parent and the offspring generation, test whether parent to offspring transmission is mainly genetic or environmental in nature56, and for ADHD57). A researcher must decide if the mentioned assumptions are appropriate for their trait of interest, but fortunately we do believe that these assumptions are reasonable for most to make. Third, correlations among the parent and child trait and the environmental measure of interest will be attenuated by measurement error. To reduce measurement error, one can do regressions according to the Familial Control Method in a structural equation modeling framework, with multiple indicators per construct. One can fit a model with as the outcome the latent child trait of interest, and as (correlated) predictors, the latent traits of both parents and the environmental measure of interest. If dropping the regression path ‘environmental measure → child outcome’ leads to a significantly worse model fit, this implies that the environmental measure is associated with the outcome above and beyond the familial effect. If the trait of interest is genetically transmitted, this equates to above and beyond genetic confounding, so suggests a direct environmental influence. The effect size here is given by the difference in explained variance in the child outcome of the models with and without the ‘environmental measure → child outcome’ path. Adopting a structural equation modeling framework with latent variables is especially advisable for constructs that are notoriously hard to measure reliably. Another advantage of this framework, compared to stepwise regressions, is that families with missing data can be retained.The Familial Control Method, using a parental trait as genetic proxy, is not watertight, and certain assumptions must be made for it to be effective for your research question  have been found to explain only 2–5% of reading ability in children59, and more recently 5–14% of “educational achievement”  in ages 7 to 16 years60. Certainly this proportion of variance explained from simple polygenic scores is not trivial, and the predictive ability of polygenic scores is anticipated to increase in the coming years. However, for most behavioral scientists the trait in the parents is not only easier to measure, but currently also a better predictor. On a related note, the value of parental traits as predictors of child outcomes has been used for decades in studying precursors of developmental disorders. In such family-risk studies, children with a family history of say dyslexia, attention-deficit/hyperactivity disorder or autism are followed from an early age, before the disorder manifests itself. These children have an increased risk to develop the disorder62.It is likely the case that for most behavioral researchers interested in the direct role of the rearing environment, the Familial Control Method is currently the most feasible proxy genetic control. It does not require data of twin or adoption families, nor collecting DNA samples. In terms of prediction, parental traits capture more of the variance in children’s outcome than polygenic scores, so likely also capture more of the genetic confound. For the example of reading ability, it has been found that the abilities of both of the parents explain 21% of the ability of children36) and GWAS (~14%35), the estimates are less than unity which indicates that genetic influences are not entirely responsible for individual differences in SES. Indeed, work examining the intergenerational transmission of SES has suggested that both genetic and environmental transmission occurs63. In this scenario where SES is transmitted through genetic and environmental pathways, when controlling for SES in data analyses, a proportion of variance attributable to other background or environmental factors is also being controlled for in the model, unintentionally leading to reduced associations between potentially important family-level predictors and child outcomes. In other words, you’d be throwing the baby out with the bathwater.Other proxy controls such as sociodemographic factors  have been used ubiquitously, but these statistical adjustments are not capable of accounting for genetic confounding as adequately as the Familial Control Method, for several reasons. First, although sociodemographic factors such as educational attainment have been significantly associated with genetic factors through twin studies (40%64). Controlling for parental SES would not control for all of the potential genetic confounds on the association between books in the home and children’s reading ability. In conclusion, when controlling for SES, other potential sources of environmental variance are also being removed from the prospective models, and at the same time would not capture the extent of genetic confounding. We believe this would happen with other proxy control measures as well, outside of the Familial Control Method described above.On the other hand, controlling for SES does not actually control for all of the genetic confounding. Say a researcher is interested in controlling for genetic confounding when examining the direct influence of books in the home on children’s reading. Parental SES is a proxy for parental reading skill, but not a perfect correlate (average correlation is 0.2661. Simply having books around the home is not enough65, yet the message that parents are getting is that it is. The take home messages from that are that either parents who do not have the resources for a home library are hurting their children, or parents with children struggling to read are at blame because they did not have quite enough books in the home. This is unfair and inaccurate. In the end, we believe that it is important to discover true environmental effects as well as how genes and environments interplay, especially when malleable, because then we can focus as a field on creating and testing interventions that have a greater chance of directly improving children’s outcomes.Here we have laid out numerous ways that genetic confounding can be controlled for when examining the rearing environment, summarized in a decision flowchart Fig. . We can Further information on experimental design is available in the Related Manuscript FileReporting summary


Jatropha curcas breeding. Thus, the present work aimed to apply the random regression technique and study its possibilities for the analysis of repeated measures in Jatropha curcas breeding. To this end, the grain yield (GY) trait of 730 individuals of 73 half-sib families was evaluated over six years. Variance components were estimated by restricted maximum likelihood, genetic values were predicted by best linear unbiased prediction and RRM were fitted through Legendre polynomials. The best RRM was selected by Bayesian information criterion. According to the likelihood ratio test, there was genetic variability among the Jatropha curcas progenies; also, the plot and permanent environmental effects were statistically significant. The variance components and heritability estimates increased over time. Non-uniform trajectories were estimated for each progeny throughout the measures, and the area under the trajectories distinguished the progenies with higher performance. High accuracies were found for GY in all harvests, which indicates the high reliability of the results. Moderate to strong genetic correlation was observed across pairs of harvests. The genetic trajectories indicated the existence of genotype × measurement interaction, once the trajectories crossed, which implies a different ranking in each year. Our results suggest that RRM can be efficiently applied for genetic selection in Jatropha curcas breeding programs.Random regression models (RRM) are a powerful tool to evaluate genotypic plasticity over time. However, to date, RRM