Body weight (BW) at different ages are of increasing importance in dairy cattle breeding schemes, because of their strong correlation with energy efficiency traits, and their impact on cow health, longevity and farm economy. In total, 15,921 dairy cattle from 56 large-scale test-herds with BW records were genotyped for 45,613 single nucleotide polymorphisms (SNPs). This dataset was used for genome-wide association studies (GWAS), in order to localize potential candidate genes for direct and maternal genetic effects on BW recorded at birth (BW0), at 2 to 3 months of age (BW23), and at 13 to 14 months of age (BW1314).-matrix, the reduced -matrix LOCO (i.e. exclusion of the chromosome on which the candidate SNP is located), and LOCO plus chromosome-wide PC. Inflation factors for direct genetic effects using FGF6, FGF23, TNNT3, and OMD. For maternal genetic effects on BW0, only three significant SNPs (according to Bonferroni correction), and four potential candidate genes, were identified. The most significant SNP on chromosome 19 explained only 0.14% of the maternal de-regressed proof variance for BW0.The first 20 principal components (PC) of the genomic relationship matrix  contains supplementary material, which is available to authorized users. Some countries with pasture-based production systems consider dairy cow live weight in overall breeding goals or in selection indices , 2. PosiOn the genomic scale, GWAS for BW or BW indicators have considered only one time point per animal –12. ZhanThe power of GWAS contributes to the detection of significant markers, and, furthermore, has an impact on the identification of associated potential candidate genes. Linkage disequilibrium (LD) is one of the parameters that affects the power of GWAS. The use of a dense 50 K single nucleotide polymorphism (SNP) chip implies that it contains markers that are closely located to the functional mutation and contribute to acceptable LD between markers and causal loci . Body weConsequently, the objectives of our study were: (1) to perform GWAS using phenotypes and de-regressed proofs for direct genetic and maternal genetic effects on BW at different ages; (2) to correct for population stratification in GWAS when using pedigree-based or genomic relationship matrices, or a combination of relationship matrices with principal components; (3) to infer (co)variance components for/between direct genetic and maternal genetic effects on different scales ; and (4) to identify associated potential candidate genes for direct genetic and maternal genetic effects.p values , 2 to 3 months of age BW23), and 13 to 14 months of age (BW1314) were available for 250,173, 42,632 and 54,768 female animals, respectively. The number of animals with phenotypic records at all three age intervals was 15,234. Animals were born between 2004 and 2016, and kept in 56 large-scale dairy cattle test-herds, which were located in the German federal states of Mecklenburg-Westpommerania and Berlin-Brandenburg. For the 250,173 calves, the gestation length of their dams ranged from 265 to 295 days (average: 279.4 days). For BW0, we discarded birth weights above 60 kg or below 20 kg. For the detection of outlier data for BW23 and BW1314, we followed the approach by Yin et al. [, and 13 p > 0.001). Only SNPs located on Bos taurus autosomes (BTA) were considered. Furthermore, we discarded animals with more than 95% identical genotypes. Quality control of SNP data was done by using the GenABEL package in R [r2) higher than 0.25 [Among the Holstein cattle with BW records, 13,827 calves with BW0 records, 4246 calves with BW23 records, and 7920 heifers with BW1314 records, were genotyped. Genotyping was performed using the Illumina Bovine 50 K SNP BeadChip V2 , or the Illumina Bovine Eurogenomics 10 K low-density chip . Animals with low-density genotypes were imputed to the 50 K chip  via AIREMLf90 from the BLUPF90 software package . The staEstimated breeding values from Model Variance components and correlations for the three BW traits explained by SNPs on all the chromosomes were estimated via genomic REML (GREML), as implemented in GCTA . Model 2mentclass2pt{minimmentclass2pt{minimp value, based on false discovery rate (FDR), was calculated for each SNP [The software package GCTA  was alsomentclass2pt{minimeach SNP . The thr-matrices using all SNPs, except those from the respective chromosome segment (for BTA1 to BTA11), or excluding all SNPs from the whole chromosome (for BTA12 to BTA30). In addition, chromosomes were separated into smaller segments according to the number of SNPs with (a) segments including 90–100 SNPs , and (b) segments including 47–50 SNPs . In order to account for the loss in similarity due to the deleted chromosome in LOCO, the first 20 PC were included as covariates (LOCO + PC20). However, consideration of 20 PC combined with the LOCO -matrix from each chromosome, and the first 3, 10 or 20 PC were included as covariates . All the similarity matrices  as mentioned above were constructed based on the full SNP dataset. An additional LOCO scenario using the pruned SNPs (LOCO_prune) was considered, in order to test the effect of LD between SNPs on inflation.The genetic similarity matrix counting , alterna” (LOCO) . Since tWe used the inflation factor  from a set of SNPs within the aforementioned window. This test followed a Chi square distribution [p-value for each of the 17,545 genes was calculated, and further adjusted according to the FDR [The database (version UMD3.1) including gene locations, start positions and end sites for all bovine genes was downloaded from Ensembl . Originaribution . Also, t the FDR . Only th the FDR .Direct pedigree-based heritabilities (i.e. using the entclass1pt{minimaentclass1pt{minimaionships . Direct The k-means clustering approach (using the first 10 PC) created three clusters including 856 cows (cluster 1), 14,305 cows (cluster 2) and 760 cows (cluster 3). Genetic distances between animals based on the two most important PC (the first two PC that contribute to genetic variation) are shown in Fig. PC1 and PC2 only explained 1.53 and 1.13% of the total genetic variation, respectively. Consequently, we observed several overlaps between the three clusters, especially for animals allocated to clusters 2 and 3. In other studies, population stratification occurred when various breeds were pooled in the same GWAS , or becaThe number of significant SNPs that contributed to direct genetic effects for the three BW traits  on BTA5 significantly contributed to the three BW traits. SNP Hapmap60480-ss46526970 was significant, regardless of the approach applied. However, SNP Hapmap57466-rs29018274 was significant only when the Hapmap60480-ss46526970) on BTA5, and the significant SNP on BTA18 (ARS-BFGL-NGS-109285), also contributed significantly to BW changes in genotyped Holstein dairy cows in the US [ARS-BFGL-NGS-109285 was significantly associated with body shape, body size, dystocia, longevity, lifetime economic merit [ARS-BFGL-NGS-39379 for BW0 and BW1314, ARS-BFGL-NGS-5139 for BW0, ARS-BFGL-NGS-107035 for BW0, and ARS-BFGL-NGS-109317 for BW0, had a significant impact on BW [Most of the significant SNPs for direct genetic effects on the three BW traits were located on BTA5 Table . Manhattn the US . On BTA1ic merit . The fouct on BW , live wect on BW , carcassct on BW , and hotARS-BFGL-NGS-61198 on BTA4 explained 2.67% of the phenotypic variation for lean rate. The significant SNP Hapmap53086-rs29025958 on BTA19 was identified as a marker for a QTL that controls fat percentage [For maternal genetic effects, only three significant SNPs according to the FDR threshold were identified when using LOCO plus chromosome-wide PC Table . Two SNPrcentage . Accordircentage , many geCorrelations between SNP effects . Weak associations between chromosome length and chromosomal genomic variances indicate that the QTL for BW are not evenly distributed across the genome [In contrast to , we foune genome .p values lower than 0.05 (according to FDR), with 51 significant genes for BW0, 12 for BW23, and 38 for BW1314; these figures reflect the smaller number of significant SNPs detected in the GWAS for BW at later ages. Six genes contributed significantly to the three BW traits and 12 more contributed to both BW0 and BW1314, but only one more gene, i.e. fast skeletal muscle troponin T (TNNT3) had a significant effect on both BW0 and BW23. Low to moderate genetic correlations between BW traits at different age points, but with some overlapping between significant genes, could indicate pleiotropic effects of the candidate genes.The identified potential candidate genes that significantly influence direct genetic effects on BW are in Additional file cytosolic thiouridylase subunit 1 (CTU1) and ENSBTAG00000037537 are highly associated with body conformation traits and kallikrein related peptidase 4 (KLK4), CTU1 and ENSBTAG00000004608 contributes to calving ease. Purfield et al. [CTU1 and ENSBTAG00000037537 contain one and two significant missense variants, respectively, that are associated with calving difficulty in a mixed bull population including Holstein–Friesian, Charolais and Limousin. Since the above-mentioned six genes also influence birth weight, the calving difficulties in these breeds are mainly due to increased BW of the newborn [Some of the potential candidate genes on BTA18 for BW traits are known to be involved in calving performance and conformation traits. For example, Abo-Ismail et al.  reportedd et al.  showed t newborn .fibroblast growth factors 6 (FGF6) and fibroblast growth factors 23 (FGF23), fast skeletal muscle troponin T (TNNT3), and osteomodulin (OMD). FGF6 and FGF23 belong to the fibroblast growth factor family, which plays an important role in a variety of biological processes, including angiogenesis, morphogenesis, tissue regeneration, and oncogenesis [cathepsin D (CTSD) is involved in the activation and degradation of polypeptide hormones and growth factors [TNNT3 produces troponin T protein in the mammalian fast skeletal muscle, with causal effects on Ca2+ muscle contractions [OMD regulates the diameter and shape of collagen fibrils, which suggest an effect on bone formation [Our analyses revealed that the identified potential candidate genes were involved in 12 biological processes Fig. : cellulaogenesis . Another factors . TNNT3 practions . OMD regormation .Fig. 5Bisolute carrier family 35 member B1 (SLC35B1), speckle-type POZ protein (SPOP), neurexophilin 3 (NXPH3), and nerve growth factor receptor (NGFR), were significantly associated with birth weight , for birth weight de-regressed proofs (c and d), for BW23 phenotypes and de-regressed proofs (e and f), and for BW1314 phenotypes and de-regressed proofs (g and h). Description: Additional file 2. Potential candidate genes for direct genetic effects on body weight recorded at birth (BW0), at 2 to 3 months of age (BW23) and at 13 to 14 months of age (BW1314).Additional file 3. Potential candidate genes for maternal genetic effects on body weights recorded at birth (BW0), at 2 to 3 months of age (BW23) and at 13 to 14 months of age (BW1314).


In genome-wide association studies (GWAS), joint analysis of multiple phenotypes could have increased statistical power over analyzing each phenotype individually to identify genetic variants that are associated with complex diseases. With this motivation, several statistical methods that jointly analyze multiple phenotypes have been developed, such as O’Brien’s method, Trait-based Association Test that uses Extended Simes procedure (TATES), multivariate analysis of variance (MANOVA), and joint model of multiple phenotypes (MultiPhen). However, the performance of these methods under a wide range of scenarios is not consistent: one test may be powerful in some situations, but not in the others. Thus, one challenge in joint analysis of multiple phenotypes is to construct a test that could maintain good performance across different scenarios. In this article, we develop a novel statistical method to test associations between a genetic variant and Multiple Phenotypes based on cross-validation Prediction Error (MultP-PE). Extensive simulations are conducted to evaluate the type I error rates and to compare the power performance of MultP-PE with various existing methods. The simulation studies show that MultP-PE controls type I error rates very well and has consistently higher power than the tests we compared in all simulation scenarios. We conclude with the recommendation for the use of MultP-PE for its good performance in association studies with multiple phenotypes. In spite of the success of GWAS in identifying thousands of associations between genetic variants and complex diseases, these identified variants only contribute to a small proportion of the phenotypic variation. In the study of a complex disease, several correlated phenotypes are usually measured for a disorder or its risk factors2 and the ignorance of the correlation structure among phenotypes4. Thus, multiple-phenotype association study that uses multiple phenotypes simultaneously has become popular.One method to use multiple phenotypes in association studies is to analyze each phenotype separately as the standard univariate association test and then aggregate the results. This approach will have a loss in power due to the penalties from the multiple testing5. OB is the most powerful test when the genetic effects are homogeneous and loses power when genetic effects are heterogeneous, especially when genetic effects have opposite directions6. van der Sluis et al.7 proposed a trait-based association test using an extended Simes procedure (TATES) that conducts association test for each phenotype and then combines the univariate p-values while correcting for the correlation between p-values. The canonical correlation analysis (CCA) conducts the linear combination of phenotypes that explain the largest amount of correlation between a genetic variant and phenotypes8. One could also use multivariate analysis of variance (MANOVA) in regression to study multiple phenotypes9. MANOVA is equivalent to CCA when canonical correlation analysis is applied to a single variant10. MultiPhen proposed by O’Reilly et al.2 can be used to detect the association between one variant and multiple phenotypes by reversing response and predictors via a proportional odds regression model. When a small number of phenotypes are included, MultiPhen and MANOVA lead to similar performance11. MANOVA and CCA require the assumption of normality of multiple phenotypes, while MultiPhen has no inflated type I error rates on non-normal phenotypes2. Some other variable reduction methods have also been proposed to test for the association between a genetic variant and the linear combination of multiple phenotypes rather than the original phenotypes14. For example, principal component of phenotypes (PCP) that maximizes the phenotype variation is the most popular dimension reduction method13. Based on PCP, Klei et al.12 developed principal component of heritability (PCH) by maximizing the heritability among all linear combination of phenotypes. Recently, Turley et al.15 introduced the Multi-Trait Analysis of GWAS (MTAG) for joint analysis of multiple phenotypes. MTAG can be applied to GWAS summary statistics from an arbitrary number of phenotypes without access to individual-level data.Several methods to detect