Our aim was to assess the influence of age, gender and lifestyle factors on the effect of the obesity-promoting alleles of FTO and MCR4.2) in the third survey, HUNT3 (2006–08), were genotyped for the single-nucleotide polymorphisms rs9939609 (FTO) and rs17782313 (MC4R); 25686 participants were successfully genotyped. Extreme sampling was chosen to increase power to detect genetic and gene-environment effects on waist-hip-ratio and BMI. Statistical inference was based on linear regression models and a missing-covariate likelihood approach for the extreme phenotype sampling design. Environmental factors were physical activity, diet  and smoking. Longitudinal analysis was performed using material from HUNT2 (1995–97).The HUNT study comprises health information on the population of Nord-Trøndelag county, Norway. Extreme phenotype participants (gender-wise lower and upper quartiles of waist-hip-ratio and BMI ≥ 35 kg/mCross-sectional and longitudinal genetic effects indicated stronger genetic associations with obesity in young than in old, as well as differences between women and men. We observed larger genetic effects among physically inactive compared to active individuals. This interaction was age-dependent and seen mainly in 20–40 year olds. We observed a greater FTO effect among men with a regular intake of artificially sweetened beverages, compared to non-drinkers. Interaction analysis of smoking was mainly inconclusive.In a large all-adult and area-based population survey the effects of obesity-promoting minor-alleles of FTO and MCR4, and interactions with life style factors are age- and gender-related. These findings appear relevant when designing individualized treatment for and prophylaxis against obesity. It is well recognized that both genetic and non-genetic factors are operative in the development and persistence of obesity. Polymorphisms of the FTO gene are shown to be the strongest genetic determinant of obesity  comparedInteractions with physical activity have been demonstrated when results from a large number of different populations have been pooled together , 5. Howe2), rather than drawing a random sample. Genotyping the phenotypic extremes is considered to increase the statistical power to detect associations between genetic variants and continuous phenotypes [The HUNT all-population survey provides data that appear to be representative of Norway and Scandinavia . Here weenotypes . In addienotypes –26. By genotypes , but to et al has previously analysed the relationship between WHR and dietary variables in HUNT3 [The HUNT study  comprisein HUNT3 . Here wein HUNT3 ). For de2. Of the 25981 extreme phenotype individuals, 25686 were successfully genotyped for both SNPs (98.9%). See Extreme phenotype participants in HUNT3 were genotyped for FTO (rs9939609) and MC4R (rs17782313). Extreme phenotypes were defined as the gender-wise lower and upper quartiles of WHR  and additionally BMI ≥ 35 kg/mThe Regional Committees for Medical and Health Research Ethics for central Norway  approved this research project.xage. The physical activity, diet and smoking variables were treated as continuous explanatory variables, xPA, xASB and xPCYR. We analysed data from men and women separately. Genetic covariates were genotypes of the FTO and MC4R SNPs, xFTO and xMC4R and coded as 0, 1 or 2 according to the number of copies of the minor-allele . Our basic regression model for over-all genetic effects wasY is a trait (WHR or BMI), ε is normally distributed with mean 0 and variance σ2, andWe assumed that in the underlying population, WHR and BMI could be modelled with linear regression models with environmental covariates , artificially sweetened beverages (ASB) and smoking (PCYR)) and genetic covariates . Age was coded into 5-year intervals and treated as a categorical variable, denoted Y between HUNT3 and HUNT2 byη is as in βFTO  while the term “estimated GEI effect” refers to estimates of βFTO*ENV , and similarly for MC4R.For longitudinal analysis, we modelled the difference in traits Δy, xage, xPA, xASB and xPCYR were observed for the full sample, while the genetic variables xFTO and xMC4R were only observed for the extreme phenotype individuals. The genetic covariates were then missing at random in the full sample, and we used likelihood methods for missing covariate data , 40–60 years (40 ≤ age < 60), 60–80 years (60 ≤ age < 80) that we analysed separately. This should eliminate to a large extent the unknown but assumed co-presence of parents and children in each sample. Importantly, such stratified analysis also allowed us to detect age- and gender-related influences on the genetic impact of FTO and MCR4.A large number of tests were performed in our analysis because we analysed six age and gender groups separately for two outcomes (WHR and BMI). For each of these, we considered over-all effects, longitudinal effects and three different GEIs; in total 120 tests. To account for the large number of tests, we have chosen to control the false discovery rate (FDR) at the 5% level using the Benjamini-Hochberg step-up procedure .We performed a power simulation study to verify that the extreme sampling design could in fact be more powerful than a random sampling design for our analysis . We usedβFTO and βMC4R in the over-all effects H0: βFTO = 0 against H1: βFTO ≠ 0, and H0: βMC4R = 0 against H1: βMC4R ≠ 0. Estimates of GEIs in H0: βFTO*ENV = 0 and H0: βMC4R*ENV = 0 are presented in Relevant sample sizes for our analysis are presented in Over-all effects of the FTO and MC4R SNPs on WHR are presented in In the HUNT population, average WHR and BMI generally increased in the decade between HUNT2 and HUNT3 in all age and gender groups . Our hypβFTO = 0.017 and βFTO*PA = −0.0029 . Anthropometric measurements in HUNT were taken by trained nurses and not self-reported. We have carefully selected environmental covariates to represent various lifestyle aspects: physical activity, smoking and diet. Furthermore we used the extreme phenotype sampling design in order to increase the statistical power of our study, as compared to randomly sampling study participants.We expectedly confirm an overall obesity-promoting effect of the FTO and MC4R minor-alleles in our study population. Of note, the effect appeared age-dependent with the strongest effect seen in the 20–40 year olds. Also our longitudinal findings indicate a diminished or absent influence of the FTO and MC4R minor-alleles at older age. An age-dependent attenuation of the FTO effect in adults has also been reported in other populations, albeit in smaller  and non-Genetic interactions with physical activity and in particular the age- and gender-dependent modulations of these effects were the main findings of our study. A previous study based on HUNT data did not find an interaction , possiblFor interactions with diet we chose a somewhat unconventional parameter, namely weekly intake of artificially sweetened beverages. The choice of this parameter was based on its strong association with central obesity, as reported by us . Other sSmoking has well recognized effects on body size, including associations to obesity. It was therefore of interest to test for interactions between smoking and genetic variants with respect to obesity. We found little evidence of interactions (only in men 40–60 years old), and effect sizes were in varying directions between age groups and genders. Smoking behaviour may differ between the genders and age groups with regard to cigarette brand choices, daily vs. occasional smoking, and association or not with intake of alcoholic beverages; such putative differences may impact the present results. Also, the diverging effects of smoking on body size should be taken into account when interpreting our results; a decreasing effect due to effects of nicotine on metabolism and appetite, increasing effects due to other factors . The pacInterpretations of our results are subject to limitation by the sizable number of non-participants. The total rate of non-participation in the HUNT3 survey was 46%. Data on non-participants has been assembled and analyzed [In conclusion, the present data from an all-adult population survey demonstrate that age and gender importantly modify interactions between lifestyle factors and the obesity-promoting effects of FTO and MCR4. These findings appear relevant when designing individualized treatment for and prophylaxis against obesity.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.S1 TableβSNP) with 95% confidence intervals for men and women in age groups 20–40 years, 40–60 years and 60–80 years. FDR-adjusted p-values for testing H0: βSNP = 0 against H1: βSNP ≠ 0.Estimated over-all effects ((PDF)Click here for additional data file.S2 TableβSNP) with 95% confidence intervals for men and women in age groups 30–40 years , 40–60 years and 60–80 years. FDR-adjusted p-values for testing H0: βSNP = 0 against H1: βSNP ≠ 0.Estimated longitudinal effects ((PDF)Click here for additional data file.S3 TableAverage differences in WHR and BMI between HUNT3 and HUNT2 for men and women in three age groups. Positive differences indicate increase from HUNT2 to HUNT3.(PDF)Click here for additional data file.