remains unexplored for the analysis of repeated measures in  Jatropha curcas L. ranks among the most relevant crops for biofuel production [Jatropha curcas as an excellent alternative for renewable energy production [oduction ,2. Its hoduction ,3,4.It has a high oil content in its seeds up to 35%), with a high oil-to-biofuel conversion efficiency, compared to other species ,5. After%, with aJatropha curcas is a perennial plant that produces during several years, and in its breeding process, genetic selection has been carried out considering only one harvest [ harvest , several harvest  or sever harvest ,10.It is worth to highlight some specific issues in perennial plant breeding that affect genetic selection. Once repeated measures are taken in the same individuals over time, analyses must consider: the permanent environmental effects and genetic and residual covariance among harvests. Thus, for quantitative traits, genetic selection must consider several harvests to maximize the selective accuracy .Jatropha curcas breeding because they consider the genetic and residual covariance among harvests and allows fitting the genetic and non-genetic trajectories (plot and permanent environment) over time [Jatropha curcas breeding.In this context, random regression models (RRM) can be a very efficient alternative for repeated measures analyses in ver time ,12. Besiver time –15. ThusJatropha curcas were evaluated for grain yield (GY) trait (kg plant-1), in six harvests (2010 to 2015) ; and, hmax is time of the last harvest (2015).The time of the harvests must be scaled to range from -1 to +1 in order to use Legendre polynomials. The scaling formula is given below :tx=−1+2Yijkl is the ith individual  on the jth harvest  on the kth replication  on the lth plot ; Rk is the fixed effect of replication;. bM is the fixed regression coefficient fitted through the fifth degree of Legendre polynomials to the common average trajectory of progenies. The random effects, gim, pikm, and sik are the random regression coefficients for the Legendre polynomials of degree m for the genetic, permanent environment, and plot effects, respectively. ϕijm is the mth Legendre polynomial for the jth harvest from the ith individual; m is the fit degree, ranging from M = 0 to M = 5, of the Legendre polynomial for the genetic, permanent environment, and plot effects, respectively; and εijkl is the residual random effect associated with Yijkl.Variance components were estimated by restricted maximum likelihood , and geny is the phenotypic data vector; b is the vector of the effects of measurement-replication combinations (assumed to be fixed) added to the overall mean; g is the vector of the individual genetic effects (assumed as random); p is the vector of the permanent environmental effects (assumed as random); s is the vector of the plot effects (assumed as random); and e is the vector of residuals (random). X, Z, W and Q refer to the incidence matrices for these effects.In the matrix notation, the above model is described as follows:g~N, p~N, s~N, and e~N; where Kg, Kp, and Ks are the covariance matrices for genetic, permanent environment, and plot effects, respectively; ⊗ denotes the Kronecker product; I is an identity matrix with appropriate order to the respective random effect; and R refers to the matrix of residual covariances. Different residual covariance structures  were tested.In this model, LogL is the logarithm of the maximum (L) of the residual likelihood function, p is the number of estimated parameters, n is the number of observations, and r(x) is the rank of the incidence matrix of fixed effect.The polynomial order in random regression models was selected using the Bayesian information criterion (BIC) , as follLogLR is the logarithm of the maximum (LR) of the residual likelihood function of the reduced model .The significance of the genetic, permanent environment and plot effects was tested using the likelihood ratio test (LRT) , as follx refers to the genetic or permanent environmental or plot covariance matrices.Variance components estimates  of the genetic coefficient covariance matrix, aiming to evaluated the genotype x measurement interaction, was calculated by the following expression [mth element of the fth eigenvector of Kg, and Φm is the normalized value of the mth Legendre polynomial.The eigenfunction  between each pair of harvests were obtained by the following expression:i and j; i and j, respectively.The areas under the genetic trajectories (A), aiming to rank the clones, were obtained by the following expression ,25:A=μ+Statistical analyses were performed using the ASReml 4.1  and R 2 softwareAccording to the BIC, the best RRM was the one of order two for the genetic effects, order five for the plot effects, and order one for the permanent environment effects; with diagonal residual variance structure . Thus, tThe genetic variance was not constant through the harvests . The estJatropha curcas progenies  . The secJatropha curcas progenies. They ranged from -0.5879 to 2.0520, and the top ten families  were: 6, 70, 48, 16, 10, 1, 34, 39, 15, 29, and 54. The complete rank are presented in the supplementary material (ρg < 0.66) in the 2010–2012, 2010–2013, 2010–2014, 2010–2015; high magnitudes (0.67 < ρg < 0.89) in the 2010–2011, 2011–2014, and 2011–2015; and very high magnitudes (ρg > 0.90) in the remaining pairs of harvests . Considering the other random effects of the model , a total of 36 covariance components were estimated. The RRM are equivalent to the covariance functions and can be considered a reduced and simplified multiple-trait model, which allows the same parameters of interest  to be obtained, but with lower parameterization and with less computational effort [Among the various criteria for selection of models, the BIC is prominent, because it is a consistent criterion. The selected model fit diagonal residual variance structure  and the projection of these genetic values for a common age, for ordering and selection purposes. Besides that, Legendre polynomials have been used to model growth curves in perennial plant breeding ,28.Jatropha curcas families. Besides that, the plot and permanent environmental effects are statistically significant , i.e., they differ from zero. The significance of genetic effects shows the potential of this population and allow the selection of superior families, even with a restricted genetic basis explored in Jatropha curcas breeding in Brazil [According to the LRT, there was genetic variability among the n Brazil ,29. It in Brazil .The RRM is mainly focused on visualizing trajectories and covariance functions over time. Therefore, the variance components and genetic parameters can vary over the harvests. In this study, it is generally observed an increasing trend in the variance components and genetic parameters over time. Such pattern was also observed by ,10,30.Jatropha curcas [The individual heritability estimates decreased from the first to the third harvest. Then, there was an upward trend until the sixth harvest, which indicates that the third harvest is more affected by the environmental conditions. According to , the hera curcas ,30. BesiJatropha curcas genotypes are more sensitive to environmental variations in the early stages of growth [Upward trend in genetic variances and heritabilities along the time, are biologically consistent. Such trends can be related to the fact that f growth ,30, due f growth , which lf growth  for an aAccording to , the seld higher . This isd higher .Jatropha curcas.The genetic trajectories describe the genetic values of each family over time and encompass the six harvests evaluated in this study. The RRM can predict the genetic value for any family in any time (between the first and sixth harvest). The trajectories demonstrated that the families