association using multiple phenotypes simultaneously have been introduced in recent years. For example, O’Brien method (OB) is proposed to combines test statistics obtained from association test for each individual phenotype6: one test may be powerful in some situations, but not in the others. Thus, one challenge in multiple phenotype analysis is to construct a test that could maintain good performance across different scenarios. In this article, we develop a novel statistical method to test the association between a genetic variant and Multiple Phenotypes based on cross-validation Prediction Error (MultP-PE). Extensive simulation studies are conducted to evaluate the type I error rates and to compare the power performance of MultP-PE with various existing methods. Our simulation studies show that MultP-PE controls the type I error rates very well and has consistently higher power than other methods we compared in all simulation scenarios.Although there are many proposed methods for joint analysis of multiple phenotypes, the performance of these methods under a wide range of scenarios is not consistentn unrelated individuals. Each individual has K  phenotypes and has been genotyped at a variant of interest. Let yik denote the kth phenotype value of the ith individual and xi denote the genotype score of the ith individual, where xi∈{0, 1, 2} is the number of minor alleles that the ith individual carries. We model the relationship between the multiple phenotypes and the genetic variant using an inverse linear regression model, in which the genotype at the variant of interest is the response variable and the multiple phenotypes are predictors. That is,We consider a sample with et al.16 used a qualitative phenotype or genotypes as response variables in linear models. To adjust the effects of covariates in rare variant association studies, Sha et al.17 also used a qualitative phenotype or genotypes as response variables in linear models. To test the association between the K multiple phenotypes and the variant, we test the null hypothesis H0:β1 = ··· = βK = 0 under model (1).We are not the first using an ordinal variable as response variable in a linear model. To correct for population stratification, Price yi = T and β = T, then the regression model in equation T and x = T. When multiple phenotypes are highly correlated, the rank of matrix Y may be less than K, then the inverse of YTY may not exist, which results in that the ordinary linear square estimate of β may not be unique18. Since multiple phenotypes in a GWAS are usually highly correlated, we propose to use Ridge regression24. Ridge regression penalizes the size of the regression coefficients. The Ridge regression estimator of β is defined as the value of β that minimizesλ (λ ≥ 0) is a tuning parameter. The solution to the Ridge regression is given by β depends on λ and we use the subscript λ to indicate that the estimator of β is a function of λ.Let ith individual out) of xi under model (1) with parameter λ in Ridge regression. Then, the statistic can be written as Tλ is the LOOCV prediction error, thus low values of Tλ would imply significance. Let pλ denote the p-value of Tλ . We define the test statistic of Multiple Phenotypes based on Prediction Error (MultP-PE) asBased on Ridge regression, we propose to use the leave-one-out cross validation (LOOCV) prediction error under model (1) as a test statistic. Let TMultP−PE. Intuitively, we need to use two layers of permutations to estimate TMultP−PE. For microarray data analysis, Ge et al.25 proposed that one layer of permutation can be used to estimate p-values. We use the permutation procedure of Ge et al. to estimate TMultP−PE. In each permutation, we randomly shuffle the genotypes at the variant. Suppose that we perform B times of permutations. Let bth permuted data for b = 0, 1, …, B and m = 1, …, M, and b = 0 represents the original data. Then, we can estimate TMultP−PE based on the bth permuted data, then the p-value of TMultP−PE is given byWe propose to use a grid search method in equation  to evaluTo apply MultP-PE to GWAS with hundreds of thousands of SNPs, we also propose an algorithm that can perform the permutation procedure described above more efficiently in the following section.Aλ = (YTY + λI)−1, xi is 26, that is, A and B, we use A*B and C, we use colSum(C) to denote the sums of the columns of matrix C. We assume n ≥ K + 1. We perform singular value decomposition of Y, that is, Y = UDV, where U is an n × (K + 1) matrix with orthonormal columns, D is (K + 1) × (K + 1) diagonal matrix with non-negative real numbers on the diagonal, and V is an (K + 1) × (K + 1) orthogonal matrix. Let D = diag. Then, Cλ = diag and j = 1, …, K + 1. Let xK). For C, U, and H only depend on phenotypes and λ1, …, λM. Thus, C, U, and H do not change in each permutation. For a GWAS, C, U, and H also do not change at different SNPs. For 1,000 permutations on one SNP, our fast algorithm is about 20 times faster than the original algorithm , then use a large number of permutations to test the selected SNPs. For example, in our real data analysis with 630,860 SNPs, we first performed 1,000 permutations and selected SNPs with p-value ≤ 0.005, then we performed 108 permutations on the selected SNPs because SNPs with p-value > 0.005 are not significantly associated with phenotypes.We use the notations in the above section and let Although we use a permutation procedure to calculate the p-value of MultP-PE, by using our fast algorithm, we can use less than one day to perform a typical GWAS. In our read data analysis on COPD in the following section, we performed a GWAS with 5,430 individuals across 630,860 SNPs and seven phenotypes. We completed the analysis in 10 hours on Intel Xeon E5-2680v3 by using a single node.G covariates we would like to consider and let T denote the covariates for the ith individual. We can adjust esch of the phenotypes by the covariates by applying the linear regression model i = 1, 2, …, n, k = 1, 2, …, K, and use the residual of yik to replace yik in MultP-PE. In our real data analysis, we used this approach to incorporate four covariates. This approach has been used in the literature. For example, Sha et al.16 and Zhu et al.14 also used the same approach to adjust phenotypes for the covariates.In the above section, we describe MulP-PE without considering covariates. If covariates are needed to be considered, we can incorporate covariates using the following approach in MultP-PE. Suppose that there are total 27. There are several methods that have been developed to control for population stratification. For example, Genomic Control (GC) approach29, Principal Component (PC) approach32, and Mixed Linear Model (MLM) approach34. Similar to most association tests for unrelated individuals, MulP-PE subjects to bias due to population stratification. To make MultP-PE robust to population stratification, we can use the PC approach. Let ci1, …, ciL denote the top L PCs of the genotypes at a set of genomic markers for the ith individual. We can use the residuals of the regression model xi and use the residuals of the regression model yik for k = 1, 2, …, K in MultP-PE to adjust for population stratification.In association studies for unrelated individuals, it has been well known that population stratification can seriously confound association results5, Trait-based Association Test that uses Extended Simes procedure (TATES)7, Optimal weight method (OW)6, Multivariate analysis of variance (MANOVA)9, and Joint model of multiple phenotypes (MultiPhen)2.We evaluate the performance of the proposed test MultP-PE by comparing it with five most commonly used methods for association studies using multiple