A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.Estimating the polygenicity ) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from a reference panel of 11 million SNPs, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities (as a fraction of the reference panel) ranging from ≃ 2 × 10 There are ∼10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype.Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ≃11 million common variants – among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants—we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities—whose product allows for lower bound estimates of heritability—vary by orders of magnitude. The genetic components of complex human traits and diseases arise from hundreds to likely many thousands of single nucleotide polymorphisms (SNPs) , most ofπ1, the proportion of causal variants in the underlying reference panel of approximately 11 million SNPs from a sample size of 503) and discoverability  summary statistics (z-scores for SNP associations with a phenotype ) to esti studies . We estiEstimates of polygenicity and discoverability allow one to estimate compound quantities, like narrow-sense heritability captured by the SNPs ; to predh2. We also perform extensive simulations on genotypes with realistic LD structure in order to validate the interpretation of the model parameters. A discussion of the relation of the present paper to other work is provided in the first section of the In previous work  we preseβ below) 15] is inIn the π1 = 5.3 × 10−3 and π1 = 2.8 × 10−3 and Given a liberal MAF threshold of 0.002, our reference panel should contain the vast majority of common SNPs for European ancestry. However, it does not include other structural variants  which may also be causal for phenotypes. To validate our parameter estimates for an incomplete reference, we reran our model on real phenotypes using the culled reference where we exclude every other SNP. The result is that all estimated parameters are as before except that π1, expressed as the fraction of the reference panel SNPs that have a non-null true β value, i.e., are “causal”; and SNP discoverability or mean strength of association , Here we present a unified method based on GWAS summary statistics, incorporating detailed LD structure from an underlying reference panel of SNPs with MAF>0.002, for estimating: phenotypic polygenicity, ncausal, and the narrow-sense common-SNP heritability, h2  are estimated to be causal. However, causal SNPs for putamen volume and HDL appear to be characterized by relatively high discoverability, respectively 17-times and 23-times larger than for schizophrenia (see In marked contrast are the lipoproteins and putamen volume which have very low polygenicity: The QQ plots (which are sample size dependent) reflect these differences in genetic architecture. For example, the early departure of the schizophrenia QQ plot from the null line indicates its high polygenicity, while the steep rise for putamen volume after its departure corresponds to its high SNP discoverability.−3; for AD the polygenicity is ≃ 10−4. Nevertheless, the GCTA approach yields a heritability estimate closer to the twin-based (broad sense) value, estimated to be in the range 60-80% We are sorry that we cannot be more positive about your manuscript at this stage. Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Xiaofeng ZhuAssociate EditorPLOS GeneticsScott WilliamsSection Editor: Natural VariationPLOS GeneticsReviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: In this report, the authors proposed a general computitional method that can infer the genetic architecture underlying complex traits. an idea similar to LD score regression, using a mixture Gaussian distribution GWAS summary statistics have been combined with a reference population (1KG); a powerful method, as demonstrated by the authors, various interesting parameters and questions, such as the proportion of causal variants and their variance can be addressed. In real GWAS summary data analysis, a range of GWAS complex traits have been tested and consistent to previous reported results in other studies.Technical comments1 Similar ideas but using individual-level data had been proposed in Bayesian approaches . In particular, their model assume the effect size is relavent to MAF. It is likely to be the case that the MAF and effect has certain correlation, negative or positive upon the scheme of the selection pattern. It is probably one of the reasons that some z-score distribution was not fitted as well as others in the manuscript.2 The kernel of the method is the introduction of the mixture model that is composed of the causal and null components. In the multinomial pdf function Eq 29 in Supplementary notes, H_i is taken as the mean 2pq for the n_i snps in the bin. However, as k_i is a small number, say k_i =1, the 2pq of this specific causal locus will be very different from the mean 2pq of n_i snps in this bin. It is not really clear H_i in Eq 29 has been updated for k_i or always taken as H_i as approximation? In gut feeling, it may effect the estimation. However, the authors emphized that the real calculation was based on convolution,3 Eq 51. H_bar is taken as 0.2165, similar critics as in the last comment.4 The LD score is estimated from the 1KG, which is presumed to be “health controls”. For case-control traits, the LD in the case-control sample will be different from “health controls”, in particular the effect size is large or the trait in under selection. Any consequence due to this leap?5 The estimation procedure should be better demonstrated in the main text, which is the kernel of the paper. As well as its step-by-step algorithm, and demo code should be provided for others to follow up.Minors1 The main text and the supplementary notes are very similar to each other.2 References are inconsistent between the main and the supplementary notes.3 Many typos. The ms has not been lined well for the reviewer to tell where those typos. In the supplementary materials it is also referred to “Supplementary Materials”, page 16 second column.4 Probably the author is from physics field, I guess. The authors used some jargons such as Dirac delta function. However, according the sup Notes, Eq 6-8, the Dirac delta function seems a standard normal distribution instead.Reviewer #2: In this paper the authors present a method for estimating the genetic architecture of a complex trait from the set of GWAS summary statistics and an estimate of the LD matrix. From a methodological perspective, the major contribution of the paper seems to be the methods, presented in the supplement, for fitting the model using summary statistics and the alone. In the model, the marginal effect sizes for each individiaul SNP are assumed to be generated due to tagging effects of multiple linked SNPs, where the causal effects of the linked SNPs follow a mixture distribution that is a combination of a point mass at zero, and a normal distribution with some variance. Such mixture distributions are fairly common in the field, but one contribution here seems to be a conceptual stand on principle that a significant number of variants should have no effect on a given trait at all, and the methods here fit such a model .In terms of the presentation, I think it would be helpful if the authors could present a bit more of the details of the model could be presented. E.g. I see no reason why equations 5 and 16 from the supplement  couldn't appear in the main text. More broadly, I think the paper could do more to help place the present work in the context of existing methods of a similar flavor (of which there are many). What specifically distinguishes the approaches laid out here from those which can be found elsewhere in the literature. The reader shouldn't have to take a detour to read 10 other paper in order to figure it out.I must confess I am a bit concerned about identifiability issues here. E.g. the authors show in the supplement that if you simulate data under diffiferent assumptions, you can somewhat seriously misestimate the number of causal SNPs. In that specific case (where effects come from a particular mixture of Gaussians), the show that their model does not fit the QQ plot well, so we might take the fact that the the model does fit the QQ plots for real traits as a reason to believe in the estimates, but this is weak evidence at best. In the one case where the authors have real data for the same trait at multiple different samples sizes (height), the estimated number of causal SNPs is correlated with GWAS sample size. It is of course hard to know exactly what to make of this, given the known structure confounding issues in the GIANT consortium data, but it is not super reassuring. If the true effect size distribution exhibits the sort of long flat tails the emerge from scale mixtures of Gaussians (i.e. the sort that is explored in the supplement), I would not be surprised to see sample size dependence of parameter estimates, particularly the polygenicity, if the effect sizes truly follow this distribution.In general, I don't like to suggest too much extra work in review, but it strikes me that it would be reassuring to see something like the UK biobank height data subsetted to different sample sizes, as a demonstration that you get consistent estimates of the number of causal sites. I.e. if you run 10 GWAS on resampled subsets of 50k UK Biobank participants, and then apply your method, do you get roughly the same average estimates for the