presented similar performance in the first harvests, which reveals that a precocious selection tends to be less efficient. The genetic correlations reinforce the inefficiency in earlier selection. The genetic correlations between the pairs of harvest presented very high values > 0.90) only between the last harvests. In addition [0 only beJatropha curcas [The RRM fitted through Legendre polynomials allows obtain the eigenfunctions and eigenvalues . Accordia curcas .Jatropha curcas progenies reinforced the presence of genotype × measurement interaction, once their trajectories are non-linear and intersect each other, which implies a different ranking in each harvest. In addition, the trajectories could also be interpreted as genetic variability. The more distant the genetic trajectories from each other, the more genetically distinct are the progenies [The genetic trajectories of the 73 rogenies .In this study, genotype ranking was performed based on the areas under the genetic trajectories. Therefore, genetic selection was based on the higher area under the genetic trajectories. The advantage of this strategy is that selection response can be predicted not only in the genotypic expression in any harvest but also in quantifying the environmental sensitivity of the trait through the genetic trajectories (robustness or responsiveness to changes in the environment). Besides that, it can be used for any number of harvests .Jatropha curcas breeding programs.The RRM can be used to help describing the observed phenotypic over time efficiently and allows genetic selection based on adaptability, stability and yield performance ,36–38. TS1 TableP.D. Gen: polynomial degree for the genetic effect; P.D. Plot: polynomial degree for the plot effect; P.D. Perm: polynomial degree for the permanent environment effect; DF: degrees of freedom; LogL: logarithm of the maximum of the restricted likelihood function; Gen. P.: total number of genetic covariances estimated; Plot P.: total number of plot covariances estimated; Perm. P.: total number of permanent environment covariance estimated; Res. P: total number of residual covariances estimated; and BIC: Bayesian information criterion. The selected model by BIC was indicated in bold.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Humans have a long history of consuming fermented foods. However, their prevalence in human diets remains largely undetermined, and there is a lack of validated dietary assessment tools assessing the intake of different fermented products. This study aimed to identify fermented foods consumed in The Netherlands and determine the relative validity of a food frequency questionnaire (FFQ) compared to multiple 24-h recalls for estimating their intake.The validation population consisted of 809 participants (53.1 ± 11.9 years) from a Dutch observational cohort (NQplus) who completed a FFQ and multiple 24-h recalls. Fermented foods from the FFQ and recalls were identified and aggregated into conventional food groups. Percent difference in mean intakes, quintile cross-classification, Spearman’s correlations, and Bland-Altman analyses were used to evaluate the agreement between the two dietary assessment methods.s ≥ 0.50) were obtained for fermented beverages , cereals/grains (wholegrain bread), and dairy (yoghurts). For ‘other bread’, quark, and buttermilk, correlations were low (rs < 0.20). Bland-Altman analyses revealed good agreement for fermented beverages , breads , pastries, chocolate, and fermented dairy (cheeses) (mean difference: 0.1–9.3).Approximately 16–18% of foods consumed by this population were fermented, and a further 9–14% were dishes containing a fermented ingredient. Fermented foods with the highest consumption included coffee , yoghurts , beer , wholegrain bread , wine , and cheese . Mean percent difference between the FFQ and recalls was small for fermented beverages (coffee), breads , and fermented dairy (cheeses) (0.3–2.8%), but large for buttermilk and quark (≥53%). All fermented food groups had > 50% of participants classified into the same or adjacent quintile of intake (58%-buttermilk to 89%-fermented beverages). Strong Spearman’s correlations , wholegrain and rye bread, and fermented dairy (cheeses). However, for less frequently consumed foods, such as quark and buttermilk, the levels of agreement were poor and estimates of intake should be interpreted with caution. This report provides the basis for developing a FFQ specific for fermented foods.The online version contains supplementary material available at 10.1186/s40795-020-00394-z. Fermented foods are foods or beverages in which microorganisms have been intentionally added or used to enzymatically transform food components . They coAccurate dietary assessment is a core tenet of nutritional epidemiology that aids in the appropriate identification of diet-health associations. To date, the food frequency questionnaire (FFQ) is one of the most common dietary assessment instruments used to estimate habitual food and nutrient intake in large populations, for reference periods of 1 month to 1 year . Since tThe validity of FFQs in estimating intakes of various nutrients, foods, and food groups has been documented in multiple studies –16. Howen = 1468) and/or 24-h recall (n = 1117). Additional data on anthropometrics, body composition, blood pressure, pulse wave velocity, advanced glycation endproduct (AGE) accumulation, and cognitive performance, were also collected. Background demographics, health, and lifestyle data were collected via validated questionnaires administered online using the open-source survey tool Limesurvey . Fasting blood samples and 24-h urine samples were also collected. All measurements were repeated at 1 and 2 years of follow-up and performed according to a standardised protocol by trained research personnel. The study was approved by the ethical committee of Wageningen University and Research and performed in agreement with the Declaration of Helsinki. Written informed consent were obtained from all participants prior to the start of the study.The Nutrition Questionnaires plus (NQplus) study is a prospective cohort study that was primarily conducted in Caucasian Dutch adults (20 to 70 years), living in or around Wageningen, The Netherlands. It was initiated as an ‘add-on’ study to the National Dietary Assessment Reference Database (NDARD) project, to gather extensive data on participant demographics, lifestyle, medical history, and cardiometabolic health outcomes. A complete description of NQplus and the NDARD project can be found elsewhere , 18. Brin = 2048), participants who did not have any dietary assessment data were excluded (n = 17), as were those who completed fewer than two phone-based recalls (n = 1081). A further ten participants with implausible energy intakes were excluded from analyses   against the difference in measures  . To visu2, while 52% were overweight or obese (61% of men and 42% of women). The majority of participants had a high educational level (65%), had never smoked (52%), and had not followed a diet in the month preceding enrolment to the study (93%). About a fifth of participants had a disease history of hypertension (22%) and high cholesterol (18%), while only a small percentage (less than 5%) had a history of cancer, diabetes, heart attack, and/or stroke.The characteristics of the participants included in the validation study are shown in Table The identification and classification of fermented foods from the FFQ and 24-h recall into food groups and subgroups, is provided in Additional file Mean energy-adjusted daily intakes and percentage of average daily energy intake for each fermented food group, the number of consumers per food group, as well as the percent and absolute differences in mean intakes, are presented in Table High intake