phenotypes. These five methods include the O’Brien’s method (OB)In simulation studies, we evaluate type I error rates of MultP-PE by generating data sets with three different sample sizes, 500, 1,000 and 2,000. For power comparison, we compare the powers of different methods by simulation data sets with 1,000 unrelated individuals.K phenotypes using models similar to the models used in Zhu et al.14 and Wang et al.35. The K phenotypes are generated from the following modely = T; ϕ =  are the genetic effects of the variant on the K phenotypes; x is the genotypic score at the variant; c is a constant number; γ is a K × R matrix; ω = T is a vector of factors with R elements and ρ is the correlation between factors, A is a matrix with elements of 1, and I is the identity matrix; ε = T is a vector of residuals, ε1, …, εK are independent, and k = 1, …, K. Based on equation  and assume Hardy-Weinberg Equilibrium (HWE). For each individual, we generate R = 1, ϕ = βT, and γ = T.There is only one factor and genotypes impact on all phenotypes with different effect sizes. That is, R = 2, γ = Bdiag, where i = 1, 2 and Bdiag means block diagonal.There are two factors and genotypes impact on one factor. That is, R = 5, γ = Bdiag, where i = 1, …, 5; k = K/5; β41 = ··· = βk4 = −β; and There are five factors and genotypes impact on two factors. That is, R = 5, γ = Bdiag, where i = 1, …, 5; k = K/5; β11 = ··· = βk1 = 0; β21 = ··· = βk2 = β; β31 = ··· = βk3 = −β; There are five factors and genotypes impact on four factors. That is, β = 0 to indicate that the genetic variant has no effect on all phenotypes. For power comparisons, we consider different values of β. To evaluate type I error rate and power, we set MAF = 0.3, the between-factor correlation is 0.14, and the within-factor correlation is 0.25. In the following simulation studies and real data analysis, we use eight different values of λ (M = 8) and set For the type I error rates, we set The R codes for implementation of MultP-PE and for simulation of data under the four models are available at Dr. Shuanglin Zhang’s homepage http://www.math.mtu.edu/shuzhang/software.html.To evaluate the type I error rates of MultP-PE, we consider different significance levels 0.01 and 0.05), different sample sizes , and different number of phenotypes . We use 1,000 permutations to calculate the p-values of MultP-PE and use 10,000 replicated samples to evaluate type I error rates of MultP-PE. For 10,000 replicated samples, the 95% confidence intervals (CIs) for the estimated type I error rates with nominal levels 0.05 and 0.01 are  and , respectively. We summarize the estimated type I error rates of the proposed test in Table  and 0.05β with K = 20 and 40, respectively. As shown in these two figures: (1) MultP-PE is the most powerful test. The power of MultP-PE is much higher than the second most powerful test; (2) as the effect size β increases, the powers of all tests increase as well; as the number of phenotypes K increases from 20 to 40, MultP-PE presents more ascendancy than the other five tests; (3) MultiPhen, OW, and MANOVA have similar powers under all four models. A similar conclusion has been reached in some published papers7; (4) OB is comparable to MultiPhen, OW, and MANOVA in models 1 and 2, but has almost no power when the genetic effects have different directions (models 3 and 4); (5) TATES is more powerful than MultiPhen, OW, and MANOVA in model 2, but is less powerful than MultiPhen, OW, and MANOVA in models 3 and 4.In power comparisons, we calculate the p-values of MultP-PE using 1,000 permutations and the p-values of MultiPhen, OW, TATES, MANOVA, OB using their asymptotic distributions. We evaluate the powers of all of the six tests using 1,000 replicated samples at a significance level of 0.05. Figures c2, with K = 20 and 40 are given in Figs 6; (3) OB is the least powerful test except under model 2 with the within-factor correlation > 0.2.Power comparisons of the six methods as a function of the within-factor correlation, c2ρ, with K = 20 and 40 are given in Figs Power comparisons of the six methods as a function of the between-factor correlation, In summary, MultP-PE is consistently the most powerful test among the tests we compared under all simulation scenarios.36. The COPDGene aims to find inherited or genetic factors that associated with COPD. The COPDGene dataset includes 10,192 participants, 3,408 of them are African-Americans (AA), and 6,784 of them are Non-Hispanic Whites (NHW). Same as Liang et al.37, we considered Age, Sex, BMI, and Pack-Years as four covariates and selected seven quantitative COPD-related phenotypes  in the following data analysis.Chronic obstructive pulmonary disease (COPD) is a terminology to describe progressive life-threatening lung diseases that causes breathlessness and serious illness, including emphysema, chronic bronchitis, refractory asthma, and some forms of bronchiectasis. A global prevalence of 251 million cases of COPD is reported in 2016 and it is estimated that COPD caused 3.17 million deaths in 201517. We regressed each phenotype on the four covariates, replaced original phenotypes with the residuals of the regression, and applied each of the six tests to detect the association between the covariates-adjusted phenotypes  and each SNP.We deleted individuals and genotypes with missing data. After excluding missing data, a set of 5,430 NHW across 630,860 SNPs was used in the analysis. Then we adjusted the phenotypes for the covariates by applying a linear regression−8 to identify SNPs that are significantly associated with the seven COPD-related phenotypes. There were total 14 SNPs identified by at least one method .Supplementary Information


This gene encodes the E2 subunit of the branched-chain α-keto acid dehydrogenase (BCKDH) complex, a core component of branched-chain amino acid (BCAA) metabolism. We causally linked a non-synonymous variant in the conserved lipoyl domain of DBT-1 to differential arsenic responses. Using targeted metabolomics and chemical supplementation, we demonstrate that differences in responses to arsenic are caused by variation in iso-branched chain fatty acids. Additionally, we show that levels of branched chain fatty acids in human cells are perturbed by arsenic treatment. This finding has broad implications for arsenic toxicity and for arsenic-focused chemotherapeutics across human populations. Our study implicates the BCKDH complex and BCAA metabolism in arsenic responses, demonstrating the power of C. elegans natural genetic diversity to identify novel mechanisms by which environmental toxins affect organismal physiology.We find that variation in the Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (ddressed . Inorganic trivalent arsenic As(III) compounds, which include arsenic trioxide (As2O3), are the most toxic forms of environmental arsenic  . HematopCaenorhabditis elegans have greatly facilitated our understanding of basic cellular processes (C. elegans laboratory strain (N2). To date, 152 C. elegans strains have been isolated from various locations around the world  complex . To determine an appropriate concentration of arsenic trioxide for mapping experiments, we performed dose-response experiments on four genetically diverged isolates of C. elegans: N2, CB4856, JU775, and DL238  . The fir37 (ωp2) . We note37 (ωp2) , suggest37 (ωp2) . Further37 (ωp2) , we focuThe increased arsenic trioxide resistance of CB4856 compared to N2 motivated us to perform linkage mapping experiments with a panel of recombinant inbred advanced intercross lines (RIAILs) that were previously constructed through ten generations of intercrossing between an N2 derivative (QX1430) and CB4856 . To capt = 