Selecting the best animals for farms has always been a fundamental aspect of animal breeding. To assist in the traditional selection performed on phenotypes and to further improve genetic selection as performed in recent years, genomic tools could be applied to select animals early in their productive careers. The aim of this study was to evaluate the application of a commercial genomic tool on animals selected from farms grouped by their average somatic cell count. The obtained results showed that farms with good animal management practices also rear animals with better genomic indexes, probably due to selection on other well-established indexes . Selecting heifers based on their wellness genomic indexes would further improve both their economic value and their disease resistance and resilience.Heifers are a fundamental resource on farms, and their importance is reflected in both farm management and economy. Therefore, the selection of heifers to be reared on a farm should be carefully performed to select only the best animals. Genomic selection is available nowadays to evaluate animals in a fast and economic way. However, it is mainly used on the sire line and on performance traits. Ten farms were selected based on their 5-year records of average somatic cell count and evenly classified into high  and low somatic cell count . Genomic indexes (regarding both wellness and productive traits) were evaluated in 157 Italian Holstein heifers reared in the selected ten farms (90 from high-cells farms and 67 from low-cells ones). Linear mixed models were fitted to analyze the effects of the abovementioned genomic indexes on related phenotypes. Results have shown that farms classified into low somatic cell count had an overall better animal genomic pool compared to high somatic cell count ones. Additionally, the results shown in this study highlighted a difference in wellness genomic indexes in animals from farms with either a high or a low average somatic cell count. Applying genomic tools directly to heifer selection could improve economic aspects related to herd turnover. Heifer selection is a critical point in farm management and economy. Since dairy producers traditionally raise most of the heifer calves born inside their farms , raisingStaphylococcus aureus) or picked up from the environment  [From a modern farming perspective, the best animals are not only the best performers but also animals that can proficiently cope with diseases and unideal environments. One way to address health issues is to select animals with an innate genetic resistance to particular diseases . Mastitiia coli) . Variousia coli) ,13. Mastia coli) . Since dia coli) . Therefoia coli) , and to ia coli) . Furtheria coli) ,18, thusia coli) . More spia coli) .The aim of this study was to investigate the possibility of using genomic indexes as a tool for heifer selection regarding their resistance to mastitis. Specifically, in this study, the relationship between genomic indexes and relative phenotypes was analyzed during the first lactation of Italian Holstein cows raised on ten different farms.A total of 10 Italian Holstein farms were selected in the provinces of Turin and Cuneo . The selection criterion was based on the average SCC calculated for the previous 5-year interval (2014–2019). Farms with an average 5-year SCC lower than 150,000 cells/mL were classified as low SCC (LS), while farms with an average 5-year SCC higher than 300,000 cells/mL were classified as high SCC (HS). The farms were evenly selected among the two groups . The two classes  were built on the two divergent tails of the farm’s SCC distribution to amplify the differences between them and to highlight the results obtained with the statistical modeling approach, similarly to what was achieved by Moretti et al., 2021 [In the selected farms, genomic selection was performed on the sires only and mainly taking into account performance genomic indexes . No selection was made on females or on wellness traits.A total of 157 Italian Holstein heifers were selected on the 10 farms: 67 heifers from LS farms and 90 from HS ones. On each farm, around 15% of the available heifers were selected. Ear tissue for the genomic analysis was sampled in conjunction with the application of ear tags; therefore, no ethical approval was required for the sampling of biological material. The animals were subsequently followed during their first lactation (considered between 5 and 305 days in milk), and data on milk yield, milk composition , and SCC were collected during routine milk recording procedures  by the staff of the Regional Breeder Association of Piedmont.Genomic testing was performed by the Clarifide Plus genomic testing system . This system offers a comprehensive package of trait predictions, economic, and wellness indexes. Specifically, two wellness indexes and three productive traits indexes were analyzed: the former are SCS and Z_MAST ; the latter are milk, fat, and protein production indexes. The first wellness index, SCS, is an indirect predictor of mastitis. When used to compare two animals, lower values indicate a lower average SCC value of the indexed animal compared to the other. The second wellness index, Z_MAST, is a specific mastitis index developed by Zoetis. When used to compare two animals, higher values indicate the animal with a lower mastitis risk. The productive indexes  give an indication regarding the genetic differences in the relative trait yield during a standard lactation of 305 days.t-test. Pearson’s correlation was also calculated. The following linear mixed model was fitted using the lmerTest R Package [ijkPheno is the phenotypic value  for each animal; Gieno is the fixed effect of the related genomic index of the i-th animal (continuous variable); iDIM is the fixed effect of Days in Milk (DIM) for the i-th animal ; jFarm_group is the fixed effect of the j-th farm classification based on the 5-year SCC average ; iAge is the fixed effect of the age at first parturition of the i-th animal (continuous variable); iAnimal is the random effect of the i-th animal ; kTest_day is the random effect of the k-th sampling day; iHYS is the random effect of the Herd-Year-Season contemporary group for the i-th animal ; ijkε the random residual effect. Fixed effects descriptive statistics  are reported in 2 for the mixed model was calculated using the r.squaredGLMM function from the MuMIn R package [2, the correlation between observed and predicted values, and the Root Mean Square Error (RMSE).All the statistical analyses and tests were performed in an R software environment . Summary Package  to evalu package , which r package . Differet-test, and the results are reported in Genomic indexes were calculated by Zoetis Clarifide Plus  genomic testing system. Each animal index was grouped by farm classification and then averaged. Differences in the means of the five genomic indexes  for the two groups were tested with a t-test, t = −2.863, df = 121.42, p-value 0.005), while no statistically significant differences were observed between the mean Z_MAST index in the two groups. Regarding the productive traits’ indexes, the difference between the mean Fat index in the two groups was statistically significant , as well as the one between the mean Protein index , while no statistically significant differences were observed between the mean Milk index in the two groups.Regarding the wellness indexes , the difference between the mean SCS index in the two groups was statistically significant , while the correlation between the Z_MAST index and the SCC phenotype was −0.10 . Similarly, a correlation was calculated between productive traits genomic indexes and their related phenotype. The correlation between the milk index and milk yield phenotype was 0.27 ; the correlation between the protein index and protein % phenotype was −0.05 ; the correlation between the fat index and fat % phenotype was 0.18 . Lastly, correlations between indexes were calculated: SCS and Z_MAST −0.59 ; SCS and Milk 0.009 ; SCS and Protein −0.14 ; SCS and Fat −0.17 ; Z_MAST and Milk −0.05 ; Z_MAST and Protein −0.09 ; Z_MAST and Fat −0.05 ; Milk and Protein 0.54 ; Milk and Fat 0.25 ; Protein and Fat 0.73 .Pearson’s correlation was calculated between the wellness genomic indexes and their related phenotypes. Specifically, the correlation between the SCS index and the SCC phenotype was 0.22  and subsequently analyzed. A data quality check was performed, and observations from animals with missing birth and first calving dates were removed, along with observations from animals with less than three functional controls. After data pruning, a total of 143 animals  were subjected to further analyses. A total of 1400 observations were therefore available in the final dataset , with an average number (mean ± SD) of observation per animal of 9.79 ± 2.35 (range = 3 to 18) and an average number (mean ± SD) of observations per farm of 140.00 ± 59.11 (range = 52 to 258). The average number of animals per farm was 14.30 ± 5.56 (range = 7 to 25). The animals’ age at functional control ranged from 1.80 to 