parameters as you do if you run 10 GWAS on different subsets of 200k individuals?I also wanted to respond briefly to note that, while it is a very minor point in the context of this paper, I think the authors have a mistaken view of what the omnigenic model is. The omnigenic model, as articulated by Boyle et al, and more recently by Liu et al, is not a claim about the total number of casual SNPs that exist for complex phenotypes. Rather, it is a conceptual model meant to explain multiple features that are commonly observed in GWAS data. One of these features is that the total number of causal SNPs is high, but I think those authors would argue that ~10k is still "high", as far as they are concerned. But another important feature is the fact that much heritability seems to be attributable to variants within genes that are not directly related to the the trait in question, but simply expressed in the relevant cell type. In fact, the "omni" in "omnigenic" is best interpretted in this context. It means roughly "all genes expressed in the relevant cell types affect the trait" not "all variants affect all traits", which is what I read the authors to be arguing against when they say in the main text that height is "very far from omnigenic".Is software to fit the model made available? I could not find any mention of it in the text.Reviewer #3: This manuscript describes a new method for estimating heritability and the number of causal SNPs for GWAS traits. The proposed method relies on a spike-slab model and perform a grid search based on MAF and LD pattern for inference. The authors applied the model to analyze 16 diverse phenotypes including eight binary disease traits. Overall, the manuscript is well written, and the analysis is well carried out. The propose algorithm makes sense and appears to work well. With that said, however, I do have a few main concerns:1. The proposed spike-slab model is identical to that used in ref #14 and one of the proposed inference algorithms  is also very similar to the one proposed in ref #14. Therefore, it would be important to provide a detailed description of the method in ref #14 and explain how the present method differs from that of ref #14. In addition, it would be important to perform comprehensive comparisons with the ref #14 method in all simulations and real data applications, to demonstrate any benefits of the proposed method.2. The authors did not provide any software. Some of the proposed algorithms are complicated. Without providing an open-source and user-friendly software along with a software manual, it would be almost impossible for anyone to use the proposed method.3. The authors did not provide the analysis code used in the paper. I am thus unable to reproduce any of their simulation and real data analysis results. It would be important to provide all analysis code along with all analyzed gwas summary statistics.4. It has been shown previously that sparse models, like the spike-slab model used in the present paper, are not suitable for heritability estimation under polygenic settings. Indeed, sparse models often under-estimate heritability, at least in studies with small samples (e.g. as illustrated in ref #71). I am thus surprised to see the h2 estimates are fine in the simulations. I was wondering if this is due to the unrealistic simulations employed in the present paper: all simulations were based on simulated SNPs but not based on SNPs from real data. Therefore, it would be important to perform realistic simulations either using real genotype data or following the simulation approach used in ref #14.5. Half of the analyzed traits in the present study are binary disease traits. It is well known that likelihood-based approaches are not suitable for h2 estimation for case control studies, as likelihood-based method would bias the estimation of h2 due to case control ascertainment (e.g. ref #75). I am thus concerned whether the proposed method can be used for analyzing binary traits. It would be important to provide mathematical details on how the proposed model can accommodate case control studies. For example, do you need to incorporate the spike-slab prior to the liability threshold model? Do you need to develop moment matching type of algorithms for inference? It would also be important to perform comprehensive simulations under ascertained case control settings, like those have been done in ref #75.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: No: demo code is not providedReviewer #2: YesReviewer #3: No:**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  such that it can be cited independently in the future. For instructions see our data availability policy requires that all numerical data underlying graphs or summary statistics are included with the submission, and you will need to provide this upon resubmission if not already present. In addition, we do not permit the inclusion of phrases such as "data not shown" or "unpublished results" in manuscripts. All points should be backed up by data provided with the submission.Please be aware that our Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool.  PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. 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Please do not hesitate to contact us if you have any concerns or questions.Yours sincerely,Xiaofeng ZhuAssociate EditorPLOS GeneticsScott WilliamsSection Editor: Natural VariationPLOS GeneticsI suggested that the authors carefully address the reviewer 3's comments, which are also the previous comments 1 and 5 by the reviewer 3.Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Congratulations!It is very helpful to provide the code for the proposed analysis.Reviewer #3: My previous main comments #1 and #5 were not well addressed. In the previous comment #1, I suggested the authors to perform a comprehensive comparison with previous methods developed in ref #14 (M2 and M3 methods) in all simulations and real data applications, due to the apparent similarity between the proposed method and the previous M2/M3 methods. The authors only criticized the previous M2/M3 methods being incorrect and applied M2/M3 models to real data. In the real data applications, the authors showed that the proposed method tends to produce a higher estimated number of causal SNPs than the previous M2/M3 methods and tends to estimate the heritability lower than the previous M2/M3 methods. Without simulations, however, it is unclear whether such difference observed in the real data is due to the inaccurate estimates from the proposed method or the inaccurate estimates from the previous M2/M3 methods.In the previous comment #5, I suggested the authors to perform simulations based on the liability threshold model to examine any potential downward bias in heritability estimation for binary traits. The authors explained that the estimates from the proposed method did produce downward bias . Because the estimates from M2/M3 models for all binary traits are all higher than that from the proposed method, I was wondering if the previous M2/M3 methods were not susceptible to case control ascertainment and thus could produce unbiased heritability estimates. It would thus be important to perform simulations based on the liability threshold model, to better understand whether the biased heritability estimation is a unique drawback for the proposed method, or whether it also applies to M2/M3.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Geneticsdata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #3: Yes**********what does this mean?). 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PLOS Genetics may also choose to issue a press release for your article. If there's anything the journal should know or you'd like more information, please get in touch via  27 Apr 2020PGENETICS-D-19-00865R2 Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model Dear Dr Holland, We are pleased to inform you that your manuscript entitled "Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. 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The current selection of the Sardinian Anglo-Arab horse (SAA) for racing is not based on any scientific method because the breeders decide only on their knowledge of pedigree and on the comparison of racing results. The comparison between competitors is a common situation in sports and games, and it might cause problems in horse performance evaluation. To overcome this problem in the game of chess, Elo suggested using a method based on winning chances. Elo’s system assumed that ranking probability for two competitors could be estimated from their rating difference. Even in horse races, the ranking of each horse, and consequently its earnings, depends on the level of its competitors. A genetic index based on Elo’s rating can clearly show the value of each animal. The strength of this method is that it allows evaluation of a horse by considering various different traits such as wins, placings, earnings over the entire career altogether.This first survey on Sardinian Anglo-Arab horse (SAA) race traits highlights important aspects for the breeding purpose of this population. The heritability of the race traits were estimated through a trivariate model; the estimates were 0.39, 0.33, and 0.30 for the number of placings, total earnings and Elo rating, respectively. The genetic progress could be improved by using an MT genetic evaluation of stallions and mares, combining information from competition traits. The Sardinian Anglo-Arab (SAA) is a very popular breed for equestrian sports, originating from crosses between old Arabian and part-Arab lines and Thoroughbreds . In ItalIn the racing competitions, the SAA horses compete only against individuals of the same breed. In the equestrian competitions, the SAA horses also compete against horses of other breeds; there are no specific events only for SAA in jumping or endurance. SAA mares are frequently crossbred with European warmbloods to obtain more competitive horses in jumping, or with Arabian stallions, to obtain more competitive horses in endurance .The current selection of SAA for racing is not based on any scientific method: the breeders only rely on the survey of the pedigree and on the comparison of racing results. The trade in live stallions and frozen semen across countries allows greater access to superior breeding stock, and this activity is an important part of the equine industry economy in many countries . TherefoIn other racing breeds, such as Thoroughbred and trotter horse, several studies have aimed to estimate the heritability of the “performance” measured by different traits ,7,8,9.Speed seems to be a very important trait to evaluate horses competing in a race, and it has been widely used in trotters, although transformed into the classical “time per km (or per mile)” . Other cA further problem with both placings and earnings is that the ranking of each horse, and consequently, its earnings, depends on the level of its competitors . The comThis study aimed to evaluate SAA estimated breeding values (EBVs), taking into consideration both career, traits such as wins, placings as well as earnings and Elo estimated racing performance.All the ancestors of the recorded horses were added to the pedigree file for the genetic evaluation, including at least four generations for each participating horse, giving a total of 43,624 horses  born from 1853 to 2013. The genealogical information was provided by the Agricultural Research Agency of Sardinia - Istituto Incremento Ippico della Sardegna (AGRIS). The pedigree file, from the first to the fifth generation, showed a completeness of 86.44%, 79.87%, 75.82%, 72.88% and 68.53%. The mean of the maximum number of traced generations reported in the Studbook was equal to 11.15. The average inbreeding coefficient (F) was 0.012 [Ten different traits for 1756 SAA born between 1995 and 2006 (ended career) were calculated using a database of career-aggregated statistics provided by H.I.D. (Hippo Information Technologies and Data S.p.A.). These traits, referred to the total career , were divided in two groups: “result traits” , and “economic traits” .The transformation of both total earnings and total earnings per start into their logarithm and square root was used to better fit a normal distribution. Since earnings data were collected in a short period (ten years), they were not further modified to account for prize development over time. Descriptive statistics were computed on the ten traits and on the effects with R software .The estimation of genetic parameters was performed on the “results traits” (4 × 4) and the “economic traits” (6 × 6) using MTDFREML software  followinThe basic model was:y = vector of observations of the corresponding trait;b = vector of fixed effects: sex organized in 3 levels  and year of birth divided into 12 levels (from 1995 to 2006);u = vector of individual additive genetic values;e = vector of residual error;X and Z = incidence matrices, fixed and random effects, respectively.a²σ the additive genetic variance and e²σ the error variance.The random-effects have the following distribution:g) from the two MT models were used to choose a “result trait” out of four and an “economic trait” out of six, so that overall response, that is the result of the direct response (i h2i σi) and all the indirect responses (i rgij hi hj σj), was maximized within each group of traits. For any mass selection of intensity i. the overall standardized selection response of each traiti was calculated as:j is any other trait in the “stat” dataset [The estimates of heritabilities (h²) and genetic correlation .Many sports and games use integer values in the 10 to 40 range for “The estimates of Elo ratings were performed by in-house Fortran 95 software.The final rating of each horse (whole career) was analyzed in a univariate model with no fixed effect because no statistical significance were obtained for sex and year of birth. The descriptive statistic (R project)  on Elo fIn order to reduce the number of sets of indexes, a final dataset was prepared by merging the “Elo” dataset with the number of placings and the square root of total earnings in a career that were found to be the traits maximizing the overall selection response in the “stat”.The final dataset included 2360 horse records . This dataset was analyzed in an MT animal model, including the fixed effects of sex and year of birth only for the two “stat” traits. The accuracy values were examined to show the improvement of using the overall genetic index evaluation in comparison to mass selection. In more detail, the number of animals without records that showed an accuracy greater than the minimum observed value in the animals with records was used for the comparison.Variance components estimation and their standard errors were performed by VCE package . EstimatDescriptive statistic results (computed with R project ) for “st2 estimates by univariate models on the “stat” dataset and Elo rating , and this could help to explain the differences among single trait and multi-trait analyses.The hThe environmental correlations were much lower than the genetic correlations, with the only exception of the correlation between the number of placings and the number of wins (0.81). The estimates of heritability and correlations by MT model for the six “economic” traits are reported in Unlike the “result” traits, a slight reduction in the estimate of h² was observed with respect to the univariate model, except for the total earnings, which slightly increased from 0.35 to 0.36. Genetic correlations of 0.95 or higher occurred among original traits and the respective derived traits  and the environmental correlations were higher than those of the “result” traits. A general view of the above-mentioned correlations, in particular the genetic ones, can lead to consider that in practice, some traits are none other than the logarithm or square root of the same trait. High genetic correlations also occur between total earnings and the earning per start (0.81), and this justifies the use of just one of these traits for the following analysis with Elo.2 values of the number of placings and the square root of total earnings decreased from 0.48 to 0.39 and from 0.42 to 0.33, respectively, whilst the Elo rating increased to 0.30. Both the genetic and environmental correlations between these three traits were high: mainly those between “stat” traits.In the final trivariate model , the h2 In p ≥0.05, data not shown).In the univariate model for the number of placings, 166 animals without records were estimated with more accuracy than the least accurate animal with records; in the univariate model for the square root of total earnings and for Elo rating, these animals were 255 and 2354, respectively. The number of animals without records with an EBV as accurate as that of animals with records was impressively increased in the final trivariate model. These animals were 9156 for the number of placings, 4646 for the square root of total earnings and 3465 for Elo rating; the animals without records that were more accurate for all the three EBVs were 3457. As expected, the accuracy reaches higher values (0.51–0.54) in the trivariate model than in the univariate model (0.31–0.38). The Spearman ranking correlation, between the EBVs estimated by univariate and EBVs estimated by MT, showed coefficients higher than 0.95  performance traits were considered, and ten of them were career traits: the effects of sex and year of birth were included in the animal model used to get estimates.Furthermore, since the traits were also averaged per start, the number of races was considered. In other research, this effect was fitted in the model as a covariate . It mustNo estimation was available for heritability of result traits in the Arabian horse: the most comparable traits seemed to be ranking where estimates were 0.25 , 0.18 3, 0.12 ± Although breeders can have different goals in the selection, since high genetic correlations between “stat” traits were obtained, they are sure that, by choosing just one trait in each group, indirect selection will, nevertheless, bring a general improvement for that group of traits.Nowadays, the breeders decide only on their knowledge of pedigree and racing results; a key step is to convince them that an EBV of a horse without record can be more accurate than that of a racing horse. Trusting in the trivariate model, the number of horses that a breeder can consider for reproduction greatly increases and this is a further benefit for the SAA population. The calculation of an official Elo rating could show the value of each animal to the horse fans in an easy and understandable way: this could help in making comparisons between horses so that new bettors are attracted. It is reasonable to expect that, with the integration of an Elo rating in the genetic evaluation system, the breeders could benefit from the same clarity; therefore, Elo ratings could have a great outlook in sport horse breeding.