levels of non-fermented dairy foods were also observed in this population , the main contributor being milk Table . While tCompared to group level percent differences in means, higher individual level percent differences in means was observed for total energy as well as multiple fermented food groups, with the most striking contrasts observed for beer, brown bread, white bread, rye bread, ‘other bread’, fermented dairy, cheeses, yoghurts, and buttermilk  for both crude and energy-adjusted intakes were obtained for fermented beverages , fermented cereals/grains (including wholegrain bread), and fermented dairy (including yoghurts). Correlation coefficients in the higher acceptable range were obtained for rye bread and cheeses (0.40 ≤ rs ≤ 0.49), while correlation coefficients in the lower acceptable range were found for brown bread, white bread, pastries, and chocolate (0.20 ≤ rs ≤ 0.39). Only for three fermented foods  was the correlation coefficient was less than 0.20. All correlations were statistically significant (p < 0.01). Crude and energy-adjusted attenuation factors for two 24-h recall replicates were consistently high for fermented beverages (0.81), coffee (0.85), beer (0.71), wine (0.64), while moderate values were observed in the range of attenuation factors for fermented cereals/grains (0.51), wholegrain bread (0.47), rye bread (0.54), fermented dairy (0.52), cheese (0.42), and yoghurts (0.53)  to 0.80 (fermented beverages) Table . Energy-, wine 0.4, while Similarly, for non-fermented food groups, strong correlations were obtained for non-fermented dairy, including milk, while acceptable correlations were obtained for butter and non-fermented soya (in the higher range), and ice cream and cream (in the lower range) Table . The crudifference ≥ 0.05). However, for wine, total fermented cereals/grains, white bread, chocolate, yoghurts, quark, and buttermilk, significant differences were found between the two dietary assessment methods . The results of the regression analyses further revealed a significant amount of proportional bias for wine, white bread, rye bread, pastries, chocolate, cheese, quark, and buttermilk (pslope < 0.0001). For certain foods , the FFQ tended to consistently underestimate their consumption compared to the 24-h recalls, while intakes were overestimated for others (rye bread and buttermilk). A small bias for coffee, beer, brown bread, and ‘other bread’ was also observed (p < 0.05) , while poor agreement was revealed for total non-fermented dairy, cream, ice cream, and milk   , albeit poor parametric assessment of differences (as indicated by the low agreement in percent difference and Bland-Altman).Fermented foods with the highest consumption levels included coffee , yoghurts , beer , wholegrain bread , wine , and cheese . These foods, with the exception of coffee, also correspond to the top fermented foods contributing to total daily energy intake in this study. Comparing our findings to studies in other European populations, mean daily intakes were similar for coffee 404 g/day), bread products (64 to 146 g/day), butter (5 g/day), cheese (25 to 58 g/day), yoghurt (95 g/day), and soya products (6 to 10 g/day) , 15, 40;4 g/day, s ≥ 0.50) were obtained for all fermented beverage groups , total fermented cereals/grains, wholegrain bread, total fermented dairy, yoghurts, total non-fermented dairy, and milk. Since the use of different dietary assessment instruments in distinct populations could affect results, we compared our results with those obtained from other studies for similar food groups. Streppel et al. [s 0.63) and wholegrain bread (rs 0.55) in our study are slightly lower than those reported in Streppel et al. [s 0.71 to 0.93 for bread). Although total bread consumption in this elderly population (126 to 133 g/day) is comparable to total fermented cereals/grains intake by the study cohort described in the current publication (129 to 143 g/day), older adults tend to have more stable diets and different dietary patterns than those of younger adults, which might account for this difference in correlation coefficients. The attenuation factors obtained for the food groups investigated in this study were considerably higher for fermented beverages, coffee, beer, and wine (range between 0.64–1.0) than for brown bread, white bread, ‘other bread’, pastries, chocolate, and quark (range between 0.14–0.77). Since attenuation factors closer to one indicates a better overall estimation of intake [Since a critical measure of success for an FFQ is its ability to accurately rank individuals into high- and low-intakes based on their habitual diet , we evall et al.  assessedl et al. , 100 Bell et al. , 1213 Gel et al. , comparil et al.  , milk (45.6–49.5% same quartile), milk and soya products (57.4% same tertile), butter (49.7–50.3% same quartile) –14, and A strength of this study is the inclusion of a large population size for validation. Moreover, we utilised multiple assessment methods, which allows for a more comprehensive evaluation of relative validity at both the individual and group level . NotwithA further aspect worth highlighting is that the FFQ used in the NQplus study was not specifically designed to assess the intake of fermented foods; consequently, not all fermented food products could be captured by this method and some may only be represented by a food group comprising fermented and non-fermented foods. Indeed, 247 fermented foods were reported for the 24-h recall compared to only 39 in the FFQ. The fermented foods in this validation study primarily consisted of foods common to the Western diet , but there exists a wealth of fermented foods from other cultures and regions that are increasingly consumed due to globalisation . Novel fermented foods are also being developed, driven by the realisation of their potential impacts on health , and thaAbout a fifth of the Dutch diet consists of fermented food items. Adequate to good relative validity was determined for the FFQ compared to the 24-h recall across all statistical measures for commonly consumed foods, including total fermented beverages, coffee, wholegrain bread, rye bread, total fermented dairy, cheeses, as well as butter (non-fermented). For wine, beer, total fermented cereals/grains, white bread, chocolate, yoghurts, as well as total non-fermented dairy, milk, and non-fermented soya, good ranking ability of participants into their levels of consumption could be established, albeit poor agreement in absolute intakes. For quark and buttermilk (fermented), as well as cream and ice cream (non-fermented), acceptable relative validity between the two methods could not be established; thus, the intakes for these food groups should be interpreted with caution in future studies using this population. Developing a FFQ specific for fermented foods would be valuable to capture global fermented foods that are increasingly consumed, and to delineate between fermented and non-fermented versions of foods that could obscure investigations between fermented food intake and health.Additional file 1:Table S1. Identification and Classification of Fermented Foods from the FFQ and 24-h Recalls in NQplus.Additional file 2:Table S2. Mean Individual Percent Difference and Test Statistics from Bland-Altman Analyses for the Validation Sample (n = 809).Additional file 3:Table S3. Attenuation Factors for the Reference 24-h Recall Compared to the Food Frequency Questionnaire.Additional file 4:Figure S1. Bland-Altman plots demonstrating relative validity of the FFQ versus 24-h recalls for fermented food subgroups.Additional file 5:Figure S2. Bland-Altman plots demonstrating relative validity of the FFQ versus 24-h recalls for non-fermented foods.Additional file 6:Table S4. Sex-Specific Spearman’s Correlations for Fermented and Non-Fermented Foods.