Streptococcus pneumoniae is a leading cause of invasive disease in infants, especially in low-income settings. Asymptomatic carriage in the nasopharynx is a prerequisite for disease, but variability in its duration is currently only understood at the serotype level. Here we developed a model to calculate the duration of carriage episodes from longitudinal swab data, and combined these results with whole genome sequence data. We estimated that pneumococcal genomic variation accounted for 63% of the phenotype variation, whereas the host traits considered here (age and previous carriage) accounted for less than 5%. We further partitioned this heritability into both lineage and locus effects, and quantified the amount attributable to the largest sources of variation in carriage duration: serotype (17%), drug-resistance (9%) and other significant locus effects (7%). A pan-genome-wide association study identified prophage sequences as being associated with decreased carriage duration independent of serotype, potentially by disruption of the competence mechanism. These findings support theoretical models of pneumococcal competition and antibiotic resistance. Streptococcus pneumoniae can cause diseases such as pneumonia and meningitis, but generally, it lives harmlessly in the nose, and is particularly common in children and the elderly.Microorganisms live in most parts of our body, including the inside of our nose. Most of the microbes are harmless and can even be beneficial to our health. However, some microbes can cause diseases – although they often go unnoticed, as our immune system can remove them before we show any symptoms. For example, the bacterium The longer the bacteria live in the nose before being killed by the immune system, the more likely they are to be transmitted to another person. The amount of time it takes for the immune system to clear the bacteria depends on various factors, such as the age of the person or the bacterium’s defense mechanism and its genetic material. A particularly important aspect is to what subtype, also known as serotype, a bacterium belongs to, which is characterized by differences in the structure of the sugar coating that surrounds the microbe. However, until now, it was not known how much each of these factors contributes.S. pneumoniae, the bacteria died sooner.Now, Lees et al. have developed a mathematical model to calculate how long the bacteria are carried in the nose before they are cleared away, and compared it with the genomic data of the bacteria. For this, over 14,000 nose swabs from almost 600 children were collected over a two-year period. In their model, Lees et al. calculated that the bacteria’s genetics explained over 60% of the variability in survival time. They also found that the serotype was the most important individual factor that influenced how long a bacterium could survive. The age of the child was less important and only accounted for 5%. In addition, Lees et al. also found that when viruses infected some A next step will be to confirm the effect of a viral infection on the bacteria’s survival time in a controlled model system, and also replicate the findings in separate population study.Understanding how long people can carry bacteria and transmit them to others may help to develop new vaccination or treatment strategies to control infections. Moreover, the discovery that viruses can negatively affect how long a bacterium lives, could motivate studies to investigate these findings further. Streptococcus pneumoniae is a human pathogen that can cause diseases such as pneumonia, otitis media and meningitis. Pneumococcal disease burden is highest in children  with hidden states corresponding to whether a child was carrying a serotype at a given time point, and observed states corresponding to whether a positive swab was observed for this serotype at this time point.The most general model for the swab data would be a vector with an entry of 0 or 1 for every possible serotype (of 56 observed in the population), corresponding to whether each serotype was observed in the swab at each time point. However, the number of parameters to estimate in this model (with over 6 million states) is much larger than the number of data points (around 14000), and in particular some serotypes have very few positive observations. Instead, we modelled each serotype separately.The models fitted, and their permitted transitions and emissions are shown in We applied all the models to 19F carriage episodes, as these had the most data available, and calculated the Akaike information criterion  for eachWe then fitted the best performing model in this test for all serotypes separately. 6A and 6C were treated as a single serotype, as they were not always distinguished in the course of the study. The models for 19F, 23F, 6A/C, 6B, 14 and non-typable (NT) converged, but other serotypes did not have enough observations to successfully fit the parameters of the model. For these less prevalent serotypes we used the transition and emission parameters from the 19F model fitted with the correct observations when reconstructing the most likely route taken through the hidden states. Results were inspected to ensure this did not cause systematic overestimation when compared with previous studies.Streptococcus pneumoniae abundance as defined by 16S survey sequencing. At 1% proportional abundance in the sample, 12% came out as culture negative , of which 2254 had a complete set of AMR data available . After rThe variation in carriage duration tability .S. pneumoniae is an important factor in determining carriage duration in this population. If environmental conditions are associated with streptococcal genotype between populations (such as host vaccination status) the heritability estimate may differ.genetics . Howevergenetics . By applgenetics , we estiA lower bound on  (hSNP2) . We used (hSNP2)  to estimS. pneumoniae, serotype and antibiogram are correlated with the overall genome sequence  in bacteria strong linkage-disequilibrium (LD) is present across the entire genome, making it difficult to pinpoint variants associated with carriage duration and not just present in the background of longer or shorter carried lineages . In S. psequence . If thessequence .A distinction has therefore been made between variants which evolve convergently and affect a phenotype independently of lineage – termed locus effects – to those which are collinear with a genotype which is associated with the phenotype, termed lineage effects . Locus eWhile the high heritability suggests many pathogen variants do affect carriage duration, it does not give information on how many of these will be locus or lineage effects. We mapped carriage duration onto the phylogeny, reconstructing the ancestral state at each node. Consistent with the high heritability of carriage duration we found that carriage length was clearly stratified by lineage : we calcWe first tested for the association of serotype with carriage duration using lasso regression and with a linear-mixed model (LMM). Serotype is correlated with sequence type  and has Not all serotypes and resistances may have an effect on carriage duration, or there may not be enough carriage episodes observed to reach significance. As including extra predictors in a linear regression always increases the variance explained, we first performed variable selection using lasso regression  to obtaiThe selected predictors and their effect on carriage duration are shown in Previous studies have used isogenic strains to look for effects of serotype of colonisation and carriage duration independent of genetic background. Resistance to killing , growth We therefore wished to test whether the detected effect of serotype and resistance on carriage duration was entirely mediated through their covariance with lineage, or whether they are independently associated with carriage duration. We first looked for differences in duration over three recent capsule gain/loss events; if there is an effect of serotype independent of genetic background, these would be predicted have the largest difference between serotypes while controlling for the relatedness of isolates. No significant difference in duration was seen between isolates with or without capsule within the same lineage p=0.39; .However, as these events were limited in number, assumed genetic independence within the clade and occurred only in part of the population, we also performed the same regression as above while also including lineage (defined by discrete population clusters) as a predictor. This therefore allows serotypes which appear in different population clusters to distinguish whether lineage or serotype had a greater effect on carriage duration. The covariance test found that 19F, erythromycin resistance and being non-typable had significant effects on the model (in that order). As these terms enter the model before any lineage specific effect, this suggested these serotypes and resistances are associated with variation in carriage duration independent of background genotypeThis lasso-based analysis may be vulnerable to confounding from unmeasured variables which may be associated with the explanatory variables (serotype and resistance). To fully account for the effect of the bacterial genome rather than relying on discrete clusters as covariates in the regression, we performed regression of these lineage effects under an LMM where the relatedness between strains was instead included as a random effect. The predictors had the same order of significance, but only serotype 19F reached genome-wide significance  have an association independent of genetic background. We also found that erythromycin resistance may be significantly associated with an increased carriage duration. While being a relatively uncommon treatment in this setting (3% of treatments captured), we did not find that other antibiotics were associated. This may be because erythromycin resistance would be expected to cause an almost four order magnitude increase in minimum inhibitory concentration (MIC), whereas other resistance acquisitions have a much smaller effect.Additionally, we calculated the mean sojourn times (average length of time children are expected to remain in the carrying state of the model with the given serotype) and mean number of carriage episodes from the fit to the HMM for commonly carried serotypes , which gave results similar to the regression performed above. These estimates are comparable to the previous analysis on a subset of these samples. The majority of carriage episodes were due to five of the seven paediatric serotypes , or non-S. pneumoniae. Colonisation by other serotypes seem to cause slightly shorter carriage episodes, though the relative rarity of these events naturally limits the confidence in this inference. That some serotypes are rarer and carried for shorter time periods may be evidence of competitive exclusion  carriage episodes of a common serotype  and around two short (under a month) carriage episodes of non-typable xclusion , as fittxclusion .These estimates are similar to previous longitudinal studies in different populations , though To search for locus effects as discussed above, we applied an LMM to all the common SNPs and k-mers in the dataset. The results for SNPs are shown in equences . To deteThe only genome-wide significant SNP hits are synonymous changes (MAF = 1%) in the replication module of the prophage in the ATCC 700669 genome , a highlS. pneumoniae lineages  and were associated with a reduced duration of carriage. As these mobile genetic elements are less weakly population stratified than other regions of the genome, they are easier to find as locus effects. The LD in this region is less than in the rest of the genome, as prophage sequence is highly variable within lineages . MultiplNeisseria meningitidis, where a specific phage sequence was found to affect the virulence and epidemiology of strains . These results are therefore evidence that infection with a specific phage sequence is associated with a slight decrease in carriage duration. A similar result has previously been found in a genome-wide screen in  strains . Additiocarriage .pblB is within the morphology module  was significantly associated with an increased carriage duration using the LMM , padR (repressor of phenolic acid stress response), pepS (aminopeptidase), aroA (aromatic amino acid synthesis), fms (peptide deformylase) and a thioesterase superfamily protein. K-mers from erythromycin resistance genes  were expected to reach significance from the above analysis, but did not: it has however previously been shown that the power to detect these elements in a larger sample set taken from the same population is limited due to the multiple resistance mechanisms and stratification of resistance with lineage . We also tested SNPs for their association with those principal components which were themselves significantly associated with carriage duration, and therefore may be driving the lineage associations .pbp regions associated with recA (DNA repair and homologous recombination), bgaA , phoH-like protein (phosphate starvation-inducible protein), ftsZ (cell division protein) and groEL (chaperonin). As 19F, the serotype most associated with carriage duration, is predominantly the pbp association may be driven through strong LD between with this serotype. dnaB (DNA replication) may be associated with altered carriage duration. Associated k-mers were also found in phtD (host cell surface adhesion), mraY , tlyA (rRNA methylase), zinT (zinc recruitment), adcA (zinc recruitment) and recJ (DNA repair). Additionally we found k-mers in the bacteriocin blpZ and immunity protein pncM  may be expected to cause a change in carriage duration . Common serotypes and resistance to erythromycin cause some of this effect , as does the presence or absence of particular prophage sequence in the genome. Other than serotype, the genetic determinants of pneumococcal carriage duration were previously unknown. By developing models for longitudinal swab data and combining the results with whole genome sequence data we have quantified and mapped the genetic contribution to the carriage duration of We provide a quantitative estimate of how closely transmission pairs share their carriage duration, and show evidence for differences both between and within serotypes. The implication of phage as having a significant effect on carriage duration has interesting corollaries on pneumococcal genome diversification through frequent infection and loss of prophage, even during carriage episodes in this dataset.comYC gene, which is frequently interrupted by prophage causing loss of function of the competence system, was associated with increased carriage duration. While the competence system is observed to remain intact over the evolutionary history of the species, these disruptive mutations spread irreversibly through the population as competent bacteria can acquire the mutation, and non-competent bacteria can no longer reverse it through recombination . 