CLOCK genes may influence obesity incidence. Although there are studies that show an association between the expression of these genes and energy intake, waist circumference or abdominal obesity phenotypes, interactions with appetite have been insufficiently investigated in relation to chrononutrition. The objective was to identify interactions between CLOCK genetic variants involved in appetite status. A total of 442 subjects  were recruited. Anthropometric, dietary and lifestyle data were collected by trained nutritionists. Participants were classified according to their appetite feelings with a Likert scale. Multiple linear regression models were used to examine associations of the type genotype x appetite status on adiposity-related variables. p values were corrected by the Bonferroni method. A significant influence was found concerning the effects of appetite on waist circumference with respect to rs3749474 CLOCK polymorphism (p < 0.001). An additive model analysis  showed that risk allele carriers, increased the waist circumference around 14 cm  by each increment in the level of appetite. The effects of appetite on waist circumference may be partly modulated by the rs3749474 CLOCK polymorphism.Chronobiological aspects controlled by  CLOCK genes exert an influence on weight regulation mediated by brain areas, that are likely to be involved in the control of relevant endocrine functions through anorexic and orexigenic hormones and neurotransmitters [CLOCK genes by transcriptional mechanisms [smitters . This cochanisms .CLOCK gene (Circadian Locomotor Output Cycles Kaput) is implicated in metabolic dysfunctions. However, more research about the biological rhythms control is needed [CLOCK gene could have negative effects on fat accumulation or mobilization in adipocytes as well as on healthy eating behaviors [Current available findings have proven an active circadian clock located in adipocytes, where genes expressed in the adipose tissue are under the influence of cyclic systems ,4. Invess needed . Severals needed . Thus, aehaviors . CLOCK genetic variants  with waist circumference [CLOCK had significantly higher waist circumference, weight and BMI (Body Mass Index) than CC subjects [Body fat location in humans is considered as important as the total fat mass content concerning metabolic complications . Thus, imference ,11. Moresubjects . CLOCK and energy intake [CLOCK polymorphisms.On the other hand, some obesity treatment approaches are being focused on the regulation of appetite and energy consumption . From a y intake . HoweverCLOCK variants on adiposity markers can contribute to increasing the knowledge about the regulatory mechanisms that depend on biorhythm regulatory genes [CLOCK genes involved in the development of obesity and associated comorbidities, according to appetite degree, since although there are studies that show an association between these processes, the scientific evidence about the interactions between both factors is scarce.Analyzing the effects of ry genes . Therefon = 442). CONSORT flow diagram is shown as Subjects recruitment for this observational study was achieved through the Platform for Clinical Trials in Nutrition and Health (GENYAL), belonging to IMDEA Food Institute . Inclusion criteria for recruitment were: free-living adults aged 18 to 70 years that had to give a written informed consent to be contacted to perform clinical trials and nutritional intervention studies. Exclusion criteria were: to suffer from any serious illness , to present dementia or impaired cognitive function, and to be pregnant or breastfeeding. A total of 1249 subjects were contacted, 954 of whom were recruited. Of these, a total of 557 participants were finally selected. Due to uncompleted questionnaires, 487 samples were left for analysis. The sample size of the variables ranges from 437 to 487 due to some missing data. No imputation was performed. The sample size was shown considering all subjects with age, sex and genotyping data for at least some one of the three polymorphisms . This question was asked by trained nutritionist always at the research center and when the subjects had been fasting for at least two hours following published protocol ,20. The A 72–h food record validated for the Spanish population , was colIn order to evaluate physical activity levels, participants had to indicate how many days per week they did physical activity as described previously . Those sCLOCK rs3749474, rs1801260 and rs4580704 genetic variants were screened following previously published procedures [ocedures . Blood socedures . The resocedures . The proloci were assessed using standard χ2 tests. Descriptive analyses were implemented for different continuous and categorical variables by sex. The p-values were obtained using Student’s t-test for continuous variables, and Fisher exact test for categorical variables. In turn, associations between 3 CLOCK  polymorphism and studied variables, were modeled through linear models. Interaction models of the type CLOCK polymorphism*appetite grade in the prediction of obesity variables were created by using linear models. Three genetic models were considered, namely: additive, dominant and codominant, and 3 CLOCK polymorphisms related to biorhythms were analyzed: rs3749474, rs1801260 and rs4580704. Different adjustment variables were considered. The p values were corrected for multiple comparisons (Bonferroni) in all the cases following stepwise criteria. Significance level was set to α = 0.05, and bilateral tests were considered. Statistical analyses were performed with R software, version 3.4.1  . DeviatiCLOCK rs3749474, rs1801260 and rs4580704 was in accordance with the Hardy–Weinberg equilibrium .A total of 442 subjects  were analyzed, considering the sample size, which included all subjects with age, sex and genotyping data for at least one of the three polymorphisms. Anthropometric measures, dietary intake, dietary habits, physical activity characteristics and frequency of studied genotypes, according to sex were completed . StatistCLOCK rs3749474, rs1801260 and rs4580704 considering anthropometric, dietary intake, dietary habits and physical activity characteristics, where no significant differences were found for either genotype adjusted by the Bonferroni method  were subsequently analyzed. Among the three genetic models evaluated , the best fit was obtained with the additive model (in which each copy of T modifies the risk by an additive amount and therefore T/T homozygotes have twice the risk of C/T heterozygotes). Of all the studied variables, statistically significant interactions were found for CLOCK rs3749474 risk allele in the prediction of waist circumference. The effect of the appetite level (low < medium < high) on the waist circumference diameter was studied according to the genotypes using marginal models. In addition, the analysis was performed with three different adjustments: age and gender (model 1), energy intake, age and gender (model 2), and exercise, energy intake, age and gender (model 3) as reporting in CLOCK rs3749474 and appetite classification were found for the three models (p ≤ 0.001) adjusted by age and gender (model 1), energy intake, age and gender (model 2) and exercise, energy intake, age and gender (model 3). In the model 1  with each unit of increment in the degree of appetite (low < medium < high). However, in the common genotype carriers (C/C) the waist circumference only increased by around 3 cm . In the model 2 and 3, similar results were obtained, since in T/T carriers, the waist circumference increased by about 14 cm , in comparison with C/C carriers, who had an increase in waist circumference of only around 3 cm. Statistically significant differences of waist circumference according to  model 1 , in minoCLOCK rs3749474 genotype and appetite degree adjusted by exercise, energy intake, age and gender (model 3) are shown in Mean measures of waist circumference according to p interaction = 0.029; Model 2: p interaction = 0.016; Model 3: p interaction = 0.008). In this case, Bonferroni correction criteria were used given the number of polymorphisms and dietary variables studied .Subsequently, the data were reanalyzed in another study with a greater number of polymorphisms to screen other possible interactions. The results again were statistically significant . In this respect, homozygous variant (T/T) carriers showed the greatest increase in waist diameter (about 14 cm) by each increment in the level of appetite (low < medium < high). The present study was designed to identify interactions between CLOCK genes and waist circumference have been demonstrated earlier, no interactions with the grade of appetite have been reported before. On this basis, the present study may constitute an advance to understand the influence of the CLOCK genes on weight control. In this sense, this study suggests that the genetic component could determine the effect that the level of appetite has on cardiometabolic markers such as waist circumference. The fact of finding differences between the genotypes has the potential to help to focus nutrition strategies on target population with certain genetic predisposition in the future. However, more research on this topic needs to be undertaken before the interaction between appetites, CLOCK polymorphism and waist circumference is more clearly understood. At the same time, our study used the