It has been claimed that efficacy estimates based on the Hamilton Depression Rating-Scale (HDRS) underestimate antidepressants true treatment effects due to the instrument’s poor psychometric properties. The aim of this study is to compare efficacy estimates based on the HDRS with the gold standard procedure, the Montgomery-Asberg Depression Rating-Scale (MADRS).We conducted a meta-analysis based on the comprehensive dataset of acute antidepressant trials provided by Cipriani et al. We included all placebo-controlled trials that reported continuous outcomes based on either the HDRS 17-item version or the MADRS. We computed standardised mean difference effect size estimates and raw score drug-placebo differences to evaluate thresholds for clinician-rated minimal improvements . We selected 109 trials  that assessed the HDRS-17 and 28 trials  that assessed the MADRS. The summary estimate (effect size) for the HDRS-17 was 0.27 (0.23 to 0.30) compared to 0.30 (0.22 to 0.38) for the MADRS. The effect size difference between HDRS-17 and MADRS was thus only 0.03 and not statistically significant according to both subgroup analysis (p = 0.47) and meta-regression (p = 0.44). Drug-placebo raw score difference was 2.07 (1.76 to 2.37) points on the HDRS-17  and 2.99 (2.24 to 3.74) points on the MADRS .Overall there was no meaningful difference between the HDRS-17 and the MADRS. These findings suggest that previous meta-analyses that were mostly based on the HDRS did not underestimate the drugs’ true treatment effect as assessed with MADRS, the preferred outcome rating scale. Moreover, the drug-placebo differences in raw scores suggest that treatment effects are indeed marginally small and with questionable importance for the average patient. The debate whether antidepressants are an effective treatment for depression is ongoing and unresolved –5. AlthoAn alternative approach to examine the efficacy of antidepressants would be to base effect size estimates on an outcome that is widely accepted as a reliable and valid measure of depression. One such outcome is the Montgomery-Asberg Depression-Rating Scale (MADRS), which was constructed to be particularly sensitive to change and to treatment effects on core depression symptoms . IndepenThe aim of this meta-analysis was thus to re-evaluate the data from short-term antidepressant trials for adults with major depression collected by Cipriani et al.  by focusSince this a post-hoc analysis of a freely available dataset, we did not write a study protocol and did not pre-register the planned analysis. That is, we did not conduct our own literature search, but relied on the work by Cipriani et al. . Except https://data.mendeley.com/datasets/83rthbp8ys/2). This is the largest database of new-generation antidepressants for the acute treatment of major depression compiled thus far. The comprehensive literature search conducted by Cipriani et al. [Our analysis was based on short-term, mostly 8-week clinical trials of antidepressants for adults with unipolar major depression, reported in a recent systematic review by Cipriani et al. . The auti et al.  yielded When trials had multiple treatment arms with different doses of antidepressants, these arms were aggregated. This is an appropriate procedure, as low-to-medium doses achieve the highest efficacy . When trWe calculated the standardised mean differences for each trial and aggregated them by means of a random effect meta-analysis based on the inverse variance method according to the procedure suggested by Munkholm et al.  using th2. Test statistics and confidence intervals were adjusted according to the procedure by Hartung and Knapp, as recommended in the Cochrane handbook [We used subgroup analysis and meta-regression to test for differences between the HDRS and MADRS. Effect size estimates with corresponding 95% confidence intervals (CI) were based on Hedges’ g standardised mean difference method and the variability of effect sizes due to heterogeneity was calculated as Ihandbook .Classifying effect sizes as small or large based on arbitrary thresholds is not recommended . We therA sensitivity analysis included a meta-regression across all antidepressants and controlled for potential confounders that differed between trials using the HDRS-17 and the MADRS. These covariates included year of publication, drop-out-rate, sample size, and publication status. We also cross-validated our findings with the standard methods for continuous outcomes proposed in the Cochrane handbook, the DerSimonian-Laird random-effects meta-analysis and the inverse-variance fixed-effects method .For the overall analysis across different antidepressants, there were 137 trials/comparisons. A full list of included studies is provided in the online supplement  and for 2 = 45%) and moderate for the MADRS (I2 = 74%). The forest plot for the meta-analysis across all drugs is shown in the online supplement  for the HDRS-17 and the MADRS were almost identical , and the difference was statistically not significant based on both subgroup analysis  and meta-regression . Observed variability across studies, which could indicate between-study heterogeneity, was low for the HAMD-17  was possible for bupropion (k = 9), duloxetine (k = 17), escitalopram (k = 12), paroxetine (k = 21), venlafaxine (k = 7), and vilazodone (k = 7). The results for the individual drugs are shown in 2: heterogeneity index, p: p-value of the meta-regression analysis with the instrument (HDRS-17 vs. MADRS) as predictor variable, k: number of trials, N: number of participants (antidepressant plus placebo arm).SMD (95% CI): standardized mean difference based on Hedges’ g and corresponding 95% confidence interval, IAs detailed in the methods section, the pre-defined threshold for a minimal improvement according to clinician evaluation is 7 points on the HDRS and 8 points on the MADRS. Corresponding cut-offs for a minimal important difference according to patient ratings are about 4 points for the HDRS-17 and 5 points for the MADRS. In the present meta-analysis, the drug-placebo difference in raw scores was 2.07 (CI = 1.76–2.37) for the HDRS-17 (possible range 0–52 points) and 2.99 (CI = 2.24–3.74) for the MADRS (possible range 0–60 points) . Drug-plIn univariate meta-regression analysis, trials using the HDRS-17 and the MADRS differed by year of publication, dropout rate, and sample size, but not by publication status. In a multivariable meta-regression analysis that included all significant confounding variables as additional predictor variables, the overall effect of the instrument (HDRS-17 compared to MADRS) was statistically not significant and very small . The results were almost identical when different meta-analytic methods were used. The inverse-variance fixed-effects model produced estimates of ES = 0.25 (0.23–0.28) and ES = 0.30 (0.27–0.34) for HDRS-17 and MADRS, whereas based on the DerSimonian-Laird random-effects model the estimates were ES = 0.26 (0.23–0.29) and ES = 0.30 (0.22–0.38).Our meta-analysis found a mean effect size of 0.30 (0.22–0.38) for the MADRS as compared to 0.27 0.23–0.30) for the HAMD-17. That is, the effect sizes for both rating scales are comparable when averaged over all drugs. Although the MADRS is considered the gold standard  and the –0.30 forWe cannot preclude with certainty that there is no difference between HDRS-17 and MADRS for all individual drugs. After correction for multiple testing, for vilazodone the effect size based on MADRS was statistically significantly larger than the effect size produced by the