15B and 15C serotypes spontaneously interconvert, so were combined. We removed two duplicated swabs .To get a good fit of the HMM, we normalised observation times for each sample. Defining infant birth as SCR_015500)  . Unobserlgorithm  with theSCR_007931), using a manually prepared reference (SCR_015501) under the GTR +gamma model (SCR_015502) (For each isolate with an inferred carriage duration (N = 2175) we extracted SNPs from the previously generated alignment against the ATCC 700669 genome . Consequeference . A phyloma model . The car_015502) . We thenWe counted 68M non-redundant k-mers with lengths 9–100 from the de novo assemblies of the genomes using a distributed string mining algorithm . We filtWe identified the presence of phage by performing a blastn of the de novo assemblies against a reference database of phage sequence . If the As we aimed to fit a multiple linear regression model to the carriage duration ribution . Residuaribution . In the which led to the residuals being much closer to being normally distributed . We applWe estimated broad sense heritability  package , using a package . To testSCR_015503) (SCR_015504) (To estimate the SNP-based heritability _015503) , which l_015503) . We ther_015504)  with defX was against the transformed carriage duration We encoded all 56 observed serotypes (including non-typables) and resistance to the six antibiotics as dummy variables. We used 6A/C as the reference level, as this had a mean carriage duration close to the grand mean in previous analysis. Orthogonal polynomial coding was used for the latter four antibiotics, where resistance could be intermediate or full. We then regressed this design matrix s of one .SCR_015505)  . We useded error  was seleλ=0.033; . The 20 0.033; λ  were usepbp genes, dyr gene and ICE transposon to calculate a kinship matrix. Restricted maximum likelihood was used to estimate the variance explained by each of these components.To estimate the variance components from serotype and resistance we used genomic partitioning , as implCapsule switch events had been previously identified by first reconstructing of the ancestral state of the serotype at each node through maximum parsimony . For eacSCR_015506) (We used the linear mixed model implemented in fast-lmm (RRID:_015506)  to assoce cutoff  of p=1.1SCR_001598) with default settings to map the significant k-mers to seven reference genomes , and the possible Tn916 sequences  (To search for variants with some level of lineage independence we used SEER (RRID:_015499) . To corrhttps://github.com/elifesciences-publications/carriage-duration.All code used for analysis is available on github, along with inferred carriage duration for each sample . A copy  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: Daniel Wilson.Thank you for submitting your article "Genome-wide identification of lineage and locus specific variation associated with pneumococcal carriage duration" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.The reviewers agree that this is an interesting and novel analysis of an impressive data set. The study sheds light on an important problem in clinical microbiology, the genetic determinants of carriage duration in pneumococcus, and the work should thus appeal to modelers in bacterial genomics and epidemiology. The reviewers find the conclusions fairly convincing and note that the association between carriage duration and the presence of prophage is an especially intriguing result that could inspire future investigations.The reviewers note, however, the statistical challenges inherent in this type of study. They provide several suggestions for how the statistics could be improved or clarified to carefully delineate the support for different conclusions. An overriding concern is that the limitations of the statistics, and especially the inability of associations to demonstrate causality, be accurately communicated. The reviewers also recommend discussing the potential biology of the phage association in more depth.Essential revisions and major points:1) Exploring the inclusion of some components , but not all components of genetic variation (i.e. the rest of the genome) on phenotype using a Lasso is fairly ad hoc, making it difficult to have confidence in the conclusions. Besides the uncertainty in distinguishing the effects of these loci from others not included, there is also the problem that unmeasured, heritable confounders may influence results, because population stratification may not be adequately controlled. Lasso can make arbitrary choices between approximately equally good variables even if all loci were included, making it good for predicting phenotype, but bad for identifying candidate causal loci. It would be appropriate to mention that in the Lasso analysis, there is less robustness to potential confounding with unmeasured variables that may be associated with the significant regressors. Inferences of causality  are therefore likely to be overstated. For more robust inference, I would suggest testing the significance of these explanatory variables over and above what is explained by, e.g. the top 30 PCs or (better) in a lineage mixed model. If these analyses do not support the same conclusions, that is important to highlight.the host traits considered here accounted for less than 5%." 2) The authors need to be more careful/speculative in their description of their findings, especially in the Abstract. For example, with respect to the role of host factors, this was far from exhaustive in this study, and there are many more host features, that were the data available could have accounted for more of the phenotype variation, so I believe it is more accurate to state "We estimated that pneumococcal genomic variation accounted for 63% of the phenotype variation, whereas The second statement in the Abstract that is overstated is 'A pan-genome-wide association study identified prophage sequences as significantly decreasing carriage duration independent of serotype.' Whereas without any evidence of causation, I believe they can only state that they have "identified prophage sequences that significantly associated with decreasing carriage duration independent of serotype." The same idea holds for the association of erithromycin resistance and carriage – the causality is not straightforward. Under the theory of Lehtinen et al. (2017), long durations drive resistance due to the sensitivity of strains with long carriage durations to the fitness effects of antibiotic use, not the other way around.3) The association of the polymorphic phage with carriage is intriguing and deserves more treatment. Phages likely affect the strain in very different ways, some potentially extending carriage duration with other others decreasing it. Can phages in the database be partitioned into categories based on predicted phenotypic impact and the association retested? What was the genetic polymorphism in the phage genes that associated with carriage duration? How many strains was this in, i.e., was it a large effect in a small number of samples or a small affect in a large number? What was the mean carriage duration for strains with and without it  Exploring the inclusion of some components , but not all components of genetic variation (i.e. the rest of the genome) on phenotype using a Lasso is fairly ad hoc, making it difficult to have confidence in the conclusions. Besides the uncertainty in distinguishing the effects of these loci from others not included, there is also the problem that unmeasured, heritable confounders may influence results, because population stratification may not be adequately controlled. Lasso can make arbitrary choices between approximately equally good variables even if all loci were included, making it good for predicting phenotype, but bad for identifying candidate causal loci. It would be appropriate to mention that in the Lasso analysis, there is less robustness to potential confounding with unmeasured variables that may be associated with the significant regressors. Inferences of causality  are therefore likely to be overstated. For more robust inference, I would suggest testing the significance of these explanatory variables over and above what is explained by, e.g. the top 30 PCs or (better) in a lineage mixed model. If these analyses do not support the same conclusions, that is important to highlight.We agree that the lasso is generally better for prediction rather than inference due to only selecting one of two equally good predictors to keep in the model at each stage. We considered applying the bootstrapped lasso , but had too few observations given the number of predictors. The covariance test for the lasso which we used instead has been shown to be more useful for inference (as cited), and we think still useful for finding potential lineage effects (which are collinear with the genetic background). To ensure robust analysis here, we also used a variance components analysis to test the variance explained and significance of all serotypes and resistances together.However, we agree that the limitations of the lasso apply to the analysis mentioned in the subsection “Serotype and drug resistance explain part of the narrow-sense heritability” where we have attempted to find whether serotype and resistance have an effect independent of genetic background. As suggested, we have performed a more robust analysis with a linear mixed model, and found that only 19F was associated independent of background. We have highlighted these differences, and mentioned the drawbacks of the lasso approach in this section as suggested. Additionally, as part of our response to major point 2), we have also been more careful in describing association/causation in this section.2) The authors need to be more careful/speculative in their description of their findings, especially in the Abstract. For example, with respect to the role of host factors, this was far from exhaustive in this study, and there are many more host features, that were the data available could have accounted for more of the phenotype variation, so I believe it is more accurate to state "We estimated that pneumococcal genomic variation accounted for 63% of the phenotype variation, whereas the host traits considered here accounted for less than 5%." The second statement in the Abstract that is overstated is 'A pan-genome-wide association study identified prophage sequences as significantly decreasing carriage duration independent of serotype.' Whereas without any evidence of causation, I believe they can only state that they have "identified prophage sequences that significantly associated with decreasing carriage duration independent of serotype." The same idea holds for the association of erithromycin resistance and carriage – the causality is not straightforward. Under the theory of Lehtinen et al. (2017), long durations drive resistance due to the sensitivity of strains with long carriage durations to the fitness effects of antibiotic use, not the other way around.We have revised the writing of the findings to ensure causality is not implied where we do not have evidence for it, and have been careful to describe associations appropriately. We have made the suggested changes to the Abstract, and similar changes to describing the effect of erythromycin resistance and the section testing for effects of capsule versus genetic background (as mentioned in the response to major point 1).3) The association of the polymorphic phage with carriage is intriguing and deserves more treatment. Phages likely affect the strain in very different ways, some potentially extending carriage duration with other others decreasing it. Can phages in the database be partitioned into categories based on predicted phenotypic impact and the association retested? What was the genetic polymorphism in the phage genes that associated with carriage duration? How many strains was this in, i.e., was it a large effect in a small number of samples or a small affect in a large number? What was the mean carriage duration for strains with and without it .