waist diameter as an indicator of visceral fat. Although waist circumference cannot discriminate between visceral and subcutaneous fat tissue, visceral fat tends to be directly proportional to waist diameter [Although evidence of associations between the diameter ,36. HoweCLOCK polymorphisms could also include the analysis of interactions with genes that encode neurotransmitters, cytokines, palatable food, or hedonic eating parameters. The current analyses could be complemented by including appetite-related hormones such as ghrelin or leptin measurements would render this research much valuable, however, this could not be carried out due to insufficient blood sample.Other researchers have also found associations, based on visual analogue scales, with respect to other polymorphisms (such as rs9939609 FTO and rs12970134 near MC4R) and the level of appetite ,37,38. IFrom the descriptive analysis, a significant difference between men and women in the appetite rate was appreciated. Simple sugars were included in the total carbohydrates. However, we considered separating out simple sugars since they may show a different outcome than overall carbohydrates given their role on sweetness and satisfaction as well as potentially on appetite . This isCLOCK genes have been shown to influence the regulation of the expression of adipocytokines such as adiponectin, resistin and leptin, the synthesis of which daily vary throughout the 24–h cycle [In turn, the expression of these genes may influence the regulation of lipid and carbohydrate homeostasis as well as the adipose tissue and abdominal fat content . On this–h cycle .CLOCK variant generates a change in the structure of the messenger RNA in such a way that the level of expression is reduced [Thus, these genes have been associated with diseases such as obesity and metabolic syndrome. Specifically, it has been reported that the rs3749474  reduced ,44. This reduced ,44. In p reduced ,44. When analyzing the results of this interaction in more detail, it can be noted that the T/T carriers + low appetite degree had a lower average waist diameter than the other genotypes carriers . Conversely, subjects with a high appetite degree + T/T carriers showed much higher levels of appetite compared to carriers of the other genotypes . These results suggest that it may be especially interesting to investigate how to reduce the degree of appetite in T/T carriers, as the impact may be particularly beneficial for a population with such genotype.CLOCK gene could be linked with a greater accumulation of visceral fat. Abdominal fat generates a multitude of adverse metabolic processes associated with inflammation and chronic disease [Interestingly, our results also found an interaction concerning to waist circumference: carriers of the T/T genotype increased by about 14 cm for each increase in the appetite rate (C/T: 7 cm and C/C: 3 cm). In addition to weight gain, previous studies have reported the association of the biorhythm genes with abdominal fat content ,45. Ther disease ,36. This disease . This evp values were: p = 0.00036 (Model 1), p = 0.00019 (Model 2) and p = 0.000095 (Model 3). In case of making the correction taking into account the rest of the dependent variables, p adjusted interaction values continued to be equally significant . Although we had a greater number of adjustments in the confirmatory analysis (81 tests), in this case the dependent variables were not taken into account since we considered not necessary to perform further correction analyses involving multiple comparisons. Failure to address multiple comparisons appropriately can introduce excess false positive results and make subsequent studies following up those results inefficient [The adjustment for multiple comparisons took into account 3 SNPs * 1 Appetite interaction test. The unadjusted fficient .CLOCK genes variants have shown preferences for eating less healthy, higher in calories and higher in glycemic index foods [CLOCK gene influence eating behavior, energy metabolism and fat tissue. Therefore, eating satiating foods could be a good strategy to control the degree of appetite and prevent the accumulation of visceral fat in this case.In addition to an increased energy intake, associations to ex foods . Thus, sex foods . By contex foods . On thisCLOCK polymorphism. Appetite control strategies in T/T carriers of this variant could be specifically useful to prevent metabolic alterations characteristic of the increase in abdominal fat. Such approach could include adding healthier foods to the diet. Further studies on the interaction between CLOCK genes and appetite levels would be interesting to confirm this novel association for personalized precision nutrition implementation.The impact of appetite on waist circumference is partially modulated by the variability in the rs3749474


Models including an interaction term and performing a joint test of SNP and/or interaction effect are often used to discover Gene-Environment (GxE) interactions. When the environmental exposure is a binary variable, analyses from exposure-stratified models which consist of estimating genetic effect in unexposed and exposed individuals separately can be of interest. In large-scale consortia focusing on GxE interactions in which only the joint test has been performed, it may be challenging to get summary statistics from both exposure-stratified and marginal (i.e not accounting for interaction) models.In this work, we developed a simple framework to estimate summary statistics in each stratum of a binary exposure and in the marginal model using summary statistics from the “joint” model. We performed simulation studies to assess our estimators’ accuracy and examined potential sources of bias, such as correlation between genotype and exposure and differing phenotypic variances within exposure strata. Results from these simulations highlight the high theoretical accuracy of our estimators and yield insights into the impact of potential sources of bias. We then applied our methods to real data and demonstrate our estimators’ retained accuracy after filtering SNPs by sample size to mitigate potential bias.https://gitlab.pasteur.fr/statistical-genetics/j2s.These analyses demonstrated the accuracy of our method in estimating both stratified and marginal summary statistics from a joint model of gene-environment interaction. In addition to facilitating the interpretation of GxE screenings, this work could be used to guide further functional analyses. We provide a user-friendly Python script to apply this strategy to real datasets. The Python script and documentation are available at  Gene-Environment (GxE) interactions are of great interest in deciphering biological mechanisms underlying complex human traits and diseases. Several theoretical approaches –3 and apTo benefit from these consortia in which only summary statistics in the joint testing framework may be available, we developed a simple tool to infer summary statistics in the groups of unexposed and exposed individuals separately, as well as summary statistics from the regression model without the GxE interaction term. First, we showed that these summary statistics can be efficiently derived from the joint model assuming independence between genotypes and exposure. We then performed a series of simulations to assess the accuracy of these estimations and to examine the impact of different potential sources of bias. Finally, we applied our pipeline to real data from the Gene-Lifestyle Interactions Working group within the CHARGE Consortium.Y, a dichotomous exposure E and a SNP G coded as the number of minor alleles. A framework to test Gene-Environment interactions is based on the generalized linear model:g denotes either the identity function if Y is a quantitative trait or the logit function if Y is a binary phenotype.Consider a trait The marginal model (i.e excluding the interaction term) in unexposed individuals (E = 0), exposed individuals (E = 1) and all individuals are defined as:βunexp and βexp in unexposed (e = 0) and exposed (e = 1) individuals respectively:Assuming independence between the genotypes and the exposure  can be derived from the joint model:Hence, the marginal genetic effect N of the study and the number of exposed individuals Ne included in the study. These two arguments are used to infer the sample sizes Nv is the sample size for the SNP. We also implemented a filtering procedure to exclude variants with a low sample size compared to the distribution of the sample sizes: a SNP with a sample size below the 9th decile of the sample size distribution divided by 1.5 is excluded from the analysis. As output, the script generates a single file containing the genetic effect size and its standard error in the group of unexposed individuals, in the group of exposed individuals and in the total sample. The script and a detailed documentation using an example are available at https://gitlab.pasteur.fr/statistical-genetics/j2s.We developed a Python script to derive summary statistics in the marginal model and in each group of individuals separately. As input, the script takes one file with the summary statistics from the joint model, that are genetic and interaction effect sizes, their standard errors, the correlation between the two effect sizes and the sample size per SNP corresponding to the number of genotypes available for this SNP (which may differ from the