4.21 years .t-test, and the results are reported in Differences in the milk yield and composition for the means of the two groups were tested with a t-test, t = 5.589, df = 1035.4, p-value < 0.001), as well as the one between the mean milk yield , mean fat % , and mean lactose % .The difference between the SCC means in the two groups was statistically significant . To obtain a better statistical result, random splitting into a training and testing dataset was performed ten times, and the total results were averaged in a bootstrapping-like procedure. The previously described model was therefore trained using only the data in the training subset. Non-significant variables were singularly removed, and each reduced model was compared to others through Following the ten iterations of the mixed model applied to the model, excluding the age of the animal at first parity, the best model was selected, and the results are reported in p-value 0.583). The effect of genomic index was statistically significant , and the DIM class II was statistically different from class I , while the DIM class III was not statistically different from class I , and the HS class was not statistically different from LS class , although close to the 0.05 threshold. Conditional R2 for the selected mixed model was 39.57%. The selected model was therefore fitted to the testing dataset to check its predicting performances. The correlation between observed and predicted data was 0.19 , and the RMSE was 1.39.The intercept of the model  was not statistically significant and, therefore, not different from 0 . Both the effects of the genomic index and DIM classes were statistically significant , while the effects of farms grouped by 5-year SCC average and age of the animal at first parity were not statistically significant. Conditional R2 for the selected mixed model was 80.33%. The selected model was therefore fitted to the testing dataset to check its predicting performances. The correlation between observed and predicted data was 0.71 , and the RMSE was 61.98.The intercept of the model  was statistically significant and therefore different from 0  and justified the dichotomous classification of the farms and the relative animals.The definition of an SCC threshold to classify milk status  is a controversial subject . Startinp-value = 0.005) between the means of LS and HS farms, while the Z_MAST index did not show statistically significant differences in the two groups . This could be due to the fact that the Italian national breeders’ association for the Italian Holstein breed added an SCS index to the national selection scheme in the first years of the XXI century, while a general udder health index was added only recently. Therefore, even if the farmers did not actively select animals with better SCS indexes, the Italian population changed accordingly to the national selection scheme. The Pearson’s correlation found between the SCS and Z_MAST index is consistent with studies found in the scientific literature that support the use of SCC as an indicator for mastitis [p-value = 0.742). Even though farmers’ preferences are known to be heterogeneous [The wellness indexes were analyzed and showed the following results: the SCS index had a statistically significant difference  and when predicting data using the testing dataset . Predictors included in our study came mainly from functional controls and, therefore, better describe variations in milk yield. Regarding the milk yield predicting model, although not statistically significant, both 5-year average SCC classification and age at first parity were not excluded from the model. Removing these two predictors reduced both the conditional R2 and the correlation between observed and predicted values and increased the RMSE of the models. Differently, regarding the SCC predicting model, while both 5-year average SCC classification and age at first parity were not statistically significant, only the latter was excluded from the model. Indeed, removing the age predictor reduced the conditional R2 from 39.63 to 39.57%; however, both the observed/predicted correlation and RMSE slightly improved . Furthermore, the anova function in R showed a statistically significant difference  between the models with and without 5-year average SCC classification, indicating the former as the best one.The fitted mixed model showed that there is a large variability among the animals for both milk yield  and SCC traits . The milk yield model performed better than SCC one, both when fitting observed data (Conditional RWelfare of heifers and their selection for improved resistance to mastitis was the focus of this study. Genomic indexes were evaluated on a sample of Italian Holstein heifers and subsequently related to their relative phenotypes. The differences in the abovementioned indexes evaluated in animals from farms with different levels of average SCC showed that it is possible to use them to classify animals and to potentially perform selection. Furthermore, the results from this study showed that a bland selection has already happened even if no direct selection had been performed on females since animals on LS farms had, on average, better genomic indexes than animals in HS farms. In addition to the good farming and management practices, a further genomic selection, based on wellness indexes other than the classical productive ones, could greatly benefit to cattle breeders. Furthermore, while in traditional farms, the genetic improvement is pursued, acting mainly on the sire-daughter line, applying genomic tools directly to heifer selection is feasible and could improve economic aspects related to herd turnover.


Determining the forces that shape diversity in host-associated bacterial communities is critical to understanding the evolution and maintenance of metaorganisms. To gain deeper understanding of the role of host genetics in shaping gut microbial traits, we employed a powerful genetic mapping approach using inbred lines derived from the hybrid zone of two incipient house mouse species. Furthermore, we uniquely performed our analysis on microbial traits measured at the gut mucosal interface, which is in more direct contact with host cells and the immune system. Several mucosa-associated bacterial taxa have high heritability estimates, and interestingly, 16S rRNA transcript-based heritability estimates are positively correlated with cospeciation rate estimates. Genome-wide association mapping identifies 428 loci influencing 120 taxa, with narrow genomic intervals pinpointing promising candidate genes and pathways. Importantly, we identified an enrichment of candidate genes associated with several human diseases, including inflammatory bowel disease, and functional categories including innate immunity and G-protein-coupled receptors. These results highlight key features of the genetic architecture of mammalian host-microbe interactions and how they diverge as new species form. The digestive system, particularly the large intestine, hosts many types of bacteria which together form the gut microbiome. The exact makeup of different bacterial species is specific to an individual, but microbiomes are often more similar between related individuals, and more generally, across related species. Whether this is because individuals share similar environments or similar genetic backgrounds remains unclear. These two factors can be disentangled by breeding different animal lineages – which have different genetic backgrounds while belonging to the same species – and then raising the progeny in the same environment.To investigate this question, Doms et al. studied the genes and microbiomes of mice resulting from breeding strains from multiple locations in a natural hybrid zone between different subspecies. The experiments showed that 428 genetic regions affected the makeup of the microbiome, many of which were known to be associated with human diseases. Further analysis revealed 79 genes that were particularly interesting, as they were involved in recognition and communication with bacteria. These results show how the influence of the host genome on microbiome composition becomes more specialized as animals evolve.Overall, the work by Doms et al. helps to pinpoint the genes that impact the microbiome; this knowledge could be helpful to examine how these interactions contribute to the emergence of conditions such as diabetes or inflammatory bowel disease, which are linked to perturbations in gut bacteria. The recent widespread recognition of the gut microbiome’s importance to host health and fitness represents a critical advancement of biomedicine. Host phenotypes affected by the gut microbiome are documented in humans , laboratDespite the importance of the gut microbiome, community composition varies significantly among host species, populations, and individuals . While aAn intriguing observation made in comparative microbiome research in the last decade is that the pattern of diversification among gut microbiomes appears to mirror host phylogeny . This phMus musculus musculus and Mus musculus domesticus (2 cross between wild-derived inbred strains of M. m. domesticus and M. m. musculus and identified 14 quantitative trait loci (QTL) influencing 29 microbial traits. However, like classical laboratory mice, these strains had a history of rederivation and reconstitution of their gut microbiome, thus leading to deviations from the native microbial populations found in nature , which reflect bacterial cell number and activity, respectively. After applying quality filtering and subsampling 10,000 reads per sample, we identified a total of 4684 amplicon sequence variants (ASVs). For further analyses, we established a ‘core microbiome’ , such that analyses were limited to those taxa common and abundant enough to reveal potential genetic signal. The core microbiome is composed of four phyla, five classes, five orders, 11 families, 27 genera, and 90 ASVs for RNA, and four phyla, five classes, six orders, 12 families, 28 genera and 46 ASVs for DNA. A combined total of 98 unique ASVs belong to the core, of which 38 were shared between DNA and RNA . The moscobacter , consistcrobiome .Importantly, inspection of the taxonomic profiles of the mapping population confirms that key features of the native mouse microbiome were retained in our wild-derived lines, despite multiple generations of breeding in the laboratory. For example, the number, identity, and relative proportions of major bacterial orders are more similar to wild-caught mice than to rederived classical laboratory strains . This is2h) of bacterial traits using lme4QTL , with estimates ranging between 39 and 83% (Oscillibacter) followed by the genus Paraprevotella, and ASV7 (genus Paraprevotella) showed the highest heritability among DNA-based traits , followed by ASV36 (genus Oscillibacter), and ASV135  had the highest heritability among RNA-based traits , i.e.We compared these heritability estimates to estimates from previous studies in other mammals , includi2h, p=0.037, Spearman’s rho = 0.47; CH, p=0.012, Spearman’s rho = 0.55; 2h, Spearman’s rho = −0.062, p=0.80; CH, Spearman’s rho = −0.091, p=0.70; 2R=0.27, F=3.202, p=0.067). Furthermore, the cospeciation rate significantly predicts the heritability estimate (p=0.022), while the median abundance does not (p=0.92). Thus, heritability estimates and cospeciation rates are associated independent of taxon abundance. These results support the notion that cospeciating taxa evolved a greater dependency on host genes, and further suggest that bacterial activity may better reflect the underlying biological interactions.In an important meta-analysis of the gut microbiome across diverse mammalian taxa, –6), a total of 1030 genome-wide significant associations were identified for individual taxa . Apart from the X chromosome, all autosomal chromosomes contained study-wide significant associations , based on 32,625 SNPs. We used a linear mixed model including both additive and dominance terms in the model to enable the identification of underdominance and overdominance in this hybrid mapping population. We included mating pair and the genotype-based genomic relatedness matrix (GRM) as random effects to control for maternal effects and relatedness, respectively . While we found no genome-wide significant associations for alpha diversity at either the DNA or RNA level  that are smaller than 1 Mb . The smaDorea and two ASVs belonging to Dorea , Dorea, ASV36 (Oscillibacter), ASV35 (Bacteroides), and ASV98 (unclassified Lachnospiraceae) (The significant genomic regions and SNPs are displayed in iraceae) .M. m. musculus and M. m. domesticus (i.e. represent fixed differences between subspecies). To gain further insight into the genetic architecture of microbial trait abundances, we estimated the degree of dominance at each significant locus using the d/a ratio , the allele associated with higher abundance is recessive or partially recessive . However, for one-third of the significant SNPs, the heterozygotes display transgressive phenotypes, i.e., mean abundances that are either significantly lower (31% of SNPs) or higher (2% of SNPs) than those of both homozygous genotypes. Interestingly, the domesticus allele was associated with higher bacterial abundance in two-thirds of this subset , indicating there are strong dominance effects. For example, Odoribacter has two significant regions which show overdominance, and ASV234 has seven significant regions which show underdominance.Next, we estimated phenotypic effect sizes by calculating the percent variance explained (PVE) by the peak SNP of each significant region. Peak SNPs explain between 3 and 64% of the variance in bacterial abundance, with a median effect size of 9.3% . The com–14) displaying 668 nodes connected by 1797 edges and an average node degree of 4.68. After retaining only the edges with the highest confidence (interaction score >0.9), this results in one large network with 233 nodes, 692 edges and ten smaller networks . Thus, in order to reveal potential higher-level biological phenomena among the identified loci, we performed pathway analysis to identify interactions and functional categories enriched among the genes in significant intervals. We used STRING  to calcunetworks .Next, we functionally annotated clusters using STRING’s functional enrichment plugin. The genes of the largest cluster are part of the G-protein-coupled receptor (GPCR) ligand binding pathway. GPCRs are the largest receptor superfamily and also the largest class of drug targets . We thenWnt signalling pathway , nor within our list of genes closest to a significant SNP .Additionally, we collected genes within genome-wide significant regions reported in seven human microbiome GWAS (mGWAS) . HoweverIyd, Nln, Slc26a3, Slc3a1, Myom2, Nebl, Tent5a, Fxr1, Cbr3, Chrodc1, Nucb2, Arhgef10l, Sucla2, Enpep, Prkcq, Aacs, and Cox7c). This is significantly more than expected by chance . Furthermore, analysing the PPI with STRING results in a significant network , solute carrier family 3 member 1 (Slc3a1), solute carrier family 26 member 3 (Slc26a3), nebulette (Nebl), carbonyl reductase 3 (Cbr3), and acetoacetyl-coA synthetase (Aacs).Subsequently, we merged the information from <i>P-value < 1.0 × 10–16, average node degree = 4.85). We identified genes with the highest connectivity and most supporting information . Next, we used STRING to construct a PPI network with this curated gene set, which is highly significant  vertical transmission could enable greater host adaptation to bacteria and/or (ii) the greater number of host genes associated with cospeciating taxa could indicate a greater dependency on the host, such that survival outside a specific host is reduced, making horizontal transmission less likely.By performing 16S rRNA gene profiling at both the DNA and RNA level, we found that 14% (DNA based) to 20% (RNA based) of bacterial taxa have significant heritability estimates, with values up to 83%. The proportion of heritable taxa and maximum value of heritability estimates are consistent with previous studies in humans and mice  . SeveralNotably, heritability estimates for RNA-based traits are significantly correlated with previously reported cospeciation rates in mammals . This paWe found a total of 172 and 305 unique significant loci for DNA-based and RNA-based bacterial abundance, respectively, passing the conservative study-wide significance threshold. Taxa had a median of four significant loci, suggesting a complex and polygenic genetic architecture affecting bacterial abundances. We identify a higher number of loci in comparison to previous QTL and GWAS studies in mice , which mdomesticus alleles are associated with higher relative bacterial abundances twice as often as musculus alleles  dominant. This suggests there is strong purifying selection against a high abundance of any particular taxon, which may help ensure high alpha diversity. For several bacterial taxa, the PVE by additive and dominance effects of significant SNPs exceeds the narrow-sense heritability, and the heterozygotes of one-third of significant SNPs displayed transgressive phenotypes. This is consistent with previous studies of hybrids , for exa alleles . AlthougEisenbergiella), Eisenbergiella and ASV32 (unclassified Lachnospiraceae) in this study, and overlaps with significant loci from three previous studies  and overlaps with associations for Clostridiales, Clostridiaceae, Lachnospiraceae, and Deferribacteriaceae   , as gut d Wnt11) . In addid Wnt11) , and CD4d Wnt11) .Tlr4 gene and was associated with the genus Dorea and several Dorea species. Dorea is a known short chain fatty acid producer  and one predominantly musculus line ; each line was represented as a dam in one cross and sire in another .Mice were kept in the same room and caged together with littermates after weaning before being separated into single cages 1 week prior to dissection . Mice were sacrificed at 91±5 days by CO2 asphyxiation. We recorded body weight, body length and tail length, and collected ear tissue for genotyping. The caecum was removed and gently separated from its contents through bisection and immersion in RNAlater . After overnight storage in RNAlater at 4°C, the RNAlater was removed and tissue stored at –20°C.We simultaneously extracted DNA and RNA from caecum tissue samples using Qiagen  Allprep DNA/RNA 96-well kits. All samples were extracted together in the same extraction round and hence timepoint. We followed the manufacturer’s protocol, with the addition of an initial bead beating step using Lysing matrix E tubes  to increase cell lysis. We used caecum tissue because host genetics has a greater influence on the microbiota at this mucosal site than on the lumen contents . We perfWe assigned sequences to samples by exact matches of MID  sequences processed 16S rRNA sequences using the DADA2 pipeline, implemented in the DADA2 R package, version 1.16.0 . ProcessWe used the phyloseq R package (version 1.32.0) to estimate alpha diversity using the Shannon index and Chao1 index, and beta diversity using Bray-Curtis distance . We defi<i>P-values < 1e-10. A total of 64,103 SNPs and all but one G2 individual were retained. Prior to mapping, we LD-filtered SNPs with r2 > 0.9 using a window of 5 SNPs and a step size of 1 SNP. We retain 32,625 SNPs for mapping.We extracted genomic DNA from ear samples using DNAeasy Blood and Tissue 96 well kits , according to the manufacturer’s protocol. We sent DNA samples from 26 G0 mice and 320 G2 mice to GeneSeek  for genotyping using the Giga Mouse Universal Genotyping Array  and two of M. m. domesticus (Germany and France) maintained at the Max Planck Institute for Evolutionary Biology . We classified SNPs as ancestry informative if they had a minimum of 10 calls per subspecies, the major allele differed between musculus and domesticus, and the allele frequency difference between subspecies was >0.3. A total of 48,361 quality-filtered SNPs from the G0/G2 genotype data were informative, including 8775 SNPs with fixed differences between subspecies samples.For each G0 and G2 mouse, we estimated a hybrid index – defined as the percentage of We computed a centred and a standardised relatedness matrix using the 32,625 filtered SNPs with GEMMA  is expressed as:We calculated heritabilities for bacterial abundances using linear mixed models implemented in the lme4qtl R package , genotypes at each locus, https://github.com/sdoms/mapping_scripts  above the proposed threshold of 1.05, we applied genomic control by dividing the chi-squared test statistic with λGC  hub genes or nearest neighbours in the ‘closest gene network’ , (2) gen This paper uses inbred hybrid mouse lines to estimate the heritability of the mucosa-associated microbiome and map variants in the mouse genome that are associated with the composition of the microbiome. The findings are of broad interest to microbiome researchers and improve on knowledge in the field, as the mapping design facilitates the identification of narrow association intervals and points to a novel correlation between heritability and cospeciation rates. The manuscript provides useful information about the approach to heritability estimation, allowing the results to be more readily placed in context. Congratulations on this important contribution to the literature. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Wendy Garrett as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Key features of the genetic architecture and evolution of host-microbe interactions revealed by high-resolution genetic mapping of the mucosa-associated gut microbiome in hybrid mice" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1. Demonstrate the robustness of the heritability analyses, including consideration of potential technical and biological confounding variables and ideally using complementary analyses .2. Demonstrate the robustness of the phylosymbiosis (heritability-cospeciation rate correlation) to controlling for taxon abundance.3. Control for population structure are well as relatedness in the trait mapping analysis.Reviewer #1 (Recommendations for the authors):1. Heritability estimates in the study are quite high, including traits that are more highly heritable than even canonical high heritability traits in humans, like height. This could be accurate, particularly in a lab-controlled environment where genetic variation is ramped up by the crossing design and hybrid origins of the founder, but I think requires closer examination. In particular, I was surprised that there does not appear to be any adjustment for covariates in the heritability analysis: in my experience, batch and technical effects are omnipresent in genomic studies, including microbiome analysis, and can affect heritability estimates in unpredictable ways. Although sex and age are controlled for in the study design, are there differences in housing, diet, sample processing, library prep, or sequencing quality/read yield/lane/batch that account for measurable variance and need to be taken into account?2. Related, part of the benefit of estimating heritability in a mixed effects models framework is the ability to flexibly control for other covariates. I don't see a benefit in using Mantel tests to correlate genetic relatedness and microbiome structure overall-as heritability is much more directly supported by direct estimation-and suggest removing this part of the analysis. I realize that the genetic relatedness-microbiome structure analysis might provide a "holistic" picture, but that could also be done by analyzing the top PCs of the microbiome data in a formal h2 analysis.3. For the individual trait mapping analysis, I have some concerns that the models might not adequately control for background population structure. It's a good step to control for genetic structure with the K matrix, but I would like to see that the results are robust to including the top PCs of the genotype data in the model as additional fixed effects. Where possible, you could also consider using LD score regression to check that there is no unexpected inflation of test statistics.4. In Figure 3 and lines 251 – 258, the results report PVE levels that are impossible (since more than 100% of variance can't be explained). While this is noted in the text, and some potential explanations are provided (lines 256 – 258), the current presentation isn't very useful because it's unclear how much trait variance is really being explained. A better alternative might be to construct the equivalent of polygenic scores for each trait and ask how much variance in each associated phenotype these composite scores explain .5. Finally, I lost the thread a bit when reading the second half of the results. All of these sections focus on one type of enrichment/annotation analysis or another. However, the hypotheses or models being tested in several of the analyses were not clear to me. E.g., why should we expect microbiome-associated genes to encode proteins that are networked to one another? Is the relationship to the GF versus conventional comparison based on the idea that if genes affect the microbiome, then changes in the microbiome should also affect the proteins those genes encode (not obvious to me)? Ultimately, what does it mean to be a "promising" candidate gene (promising for what kind of application), and how is that defined by these enrichment analyses?Rather than having readers try to fill in the rationale for these analyses, it would be helpful to clarify the motivation in the paper itself-or even shorten these sections to focus on those where there is the clearest underlying model or hypothesis. This might also avoid diluting the more interesting, less speculative findings presented earlier.Reviewer #2 (Recommendations for the authors):Overall, this was an excellent study and I have few comments for improvement. A couple of points:The association between heritability of taxa and their co-speciation rates is interesting, but I wonder whether this could be explained by an association between abundance and statistical power to detect heritability/co-speciation. Have the authors considered modeling heritability as a function of co-speciation rate and relative abundance? Such an analysis would be important to determine whether heritability and co-speciation rate are associated independently of abundance.Relative abundances of microbes were used for all analyses (as opposed to absolute abundance quantification). Because findings based on relative abundances can be difficult to interpret, more discussion on this limitation would be helpful.My understanding is that heritability estimates were generated only for ASV relative abundances. However, previous heritability studies have examined taxonomic levels from ASV to phylum. It would be interesting to extend the analyses presented to higher taxonomic levels beyond ASVs (eg species to phylum).Reviewer #3 (Recommendations for the authors):The main point of concern for me are the heritability estimates. The paper reports heritability results that may be a bit surprising considering the current knowledge and literature: the heritability values are very high, with several values around 90% or higher. This is unexpected, and I am not sure how this is reconciled