0.56) . Taken t = 0.56) . The PC1 = 0.56) . HoweverNext, we constructed near-isogenic lines (NILs) to isolate and narrow the chromosome II QTL in a controlled genetic background. We introgressed genomic regions from the CB4856 strain on the left and right halves of the confidence interval into the N2 genetic background. In the presence of arsenic trioxide, both of these NILs recapitulated the parental CB4856 PC1 phenotype  and had C. elegans strains  mapping experiment by quantifying the responses to arsenic trioxide for 86 wild  strains   and is predicted to encode a cysteine-to-serine variant at position 78 (C78S). Although it is possible that the causal variant underlying differential arsenic trioxide response in the C. elegans population is an intergenic variant, we focused on the DBT-1(C78S) variant as a candidate to test for an effect on arsenic response.To fine map the PC1 QTL, we focused on variants from the  dataset  that are dataset . Only onC. elegans dbt-1 gene encodes the E2 component of the branched-chain α-keto acid dehydrogenase complex (BCKDH)  . The BCK (BCKDH) . The BCK (BCKDH) . All thr (BCKDH) . In C. eTo confirm that the C78S variant in DBT-1 contributes to differential arsenic trioxide responses, we used CRISPR-Cas9-mediated genome editing to generate allele-replacement strains by changing the C78 residue in the N2 strain to a serine and the S78 residue in the CB4856 strain to a cysteine. When treated with arsenic trioxide, the N2 DBT-1(S78) allele-replacement strain recapitulated 56.4% of the phenotypic difference between the CB4856 and N2 strains as measured with the first principal component (Cohen’s F)  Figure . Similarelo-5, elo-6, or dbt-1 produce less C15ISO and C17ISO mmBCFAs, arrest at the L1 larval stage, and can be rescued by supplementing the growth media with C15ISO or C17ISO (Mono-methyl branched chain fatty acids (mmBCFA) are an important class of molecules that are produced via BCAA catabolism . The pror C17ISO  -dependent difference in progeny length between the N2 and CB4856 strains after arsenic trioxide treatment might be caused by differential larval arrest through depletion of downstream mmBCFAs. To test this hypothesis, we quantified the abundance of the monomethyl-branched (ISO) and straight-chain (SC) forms of C15 and C17 in the N2, CB4856, and allele-replacement genetic backgrounds. We measured the metabolite levels in staged L1 animals and normalized the detected amounts of C15ISO and C17ISO relative to the abundances of C15SC and C17SC, respectively, to mitigate the confounding effects of differences in developmental rates that could be caused by genetic background differences after arsenic trioxide exposure. Generally, the ratios of C15ISO/C15SC and C17ISO/C17SC were reduced in arsenic-treated animals relative to controls –3.C. elegans strains.However, arsenic trioxide treatment had a 7.6-fold stronger effect on the C15ISO/C15SC ratio in N2, which naturally has the C78 allele, than on the N2 DBT-1(S78) allele replacement strain. This difference suggests that the DBT-1(C78) allele is more strongly inhibited by arsenic trioxide . Similarly, we observed a 6.6-fold arsenic-induced reduction in the C17ISO/C17SC ratio when comparing the N2 DBT-1(C78) and N2 DBT-1(S78) strains . When comparing the CB4856 DBT-1(S78) and CB4856 DBT-1(C78) strains, we observed a 2.8-fold lower C15ISO/C15SC ratio  and 1.5-fold lower C17ISO/C17SC ratio  in the in the CB4856 DBT-1(C78) strain. We noted that the C17ISO/straight-chain ratio difference was not significantly different between the two CB4856 genetic background strains. However, we observed a significant arsenic-induced decrease in raw C17ISO production in the CB4856 DBT-1(C78) strain  and no significant difference in the CB4856 DBT-1(S78) strain  . ImportaIn addition to arsenic-induced differences in branched chain fatty acid production, we observed significant differences in branched/straight-chain ratios between the parental and allele replacement strains when L1 larval animals were grown in control conditions –3. StraiC. elegans by reducing ISO fatty acid biosynthesis, and the DBT-1(C78) variant can be partially rescued by supplementation with mmBCFAs.To test the hypothesis that differential arsenic-induced depletion of branched-chain fatty acids in strains with the DBT-1C78S) causes physiological differences in growth, we tested if mmBCFA supplementation could rescue the effects of arsenic trioxide-induced toxicity. We exposed the parental and the DBT-1 allele-replacement strains to media containing arsenic trioxide alone, C15ISO alone, or a combination of arsenic trioxide and C15ISO. In agreement with previous experiments, the PC1 trait was more strongly correlated with the animal length, optical density, and fluorescence traits than the brood size trait 8S causes. C15ISO C. elegans translate to human variation in arsenic sensitivity, we tested the role of DBT1 variation on arsenic trioxide responses and mmBCFA biosynthesis in human cells. The human DBT1 enzyme contains a serine at position 112 that corresponds to the C78 residue in C. elegans DBT-1  allele are more sensitive to arsenic trioxide than strains carrying the DBT-1(S78). Furthermore, we show that the increased sensitivity of the DBT-1(C78) allele is largely explained by differences in the production of mmBCFAs  allele  allele –4 might jima’s D   used for linkage mapping have been described previously , containing 1% agar and 0.7% agarose to prevent burrowing of the wild isolates . For eaceviously . Strainshttps://doi.org/10.6084/m9.figshare.7458980.v2).We used the high-throughput fitness assay (HTA) described previously . In shoreasysorter, which was specifically developed for processing this type of data , animal extinction (EXT), and three fluorescence channels , which correspond to animal length, optical density, and ability to pump fluorescent beads, respectively. Measured brood sizes (n) are normalized by the number of animals that were originally sorted into each well (norm.n). For mapping experiments, a single well replicate for each strain is summarized using the sumplate function. For follow-up experiments, multiple replicates for each strain indicated by a unique plate, well, and column were used. After summary statistics for each well are calculated, we accounted for differences between assays using the regress(assay = TRUE) function in the easysorter package. Outliers in the GWA and linkage mapping experiments were identified and eliminated using the bamf_prune function in easysorter. For follow-up experiments that contained multiple replicates for each strain, we eliminated strain replicates that were more than two standard deviations from the strain mean for each condition tested. Finally, arsenic-specific effects were calculated using the regress(assay = FALSE) function from easysorter, which accounts for strain-specific differences in growth parameters present in control conditions.Phenotype data generated using the BIOSORT were processed using the R package  of data . Brieflyeasysorter package to generate various summary statistics of each measured parameter, including five distribution quantiles and measures of dispersion , optical density (EXT), fluorescence . We use these data to calculate the total number of animals in a well and then normalize by the number of animals initially sorted into the well (brood size). All these measurements were then summarized using the spersion . We usedion in R . EigenveAll dose-response experiments were performed on four genetically diverged strains  in technical quadruplicates prior to performing GWA and linkage mapping experiments . Animalslmer function in the lme4 package with the following linear