sample size of the study because of missing data). This file corresponds to the output of the METAL software to meta-analyze GxE screenings using the joint test . In addiY = βGG + βGE + βGEG × E + ε as a linear combination of the SNP G, the exposure E and the G × E interaction term with randomly chosen effect sizes βG, βE and βGE and a random noise βG, βE and βGE were drawn from a uniform distribution on  with a randomly and equiprobably chosen sign. Note that in this design, genotypes G and exposure E were drawn independently. Then, on the one hand, we computed the summary statistics from the joint model including the GxE interaction term using individual level data. On the other hand, we applied linear regressions without the GxE interaction term in each group of individuals (unexposed and exposed) separately and in the pooled sample to compute the summary statistics of the genetic effect in each group of individuals and in the marginal model. Using the estimators derived from the joint model, we also inferred these summary statistics in each group and in the marginal model using our pipeline. Comparisons of the empirical and inferred summary statistics showed high accuracy of the estimators, with intraclass correlation coefficient (ICC) between “real” and “estimated” equal to 1 in all scenarios  and the estimated (computed in the whole sample) proportions of exposed individuals, the larger are the discrepancies between the summary statistics  compared to the maximum sample size. To evaluate the impact of such a misspecification, for each SNP, we selected a subset of individuals to include only a randomly selected proportion of individuals while intentionnaly misspecifying the proportion of exposed individuals. For each subset, we removed a randomly selected number of exposed individuals. We the compared the summary statistics obtained using the individual-level data and those estimated using the pipeline. As expected from the theoretical derivations detailed above, misspecifying the proportion of exposed individuals, quantified as entclass1pt{minimacs Figs. .Third, bias in our estimations can also occur due to differences in phenotypic variance between unexposed and exposed individuals. To explore this, we simulated a phenotype with exposure-dependent variance by adding statistical noise to the phenotypes of exposed individuals and performed the same simulation study as described above. A different phenotypic variance in the two groups of individuals did not bias estimation of the summary statistics in the marginal model but it clearly biased the estimation of summary statistics in the exposed and unexposed individuals Figs. , S4. Altp-values with only one of the two methods  can be observed, confirming the impact of these source of bias  under a null model with neither a genetic effect nor an interaction effect to assess the control of the type I error rate and quantify the discrepancies in significance results that can arise because of these different sources of bias Figures S. Globall Figure S.We assessed the accuracy of our estimations using real data from the Gene-Lifestyle Interaction Working Group of the CHARGE consortium . This WoIn this work, we aimed at inferring marginal genetic effects in exposed and unexposed individuals separately and in the whole sample using summary statistics of the joint test performed in the context of GxE interaction studies. We analytically derived estimators of marginal genetic effects in the different groups of individuals and in the total sample. We validated the method through simulation studies and real data applications which both highlighted the accuracy of our estimations. Notably, this method can be applied without loss of accuracy to quantitative and binary traits.As demonstrated by our simulation studies, differences between true and estimated parameters are observed for SNPs with the lowest sample sizes. This is explained by a different proportion of exposed individuals for this particular SNP and in the whole sample. Also, our method also provides basic estimates of the expected sample size in the groups of exposed and unexposed individuals. For the same reason, these estimates could be biased for SNPs with low sample size compared to the total sample size. Consequently, we implemented a procedure to filter out variants with low relative sample size to minimize this potential bias.Our estimations rely on the independence between genotypes and exposures. Relaxing this assumption leads to biased estimations of the marginal effect size standard deviation in the marginal model, but does not impact the accuracy of the estimations in the stratified models. As correlation between SNPs and the exposure cannot be retrieved using summary statistics from the joint model, although this assumption may not hold only for a very limited number of SNPs,existing litterature may be helpful to identify variants which should be discarded from the analyses because of existing correlation with the considered exposure. The correlations between genotypes and exposures are expected to be low, resulting in little overall impact, as observed when validating our estimators using real data from the Gene-Lifestyle Interaction Working Group.Finally, we evaluated our estimations in the case of exposure-dependent phenotypic variance. Although our simulations showed clear impacts on the estimations in the stratified models, we noted that the error increased with the magnitude of this difference. In real data applications, such differences in phenotypic variance are expected to be small and should consequently have only a limited impact on the estimations in each exposure stratum. Application to real data sets confirmed this notion as our estimations were highly concordant with real data.Overall, an advantage of exposure-stratified models is that they allow for a comparison between genetic effects in each group of individuals. This different way of quantifying GxE interactions makes the interpretation more intuitive compared to the joint test by comparing genetic effects between the two groups. In addition, exposure-stratified summary statistics can also be used to apply further analyses such as biological pathways  or herithttps://gitlab.pasteur.fr/statistical-genetics/j2s.In this work, we derived accurate estimations of the marginal genetic effects in unexposed and exposed individuals separately and in the whole sample in the context of genome-wide GxE interaction screenings using the joint test. This method can not only lead to a more intuitive understanding of GxE interactions but also be used to perform additional studies that can guide further functional analyses. We implemented j2s, a Python3 script to easily apply this method, available at Project name: j2sProject home page:https://gitlab.pasteur.fr/statistical-genetics/j2sOperating systems: LinuxProgramming language: Python3Other requirements: NoneLicense: MITAny restrictions to use by non-academics: NoneAdditional file 1: Figure S1. Comparison between summary statistics derived from individual-level data (True) and their estimations (Estimated) in unexposed (A) and exposed (B) individuals and in the marginal model (C) using simulated data in the case of a binary phenotype. Figure S2. Comparison between summary statistics derived from individual-level data (True) and their estimations (Estimated) in unexposed (A) and exposed (B) individuals and in the marginal model (C) using simulated data in the case of a quantitative phenotype when relaxing the genotype-environment independence. Figure S3. Comparison between summary statistics derived from individual-level data (True) and their estimations (Estimated) in unexposed (A) and exposed (B) individuals and in the marginal model (C) using simulated data in the case of differences between the proportion of exposed individuals for the SNP and the proportion of exposed individuals in the whole sample. Figure S4. Comparison between summary statistics derived from individual-level data (True) and their estimations (Estimated) in unexposed (A) and exposed (B) individuals and in the marginal model (C) using simulated data in the case of different phenotypic variance conditionally on the exposition. Figure S5. Comparison of the Type I error rate evaluated between summary obtained using individual-level data (blue) and summary statistics estimated using the pipeline (orange) with respect to the different quintiles of the different sources of bias: G-E correlation(A), misspecification of the proportion of exposed individuals (B) and different phenotypic variance in the strata of the exposure (C). Type I error rate were evaluated for the marginal model in all individuals (left), in unexposed individuals only (middle) and in exposed individuals only (right). The dashed line represents the nominal significance threshold (5%). Figure S6. Proportion of SNPs with discordant significance results between summary statistics obtained using individual-level data and summary statistics estimated using our pipeline with respect to the different quintiles of the different sources of bias: G-E correlation(A), misspecification of the proportion of exposed individuals (B) and different phenotypic variance in the strata of the exposure (C). Type I error rate were evaluated for the marginal model in all individuals (left), in unexposed individuals only (middle) and in exposed individuals only (right).