with the expectation that most variation in the microbiome is environmental rather than genetic. Although some potential reasons are given in the Discussion , it still makes me a bit uneasy to see such high heritability estimates. One potential way to approach this is to try a different statistical approach for calculating heritability. Another would be to compare heritability estimates from this study with estimates from other studies – by now there are quite a few studies that report microbiome-wide heritability estimates from humans, mice, and other host species. It could be useful to correlate the heritability estimates in the current study with those from these studies. Lastly, it would be good to compare microbiome heritability estimates from this study to heritability’s of other complex traits in the same system – are there other known phenotypes that have heritability estimates that are this high?eLife. Your revised article has been evaluated by Wendy Garrett (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Key features of the genetic architecture and evolution of host-microbe interactions revealed by high-resolution genetic mapping of the mucosa-associated gut microbiome in hybrid mice" for further consideration by All reviewers appreciate the thorough revisions and re-analyses, which have resulted in a much improved manuscript. However, there are a few remaining issues that need to be addressed, as outlined below :1) Address the concern of Reviewer 3 below, which concern inflated heritability values in the main text. While the correlations in h2 estimates between methods are reassuringly high, Figure 8 in the response to reviewers also shows that the h2 reported in the paper (from lme4QTL) are typically several-fold (and sometimes an order of magnitude) higher than obtained from three alternative methods. This information would not be available to readers in the manuscript's current form. The response to reviewers indicates that lme4qtl was chosen because it had a high R2 with the PVE explained by the additive effects of significant SNPs . However, both GEMMA and sommer produce similar R2 values  but much lower heritability estimates. Do these lower estimates also correlate with co-speciation rates?2) Consider the polishing revisions to the code repository suggested by Reviewer 3.3) Consider integrating the useful explanation in the response to reviewers about experimental design controls for technical and batch effects in the main manuscript, as future readers may have similar questions.Reviewer #2 (Recommendations for the authors):The authors addressed all of my comments from the previous round of review. The inclusion of multiple alternative calculations of heritability have improved the manuscript substantially.All 16S rRNA gene sequence data have been deposited to NCBI.Reviewer #3 (Recommendations for the authors):I thank the authors for a comprehensive revision that addressed many of my concerns. It was good to see that the heritability values are correlated across different methods, and that several other methods produced heritability values smaller than those generated by lme4QTL. This, together with the fact that heritability values for some bacteria are higher than those for traits like length and weight, supports my notion that lme4QTL heritability values are overestimated. I am not sure what the reason is -- it's hard to know without spending time digging into the data, and I might not have the statistics background to advise on this -- but I am not confident that these values are robust. I would encourage the authors to investigate these analyses very thoroughly, making sure that all potential confounders are accounted for, the models are reasonable, and there are no artifacts in the data. I would suggest the text includes the results of heritability analysis with other approaches (in addition to lme4QTL) more prominently: Figure 1 should report and visualize heritability values from all methods used, and visualize the correlations between them. The text should describe these results, the methods used, the heritability values reported and their correlation. There should be a clear discussion about the possible reasons for the high heritability values (and why they are lower using other methods). I would also suggest including the analysis comparing heritability values across studies in the text, and include a visualization of these correlations, rather than just reporting the p-value.Regarding code availability, I want to thank the authors for enhancing the README file on the github repository, which now provides a nice description of the pipeline and analysis steps. However, I am not sure if this is sufficient for readers who want to reproduce the results: looking at the code itself, it seems like there are commands to load scripts that are not included in the repository (e.g. the snp_heritability_lme4qtl.R script loads the script function_for_gemma.r that I couldn't find anywhere), and these scripts might not be able to be run on other machines. I recommend amending the github repository and scripts so that anyone who wishes to do so is able to run the analysis and reproduce the results. Essential revisions:1. Demonstrate the robustness of the heritability analyses, including consideration of potential technical and biological confounding variables and ideally using complementary analyses .2. Demonstrate the robustness of the phylosymbiosis (heritability-cospeciation rate correlation) to controlling for taxon abundance.3. Control for population structure are well as relatedness in the trait mapping analysis.Reviewer #1 (Recommendations for the authors):1. Heritability estimates in the study are quite high, including traits that are more highly heritable than even canonical high heritability traits in humans, like height. This could be accurate, particularly in a lab-controlled environment where genetic variation is ramped up by the crossing design and hybrid origins of the founder, but I think requires closer examination. In particular, I was surprised that there does not appear to be any adjustment for covariates in the heritability analysis: in my experience, batch and technical effects are omnipresent in genomic studies, including microbiome analysis, and can affect heritability estimates in unpredictable ways. Although sex and age are controlled for in the study design, are there differences in housing, diet, sample processing, library prep, or sequencing quality/read yield/lane/batch that account for measurable variance and need to be taken into account?We thank the reviewer for raising this important concern and agree that the heritability estimates appear high. We accordingly performed several analyses, which do confirm the robustness of the results. Details are given below in response to comment 3 and the major issue of Reviewer 3. In short, this resulted in the inclusion of a “sub-cross identifier” as an additional random effect . The current model includes the sub-cross identifier with the mating pair identifier nested within it . We now We also agree that batch and technical effects are important to account for. Due to the setup of our extraction and sequencing library prep procedures, these factors were inherently accounted for at numerous levels. The samples were extracted using the Qiagen DNA/RNA Allprep kit in 96-well plate format, such that all samples were extracted in the same extraction round and hence timepoint. Individual sequencing libraries were subsequently prepared in parallel for all DNA and RNA samples, respectively, resulting in one MiSeq library each. Accordingly, all individual 16S rRNA gene profiles within a given DNA- or RNA-based mapping analysis were generated by a single sequencing run. Thus, only direct comparisons between DNA- and RNA-based traits could be possibly confounded by sequencing run, and all analyses were performed independently for these two categories.2. Related, part of the benefit of estimating heritability in a mixed effects models framework is the ability to flexibly control for other covariates. I don't see a benefit in using Mantel tests to correlate genetic relatedness and microbiome structure overall-as heritability is much more directly supported by direct estimation-and suggest removing this part of the analysis. I realize that the genetic relatedness-microbiome structure analysis might provide a "holistic" picture, but that could also be done by analyzing the top PCs of the microbiome data in a formal h2 analysis.Thank you for this suggestion, we agree that this result is not among the most important of our study and followed this advice as an opportunity to make the Results section more concise, also in reference to comment #5 of the same reviewer below.3. For the individual trait mapping analysis, I have some concerns that the models might not adequately control for background population structure. It's a good step to control for genetic structure with the K matrix, but I would like to see that the results are robust to including the top PCs of the genotype data in the model as additional