mixed model (phenotype ~1 + (1|strain)) ) . H2 was Heritability estimates for the linkage mapping experiment were calculated using two approaches. In both approaches, we used the previously described RIAIL genotype matrix to compute relatedness matrices . In the tors, h2 , using tmmer function in the sommer package with the formula (y ~ A + E) to estimate variance components, where y is a vector of RIAIL phenotypes, A is the realized additive relatedness matrix, and E is the epistatic relatedness matrix. This same approach was used to estimate heritability for the GWA mapping phenotype data, with the only difference being that we used the wild isolate genotype matrix described below. Heritability estimates for RIAIL and wild isolate data are in The second approach utilized a linear mixed model and the realized additive and epistatic relatedness matrices . We usedlm R function. Next, we extracted effect sizes using the anova_stats function from the sjstats R package (We first fit a linear model with the formula (phenotype ~strain) for all measured and principal component traits for each concentration of arsenic trioxide using the  package . Effect A total of 262 RIAILs were phenotyped in the HTA described previously for control and arsenic trioxide conditions . The phet values . The maxt values . We randt values .NILs were generated by crossing N2xCB4856 RIAILs to each parental genotype. For each NIL, eight crosses were performed followed by six generations of selfing to homozygose the genome. Reagents used to generate NILs are detailed in the Key Resources Table. The NILs responses to 1000 µM arsenic trioxide were quantified using the HTA described above . NIL whoC. elegans isotypes . To perform fine-mapping, we defined confidence intervals from the genome-wide mapping as +/- 100 SNVs from the rightmost and leftmost markers above the Bonferroni significance threshold. We then generated a QTL region of interest genotype matrix that was filtered as described above, with the one exception that we did not perform LD pruning. We used PLINK v1.9 to extract the LD between the markers used for fine mapping and the peak QTL marker identified from the genome-wide scan. We used the same command as above to perform fine mapping, but with the reduced variant set. The workflow for performing GWA mapping can be found on https://github.com/AndersenLab/cegwas2-nf .Genome-wide association (GWA) mapping was performed using phenotype data from 86 isotypes . Genotypisotypes . We usedisotypes  to filteisotypes  to LD-pr package   repair templates were injected at 1.34 µM and 4 µM, respectively. Cas9 protein  was injected at 23 µM. To generate injection mixes, the tracrRNA and crRNAs were incubated at 95°C for 5 min and 10°C for 10 min. Next, Cas9 protein was added and incubated for 5 min at room temperature. Finally, repair templates and nuclease-free water were added to the mixtures and loaded into pulled injection needles . Individual injected 0P animals were transferred to new 6 cm NGM plates approximately 18 hr after injections. Individual 1F rollers were then transferred to new 6 cm plates to generate self-progeny. The region surrounding the desired S78C (or C78S) edit was then amplified from 1F rollers using primers oECA1163 and oECA1165. The PCR products were digested using the SfcI restriction enzyme . Differential band patterns signified successfully edited strains because the N2 S78C, which is encoded by the CAG codon, creates an additional SfcI cut site. Non-Dpy, non-Rol progeny from homozygous edited 1F animals were propagated. If no homozygous edits were obtained, heterozygous 1F progeny were propagated and screened for the presence of the homozygous edits. 1F and 2F progeny were then Sanger sequenced to verify the presence of the proper edited sequence. The phenotypes of allele replacement strains in control and arsenic trioxide conditions were measured using the HTA described above  concentrations to assay rescue of arsenic effects .For L1 larval stage assays, chunks (~1 cm) were taken from starved plates and placed on multiple fresh 10 cm plates. Prior to starvation, animals were washed off of the plates using M9, and embryos were prepared by bleach synchronization. Approximately 40,000 embryos were resuspended in 25 ml of K medium and allowed to hatch overnight at 20°C. L1 larvae were fed 15 mg/ml of HB101 lysate the following morning and allowed to grow at 20°C for 72 hr. We harvested 100,000 embryos from gravid adults by bleaching. These embryos were hatched overnight in 50 ml of K medium in a 125 ml flask. The following day, we added arsenic trioxide to a final concentration of 100 µM and incubated the cultures for 24 hr. After 24 hr, we added HB101 bacterial lysate (2 mg/ml) to each culture. Finally, we transferred the cultures to 50 ml conical tubes, centrifuged the cultures at 3000 RPM for 3 min to separate the pellet and supernatant. The supernatant and pellets from the cultures were frozen at −80°C and prepared for analysis. For young adult stage assays, 45,000 animals per culture were prepared as described above but in S medium, at a density of three animals per microliter, and fed HB101 lysate (5 mg/mL). These cultures were shaken at 200 RPM, 20°C in 50 mL Erlenmeyer flasks for 62 hr. For harvesting, we settled 15 mL of cultures for 15 min at room temperature and then pipetted the top 12 mL of solution off of the culture. The remaining 3 mL of animal pellet was washed with 10 mL of M9, centrifuged at 1000 g for one minute, and then the supernatant removed. This wash was repeated once more with M9 and again with water. The final nematode pellet was snap frozen in liquid nitrogen.Pellets were lyophilized 18–24 hr using a VirTis BenchTop 4K Freeze Dryer until a chalky consistency was achieved. Dried pellets were transferred to 1.5 mL microfuge tubes and dry pellet weight recorded. Pellets were disrupted in a Spex 1600 MiniG tissue grinder after the addition of three stainless steel grinding balls to each sample. Microfuge tubes were placed in a Cryoblock (Model 1660) cooled in liquid nitrogen, and samples were disrupted at 1100 RPM for two cycles of 30 s. Each sample was individually dragged across a microfuge tube rack eight times, inverted, and flicked five times to prevent clumping. This process was repeated two additional rounds for a total of six disruptions. Pellets were transferred to 4 mL glass vials in 3 mL 100% ethanol. Samples were sonicated for 20 min (on/off pulse cycles of two seconds at power 90 A) using a Qsonica Ultrasonic Processor (Model Q700) with a water bath cup horn adaptor (Model 431C2). Following sonication, glass vials were centrifuged at 2750 RCF for five minutes in an Eppendorf 5702 Centrifuge using rotor F-35-30-17. The resulting supernatant was transferred to a clean 4 mL glass vial and concentrated to dryness in an SC250EXP Speedvac Concentrator coupled to an RVT5105 Refrigerated Vapor Trap (Thermo Scientific). The resulting powder was suspended in 100% ethanol according to its original dry pellet weight: 0.01 mL 100% ethanol per mg of material. The suspension was sonicated for 10 min (pulse cycles of 2 s on and 3 s off at power 90 A) followed by centrifugation at 20,817 RCF in a refrigerated Eppendorf centrifuge 5417R at 4°C. The resulting supernatant was transferred to an HPLC vial containing a Phenomenex insert (cat #AR0-4521-12) and centrifuged at 2750 RCF for five minutes in an Eppendorf 5702 centrifuge. The resulting supernatant was transferred to a clean HPLC vial insert and stored at −20°C or analyzed immediately.