Over the last 100 years, significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs. Technological progress has enabled a shift from labour intensive, on-farm collection and processing of samples that assess yield and fat levels in milk, to large-scale processing of samples through centralised laboratories, with the scope extended to include quantification of other traits. Fourier-transform mid-infrared (FT-MIR) spectroscopy has had a significant role in the transformation of milk composition phenotyping, with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs. This includes traits related to the nutritional value of milk, the processability of milk into products such as cheese, and traits relevant to animal health and the environment. The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits, and the genetic correlations between the FT-MIR predicted and actual trait values. Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis. The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes. Additionally, there are other molecular phenotypes such as those related to the metabolome, chromatin accessibility, and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest. Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets, and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs. Characterisation of milk composition in dairy cattle has a long history of scientific and commercial interest, with many countries establishing formal milk testing programs by the early 1900’s . InitialFourier-transform mid-infrared spectroscopy uses light from the mid-infrared region to scan milk samples and determine the presence of specific chemical bonds. Results are presented as an absorption profile, consisting of the absorbance values for individual infrared light wavenumbers across the mid-infrared region. Traits are predicted as a function of the individual FT-MIR wavenumber absorbances, enabling rapid, high-throughput phenotyping of milk traits such as fat and protein yields, at a fraction of the cost of estimating the components using other methods. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising FT-MIR data to predict other novel traits of interest to the industry, because the spectra are already available as a by-product of routine milk testing. Many of these traits are relevant to consumer expectations and concerns about the nutritional quality of milk, and the impact of dairy production systems on animal health and the environment; and are also relevant to farmers as they seek to improve farming systems and select cows based on their productivity, reproductive performance and disease resistance.Successful phenotyping using FT-MIR data is dependent on the magnitude of the phenotypic correlation between the predicted trait and the trait as measured by a benchmarked standard reference method. The successful incorporation of a FT-MIR predicted trait into a breeding program is further dependent on the heritability of the spectral-based predictions and on the genetic correlation between the spectral-based predictions and the trait as measured by the benchmarked standard , 4. ImprEstablishing causal links between the genome and observed phenotypes may be assisted by employing the individual FT-MIR wavenumbers, and other molecular phenotypes such as those related to the metabolome, chromatin accessibility, transcript levels, and RNA editing. Here we review the shifting role of FT-MIR datasets in dairy cattle improvement as we seek to predict new traits of importance to milk payment and animal evaluation systems. We discuss the broader topics of improving FT-MIR data quality and prediction model accuracy in phenotyping applications; and review existing studies of the genetics of FT-MIR predicted traits and individual FT-MIR wavenumbers. We also discuss opportunities for consolidating FT-MIR spectra datasets with other genomic and molecular data sources, to improve our knowledge of the genetic mechanisms of milk composition and enhance future dairy improvement programs.Fourier-transform mid-infrared spectroscopy uses infrared light to scan a milk sample and determine the presence of specific chemical bonds. As the light passes through the sample, it interacts with the molecules present, causing vibrations and rotational changes in the molecular bonds, resulting in absorption of some of the light. The light absorption is typically represented as an absorption spectrum, consisting of the absorbance values for individual infrared light wavenumbers across the mid-infrared range. Traits of interest are subsequently predicted as a function of the individual FT-MIR wavenumber absorbances. Utilising FT-MIR data for the prediction of milk composition and other novel traits has been widely studied and recently reviewed , 4, 6, 7Trait prediction using FT-MIR spectra requires development of a calibration model, typically using a modest set of samples that have corresponding trait values, measured by a benchmarked technique. The most widely used method for developing calibration models from FT-MIR spectra has been partial least squares (PLS) regression. Fewer studies have employed Bayesian methods to develop calibration models , 21, 22,Pre-processing treatments are commonly applied to FT-MIR spectra before generating a calibration model. The objective of pre-processing is to retain important discriminatory features of the spectra, but address baseline and scaling effects caused by light scattering, that can erode prediction accuracy. Baseline effects are additive and represent a baseline offset in the spectral response, whereas scaling effects are multiplicative and scale the spectral results by a given factor. One common group of methods for pre-processing are the multiplicative scatter methods , 39. MulOverall, there is no consensus about the best pre-processing treatment to apply to FT-MIR spectra. For example, some studies report that pre-processing spectra provides no significant gains to model prediction accuracy , 36, whiOutliers in FT-MIR datasets are often identified using a Mahalanobis distance (MD) metric, where the MD is a multivariate indicator of the distance between a spectral record and the average spectral response. Many studies are based on spectra from a single instrument, and are therefore not required to account for the different variance-covariance structures of measurements from different instruments. In a study of spectra from 66 instruments, Grelet et al.  showed c− 1) and O–H stretching bands (>~ 3000 cm− 1). These regions have low signal-to-noise ratios, with varying boundaries reported across publications: 1600 to 1700 cm− 1 and 3040 to 3470 cm− 1 [− 1 and 3052 to 3669 cm− 1 [− 1 and 3008 to 5010 cm− 1 [DGAT1 gene, and Toledo-Alvarado et al. [− 1 wavenumber and pregnancy status. More generally, Bittante and Cecchinato [Bands of the infrared spectrum with low repeatability of sample measurement due to the water content in milk are typically reported in the O–H bending  based measurements are shown in Table Fewer studies exist of the genetic parameter estimates of FT-MIR predicted individual milk proteins. Sanchez et al.  reported30). Heritability estimates for FT-MIR predicted minerals ranged from 0.32 to 0.56, with phosphorus having the highest estimated heritability, and sodium having the lowest estimated heritability in both studies presented [Heritability estimates and genetic correlations between measured and FT-MIR predicted milk processability traits are shown in Table resented , 65. Herresented , 67. Bitresented  was the Health and fertility traits are valuable targets for breeding programs and selection for these