HDRS-17. However, it is important to note that vilazodone’s HDRS-17 effect size (ES = 0.09) was considerably lower than the summary estimate for all drugs (ES = 0.27), whereas vilazodone’s MADRS effect size (ES = 0.41) was above the average for all drugs (ES = 0.30). This is a peculiar inconsistency, because if vilazodone was truly more efficacious than other drugs, this should show in an above-average score for both HDRS-17 and MADRS, but not in a very low HDRS-17 score and in a high MADRS score. This inconsistency might be explained by the low proportion of published vilazodone trials using the HDRS-17 , whereas all three trials using the MADRS were published (100%). It is known that the trials published in the scientific literature exaggerate efficacy estimates , 29. ThePrevious research tried to minimise biases assumed to underlie the full HDRS scale by focusing on selected items. For instance, Bech developed a HDRS subscale that contains only core depression symptoms . In a reHieronymus et al.  chose anThis study is not without limitations. First, it is important to note that the MADRS was applied in relatively few trials and comparisons between MADRS and HDRS-17 for individual drugs are thus limited to bupropion, duloxetine, escitalopram, paroxetine, venlafaxine, and vilazodone. However, it is unlikely that this restriction has introduced systematic bias. Gartlehner et al.  concludeThe effect size estimates reported in recent meta-analyses of antidepressants that were mostly based on the highly criticised HDRS-17 do not appear to underestimate the drugs treatment effects. When efficacy estimates are based on the MADRS, the preferred rating scale for depression that is particularly sensitive to change in core depression symptoms, the effect size estimates do not differ meaningfully. Moreover, we found that the average drug-placebo difference in raw scores for both the HDRS-17 and MADRS fall short of the cut-offs for a minimally important improvement. Prominent claims that the HDRS has significantly underestimated antidepressants’ true efficacy are thus not based on convincing evidence. A more plausible explanation would be that the efficacy of antidepressants is indeed poor and of questionable importance to the average patient.S1 Checklist(DOC)Click here for additional data file.S1 File(PDF)Click here for additional data file.


Populous tomentosa germplasm resources can significantly improve the efficiency of parental matching. However, due to some factors such as loose powdering time and pollen competitiveness, the offspring derived from open-pollination families which do not undergo completely random mating. Although hybrid combinations based on the male identification method have a high combining ability, this method cannot easily cover the mating combinations of all male and female specimens in the germplasm bank. In addition, the performance of superior plants in open-pollinated families also affects the selection result. If the trait performance value is higher than the population average, then the special combining ability of the reconstructed hybrid combination may be overestimated. Obtaining a solution to the above problems is of great significance for improving the efficiency and accuracy of selecting hybrid parents of P. tomentosa. In this study, 24 pairs of SSR (Simple Sequence Repeats) molecular markers were used to analyze the genetic differentiation of P. tomentosa germplasm resources. The results showed that the genetic variation of the P. tomentosa population was derived from individuals within the provenance, indicating that high genetic diversity is preserved in provenances. The correlation analysis showed that there was a significant positive correlation between the special combining ability of planting height and diameter at breast height (dbh) of the 34 full-sib progeny population and the genetic distance between the parents. Then, the genetic distance between 18 female plants with high fertility and 68 male plants with large pollen quantity was analyzed using this correlation. Fifteen female parents and 12 male parents were screened out, and 52 hybrid combinations with high specific combining ability for growth traits were predicted. Furthermore, for the male parent identification of superior individual plants, we constructed the breeding parent population including 10 female parents and 5 male parents, generating 14 hybrid combinations with potentially high combining ability. The results of the hybridization test showed that the specific combining ability of plant height and dbh was significantly higher than the controlled pollination. Moreover, genetic distance and paternal identification can be used to rapidly and efficiently construct hybrid parent combinations and breeding parent populations.Parent selection is the core of hybrid breeding. The breeding strategy involving the parental identification of superior open-pollinated progeny of  The strength of parent combining ability determines the strength of the heterosis and a hybrid combination with strong advantages can only be developed via the breeding of parents with high specific or general combining ability. The traditional selection method relies on a pre-mating design to establish a breeding parent population for the detection and selection of offspring. This method requires considerable time and manpower and physical resources, and the cost is extremely high. Thus, the traditional breeding method has great limitations. Therefore, developing a method to quickly and accurately predict the hybrid parental combination has become a problem with which breeding researchers are extremely concerned and committed to solving.1 proposed a BWB (breeding without breeding) breeding strategy that uses trees germplasm with a known pedigree to construct a breeding population. This method requires fewer resources than controlled pollination in traditional breeding and has been performed to reconstruct the pedigree of open-pollinated families2. However, there is a clear sampling error deficiency in the BWB breeding strategy. In other words, the performance of superior individuals in the plantation will affect the selection results. The phenotypic value of the superior plants is higher than the population average, which will lead to overestimation of the special combining ability of the reconstructed hybrid combinations. However, the phenotypic value of superior plants is lower than the population average, which will lead to the underestimation of the special combining ability of reconstructed hybrid combinations. In addition, the generation of open-pollinated families is often affected by many factors, such as paternal pollen competitiveness, flowering consistency, parental location in the group, and wind direction. This is not a completely randomized genetic environment, and certain parents with high combining ability will not have the opportunity to mate to produce offspring. Thus, the hybrid combinations with high combining ability predicted by paternal identification were not comprehensive.For the genetic improvement of forest trees, EI-Kassaby et al.3 demonstrated that genetic distance can be used as a basis for selecting a superior rice combination. Phumichai et al.4 also showed that the genetic distance was significantly positively correlated with the special combining ability of maize yield. Jovan et al.5 proved that there was a significant positive correlation between genetic distance and the special combining ability of maize economic traits. In addition, Tian et al.6 used molecular