Brassica napus (oilseed rape). Based on 1.92 × 1012 bases of leaf mRNAseq data, functional genotypes, comprising 355 536 single‐nucleotide polymorphism markers and transcript abundance were scored across a genetic diversity panel of 383 accessions using a transcriptome reference comprising 116 098 ordered coding DNA sequence (CDS) gene models. The use of the platform for Associative Transcriptomics was first tested by analysing the genetic architecture of variation in seed erucic acid content, as high‐erucic rapeseed oil is highly valued for a variety of applications in industry. Known loci were identified, along with a previously undetected minor‐effect locus. The platform was then used to analyse variation for the relative proportions of tocopherol (vitamin E) forms in seeds, and the validity of the most significant markers was assessed using a take‐one‐out approach. Furthermore, the analysis implicated expression variation of the gene Bo2g050970.1, an orthologue of VTE4 (which encodes a γ‐tocopherol methyl transferase converting γ‐tocopherol into α‐tocopherol) associated with the observed trait variation. The establishment of the first full‐scale Associative Transcriptomics platform for B. napus enables rapid progress to be made towards an understanding of the genetic architecture of trait variation in this important species, and provides an exemplar for other crops.An updated platform was developed to underpin association genetics studies in the polyploid crop species  Brassica napus, based on functional genotypes, enables the genetic architecture of essentially any trait to be addressed in this important crop species.The availability of a full‐scale association genetics platform for  A broad aim of crop science is the identification of the genetic bases for trait variation, including both the identification of beneficial alleles and the development of molecular markers to accelerate their introduction into elite germplasm. Genetic diversity panels, typically comprising past and current cultivars along with wild relatives, are usually available for crop species. Such panels represent ideal resources for genome‐wide association studies (GWAS), which exploit historical recombination between molecular markers and loci associated with trait variation. Where recombination between loci is observed proportionately less frequently than expected for unlinked loci (i.e. < 0.5), those loci are said to be in linkage disequilibrium (LD). The approach of identifying molecular markers in LD with loci associated with trait variation is an important tool used in human genetics studies, and has been applied successfully in several plant species  and a new trait (the relative content of γ and α forms of tocopherol in seeds).The first AT panel reported for B. napus doubled haploid (DH) or inbred accessions was assembled, with the aim of covering the breadth of genetic variation available in the species. This panel included the breadth of crop types of B. napus, and comprised 362 inbred lines previously used by Bus et al. .Functional genotypes were produced for the panel based on leaf RNA, with 100‐base read length mRNAseq data produced using the Illumina HiSeq 2000 platform. A total of 1.92 × 10psiko  for the erucic acid trait was estimated from the SNP analysis as 0.794. A total of 318 genome‐assigned SNP markers above the Bonferroni‐corrected significance threshold of P = 0.05  were detected across association signals on chromosomes A5, A8, A9, A10 and C3 , represented by gene models Cab035983.1 and Bo3g168810.1, are near the centres of these SNP association peaks, within six genes (approximately 42 kb) and nine genes (approximately 56 kb) from the closest significantly associated gene, respectively, according to the reference sequence  are transcriptionally inactive in the tissue (leaves) sampled for the production of the functional genotypes. We are still able to detect both SNP and gene expression marker (GEM) association peaks through markers in LD with FAE1 on A8 and C3, however, as illustrated in Figure et al., In addition to association analysis using SNP markers, AT also reveals associations between gene expression markers  and trait variation. In the case of seed erucic acid content, the main control genes  show particularly strong transcript abundance associations, which we interpret as consequences of the extensive structural variation in these regions of the genome  and α‐tocopherol (51–229 mg kg−1), the relative proportions of which (measured as the γ/α‐tocopherol ratio) varied greatly, ranging from 0.485 to 5.00, with δ‐tocopherol representing a minor component (1.8–9.9 mg kg−1). Analysis of tocopherol characteristics by crop type showed that γ‐tocopherol content tended to be higher in spring crop types and α‐tocopherol content tended to be higher in winter crop types, as illustrated in Figure We selected tocopherols in seeds as test traits of unknown genetic basis, quantifying α, γ and δ forms. Tocopherols were purified from seeds and quantified for 377 accessions of the RIPR panel. The results are summarised in Appendix R2 = 0.13; P < 0.001).Given that the purpose of tocopherols in seed oil is to protect against oxidation, we assessed the diversity panel for correlations of tocopherol traits with the proportions of the fatty acids found in seed oil that are most susceptible to oxidation, the PUFAs linoleic and linolenic. The content of these fatty acids had been determined alongside that of erucic acid Appendix . A weak B. napus breeders. We interpret the very sharp association signal as indicative of this lack of selection, and consider this to be consistent with LD across most of the genome. The association peak on chromosome C2 includes 33 genome‐assigned markers above the Bonferroni‐corrected significance threshold , an enzyme that converts γ‐tocopherol into α‐tocopherol  were identified on chromosomes C2, C5 and C7 , which reflect the estimated narrow‐sense heritability (h2) of 0.452 for the γ/α ratio. These SNPs and GEMs can therefore be used as promising markers in marker‐assisted breeding.To investigate whether the top selected markers are predictive for the γ/α ratio, we performed a set of ‘take‐one‐out’ permutations for the SNP and GEM markers identified from association analysis of 377 accessions adapted from Harper et al. . Markerset al.  were selVTE4 orthologue in the associated region of C2 (Bo2g050970.1), we used the transcript quantification data that were obtained alongside the transcriptome SNP data as part of the functional genotypes. As illustrated in Figure R2 = 0.41, P < 0.001). This is consistent with the predicted γ‐TMT activity of the gene encoded by Bo2g050970.1 (i.e. lower expression leading to less conversion of γ‐tocopherol to α‐tocopherol). There had been no significant associations between SNPs within Bo2g050970.1 and the γ/α ratio, consistent with the basis of the allelic variation being variation in gene expression rather than variation in gene sequence.In order to confirm the role of the et al., B. napus, as was recently shown by Chalhoub et al.  on chromosome C2 as a candidate gene, based on inference of gene function from studies of its orthologue in A. thaliana. Although there was no evidence of the presence of any specific allelic form of the VTE4 orthologue associated with γ/α‐tocopherol ratio, this gene has been easily identifiable by the presence of SNPs in surrounding genes. This set of tightly linked markers exhibited excellent predictive ability  is known to use δ‐ and γ‐tocopherols as substrates to produce β‐ and α‐tocopherols, respectively  and Arabidopsis  forms accumulated in seeds. We analysed seed tocopherols in 377 rapeseed accessions for their type and content. The profiles presented here showed a high degree of variability for the γ/α‐tocopherol ratio (Coefficient of Variance = 53%), displaying distinct patterns for different crop types, that allowed us to identify gene y Figure . The assBy assembling and developing functional genotypes (i.e. comprising both gene sequence variation and gene expression variation) for a diversity panel representing species‐wide genetic diversity, we have established a resource for the whole rapeseed research community to use. Furthermore, the success of the approach of Associative Transcriptomics for the identification not only of linked markers but of candidates for causative genes serves as an exemplar for plant and crop science more broadly.B. napus accessions is available from the John Innes Centre ( https://www.jic.ac.uk). It was planted in a randomized block design of five biological replicates under controlled conditions of two polytunnels at the University of Nottingham, as described by Thomas et al.  for analysis using a DANI Master GC fitted with an SGE‐BPX70 double column ( https://dani-instruments.com).For the analysis of fatty acid methyl esters (FAMEs), 30 mg of seeds were homogenized in a glass vial with 5 mL of heptane. To the homogenate, 500 μL of 2 M potassium hydroxide was added, left for 1 h and then neutralized with sodium hydrogen sulphate monohydrate. The upper phase was transferred into crimp‐cap Chromacol 0.8‐ml vials , phase B was heptane:dioxane . The internal standard, α‐tocopherol acetate (Sigma‐Aldrich), was added to each sample at a concentration of 25.4 μM (12 μg mL−1).The α‐, γ‐ and δ‐tocopherol  were extracted from a homogenous mixture of 80 mg rapeseed seeds and analysed by normal‐phase HPLC, as described previously  > 0.01. The markers were also classified as those that can be assigned with confidence to the genomic position of the CDS model in which they are scored  mapping population), and those that cannot, as the polymorphism may be in either homoeologue of the CDS model in which they are scored . Transcript abundance was quantified and normalized as reads per kb per million aligned reads (RPKM) for each sample for 116 098 CDS models of the pan‐transcriptome reference. Significant expression (> 0.4 RPKM) was detected for 53 889 CDS models.The growth conditions, sampling of plant material, RNA extraction and transcriptome sequencing was carried out as described by He et al. . The RNA et al.,  as refer, et al.  of mRNAsr script developed in‐house. r package ‘phangorn’ was used for generating a distance matrix with the JC69 model  was observed to be greater than 1.Association analysis for SNPs and GEMs was performed using et al., r script was performed with adaptation from Harper et al. (R2 value is calculated from predicted trait values regressed against the observed trait values, which indicates the predictive power of the top selected GEMs and SNPs.The predictive power of the best GEMs and SNPs were assessed using a ‘take‐one‐out’ approach (Harper r et al. , with a Sequence data from this article can be found in the SRA data library under accession number PRJNA309367.The authors declare no conflicts of interest.http://www.yorknowledgebase.info/.The smaller data sets are hosted as supporting information online.Supporting data are available. The largest data sets, representing the functional genotypes of the RIPR panel, are accessible via a data distribution website: Figure S1. Genome‐wide linkage disequilibrium analysis for the RIPR diversity panel.Click here for additional data file.Figure S2. Histograms of seed tocopherol composition of the RIPR diversity panel in different crop types.Click here for additional data file.Figure S3. Quantile–quantile plots from GEM and SNP association analysis for erucic acid and γ/α‐tocopherol ratio.Click here for additional data file.Appendix S1. List of cultivars, crop type classifications and Illumina read mapping statistics.Click here for additional data file.Appendix S2. Ordered list of CDS gene model‐based Brassica AC pan‐transcriptome.Click here for additional data file.Appendix S3. Seed fatty‐acid composition of the RIPR diversity panel.Click here for additional data file.Appendix S4. Markers and genomic regions showing association with variation for erucic acid content.Click here for additional data file.Appendix S5. Seed tocopherol composition of the RIPR diversity panel.Click here for additional data file.Appendix S6. Markers and genomic regions showing association with variation for γ/α‐tocopherol ratio.Click here for additional data file.Appendix S7. Gene expression markers showing association with variation for γ/α‐tocopherol ratio.Click here for additional data file. Click here for additional data file.