fixed effects. Where possible, you could also consider using LD score regression to check that there is no unexpected inflation of test statistics.Thank you, we agree these suggestions would help show the robustness of the mapping analysis. Accordingly, we compared three different models and ran them on a representative subset of microbial traits (n=27 RNA-based genus level traits): (1) Current model with the mating pair and the kinship matrix as random effects, (2) Current model + PC1-PC3, (3) Current model + the mating pair id nested within the sub-cross identifier . We first compared the BIC scores of these models without a SNP effect . We see Next, we calculated the genomic inflation factors  as well 4. In Figure 3 and lines 251 – 258, the results report PVE levels that are impossible (since more than 100% of variance can’t be explained). While this is noted in the text, and some potential explanations are provided (lines 256 – 258), the current presentation isn’t very useful because it’s unclear how much trait variance is really being explained. A better alternative might be to construct the equivalent of polygenic scores for each trait and ask how much variance in each associated phenotype these composite scores explain .We thank the reviewer for this suggestion. We used a linear mixed model that includes all the significant SNPs per trait to calculate a polygenic score equivalent. We adapted the manuscript Figure 3 accordingly and added a section to the Methods (line 908-917):"The total variance explained by all significant SNPs was calculated similarly with the exception of including all significant SNPs as fixed effects instead of only the lead SNP. This was performed using the additive genotypes only and both the additive and the dominance genotypes."2R value (0.14).We used these "polygenic scores" to determine the choice of linear models and software/methods for calculating the heritability estimates. As seen in 5. Finally, I lost the thread a bit when reading the second half of the results. All of these sections focus on one type of enrichment/annotation analysis or another. However, the hypotheses or models being tested in several of the analyses were not clear to me. E.g., why should we expect microbiome-associated genes to encode proteins that are networked to one another? Is the relationship to the GF versus conventional comparison based on the idea that if genes affect the microbiome, then changes in the microbiome should also affect the proteins those genes encode (not obvious to me)? Ultimately, what does it mean to be a "promising" candidate gene (promising for what kind of application), and how is that defined by these enrichment analyses?Rather than having readers try to fill in the rationale for these analyses, it would be helpful to clarify the motivation in the paper itself-or even shorten these sections to focus on those where there is the clearest underlying model or hypothesis. This might also avoid diluting the more interesting, less speculative findings presented earlier.We thank the reviewer for this constructive input. We now added and/or re-worded text at the beginning of each respective subsection to more explicitly state the motivation/hypothesis behind these additional analyses, which we believe are ultimately important to draw higher-level conclusions/generate novel hypotheses to serve as a basis of future work .Reviewer #2 (Recommendations for the authors):Overall, this was an excellent study and I have few comments for improvement. A couple of minor points:The association between heritability of taxa and their co-speciation rates is interesting, but I wonder whether this could be explained by an association between abundance and statistical power to detect heritability/co-speciation. Have the authors considered modeling heritability as a function of co-speciation rate and relative abundance? Such an analysis would be important to determine whether heritability and co-speciation rate are associated independently of abundance.2R=0.27, F=3.202, P=.067). Further, the cospeciation rate significantly predicts the heritability estimate (P=.022), while the median abundance does not (P=.92). We thus conclude that the heritability estimates and the cospeciation rates are associated independent of the abundance of a taxon in question (lines 209-215).Thank you for this suggestion. We used a multiple linear regression model to test if the cospeciation rate and the median relative abundance significantly predict the heritability estimate. The overall regression model was not significant . Because findings based on relative abundances can be difficult to interpret, more discussion on this limitation would be helpful.We thank the reviewer for pointing out this important aspect. We added a specific passage in the Discussion section (lines 670-675):“A limitation of the current and previous genetic studies is that the phenotypes used for mapping are based on relative abundance rather than absolute, quantitative estimates. Specifically, an increase in a given taxon’s abundance will necessarily lead to a decrease in abundance among all other taxa, which can lead to a number of potential biases . Thus, future studies incorporating absolute abundance estimates may improve the detection of host-microbe interactions.”My understanding is that heritability estimates were generated only for ASV relative abundances. However, previous heritability studies have examined taxonomic levels from ASV to phylum. It would be interesting to extend the analyses presented to higher taxonomic levels beyond ASVs (eg species to phylum).We calculated heritability estimates with the updated model for all taxonomic levels (see reply to the comments of Reviewers 1 and 3). The labels on Figure 1A-B of the article are colored by taxonomic level.Reviewer #3 (Recommendations for the authors):The main point of concern for me are the heritability estimates. The paper reports heritability results that may be a bit surprising considering the current knowledge and literature: the heritability values are very high, with several values around 90% or higher. This is unexpected, and I am not sure how this is reconciled with the expectation that most variation in the microbiome is environmental rather than genetic. Although some potential reasons are given in the Discussion , it still makes me a bit uneasy to see such high heritability estimates. One potential way to approach this is to try a different statistical approach for calculating heritability. Another would be to compare heritability estimates from this study with estimates from other studies – by now there are quite a few studies that report microbiome-wide heritability estimates from humans, mice, and other host species. It could be useful to correlate the heritability estimates in the current study with those from these studies. Lastly, it would be good to compare microbiome heritability estimates from this study to heritability’s of other complex traits in the same system – are there other known phenotypes that have heritability estimates that are this high?We agree with the Reviewer that it is important to critically evaluate the robustness of heritabilities. Accordingly, we calculated the heritability estimates using gaston , sommer , and GEMMA , as well as lme4QTL . To compare the heritability estimates between the different methods, we used the same model where the first genotype PC is included as a fixed effect and the genetic relatedness matrix (GRM) is included as a random effect, as GEMMA does not allow categorical covariates such as mating pair ID . We used identical GRMs in lme4QTL and GEMMA. However, sommer and gaston incorporate estimation of GRMs when calculating heritabilities.P < 2.2 e-16, Spearman's correlation; Heritability estimates from each of the alternative methods are significantly correlated with estimates from lme4QTL This results in fewer taxa with significant heritability’s, and estimated values are lower. Importantly, the correlation of the cospeciation rate with the heritability estimates remains significant  and with the heritability estimates from one human study by  .Next, we compared our heritability estimates with other studies. Other microbiome QTL/GWAS studies in humans and mice also reported high heritability estimates for some taxa: 82 % in , 92% in , and 99% in . We calculated the correlation of our heritability estimates with seven human studies , two mouse studies , one pig study , and one primate study . We found our DNA-based heritability estimates to be positively correlated with DNA-based heritability estimates of male mice in   and body length (67%) are also high and comparable to previous studies in mice . This has been added to results .All reviewers appreciate the thorough revisions and re-analyses, which have resulted in a much improved manuscript. However, there are a few remaining issues that need to be addressed, as outlined below :1) Address the concern of Reviewer 3 below, which concern inflated heritability values in the main text. While the correlations in h2 estimates between methods