- ions in negative ionization mode. Peaks of known abundant ascarosides and fatty acids were used to monitor mass accuracy, chromatographic peak shape, and instrument sensitivity for each sample. Processed metabolite measures can be found in Reversed-phase chromatography was performed using a Dionex Ultimate 3000 Series LC system  controlled by Chromeleon Software (ThermoFisher Scientific) and coupled to an Orbitrap Q-Exactive mass spectrometer controlled by Xcalibur software (ThermoFisher Scientific). Metabolites were separated on a Kinetex EVO C18 column, 150 mm x 2.1 mm, particle size 1.7 µm, maintained at 40°C with a flow rate of 0.5 mL/min. Solvent A: 0.1% ammonium acetate in water; solvent B: acetonitrile (ACN). A/B gradient started at 5% B for 30 s, followed by a linear gradient to 95% B over 13.5 min, then a linear gradient to 100% B over 3 min. 100% B was maintained for 1 min. Column was washed after each run with 5:1 isopropanol:ACN, flow rate of 0.12 mL/min for 5 min, followed by 100% ACN for 2.9 min, a linear gradient to 95:5 water:ACN over 0.1 min, and then 95:5 water:ACN for 2 min with a flow rate of 0.5 mL/min. A heated electrospray ionization source (HESI-II) was used for the ionization with the following mass spectrometer parameters: spray voltage: 3 kV; capillary temperature: 320°C; probe heater temperature: 300°C; sheath gas: 70 AU; auxiliary gas flow: 2 AU; resolution: 240,000 FWHM at m/z 200; AGC target: 5e6; maximum injection time: 300 ms. Each sample was analyzed in negative and positive modes with m/z range 200–800. Fatty acids and most ascarosides were detected as [M-H]TukeyHSD function with an ANOVA model with the formula (phenotype ~strain). p-Values of individual pairwise strain comparisons are reported in each figure legend.All p-values testing the differences of strain phenotypes in the NIL, allele-replacement, and C15ISO experiments were performed in R using the The 293 T cells were sourced from CCLE. Identity authenticated by SNP profiling. Cells were regularly tested for Mycoplasma (~bimonthly). Gene-editing experiments were performed in a single parallel culture experiment using human 293 T cells (ATCC) grown in DMEM with 10% FBS. On day zero, 300,000 cells were seeded per well in a six-well plate format. The following day, two master mixes were prepared: a) LT-1 transfection reagent (Mirus) was diluted 1:10 in Opti-MEM and incubated for 5 min; b) a DNA mix of 500 ng Cas9-sgRNA plasmid –2 with 2Genomic DNA was extracted from cell pellets using the QIAGEN  Midi or Mini Kits based on the size of the cell pellet  according to the manufacturer’s recommendations. DBT1 loci were first amplified with 17 cycles of PCR using a touchdown protocol and the NEBnext 2x master mix (New England Biolabs M0541). The resulting product served as input to a second PCR, using primers that appended a sample-specific barcode and the necessary adaptors for Illumina sequencing. The resulting DNA was pooled, purified with SPRI beads , and sequenced on an Illumina MiSeq with a 300-nucleotide single-end read with an eight nucleotide index read. For each sample, the number of reads exactly matching the wild-type and edited DBT1 sequence were determined .Mass spectroscopy experiments used human 293 T cells (ATCC) grown in DMEM with 10% FBS. On day zero, 150,000 cells were seeded into 15 cm tissue cultures dishes with 15 mL of either 2.5 µM arsenic or no arsenic media. Each condition had five replicates. On day 3, the no arsenic cells were approaching confluence and required passaging. Arsenic conditions were at ~30% confluence and received a media change. On day seven, both conditions were near confluence, media was removed, and plates were rinsed with ice cold PBS, remaining liquid removed. Plates were frozen at −80°C before processing for mass spectrometric analysis. Cells were scraped off the plates with PBS and pelleted in microfuge tubes. Cell pellets were lyophilized 18–24 hr using a VirTis BenchTop 4K Freeze Dryer and extracted in 100% ethanol using the same sonication program as described for nematode extraction. Following sonication, samples were centrifuged at 20,817 RCF in a refrigerated Eppendorf centrifuge 5417R at 4°C. Clarified supernatant was aliquoted to a new tube and concentrated to dryness in an SC250EXP Speedvac Concentrator coupled to an RVT5105 Refrigerated Vapor Trap (Thermo Scientific). The resulting material was suspended in. 1 mL 100% ethanol and analyzed by LC-MS as described. Metabolite measurements can be found in https://elegansvariation.org/data/release/20160408) to calculate Tajima’s D. Tajima’s D was calculated using the tajimas_d function in the cegwas package using default parameters  (We used the VCF corresponding to CeNDR release 20160408 (up = N2) . Isolati In the interests of transparency, eLife includes the editorial decision letter, peer reviews, and accompanying author responses.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed.]C. elegans arsenic toxicity is explained by differences in branched chain amino acid metabolism". The reviews follow below. As you will see, while both reviewers appreciate the scope and complexity of the analysis, neither reviewer is fully satisfied with the revisions and they continue to have major reservations. Given this state, the Reviewing Editor's assessment is that "major issues remain unresolved". If you elect to go forward with publication, this assessment will appear below the Abstract and at the top of the Decision Letter.The editors and two reviewers have re-assessed your work, "Natural variation in Reviewer 1 in particular remains unconvinced regarding the justification for the PCA. Reviewer 2 notes that it has not been demonstrated that the increased cell proliferation in HEK293 cells is due to increased BCAA levels. More details are in the reviews below.Reviewer #1:eLife process. Thus the presence of comments related to editorial approach of the journal, which are irrelevant to the authors. I was trying to be very clear in my concerns and alternative interpretations for the benefit of the editorial process. So while the concerns that I raised are valid (and in many cases remain), my phrasing was more blunt that I would want to normally convey, especially given the scope and complexity of the work. It is my responsibility for not recognizing how the process was proceeding and not further editing my comments as part of the final decision process. This is obviously especially important given the public nature of this new review process.Thanks to the authors for addressing reviewer concerns. I would first like to start by apologizing for some of the phrasing that made it into the first review. My intention was to initiate a conversation with the other editor and other reviewers that would then be synthesized into an overall statement, as is my understanding of the With that preamble, I appreciate that the authors have clarified a great deal of the analysis, particularly the use of line means in the quantitative genetic analysis. The revision remains as the initial submission: a comprehensive analysis of natural variation in arsenic sensitivity. The overall conclusions are still well supported. Nevertheless, I find that the multivariate approach for the mapping analysis is not well justified. This need not be blown out of proportion, but must still be pointed out.1) The authors have now greatly clarified the conversion of the raw observations into the mapping approach. This was a bit opaque before, but it is now clear that this is a line-means analysis, which is perfectly appropriate for mapping and there newly reported results show that there is substantial among-line variation (H2 for their traits). My original point relative to inferences within natural populations remains, however. These line-based differences actually explain very little about the susceptibility