Whole-genome sequencing and imputation methodologies have enabled the study of the effects of genomic variants with low to very low minor allele frequency (MAF) on variation in complex traits. Our objective was to estimate the proportion of variance explained by imputed sequence variants classified according to their MAF compared with the variance explained by the pedigree-based additive genetic relationship matrix for 17 traits in Nordic Holstein dairy cattle.Imputed sequence variants were grouped into seven classes according to their MAF . The total contribution of all imputed sequence variants to variance in deregressed estimated breeding values or proofs (DRP) for different traits ranged from 0.41 [standard error (SE) = 0.026] for temperament to 0.87 (SE = 0.011) for milk yield. The contribution of rare variants (MAF < 0.01) to the total DRP variance explained by all imputed sequence variants was relatively small . Rare and low-frequency variants (MAF < 0.05) contributed a larger proportion of the explained DRP variances (>13%) for health-related traits than for production traits (<11%). However, a substantial proportion of these variance estimates across different MAF classes had large SE, especially when the variance explained by a MAF class was small. The proportion of DRP variance that was explained by all imputed whole-genome sequence variants improved slightly compared with variance explained by the 50 k Illumina markers, which are routinely used in bovine genomic prediction. However, the proportion of DRP variance explained by imputed sequence variants was lower than that explained by pedigree relationships, ranging from 1.5% for milk yield to 37.9% for the health index.Imputed sequence variants explained more of the variance in DRP than the 50 k markers for most traits, but explained less variance than that captured by pedigree-based relationships. Although in humans partitioning variants into groups based on MAF and linkage disequilibrium was used to estimate heritability without bias, many of our bovine estimates had a high SE. For a reliable estimate of the explained DRP variance for different MAF classes, larger sample sizes are needed.The online version of this article (doi:10.1186/s12711-017-0336-z) contains supplementary material, which is available to authorized users. Associations of common genetic variants with complex diseases and quantitative traits have been successfully identified in humans and livestock –3. HowevRare variants may play a significant role in quantitative trait variation , 13, 14 When whole-genome sequence data are available, linkage disequilibrium (LD) between SNPs and causal variants increases and a large fraction of the causal variants themselves will be available for analysis. Therefore, an increase in the proportion of the variance that can be explained for quantitative traits is expected when whole-genome sequence variants are used compared with the use of SNP array data , 8.However, using whole-genome regressions which regress phenotypes on the whole-genome sequence variants using a linear model to infer the proportion of variance explained for a trait may result in biased estimates , 18. FirRecently, Yang et al.  proposedThe objectives of this study were to: (1) estimate the proportion of variance explained by whole-genome sequence variants for 17 traits in Nordic Holstein cattle; (2) estimate the proportion of variance explained by partitioning variants according to MAF, and with or without taking LD heterogeneity into consideration; and (3) compare estimates of the proportions of genetic variance explained by relationships based on pedigree, 50 k SNPs, and imputed whole-genome sequence variants.http://www.nordicebv.info/ntm-and-breeding-values. The number of bulls with both genotype data and DRP for different traits ranged from 4485 to 4949  array version 1 or 2 . The phenotypes used in this study were deregressed estimated breeding values or proofs (DRP) with a minimum reliability of 0.2 for 17 traits  for each of the seven classes of variants were calculated following  and fittsoftware , 31. Theg et al. . First, g et al. , resultiThe GRM used in GCTA, assumes that allelic effects of both common and rare variants follow the same distribution, similar to VanRaden’s method 2 , 32. ThiThe phenotypes used in our analysis, as is often the case in animal breeding, were DRP derived from estimated breeding values with varying reliabilities. Weights derived from those reliabilities are commonly used in analyses that use DRP. However, the GCTA software does not support the use of weights, because it was developed in the context of human data analysis where the phenotypes used are typically directly measured on the genotyped individuals. However, the average reliability of the DRP used here were quite high Table . For exa1 is a vector of 1s, The genetic variance estimated by using the pedigree relationship matrix was compared to the variance explained by the imputed sequence variants and the 50 k SNPs. The proportions of DRP variance explained by pedigree and genomic relationships were estimated by fitting pedigree and 50 k data separately or jointly in the model as described below:1 is a vector of 1s. The factor Models , 3) and and3) anmethod 1 :\documensoftware . In addi Legarra .Additional file DGAT1 gene [Interestingly, we observed that the relative contribution of variants with a MAF between 0.2 and 0.3 was substantially higher than that of other classes for MILK, FAT and PROT, as well as for LEG and LONG, while the imputed sequence variants were more or less evenly distributed across each MAF class Fig. . This miAT1 gene  (locatedThe proportion of DRP variance explained by rare MAF < 0.01), low-frequency (MAF = 0.01–0.05) and common variants  in Additional file , low-freThe proportions of DRP variance explained for 17 traits by the different models  and using different information sources to construct relationship matrices  are in Table Imputed sequence variants explained more DRP variance than 50 k SNPs for most traits Table . HoweverThe variance explained by fitting both pedigree and genomic relationship matrices (GRM) using the 50 k data in the PED + 50 k-DMU model, relative to the variance explained by the pedigree-based relationship matrix alone (PED-DMU), ranged from 109.2% for MASTI to 90.3% for FERT  for HEALTH . “-” means that there is no result for this case. Estimates that are larger than one time the standard error are in boldface.Additional file 2: Table S2. Number of variants in different MAF classes, imputation accuracy for different MAF classes and proportion of DRP variance explained and standard errors for seven MAF by four LD classes for 17 traits. The numbers were presented as the proportion of explained DRP variance ± standard error. The traits where the model did not converge were not presented in this table. For each column of the table, the imputed sequence variants were classified into 7 classes based on their MAF . For each row of the table, the variants within each of the 7 MAF classes were stratified into 4 equally sized LD groups based on increasing mean LD scores, resulting in 28 groups in total. Estimates that are larger than one time the standard error are in boldface.Additional file 3: Table S3. Estimates of residual and total variance of DRP using different models and different information sources to construct the GRM. The total explained DRP variance for PROT in PED-DMU is scaled to 100.0 and used as a reference to scale other numbers across models and traits. There are two rows for each trait. The relative residual variance is presented on the first row and total variance of DRP is shown on the second row. “GREML-MS” is the relative residual variance and DRP variance calculated using the GREML-MS method with partitioning of imputed sequence variants into MAF groups. “REML-GRM” is the relative residual variance and DRP variance calculated by fitting 50 k SNPs with the REML-GRM model implemented in GCTA. “REML-PED” is the relative residual variance and DRP variance calculated by fitting pedigree relationships with the REML-PED model implemented in DMU. “REML-PEDGRM” is the relative residual variance and DRP variance calculated by fitting both 50 k SNPs and pedigree relationships with the REML-PEDGRM model implemented in DMU. “-” means that the model did not converge.Additional file 4: Table S4. The Akaike information criterion (AIC) for different models compared to REML-PED. The log likelihoods for different models were used to calculate AIC following [ollowing . The AICAdditional file 5: Figure S1. LD score on a segment of chromosome 1 in a sample of Holstein individuals. The yellow dots are the LD score for each variant. The LD score was defined as the sum of the LD measure r2 between this SNP and other SNPs in a 20-Mb region centered on this SNP. The blue dots are the average LD score for a sliding window of 100 kb.


OXTR) gene and overeating, and to examine OXTR links with relevant endophenotypes of overeating related to reward and stress sensitivity, and to food preferences.Oxytocin (OXT) is an evolutionarily ancient neuropeptide with strong links to affiliative and prosocial behaviors, and the management of stress. Increases in OXT also tend to decrease food intake, especially of sweet carbohydrates. The social correlates of low OXT levels mesh with the social deficits and stress proneness identified in interpersonal models of overeating, as well as the increased appetite for highly palatable foods typically seen in chronic overeaters. The objectives of this study were to investigate links between polymorphisms of the oxytocin receptor  of the Analyses identified a four-marker haplotype that was significantly related to food preferences. Individual genotype analyses also found that at least one of the markers was related to each of the phenotypic variables. In addition, an empirically derived structural equation model linking genetic and phenotype variables produced a good fit to the data.OXTR variation is associated with overeating, and with endophenotypic traits such as sweet and fatty food preferences, and reward and punishment sensitivity. In general, the genetic findings also favor the view that overeating may be associated with relatively low basal OXT levels.The results of this preliminary study have demonstrated that  In accord with its role in sugar-specific satiety, mice deficient in OXT also tend to overconsume sucrose solutions, both initially and over a sustained period of time.16 Of relevance, however, are the findings that chronic sucrose overconsumption tends to downregulate the anorexigenic OXT system in such a way that an elevated food-load ‘threshold’ is required to induce satiety.17 For instance, a recent study found that binge-prone rats consumed ~30% more sucrose in a non-stressful situation, and 60% more sucrose in a stressful environment, compared with their non-binge-prone counterparts—findings that may also be moderated by OXT levels.18 Likewise, there is good clinical evidence that chronic overeaters have higher reward responsiveness to palatable food, and tend to experience greater food cravings, compared with their normal-weight counterparts.19 Of relevance is the evidence that OXT is a well-established anxiolytic agent that tends to buffer cortisol responses to stress, especially in those with poor emotion-regulation abilities.20To date, studies of OXT signaling in the risk for, and development of, eating disorders are relatively scarce and mostly based on small-sample research.21 For instance, several studies have identified poor interpersonal skills, avoidance of emotional expression, interpersonal distrust and a diminished ability to cope with negative feelings across diverse samples of adults with clinically significant overeating.23 Furthermore, significant links have been established between attachment avoidance and binge eating,24 and between attachment insecurity and increased risk for disordered eating in general.25 Binge eating in both mothers and fathers has also been associated