are reassuringly high, Figure 8 in the response to reviewers also shows that the h2 reported in the paper (from lme4QTL) are typically several-fold (and sometimes an order of magnitude) higher than obtained from three alternative methods. This information would not be available to readers in the manuscript's current form. The response to reviewers indicates that lme4qtl was chosen because it had a high R2 with the PVE explained by the additive effects of significant SNPs . However, both GEMMA and sommer produce similar R2 values  but much lower heritability estimates. Do these lower estimates also correlate with co-speciation rates?Thank you for the further input to improve our heritability results. After careful inspection, we determined that the analyses described in the previous reply  were not appropriate. Including principle components of the SNP-based relatedness matrix as fixed effects in the model only makes sense if the kinship matrix included as a random effect is pedigree-based. This is not necessary because we include the full SNP-based relatedness matrix as a random effect in the model following the approach in GEMMA, because “using a lower-rank relatedness matrix compromises the ability of the linear mixed model to control for sample structure.” .We recalculated heritability estimates in GEMMA without including PCs, and address differences in heritability estimates in detail below, in response to Reviewer 3.The previous RNA-based heritability estimates from GEMMA and sommer do significantly correlate with co-speciation rates (Author response images 6-8). In the revised manuscript, we report that both chip heritability  and narrow-sense heritability estimates for RNA are correlated with co-speciation rates (lines 142 – 161 and 177-182).2) Consider the polishing revisions to the code repository suggested by Reviewer 3.Please see reply below.3) Consider integrating the useful explanation in the response to reviewers about experimental design controls for technical and batch effects in the main manuscript, as future readers may have similar questions.Thank you for this advice. We added the explanation in the respective Methods sections .Reviewer #3 (Recommendations for the authors):I thank the authors for a comprehensive revision that addressed many of my concerns. It was good to see that the heritability values are correlated across different methods, and that several other methods produced heritability values smaller than those generated by lme4QTL. This, together with the fact that heritability values for some bacteria are higher than those for traits like length and weight, supports my notion that lme4QTL heritability values are overestimated. I am not sure what the reason is -- it's hard to know without spending time digging into the data, and I might not have the statistics background to advise on this -- but I am not confident that these values are robust. I would encourage the authors to investigate these analyses very thoroughly, making sure that all potential confounders are accounted for, the models are reasonable, and there are no artifacts in the data. I would suggest the text includes the results of heritability analysis with other approaches (in addition to lme4QTL) more prominently: Figure 1 should report and visualize heritability values from all methods used, and visualize the correlations between them. The text should describe these results, the methods used, the heritability values reported and their correlation.We agree the differences between estimates from different methods needed further explanation. We thoroughly scrutinized the publications describing each method, together with input from a colleague who is an expert in quantitative genetics .sommer calculates the GRM following ). This approach assumes causative alleles are rare in the mapping population, as might be expected when mapping disease traits in humans or mapping in a sample from a natural population. As our mapping population was produced by intercrossing eight inbred strains, causative alleles are not expected to be rare, even for deleterious phenotypes. In this case, GEMMA recommends using a centered GRM for mapping , which we followed. However, when calculating heritability estimates, GEMMA always accounts for allele frequency differences – by multiplying the genetic variance component by a standardization factor to . This is because the ‘chip heritability’ calculated in GEMMA only estimates the heritability that can be explained by genotyped SNPs  – in contrast to narrow-sense heritability (2h), which is the proportion of phenotypic variance explained by all additive genetic effects. Chip heritability is useful for genotype prediction, such as in animal breeding and predicting disease risk in humans. Typically chip heritability values are much lower than narrow-sense heritability estimates from other methods . This explains why the heritability estimates from GEMMA  were lower than estimates from lme4QTL (narrow-sense heritability).The differences in estimates are due to the way in which the genetic relatedness matrix (GRM) is calculated. Several methods use standardized GRMs, which standardize the variance across SNPs by accounting for allele frequencies , for ease of comparison with some previous studies, which report estimates of chip heritability.We are interested in estimates of narrow-sense heritability, which is the upper bound of what could be explained if all additive effects are known. However, we decided to report both chip heritability and 2h , after first verifying that using the standardized GRM without covariates in lme4QTL produces the same results as GEMMA.It is not possible to include categorical covariates in GEMMA, therefore we used lme4QTL to calculate both chip heritability and 2h estimates to PVE by significant SNPs in Results lines 279 – 287, Figure 1A-B.In addition, we added a comparison of There should be a clear discussion about the possible reasons for the high heritability values (and why they are lower using other methods).We added an explanation of the difference between chip heritability and narrow-sense heritability estimates to the Discussion (lines 461-468).I would also suggest including the analysis comparing heritability values across studies in the text, and include a visualization of these correlations, rather than just reporting the p-value.We agree. We added a plot  showing the correlation of the narrow-sense heritability estimates of this study (lme4QTL) with Org et al., 2015 and Turpin et al., 2016. Supplementary File 1 includes heritability estimates from previous studies, along with our estimates.Regarding code availability, I want to thank the authors for enhancing the README file on the github repository, which now provides a nice description of the pipeline and analysis steps. However, I am not sure if this is sufficient for readers who want to reproduce the results: looking at the code itself, it seems like there are commands to load scripts that are not included in the repository (e.g. the snp_heritability_lme4qtl.R script loads the script function_for_gemma.r that I couldn't find anywhere), and these scripts might not be able to be run on other machines. I recommend amending the github repository and scripts so that anyone who wishes to do so is able to run the analysis and reproduce the results.Thank you for spotting that this script was missing. It has now been added. We however respectfully maintain that further development of our documented pipeline would amount to the creation of a new analysis package, which we feel would go beyond the scope of the current study. The github repository was rather intended as a basis for researchers to adapt these approaches to their individual datasets.References:J Dairy Sci, 91 (11), 4414-23.VanRaden, PM (2008), ‘Efficient methods to compute genomic predictions.’, Nat. Genet., 44 (7), 821-24.Zhou, Xiang and Matthew Stephens (2012), ‘Genome-wide efficient mixed-model analysis for association studies’, PLoS Genet., 9 (2), e1003264.Zhou, Xiang, Peter Carbonetto, and Matthew Stephens (2013), ‘Polygenic modeling with Bayesian sparse linear mixed models’,