markers to analyze the correlation between genetic distance and Brassica napus economic traits, and the results showed that there was a significant positive correlation of the two indexes. However, there are exceptions. Perenzin et al.7 found that genetic distance has a certain correlation with heterosis in wheat, but the correlation coefficient is small and insufficient to predict heterosis. Dong et al.8 analyzed the relationship between genetic distance and heterosis in Pinus massoniana and found that genetic distance can be used to predict combining ability and heterosis within a certain genetic distance. The difference in genetic distance and combining ability between the above parents is related to the research material and type of marker applied. Not all molecular markers are suitable for estimating the combining ability between parents. The correlation between genetic distance and combining ability among parents has a close relationship with the research object. Thus, could a positive correlation occur between genetic distance and combining ability in P. tomentosa? Can genetic distance be used to predict the high combining ability of hybrid parents to make up for the deficiency of the parent population constructed by paternal identification of open-pollinated superior plants?With the development of molecular marker technology as well as techniques to determine the genetic diversity and genetic differentiation of species provenance, new methods of indirectly predicting the combining ability, which improve breeding efficiency, were developed. In crops, Wang et al.P. tomentosa seedlings height and diameter at breast height (dbh) of 34 hybrid combinations and determined the correlation between SSR marker genetic distance and the special combining ability of 19 parentals derived from 5 provenances. The possibility of using genetic distance to predict high combining ability for hybrid combination was discussed, and the open-pollinated individual male identification results were compared with the primary breeding parent population based on genetic distance. Constructing a more reliable high combining ability breeding parent population provides a theoretical reference for overcoming the blindness of hybrid combination and predicting high combining ability combinations of P. tomentosa.In this study, we analyzed the P. tomentosa Research Institute of Beijing Forestry University  organized the provincial P. tomentosa research collaboration organization to carry out a preliminary resource survey. In the Huanghe-Huaihe-Haihe river basin, the P. tomentosa distribution area is 1 million km2  in 100 counties, and 1047 high-quality P. tomentosa trees were selected. In 1986, the germplasm resources bank of P. tomentosa in Guan Xian County of Shandong Province was established. At present, 441 genotypes of excellent diploid trees from Beijing, Hebei, Shandong, Henan, Shanxi, Shaanxi, Gansu, Anhui, and Jiangsu provinces are preserved in the germplasm bank, 18 of which have been screened for good fertility and high combining ability of the female plants9, and 68 male plants with large pollen quantities were also identified10. The female plants are numbered T-F-1 to T-F-18, and the male plants are numbered T-M-1 to T-M-68. In addition, 17 female plants and 2 male plants distributed in 11 counties , common reverse primers, and fluorescent  M13 primers. The specific PCR procedure is as follows: 94 °C for 5 min; 94 °C for 30 s; 25 cycles of 72 °C for 30 s; 94 °C for 30 s, 53 °C for 30 s, 72 °C for 30 s, 8 cycles; and 72 °C for 8 min. The products were then stored at 4 °C. The PCR products were generated by Ruibo Xingke Biotechnology (Beijing) Co., Ltd. on an ABI-3730XL Genetic Analyzer, and the results were read and analyzed using GeneMarker 1.75 software12.Total DNA extraction was performed using a plant genomic DNA extraction kit . In this study, three types of primers were required according to Schuelke's methodA total of 536 pairs of SSR primers in the SSR database  published by the International Populus Genome Consortium were randomly selected as screening objects. All the forward primers were labeled with M13 fluorescent dye at the 5′ tail end, and all the primers  were entrusted to Beijing Ruibo Xingke Biotechnology Co., Ltd. for synthesis. The above primers were used for TP-MI3-SSR PCR to screen for SSR polymorphism primers with differences among provenances.13. The number of allele loci (NA), effective allele number (NE), expected heterozygosity (HE), observed heterozygosity (Ho) and Shannon's polymorphism index (I) were calculated by software POPGEN Version 1.3214 for all clones of the P. tomentosa populations. These parameters can ideally reflect the genetic diversity of the population as a whole and sub-regions. The F-statistic index 16 was estimated by FSTAT 2.9.3 software17, where the genetic differentiation coefficient (Fst) reflects the degree of the population genetic differentiation. The fixation indices FIT and FIS represent degree of inbreeding at the total population level and between individuals in each subpopulation, respectively18. The estimated allele frequencies were imputed by GenAlEx version 6.519 software to compute the average value across markers of the standardized genetic differentiation measure (Gst) proposed and recommended for SSR data by Hedrick20.The original data were converted as needed using CONVERT VERSION 1.3.1 software21. Rousset's genetic distance model 22. The geographical distance was based on the latitude and longitude of the sampling point, and Vincenty's formula (https://www.movable-type.co.uk/scripts/latlongvincenty.html) was used to calculate the geographic distance (km) between the provenance and the individuals within the provenance. In addition, the Distance Web Service (Version 3.23) was used to perform a Mantel correlation test of genetic distance and geographic distance24.Polymorphism information content (PIC) was calculated using PIC-CalcVersion 0.6 software to evaluate genetic diversity25, and a Pearson correlation analysis was performed using Origin pro version 9.0. There were F families, with the observed value of a single plant as the statistical unit. Tree height and diameter at breast height data were analyzed by the following mixed linear model for restricted maximum likelihood analysis: ij is the observation of the i-th male parent and the j-th female parent, μ is the universal mean, and eij is the residual effect.The height and diameter at breast height data for 34 full-sib families Table  were obtFi, which is the random effect of general combining ability of the parent i, E(Fi) = 0, Var(Fi) = σ2f;Mj, which is the random effect of general combining ability of the father j, E(Mj) = 0, Var(Mj) = σ2m;FMij, which is the special combining ability random interaction effect of female parent i and male parent j, E (FMij) = 0; Var(FMij) = σ2fm28.The random effects of genetic variance include:P. tomentosa germplasm resource bank (Supplementary Table Genetic differentiation was performed at 24 polymorphic SSR loci (Supplementary Table Fis) was negative in the nine provenances, ranging from − 1.0 to − 0.237  and 4 male  parents  and six male  parents were randomly selected. In February 2016, 8 full-sib hybrids populations were constructed, and the effectiveness of hybridization of parents based on genetic distance and parental identification of superior plants was verified. Among them, four hybrid combinations based on genetic distance and parental identification of superior plants were selected: T-F-15 × T-M-14, T-F-14 × T-M-45, T-F-14 × T-M-43, and T-F-18 × T-M-45. Only T-F-15 × T-M-41 and T-F-18 × T-M-43 were predicted by paternal identification. The two prediction methods predicted T-F-15 × T-M-2 and T-F-14 × T-M-27. Through the construction of the full-sib hybrid progeny population, a total of 34,020 hybrid seeds were obtained, 5415 full-sib child progeny hybrid seedlings were obtained, and 3984 seedlings were obtained. The emergence rate was 15.9%, and the survival rate was 73.6%  and low genetic differentiation among P. tomentosa provenance (FST = 0.037), which may be due to the long-term cultivation and introduction of P. tomentosa. The same pattern of genetic variation was also found in close relatives of P. tomentosa, Populus tremula31. To carry out the genetic improvements of P. tomentosa, individual differences within the population should be considered, and hybrid parents with large genetic distances should be selected to acquire high heterosis. A correlation analysis between individual genetic distance and geographical distance revealed that the geographic distance is not important. Furthermore, the 34 full-sib hybrid family derived from the same provenances or different provenances are analyzed. The results show that the genetic distance between the parents is significantly positively correlated with the special combining ability of the full-sib growth traits, and a larger genetic distance corresponds to strong heterosis. Therefore, the analysis of the correlation between the genetic distance and the special combining ability in P. tomentosa has important guiding significance for the selection of hybrid combinations. In the P. tomentosa population, the breeding strategy in which strong heterosis combinations are selected based on the parents genetic distance can be used to significantly reduce the selection range of parents, thus saving breeding time and a large amount of human and material resources; moreover, it lays the foundation for the rapid and efficient construction of high combining ability hybrid parent groups.In this study, 24 pairs of SSR primers were used in the genetic analysis of 441 clones of 32. In this study, a significant correlation is not observed between the general combining ability and special combining ability of height and diameter at breast height. This relationship has been reported in recent research34. The relationship between the special combining ability and the genetic distance was verified, and the genetic distance in P. tomentosa was significantly positively correlated with the special combining ability, indicating that the breeding parents could be predicted by the genetic distance. However, Tian et al. used the genetic distance of rapeseed economic traits to predict heterosis and combining ability and found that there was no significant correlation between the two indices6, indicating that the genetic distance is not capable of predicting heterosis and combining ability40. This finding may be related to the lack of a relationship between the determinant gene of the measured trait and the molecular marker utilized to estimate the genetic distance or differences between individual genomes of the offspring and interactions between genes and the environment44, thus illustrating the complexity of quality traits36.The general combining ability reflects the average performance of the progenies resulting from several mating combinations in a mating group and reacts to the parental additive gene effect, and the additive gene can be transmitted to the offspring in the mating population. The special combining ability reveals the deviation between the performance of a particular mating combination and the predicted performance of the parent general combining ability. The additive effect and the superior effect of the reactive gene can only be expressed when specific genes are combined and cannot be passed on to the offspring45. Here, we included T-M31 in the breeding parent population, which was the final constructed breeding parent group that included 10 female parents and 5 male parents. The full-sib offspring representative assay was used to verify that height and diameter at breast height of the predicted high specific combining ability hybrid combination were significantly higher than those of the same female parental control hybrid combination. The results showed that the genetic distance and the paternal identification results of the half-sib progeny are able to quickly and effectively construct breeding parent groups and confirm breeding parents.Based on the genetic distance analysis, a primary breeding parent population with potentially high specific combining ability was constructed, and the high specific combining ability hybrid combination was predicted; however, only 14 hybrids were consistent with the results of the paternal identification of open-pollinated individuals. The reason may be related to the inconsistency of the loose powder time of the male plants or the difference in the competitiveness of the pollen, meaning that the parent population identified based on open-pollinated excellent plants is not obtained under a genetic environment with completely random mating. However, 35 of the reconstructed hybrid combinations based on the parental identification were different from those with high special combining ability predicted by genetic distance, which may be due to the influence of random sampling error. Therefore, the selected superior plants were the most prominent individuals in the progeny population, and the average value of the growth traits of the full-sib population was relatively low, which may also explain why the male plant T-M-31 can be combined with 13 high fertility and high combination ability female plants as a high special combining ability hybrid combination via the genetic distance prediction but not by the parental identification method2. However, there are obvious shortcomings in the BWB breeding strategy, such as sampling error and incomplete randomization of the mating genetic environment, which may result in the selection of superior trees that do not represent the comprehensive performance of the progeny population of elite hybrid combinations. This study combines genetic distance and BWB breeding strategies to improve the accuracy and effectiveness of parental selection and breeding parent population construction. Based on the above research, this paper proposes a breeding parent selection strategy based on a high special combining ability. For the selection of excellent P. tomentosa plants and even similar species, the germplasm resource bank is first used to observe and screen parents with good fertility through pollen and seed formation. Molecular markers are used to analyze the genetic distance of the fertile parents. Based on certain genetic distance criteria, the parental hybrids with potential high specific combining ability are screened to construct the primary breeding parent population. Furthermore, the open-pollinated family is constructed by using the female parents of the primary breeding parental group, excellent offspring trees are screened from this group, and the paternal identification of open-pollinated excellent individuals is analyzed by molecular markers. On this basis, a primary breeding parental group and parent identification results are synthesized using the genetic distance to select parents that match the high special combining ability hybrid combination.In forest genetic improvement, BWB strategies have effectively improved breeding efficiencySupplementary Information.