with poor parent–child interactions during feeding and with the child’s emotional–behavioral problems over time.26 Similarly, overeating—especially among those who binge eat—has been associated with an elevated proneness to stress, and with poor emotional regulation.27 Interestingly, a large population study recently found that across all forms of disordered eating, patients were more likely to be childless than were their healthy counterparts with an odds ratio of 1.86.28 Such outcomes might have as much to do with relationship and social-bonding deficits as with poor physical reproductive health.It is striking that the social deficits associated with relatively low levels of OXT—such as attachment avoidance, relationship difficulties, parental insensitivity, and stress proneness—show remarkable similarities to the social impairments identified in interpersonal models of overeating.Based on the body of research reviewed above, it is proposed that OXT may have important implications in the risk for, and development of, clinically significant overeating. Relatively low trait levels of OXT may foster a propensity to overconsume, especially in an environment where highly palatable temptations are ubiquitous, easily available and reasonably inexpensive, and among those with elevated sensitivity to the rewarding properties of such foods. Over time, chronic consumption of a sugar-laden diet may exacerbate the consumption of sweet substances. Relatively low OXT levels are also associated with a myriad of interpersonal deficits, which may contribute to elevated stress, particularly in those who are sensitive to social rejection and anxiousness. Taken together, these factors may foster increases in palatable food consumption as a means of emotional comfort. Such a stress-diathesis model of overeating meshes with the interconnected influences of the OXT system on complex human behaviors and personality traits.exploratory study was to examine links between markers of the OXTR gene and overeating. We also investigated associations between the OXTR markers and established endophenotypes of overeating such as sensitivity to both punishment and reward, and palatable food preferences.30 In a relatively large sample of adult men and women, structural equation modeling (SEM) was used to assess simultaneously the relationships proposed in The aim of the current −2).The sample consisted of 460 adults  between 24 and 50 years of age. Inclusion criteria were residence in North America for at least 5 years before enrollment in the study, and fluency in written and spoken English. Women were also required to be premenopausal as indicated by the self-report of a regular menstrual cycle. Exclusion criteria included serious medical illnesses such as cancer or diabetes, or severe physical disabilities such as cerebral palsy. Those with a current axis I diagnosed disorder, with the exception of unipolar depression, were also excluded. In addition, women with a pregnancy in the previous 6 months, or who were lactating, were not included. The majority of the sample was Caucasian (79%), with 15% identifying as African descent and the remainder as ‘Other’. The sample represented a broad range of body mass index (BMI) values  in the 34 higher levels of trust and empathy,35 more sensitive parenting36 and increased sensitivity to social support.37 Additionally, the GG genotype carriers were more likely to report dieting to lose weight and less likely to eat sugary and fatty foods, such as cakes and puddings.38For instance, the G allele at the intron-3 of the rs53576 marker has been associated with higher reward dependence, increased amygdalar activation during the facial expression face-matching task,41 There is other evidence that this SNP may impact brain-reward circuitry. For instance, the TT genotype group displayed decreased activation in mesolimbic regions  in response to a monetary-rewards task.3 The T allele was also associated with poorer performance on a composite social cognition index.42At the intron-3 of the rs2268493 marker, the T allele has been associated with social impairment, autism spectrum disorders, and attention deficit/hyperactivity disorder.43 Using a cumulative risk index including the AA genotype of this SNP, another recent study found less empathic concern to their romantic partner’s distress and lower social reciprocity in support-giving interactions.44The A-allele carriers at another intron-3 marker, rs2268494, has been associated with reduced anger responses in a laboratory game focused on interpersonal interactions.46 Those carrying the G allele also showed lower amygdala response in tasks involving socially relevant face stimuli.47 In addition, a case–control study of patients with eating disorders found that A-allele carriers had a greater preoccupation with food and weight, and heightened social anxiety.32The G allele at the intron-3 of the rs2254298 marker has been associated with reduced social anxiety, decreased responsivity to stressors and lower social impairments.48 and in another study, the TT genotype carriers displayed increased callous-unemotional traits compared with their counterparts.49 Of relevance, the prepulse inhibition of the startle reflex was significantly lower in the TT genotype.50 As low prepulse inhibition is associated with depression, these findings mesh with the results of the studies described above.The TT genotype at the intron-3 of the rs237885 marker has been broadly overrepresented in schizophrenia patients,51 and on a measure of social-perception abilities including the strength of social bonding, such as kinship and intimacy.52 The TT genotype was also associated with the lowest scores on measures of fear and sadness, as well as on negative emotionality.53The T allele of the promoter rs2268498 marker has been associated with higher scores on a measure of empathy,54Finally, the C allele at the 3′ rs237878 marker has been associated with higher levels of extraversion—a personality construct with strong positive links to reward sensitivity (RS) and sensation seeking—compared with the T allele.The three psychological independent variables , and the dependent variable (overeating), were operationally defined as separate composite variables  to reflect the multidimensionality of each construct.approach motivation—one of three evolutionarily adaptive brain systems underlying current psychobiological theories of personality.55 This neurocircuitry regulates the incentive to obtain rewards as well as the hedonic experience of pleasure derived from these rewards.56 The 30-item Barratt Impulsivity Scale identifies facets of impulsiveness such as non-planning, and the tendency to act rashly and to make quick decisions.57 The Reward Subscale of the Sensitivity to Punishment and Sensitivity to Reward Questionnaire contains 24 forced-choice items assessing the respondent’s approach responses under various conditions of reward—including tangible rewards such as money and pleasure-inducing substances, as well social rewards such as receiving admiration from others.58 And finally, the 13-item Behavioral Activation Scale comprises three subscales, which collectively assess (i) anticipation of, and positive response to, rewards, (ii) the degree of persistence in achieving rewarding goals, and (iii) the strength of one’s desire for these rewards.59RS comprises three questionnaires, which were designed to assess avoidance motivation—another evolutionarily adaptive brain system regulating the drive to avoid or retreat from situations of threat or punishment, and from other aversive events.60 The 35-item Harm Avoidance Scale of the Temperament and Character Inventory reflects the tendency to inhibit behavior in response to aversive or punishing stimuli.61 The Punishment Subscale (RS) of the Sensitivity to Punishment and Sensitivity to Reward Questionnaire contains 24 forced-choice items assessing the respondent’s avoidance responses in general situations involving worry and the threat of mistreatment, failure and the anticipation of non-reward.58 And finally, the 7-item Behavioral Inhibition Scale assesses both fear and anxiety responses to aversive social and environmental stimuli and situations.59PS includes three questionnaires, which reflect 62 This questionnaire provides hedonic ratings for 72 common food items arranged according to a 2 (FAT: high vs low) x 3  matrix with 12 items in each cell. In the current study, High Sugar and High Fat Foods is reflected by the 12 items from the high fat/high simple sugar cell; High Sugar Foods includes the 12 items from the High Simple Sugar/Low Fat cell and the High Fat Foods is the total (24 items) of the High Fat/High Carbohydrate and High Fat/High Protein subscales. The 72 food items of the Food Preference Questionnaire vary systematically and significantly with respect to their macronutrient content.Food-reward preferences were assessed by subscales of the well-validated Food Preference Questionnaire.Binge Eating was assessed by the five-item subscale of the Binge Eating Questionnaire, which includes frequency and severity of symptoms such as loss-of-control overeating, and negative affect following a binge.63 The Emotional Eating and Snacking on Sweets subscales  of the Eating Behavior Patterns Questionnaire were also included.64 The former assesses eating in response to negative moods and often in the absence of hunger. It also reflects the use of palatable food to comfort a distressed state. The latter describes an eating pattern characterized by the frequent consumption of sugary snacks throughout the day, and sometimes as a replacement for regular meals.Overeating comprises three scales representing different aspects of overconsumption of (mostly) highly palatable foods. 65 Genotyping was conducted on OpenArray plates  using a PCR-based method in the QuantStudio 12 K Flex Real-Time PCR System. We randomly regenotyped 10% of the samples for quality control purposes.Genomic DNA was purified from blood lymphocytes using the high-salt method as described previously.66 Single-marker analyses were conducted using SPSS , whereas haplotype analyses were performed with minimum haplotype frequency threshold of 0.05 in UNPHASED version 3.1.7.67For the genetic analyses, departure from Hardy–Weinberg equilibrium was tested for each marker, and linkage disequilibrium (LD) values between markers were determined in Haploview version 4.2 .OXTR markers and endophenotypes associated with overeating. All parameters were determined a priori to testing. A model was tested in which RS, PS, and food preference each predicted overeating, with the SNPs added as predictors of the appropriate variables according to the significant effects identified in the preliminary analyses. The four factor scores were used instead of latent variables to avoid issues of collinearity in testing the SEM model, and covariances were added among the four OXTR SNPs given they were all markers on the same gene.The statistical analyses were conducted in two stages. Initially, four one-way analysis of variance procedures were conducted for each SNP, with genotype as the independent variable and RS, PS, food preferences, and overeating as separate dependent variables. These preliminary analyses identified relevant SNPs to include in the SEM illustrated in 68 The χ2 test and a number of ‘approximate’ or ‘global’ fit tests were calculated. The χ2 analysis is known as the ‘exact-fit’ test in SEM, because a nonsignificant value requires the model-implied population covariance matrix to have zero discrepancies from the actual sample observed covariance matrix.SEM was performed using SPSS AMOS 24. Missing data were handled using the AMOS estimate means and intercepts function, which applies maximum-likelihood methods.Principal component analysis (SPSS version 23) was used to create the four composite variables, as described in the Methods section. Total scores, not individual items, were entered into the principal component analyses. Each analysis extracted only one component with an eigenvalue>1. 