Background: Statins are the most popular agents for the primary and secondary prevention of cardiovascular disease; however, the pharmacokinetic parameters and associated genetic factors in the Korean population have not been fully elucidated. This study explored the pharmacokinetic properties of atorvastatin and the association between genetic variations and atorvastatin pharmacokinetics in healthy Korean subjects.Methods: Atorvastatin (80 mg) was administered to 35 healthy Korean volunteers. Plasma levels of atorvastatin and its metabolites were measured sequentially using liquid chromatography-tandem mass spectrometry from 0 to 24 h after atorvastatin administration. Customized next-generation sequencing analysis was performed covering all coding exons of 15 genes, as well as 46 single-nucleotide variants in 29 genes related to statin pharmacokinetics.Results: The mean area under the concentration-time (AUC) and Cmax (maximum peak concentration) were 269.0 ng/ml∙h and 84.3 ng/ml, respectively, which were approximately two times higher than those reported in Caucasians. Genetic analysis revealed that eight genetic variants in ABCB1, ABCG2, APOA5, CETP, and CYP7A1 contributed to the AUC of atorvastatin. The atorvastatin AUC0–24 h prediction model was developed based on age and eight genetic variants using multivariate linear regression .Conclusion: This study shows that the pharmacokinetic properties of atorvastatin in Koreans are different from those in Caucasians and that atorvastatin AUC0–24 h could be predicted based on age and eight genetic variants of ABCB1, ABCG2, APOA5, CETP, and CYP7A1. Statins are used as a first-line treatment for the primary prevention of atherosclerotic cardiovascular disease (ASCVD) in patients with elevated low-density lipoprotein cholesterol (LDL-C) levels, those with diabetes mellitus between the age of 40–75 years, and those who are at sufficient ASCVD risk as assessed by clinician-patient risk discussion . AtorvasSLCO1B1 variant is a commonly cited example of formal guidance regarding pharmacogenetics related to statin toxicity  . Althougtoxicity .Reports on the effects of genetic variations on statin pharmacokinetics have been inconsistently replicated, and there are limited data available in Asian populations . MoreoveThe aim of our study was to explore the PK properties of atorvastatin, to investigate the association between atorvastatin PK and genetic variants during the early phase of PK after oral administration of atorvastatin in healthy Korean subjects, and to establish a PK prediction model based on genetics.This study was approved by the Institutional Review Board of Konkuk University Medical Center (KUH1200102) and the Korean Ministry of Food and Drug Safety (KMFDS). Thirty-five healthy Korean volunteers between the ages of 20 and 55 were enrolled, with written informed consent from each participant. All the subjects were confirmed to be eligible for the study, not having any current or pre-existing medical condition, not taking any medications, and showing no abnormal findings on the laboratory tests. Medical record review and routine check for admission of inpatients, including physical examination, vital signs, electrocardiogram, and laboratory tests, were performed. The subjects received a single oral dose of 80 mg atorvastatin calcium at 8:30 a.m. in an overnight fasting state. After 4 h of additional fasting, lunch and dinner were permitted. Venous blood was collected via an intravenous catheter prior to drug administration (0 h) and at 0.25, 0.5, 0.75, 1, 1.5, 2, 3, 4, 6, 8, 11, and 24 h after dosing for pharmacokinetic analysis. After collection, all samples were immediately centrifuged, aliquoted, and stored frozen. Genetic analysis was performed using the first blood sample (0 h).The plasma concentrations of atorvastatin and its two active metabolites [para-hydroxy (p-OH) and ortho-hydroxy (o-OH) atorvastatin] were measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS) using a 5500 QTRAP mass spectrometer  at Green Cross Laboratories . Standard compounds of atorvastatin, p-OH atorvastatin, o-OH atorvastatin, and deuterium-labeled atorvastatin (atorvastatin-d5) were purchased from TLC Pharmaceutical Standards Ltd. . LC-MS/MS analysis was performed with the protocol that authors have presented as poster at the 11th Annual North American Conference of The Association for Mass Spectrometry: Applications to the Clinical Lab , by usinmax) and time to achieve Cmax (Tmax) were determined directly from the plasma concentration. The rate constant was calculated by performing a linear regression of the logarithmic concentration-time curve in the terminal phase. The area under the plasma concentration curve from 0 h to infinity (AUC∞) was calculated using a combination of the linear trapezoidal method and extrapolation to infinity by the elimination rate constant.The actual concentration-time profile was plotted, and the PK parameters were determined using non-compartmental analyses by BA-Calc 2007  and PKSolver . The maxhttp://bio-bwa.sourceforge.net), and duplicated reads were marked with Picard, version 1.138 (http://picard.sourceforge.net). Local alignment, base quality recalibration, and variant calling of each sample was performed using the HaplotypeCaller of GATK, version 3.5  (As shown in  States)  in the - States)  and non- States) .p < 0.00001) were removed. The removed SNPs were rs4987144, rs1985842, rs111564371, rs112568578, rs113889384, rs28371713, and rs111606937 in CYP2D6. As quantitative trait association analysis assumes that the phenotypes are normally distributed, a rank-based inverse normal transformation method was applied to the raw phenotype values according to a previous report (http://pngu.mgh.harvard.edu/purcell/plink)   after adp-value ≤0.05. Stepwise regression was performed in order to develop Statin AUC prediction model. The regression was run in R version 4.0.5 using the stepAIC function from the MASS library. Starting from a model that included all variables (24 SNPs and age), either deleted the variable that was least helpful to the standard statistic or added the variable that improved the standard statistic most, among the missing variables in the model. Then, the addition or deletion of these SNP variables had been repeated. As a result, eight of the 24 variants were selected with age as the essential variable, and the dosage information of all 35 samples was the same as 80 mg. We adjusted the formula using the dosage value/80  was 1.4 h, and elimination half-life (T1/2) was 5.5 h  (The PK parameters of atorvastatin were as follows: the mean AUCas 5.5 h . PK paractively) .The plasma concentration-time plots of atorvastatin after administration are shown as mean ± standard deviation (SD) values in p < 0.01 in linear regression analysis; p-value of <0.05.A total of 467 variants were identified. Twenty-one genetic variants from seven genes were associated with PK parameters according to genotype   .Two individuals had PK parameter values higher than twice the average values for the other patients: one in AUC∞ and Cmax of atorvastatin , and the other in AUC0-24 h and Cmax of metabolites . Genetic variants identified only in these two individuals are shown in 0–24 h and Cmax in this study were relatively higher than those reported by Woo et al. for the Korean population, which may be associated with the relative decrease in atorvastatin elimination capability  due to differences in the composition of the study subjects   . In the 4 years) , and thi4 years) . This pr0–24 h and Cmax in this study were about two times higher than those reported in a white population   . This fictively) . The AUCctively) . A compactively) . The geoctively) . Furthertin acid . Howevertin acid . The finp < 0.01 in linear regression analysis; ABCB1 , ABCG2 (rs759701118_A), APOA5 , CETP (rs12708974_T), and CYP7A1 (rs3808607_T) and age were selected to predict atorvastatin AUC0–24 h   .ABCB1  and ABCG2 (rs759701118_A) were significantly associated with the level of atorvastatin exposure (AUC)  . The effre (AUC) . ABCB1 texposure  and affeexposure . The fun statins . The fun statins . Geneticucasians . Howeverulations . Since ACYP7A1  is significantly associated with a poor response to atorvastatin (CYP7A1 (rs3808607) may have a decreased response to atorvastatin compared to patients with the TT genotype (CYP7A1 (rs3808607) showed lower atorvastatin AUC values than those with the TT genotype (Bile acid biosynthesis by cholesterol 7α-hydroxylase (CYP7A1) is a major pathway for the removal of cholesterol into bile. A common promoter variant in vastatin , which mvastatin . Patientgenotype . In thisgenotype , which sAPOA5  and CETP (rs12708974_T) are associated with the AUC of atorvastatin. CETP plays an important role in cholesterol metabolism by bringing cholesterol esters into the liver and transferring triglycerides from LDL to HDL  elevate triglyceride levels and shift the entire lipoprotein subclass distribution to atherosclerotic dyslipidemia in patients with high cardiovascular risk  technology for genomic analysis, it provides more comprehensive information than previous sequencing studies. Our findings may contribute to the basic data needed for therapeutic drug monitoring. Further studies on the correlation between the AUC or C0–24 h prediction model based on the eight genetic variants related to atorvastatin pharmacokinetics. This study suggests that genetic variants of ABCB1, ABCG2, APOA5, CETP, and CYP7A1 contribute to the variability of atorvastatin exposure in the Korean population.We demonstrated the PK parameters of atorvastatin and its active metabolites and analyzed the values 24 h after single high-dose statin administration in a healthy Korean population. The mean atorvastatin area under the concentration-time and maximum peak concentration was approximately two times higher than that reported in Caucasians, which is consistent with a previous report of increased statin sensitivity in Asian subjects. Furthermore, we also developed an atorvastatin AUC