traits would be considerably enhanced if they could be reliably predicted from FT-MIR. A recent review by Bastin et al.  across a4) and nitrogen outputs from dairy systems, there have been few reports of the genetic parameter estimates of these FT-MIR predicted traits, or of the genetic correlations between measured and FT-MIR predicted trait values. Kandel et al. [4 emission and 0.17 to 0.18 for log-transformed predicted CH4 intensity. There is, therefore, some potential for the future incorporation of FT-MIR predicted methane traits into breeding programs. However, there are still issues to be resolved to address uncertainties and discrepancies in methane datasets and measurement methods, and to improve the accuracy and robustness of prediction equations to make them applicable across a broader range of production systems and environments [Despite increasing interest in FT-MIR predictions of environmental traits related to methane  concentrations are routinely predicted using FT-MIR spectroscopy , howeverIn contrast to the prevalence of studies reporting genetic parameter estimates of FT-MIR predicted traits, there are relatively few studies reporting genetic parameter estimates for the individual spectral wavenumbers. Nevertheless, the transmittance of FT-MIR spectra wavenumbers is moderately to highly heritable across a large proportion of the mid-infrared region , 77–79. Many GWAS have been published in the last decade for FT-MIR predicted major milk production traits –87, and O (mn2) or O (m2n) time per trait, where m is the number of variants and n is the number of animals. These models become prohibitively slow as the numbers of genotyped individuals and variants increase [O (mn1.5) time; however, it makes assumptions that are valid only for larger sample sizes. Recent versions are capable of running the entire UK biobank data set (n = 459 k) in a few days on a single computational node [O (mn) time. These improvements mean that it is capable of running n = 400 k UK biobank samples in around 20 min, compared to 22 h for BOLT-LMM on the same hardware. Developments such as this make GWAS across sizeable populations with large numbers of FT-MIR phenotypes feasible.Over the last two decades, the scope of genomic resources available for GWAS has increased, both in terms of the number of genotyped individuals, and in terms of variant density. Developing strategies for managing GWAS on large numbers of densely genotyped individuals is an active area of research, as we look to generate new, more efficient algorithms that will enable the processing of these datasets within acceptable timeframes and computational limits of RAM and CPU. The importance of efficient algorithms is further highlighted when we conduct GWAS across large numbers of FT-MIR predicted traits and the individual FT-MIR wavenumbers. Existing mixed-linear model-based methods for conducting GWAS, such as GCTA-MLMA  primarilincrease . The eveincrease , 96 runsnal node , availabAfter conducting a GWAS, it is useful to identify the candidate genes and mutations underlying genomic loci with signal for a trait of interest. This can aid marker-assisted selection and improve our understanding of the biological pathways regulating the trait. Moreover, it has been shown that genomic prediction can be improved by including variants close to the causative mutations . SoftwarAssuming a causality chain hypothesis, as illustrated in Fig. Similar to eQTL analysis, a range of additional omics data sources can be used for QTL mapping, and the resulting QTL could be applied to identify causative genes for FT-MIR predicted traits and individual FT-MIR wavenumbers. The factors yielding these omics data sources can occur before or after mRNA transcription. Factors acting before transcription, such as DNA methylation and chromatin accessibility, can help unravel causative regulatory variants by highlighting actively-transcribed regions of the genome, and the variants that sit within them. One of these factors is chromatin accessibility. Transcriptionally active genes, as well as active regulatory elements (such as enhancers), are found in regions of open chromatin (euchromatin); whereas inactive regions of the genome are typically much more densely compacted into a structure known as heterochromatin. Genome features found in euchromatin are therefore more accessible to transcription factors and other factors involved in gene expression, and so are more likely to influence traits of interest compared to factors located in inactive regions. Methods to assay chromatin accessibility include ChIP-seq , DNase-sDrosophila [CSF2RB/NCF4 locus [Other factors acting during or after transcription provide intermediate phenotypes that can aid in understanding the underlying biological control of these traits . One sucosophila , followeosophila  and humaosophila . Recentlosophila , and subF4 locus . That stAbsorbance levels at individual FT-MIR wavenumbers provide insights into the presence of particular chemical bonds in the sample and accordingly provide information as to the chemical composition of a milk sample. Analysing the chemical composition of a sample in more detail, using methodologies such as nuclear magnetic resonance (NMR) spectroscopy or mass spectroscopy (MS), yields the metabolome, i.e., a more complete set of all small molecules present in a tissue sample. Metabolomics can provide detailed information about enzymatic activity in the pathways that exist between gene expression and FT-MIR predicted traits, providing a near-terminal link in the chain of causality. For example, rumen volatile fatty acid (VFA) levels can provide information on measuring and controlling methane production . Levels Over the last 100 years, milk composition phenotyping for dairy cattle has evolved from manual on-farm methods for determining yield and fat levels in milk, to high-tech analysis at centralised laboratories, with many novel FT-MIR predicted traits now being considered for incorporation into improvement programs. Multiple studies have demonstrated that the accuracy of FT-MIR predictions are strongly influenced by how well the variation in the prediction population is represented in the calibration population. Trait prediction accuracy is also strongly affected by how well instrument-specific measurement differences are accounted for, particularly when transferring calibration equations developed on one instrument to spectra collected on other instruments. Utilising FT-MIR data to generate proxies for novel traits has grown in popularity, however, compared to FT-MIR predictions of major milk components, there are relatively few studies of the genetics of other FT-MIR predicted traits, and even fewer of the genetics of the individual wavenumbers. This is despite the individual wavenumbers exhibiting additional genetic signal that is often not observed in FT-MIR predictions of major milk composition traits. Integrating results from GWAS applied to FT-MIR predicted traits and GWAS applied to individual wavenumbers with other molecular datasets could improve our understanding of the underlying biological systems controlling traits of interest. However, integration of these data sources also brings computational challenges due to the size and complexity of the datasets involved. Resolving the challenges of effectively integrating FT-MIR datasets with other omics data sources will require a mix of both bioinformatics and molecular biology approaches. Successfully consolidating these approaches promises to improve our knowledge of milk composition and enable the future enhancement of animal breeding programmes.