70 and a more pronounced sensitivity to punishment,71 whereas males demonstrated a great sensitivity to reward.72Prior to the analyses, all the composite-variable factor scores were screened for normality. None deviated significantly as indicated by skewness values<±1. OXTR SNPs used in the following analyses. None of the genotypes for the seven OXTR SNPs deviated significantly from Hardy–Weinberg equilibrium (P>0.05). Owing to the low frequency of the minor allele for the rs2268493, rs2268494 and rs2254298 SNPs, the homozygous minor-allele group was combined with the heterozygous group to form a binary genotype variable. Additionally, and in light of arguments which favor the reduction of type II errors  in exploratory and preliminary research,73 we did not control for multiple testing in the genotype analyses.There were no group effects for rs53576, rs2254298 and rs237878. However, the remaining four SNPs demonstrated significant genotype differences on at least one endophenotypic variable. Findings for the SNPs with significant effects are summarized in Post hoc comparisons using the least significant difference test indicated that the CC group was higher than both the CT and TT groups  on PS, whereas the CC group was only higher than the CT group regarding the RS variable (P=0.015).For rs2268498 there were main effects for both RS and PS with the homozygous C genotype reporting higher sensitivity on both measures. post hoc comparisons demonstrated this group had greater scores than both the TT and the TG groups .There was also a rs227885 main effect for RS indicating higher scores among the G homozygous group. Least significant difference χ2 , this result is unsurprising given the sample size.74 Consequently, it is conventional to examine indices other than the exact-fit χ2 to determine the quality of the fit of an SEM model. Following is a summary of these statistical indicators.Although the current model did produce a significant χ2 (CMIN/d.f.) measure divides the χ2 statistic by its degrees of freedom. A value below 3 is deemed an acceptable value for a well-fitting model,74 and the current model produced a relative χ2 of 2.296. The root mean square error of approximation is the most popular measure of goodness-of-fit, with an acceptable fit indicated by values below 0.06 and lower and upper confidence interval limits below 0.05 and 0.08, respectively.75 The model had a root mean square error of approximation of 0.051 . The Tucker–Lewis index, comparative fit index, and normed fit index should be at least 0.90, with a well-fitting model scoring 0.95 or greater.75 The model resulted in Tucker–Lewis index, comparative fit index, and normed fit index values of 0.870, 0.949 and 0.918, consecutively.The relative R2 (squared multiple correlations). For overeating, the R2 value is 0.298 (0.301 when controlling for sex in the model). All parameter estimates were in the expected direction and statistically significant, with the exception of the effect of the rs2268498 SNP on RS (P=0.257) and the covariance between the rs2268493 and rs2268498 (P=0.966). See P=0001), to RS  and to the overeating  variable. The model fit indices with sex added to the model are: χ2=2.778, P<0.0001, CMIN=49.996, Tucker–Lewis index=0.794, comparative fit index=0.917, normed fit index=0.884, root mean square error of approximation=0.060 (0.040–0.079). As negligible differences resulted in the pathway estimates when controlling for sex, it was decided to leave sex out of the model since the fit indices are somewhat better without sex in the model).Overall, and according to well-established statistical criteria, the model was therefore a good fit to the data. AMOS produces estimates of P=0.0055; haplotype frequency=6.06% estimated additive value=0.74; 95% confidence interval: 0.32–1.16; haplotype-specific P=0.00046). .Linkage disequilibrium analysis showed that the rs237885, rs2268493, rs2268494 and rs2254298 SNPs formed one haplotype block, and rs53576 and rs2268498 formed another, as seen in 76 Generally speaking, a robust OXT system is more resilient and receptive to social reward, and better able to ameliorate anxiety and reduce stress.76 These are all characteristics that tend to be diminished in many individuals with chronic overeating.77It is well established that large individual differences exist both in basal OXT levels and in the reactivity of the OXT system and the abundance of its receptors—factors that influence responsiveness to social stimuli and that aid in the regulation of food intake.OXTR SNPs. Results indicated that our model was a good fit to the data. All specified paths were statistically significant in the predicted direction except the path between rs2268498 and RS, which did not survive the P-value imposed by SEM to account for multiple simultaneous tests. Independent and additive associations, in the positive direction, were found for RS, PS and sugar/fat food preferences on overeating (R2=30%). To date, only a handful of studies has examined the simultaneous relationships between RS and PS and eating-related problems, and are in accord with the current findings.79 Previous evidence also indicates a clear relationship between overeating and elevated BMI, and palatable food preferences.19To the best of our knowledge, this is the first study to examine genetic markers of the OXT system from a broad ‘OXT-deficiency’ perspective in relation to overeating, and endophenotypes of overeating. SEM was used to assess multiple simultaneous relationships in a theoretical model proposing the influence of psychobehavioral traits on overeating, and their underlying biological links as indicated by relevant 50 and lower unemotional traits.49 The rs2268493 T allele and the rs2268494 A allele have each been associated with poorer social cognition,42 and less emphatic concern.44 Similarly, the GG genotype of rs2254298 has also been associated with lower empathic concern and social reciprocity, and lower salivary OXT levels.81 Empathy is a key component of prosocial-emotional development with heritability estimates above 50%.82 In summary, our haplotype findings suggest that reduced OXT signaling, which is related to social deficits and to lower social responding, may also influence sweet and fatty food preferences. Such a link meshes with the association between low OXT levels and increased sucrose intake.10The significant association between the G–T–A–G haplotype and high sugar/fat food preferences is supportive of the OXT-deficiency viewpoint in the context of overeating. In previous research, the rs237885 G allele was associated with the highest levels of prepulse inhibition, low depressive symptomsOXTR SNPs included were associated with at least one of the composite-dependent variables. In accord with the haplotype analysis, the A allele carriers at rs2268494 reported higher sweet and fatty food preferences than those with TT. Relatedly, the TT genotype at rs2268493 reported greater overeating compared with the combined CC+CT group. In addition, the GG genotype of the rs237885 marker was related to higher RS compared with the other genotype groups. Finally, the homozygous C group for rs2268498 reported significantly higher RS and PS scores.Single-marker analysis found that four of the seven social salience theory of OXT whereby the effects of OXT are nuanced by stable characteristics of the individual and the environmental context,2 so that OXT ‘does not cast a rose-colored hue for all’2 (p. 105). The role of other individual differences is therefore important when interpreting the role of OXT on psychobehavioral outcomes,83 and an important step forward for future researchers in this area of investigation.It is important to note, in relation to OXT markers, that while there is relative consistency across studies concerning a marker’s association with a particular trait or characteristic, the ‘risk’ allele may differ across samples. That is, the direction of the effect is sometimes inconstant. Such findings support the OXTR gene, we hope that other investigators will attempt to confirm our findings in future research. Relatedly, it is necessary to assess findings from Caucasian-specific single-SNP analysis in larger independent samples. As mentioned in the Note 2 above, however, the direction of the current findings were not changed, and remained significant, when the haplotype analysis was carried out in the Caucasian-only sample.In conclusion, this study presents an innovative psychogenetic risk model of overeating and its relevant endophenotypic correlates. As such, it has several strengths including a sound model based on theoretically and empirically supported relationships. Nevertheless, it is important to also address some limitations in this research. Importantly, replication is essential in all SNP–association studies to strengthen confidence in the current findings. As this was the first study to examine relationships between overeating and its endophenotypes, and markers of the


We have established a pig resource population specifically designed to elucidate the genetics involved in development of obesity and obesity related co-morbidities by crossing the obesity prone Göttingen Minipig breed with two lean production pig breeds. In this study we have performed genome wide association (GWA) to identify loci with effect on blood lipid levels. The most significantly associated single nucleotide polymorphisms (SNPs) were used for linkage disequilibrium (LD) and haplotype analyses. Three separate haploblocks which influence the ratio between high density lipoprotein cholesterol and total cholesterol (HDL-C/CT), triglycerides (TG) and low density lipoprotein cholesterol (LDL-C) levels respectively were identified on Sus Scrofa chromosome 3 (SSC3). Large additive genetic effects were found for the HDL-C/CT and LDL-C haplotypes. Haplotypes segregating from Göttingen Minipigs were shown to impose a positive effect on blood lipid levels. Thus, the genetic profile of the Göttingen Minipig breed seems to support a phenotype comparable to the metabolic healthy obese (MHO) phenotype in humans. Obesity is defined as excessive accumulation of fat in the body to the extent that it may have a negative effect on health. According to the World Health Organization (WHO), in 2016 obesity had more than doubled worldwide since 1980. Obesity can be socially stigmatizing, however, in itself it is not the primary health problem. Rather, it is the co-morbidities such as cardiovascular disease (CVD) and type 2 diabetes (T2D) which pose the major health problems. Common to many of the co-morbidities is an unhealthy metabolic profile with insulin resistance and dyslipidemia characterized by elevated triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) levels and by decreased high-density lipoprotein cholesterol (HDL-C) concentrations . These iTo understand why MHO individuals have a better prognosis than the metabolically unhealthy individuals it is relevant to elucidate the genetic mechanisms underlying regulation of blood lipids such as cholesterol and TG.Cholesterol is a ubiquitous steroid and a vital component of cellular membranes in vertebrates. But at the same time, subendothelial accumulation of cholesterol is the cause of atherosclerotic lesions leading to vascular diseases, heart attacks, aortic aneurysms and peripheral vascular diseases  which, aCholesterol is transported in plasma in lipoprotein particles. These consist of a major structural apolipoprotein, peripheral apolipoproteins, structural lipids (phospholipids and cholesterol) and a cargo of TG and steryl esters. Dietary lipids are absorbed via the small intestine and packaged as TG into large particles known as chylomicrons (CM) which distribute lipids throughout the body for direct use and storage. CM remnants return to the liver where the remaining lipid cargo enters hepatic lipid pathways and a second class of lipoprotein particles is produced; the very low density lipoproteins (VLDL). Like CM, VLDL distributes TG to the periphery. As VLDL delivers TG they shrink and shed peripheral apolipoproteins, hence the proportion of cholesterol increases and VLDL becomes low-density lipoproteins (LDL). Both VLDL and LDL are characterized by the major structural apolipoprotein APOB. LDL can bind to cells expressing the LDL-receptor (LDLR) and as such, LDL acts as an efficient cholesterol delivery system into cells .The reverse transport of cholesterol from the periphery to the liver is mediated by high-density lipoproteins (HDL) containing the major structural apolipoprotein APOA1. Nascent HDL particles contain few lipids but collect free cholesterol and phospholipids from peripheral tissues. Different mechanisms enrich the HDL particles with lipids, which ultimately are delivered to the liver via Scavenger Receptor B1 (SCARB1) for subsequent excretion with the bile .Heritability for LDL-C and HDL-C levels in humans are estimated to be around 70%.. HoweverA way to elucidate novel mechanisms involved in blood lipid regulation is to study animal models. Animal models provide the benefits of a strictly controlled diet and environment which is impossible in large human studies. Consequently, random noise is greatly reduced and a corresponding increase in power is presumed when animal models are used to study human conditions. Also, the individual breeds are genetically more homogeneous than humans due to domestication and artificial selection [e.g. ref. The pig is an animal model with a close similarity to humans in body size, physiology, organ development and disease progression. It has been widely used as a model for cardiovascular and metabolic diseases . The preIn the present study, a GWAS aimed at identifying loci with effect on blood lipid levels is reported. The GWAS was performed in two pig crosses established by using Göttingen Minipig as the parental boar line in both crosses whereas Duroc and Yorkshire production pigs were used as parental sow lines in the two crosses respectively. The Göttingen Minipig is an obesity prone pig breed  often usTwo populations were produced as F2 crosses using purebred Göttingen Minipig (M), Duroc (D) and Yorkshire (Y) as parental lines . The Minipig-Duroc (MD) crossbred F1 animals were founded by seven M boars and seven D sows and 28 F1 MD gilts and 16 F1 MD boars were used to produce 285 F2 animals. Similarly, the Minipig-Yorkshire (MY) crossbred F1 animals were founded by seven M boars and seven Y sows and 279 F2 animals were produced by 26 MY gilts and 13 MY boars .All pigs were raised under controlled environmental conditions and fed the same diet ad libitum. The project was approved by the Danish Animal Experiments Inspectorate. Animal care and maintenance were conducted according to the Danish “Act about Animal Husbandry” and “Animal Protection Act” . All pigs were housed at a regular pig farm, and slaughtered at a commercial slaughterhouse by stunning and bleeding under veterinary supervision.Blood samples for blood lipid analysis were drawn from the jugular vein at about two month of age  and at slaughter . Plasma lipid levels were assayed by standardized techniques using a Konelab 20 Clinical Chemistry Analyzer  and commercial reagent kits from Roche Diagnostics for Total Cholesterol (CT) and from ThermoElectron for TG and HDL-C levels (direct method). LDL-C levels were calculated using the Friedewald formula. Observations >5 SD from the mean were considered outliers and excluded. Box-Cox transformation was used to adjust for non-normality. After transformation, skewness and kurtosis were calculated for each phenotypic distribution and Q-Q plots were made to evaluate normality.ANIMAL2 and Iσε2, respectively. A is the additive genetic relationship matrix based on pedigree information, σANIMAL2 the additive genetic variance, I an identity matrix of appropriate size and σε2 the residual error variance.Genetic parameters were estimated using Best Linear Unbiased Prediction (BLUP) based on Average-Restricted Maximum Likelihood (AI-REML) using DMU version 6, release 5.2 . Varianc1 and y2 are vectors of the transformed phenotypic measurements of Trait 1 and Trait 2 respectively. X1 and X2 are design matrices connecting the measurements to b1 and b2 which are vectors of the environmental fixed effects, while Z1 and Z2 are design matrices linking the measurements to a1 and a2 which are vectors for the random genetic effects for each trait. Finally, e1 and e2 are vectors of the random environmental effects. The effects fitted in the bivariate linear mixed models are the same as the ones fitted in the univariate linear mixed models. When fitting the bivariate model, the non-diagonals of the additive genetic variance and covariance matrix G for animal effects and the variance and covariance matrix R for random residual effects were included as follows:A12 = covA21 is the additive genetic covariance and covE12 = covE21 the random residual covariance between Trait 1 and 2. The phenotypic covariance covP12 = covP21 between Trait 1 and 2 can be calculated as: covP12 = covA12 + covE12 [A bivariate linear mixed model was fitted to estimate phenotypic and genetic correlations between pairs of traits (Trait 1 and Trait 2):+ covE12 .Each animal was genotyped using the 60k porcine Illumina SNPchip. Sex chromosomes were excluded from the single-marker analyses. The Sus Scrofa 10.2 pig genome assembly was used to derive map positions for all SNPs. After genotyping, quality control (QC) was conducted using GenABEL version 1.8–0  excludini is the random additive polygenic effect for animal i based on identity by state (IBS). Ai is assumed to be normally distributed with a (co)variance of GσANIMAL2 where G is the genomic relationship matrix and σANIMAL2 the additive polygenic variance. Estimation of effects was performed by means of maximum likelihood (ML) [GWAS was carried out for each phenotype using a single-marker test as implemented in GenABEL . The anaood (ML) .m is assumed a fixed effect with 3 genotype scores 0, 1 and 2 referring to the number of minor allele copies for the m’th SNP and εm is the residual error. Each single SNP was modeled independently. The allele substitution effect of the m’th SNP was calculated as the average phenotypic change when replacing a major allele with the minor allele [Step 2 use estimated residuals from step 1 (contains part of the QTL variance) to estimate SNP effects :ε^i=μ+Sr allele .eff = 12916. Missing genotypic values were replaced with the common allele genotype in the genotype matrix prior to the analysis. It resulted in a cut off value of–log10(0.05/12916) = 5.41 corresponding to a nominal significance level (α) of 3.87*10−6.SimpleM –35 was um is the phenotypic variance explained by the m’th SNP, qm is MAF for the m’th SNP and SNP / σA2. The additive genetic effect (α) for each top-SNP was calculated using BLUP as implemented in DMU where the effect of having 0, 1 or 2 copies of a specific SNP was fitted in the models described above (Model 1 and Model 2).For each significant SNP, the phenotypic variance explained by the SNP was calculated as :VSNP=VmThe method described by Gabriel et al.(2002)  as impleTo evaluate haplotype effects in individuals with high- and low-BMI respectively, all F2 animals were sorted according to BMI and the one-third with highest BMI (high BMI) and lowest (low BMI) respectively were selected. Each of the groups were subdivided according to haplotypes in the HDL-C/CT and the LDL-C QTL regions and mean and standard deviation for BMI_Age2, CT_Age2, TG_Age2, HDL-C/CT_Age1, HDL-C_Age2, HDL-C/CT_Age2, LDL-C_Age2 and LDL-C/HDL-C at Age2 were calculated separately using Students-t test for animals which were homozygous for either the GM or the Yorkshire/Duroc haplotypes in the HDL-C/CT and LDL-C QTL regions.A moderate genetic correlation was found between Age1 and Age2 both in regard to LDL-C and HDL-C/CT, whereas, for TG there was no correlation between measurements at the two ages see .sus scrofa chromosome 3 (SSC3)  see . A singlLD analysis  revealedThe additive genetic effect of haplotypes present in more than 20 animals is shown in For the LDL-C associated LD block, the haplotype AGGAGCCAAA originating from the Göttingen Minipig founders, decrease the LDL-C level. The Yorkshire founder specific haplotypes, GGAAGAAGAG and GGAAGAAGCG, increase LDL-C level; however, there is also one Yorkshire haplotype, GGAAGAAACG, which decreases LDL-C level. Two of the Duroc haplotypes increase the LDL-C level slightly. The effect of each haplotype is the same at Age 1 and Age 2 (data not shown) even though the blocks are only loosely in LD according to the LD analysis.To further evaluate the phenotypic consequences of the haplotypes segregating from Göttingen Minipig and Yorkshire/Duroc respectively we compared lipid and TG levels in animals homozygous for either Göttingen Minipig haplotypes or the Duroc/Yorkshire haplotypes. The comparison was performed in the one-third of the F2 animals with the highest and one-third with lowest BMI, respectively. The mean BMI in the combined F2 population  is 127±20 while in the selected high BMI group  the mean BMI is 154±13, and in the selected low BMI group  the mean BMI is 111±9. As seen in In agreement with previously performed GWA studies in pigs , 40, 41,RHOB) is located in the LDL-C associated haploblock. RHOB is a member of the RHO GTP-binding protein family which regulates expression of CD36 [APOB) is located at 125.23–125.35 Mb. It is an obvious candidate gene for lipid related traits but in the present study it is not included in associated LD-blocks even though it was covered by four SNPs with a MAF above the exclusion threshold. The gene encoding lipid droplet associated hydrolase (LDAH) is located at 125.42–125.53 Mb which is also outside the identified LD-blocks. Further studies including sequencing and genotyping of additional markers are warranted to ascertain LD structure and to identify genetic variation. It is very likely that the observed differences in blood lipids are caused by variation in regulatory components within the identified QTL regions in particular since a high number of different genes involved in regulation of lipoproteins are located within this region of the genome.There are no annotated known genes in the HDL-C/CT and TG associated SSC3 haploblocks in the Sscrofa 10.2 assembly but the gene encoding ras homolog family member B . A likely explanation for the MHO profile in Göttingen Minipigs is that the obesity prone minipig founders have been adapted to overcome obesity by natural selection. Thus, unexpectedly, the results presented here indicate that the Göttingen Minipig breed is not well suited for studies of the obesity related co-morbidities but may be a valuable model to advance understanding of the MHO phenotype in humans.The MHO profile identified in our F2 population is comparable to the MHO profile in humans which appear to be protected against obesity related metabolic complications. However, although MHO is an important, emerging phenotype in humans no universally accepted definition has been established for this phenotype yet . It is aRHOB, APOB, LDAH) comprise regulatory elements of importance to the regulation of lipid metabolism. Interestingly, the largest additive genetic effects of the haplotypes identified in this study show that haplotypes segregating from the obesity prone Göttingen Minipig breed is able to uphold a healthy lipid profile despite development of obesity. Thus, the genetic profile of the Göttinging Minipig breed seems to support a phenotype comparable to the MHO phenotype in humans promoting this pig breed as a model for further studies of this particular phenotype. Our future studies will be directed at identification the genetic variation in the regulatory components involved in lipid metabolism and further genetic characterization of the healthy metabolic phenotype.In conclusion, we have substantiated that different genetic loci have an effect on TG early and late in life. We have also substantiated that the genomic region close to genes implicated in lipoprotein metabolism (