of individual worms to arsenic, which is largely due to random effects. The experimental design concerns discussed here mean that the value that I calculated are actually upper bound to the actual broad sense heritability in the population. This may or may not be important for the problem at hand, but it is important for the interpretation of these results from the standpoint of worm populations.2) In the first version of the paper, the authors reported that they did not identify any significant peaks for the single trait analysis so they performed the mapping on the PCA. This is what was meant by "no results turned up for the single-trait analysis". The reviewer did not miss the summaries and supplementary materials. It did not mean that no results were presented, which is something quite different. The current manuscript has been modified to simply gloss over these issues and go directly to the PCA. While the authors "respectfully disagree" with criticisms of the PCA approach, they do not address the issues. The approach is statistically, mathematically, and biologically invalid. There is simply no justification for throwing summary statistics that are functions of one another into a PCA except to try to minimize variance. Showing that an arbitrary and invalid PCA is correlated with a biological measure begs the question as why one would not simply use the measure itself. Yes, it is possible to perform a PCA on a singular covariance matrix, but the outcomes tend to be highly unstable in the face of sampling variance. It is the eigenvectors that are the elements that are subject to interpretation. It is still the case that nothing about this analysis makes sense except that it yields a result that is consistent with the other, more convincing parts of the paper. It still feels that the main goal here was to derive a trait that yielded a significant result. The non-significant pileups of the other traits could have been subjected to joint probability test to achieve a similar goal in a much more straightforward and interpretable fashion.3) The genomic approach and markers used in the GWAS is now much clearer.4) The selection experiments in the human cell lines now contain error bars and significance tests, which is obviously a good thing. These effects are obvious not particularly large and selection among human cells seems like it could be subject to a number of complicating interpretations, but the result is well consistent with the overall results and interpretation.Reviewer #2:C. elegans with more biological replicates and stronger statistical power. However, my comments on BCAA changes in 293 cells have not been addressed. To demonstrate the evolutionary conservation, the authors showed that the cysteine variant is associated with increased cell proliferation in 293 cells treated with arsenic. They suggest that this increased cell proliferation is due to the induction of BCAA levels. However this is not supported by the data using 293 cells.In the revised manuscript, the authors have addressed my concern on BCAA changes in  Reviewer #1:C. elegans to genetic mapping to gene identification to biochemical characterization. Overall, the topic is very well introduced and motivated. In some ways, the scope is almost too large for any single reviewer to assess. For this purpose, I will therefore concentrate on the genetics side of the work and let others assess the biochemistry and health implications (which does feel a bit cloying at times).This paper provides an unusually broad and comprehensive approach of moving from natural variation for arsenic response in The authors use a four-fold strategy to identify the arsenic resistance locus: QTL mapping, NIL construction, GWAS, and genetic transformation via CRISPR. There can be little doubt that they have identified a gene that generates increased arsenic resistance the CB4856 line, as the allele-specific replacements via CRISPR would be the gold standard for this. On this basis, the fundamental results and conclusions of the paper appear to be on a solid foundation, providing an appropriate context for the biochemical characterization that forms the second half of the paper.We thank the reviewer for noticing the comprehensive analyses we provided and are happy to hear that the “fundamental results and conclusions of the paper appear to be on a solid foundation.”The primary concerns that I have with the work relate to the lead in to mapping results, especially around the GWAS results and how they are portrayed. While the paper definitely appears to identify "a gene" for arsenic resistance, we are given very little context for evaluating how important this gene is in the context of genetic variation for arsenic resistance per se. Instead, the mapping results are used to move directly to proposed significance in human health, but we do not really know what the relevance to the worm themselves are in the first place.We thank the reviewer for these observations. We addressed this criticism by quantifying and more thoroughly reporting the effect sizes of the traits that we discussed. Specifically, we included the following:1) Effect-size calculations from the dose-response experiment .2) Heritability estimates from the dose-response experiment .3) Heritability estimates for all traits measured in the RIAIL population 4) Heritability estimates for all traits measured in the wild strain populations 5) Explicit mentions throughout the text of the effect sizes for all experiments following the mapping experiments, which include the NIL, allele-swap, and C15ISO rescue experiments. In all cases we relate our findings in these experiments to the results originally obtained in the linkage mapping experiment because the strains we used for follow-up experiments were N2 and CB4856, which were the parental strains used to generate the RIAIL population.This paper clearly approaches this problem from a quantitative genetic perspective, and yet we are provided very little quantitative genetic context in which to evaluate the mapping approach, especially for the GWAS. Specifically, there is no presentation of a partitioning of within and among line variance, which is necessary to understand what kind of expectations one might have in terms of mapping outcomes, especially with respect to power. In the "transparent reporting" document that accompanies the manuscript, it is stated that "Replicates and power were determined by the throughput required for the particular assay or variance explained." First, it is not even clear what this statement means other than waving off the reporting standard being imposed by eLife. More importantly, however, this simply cannot be the case for the GWAS-or at least it cannot be evaluated in the absence of some sort of analysis of the quantitative genetics of the traits involved.To attempt to address this concern, at a fairly high degree of difficulty, I worked to figure out how the data for this experiment actually worked. While we are at a brave new world of open data, which is a good thing, there could be some debate as to whether the data standard should be to simply make all raw data available or if there should be some effort to curate the data such that an external reader is told what the columns mean , which are critical for evaluation of the results , etc.We sincerely apologize for the perceived failure to report the data formats used throughout the manuscript. To address this issue, we included detailed descriptions of all columns for all Supplementary Files used in the manuscript. This information can be found at the end of the manuscript under the section labelled “Format of Supplementary Files.”I note that the Materials and methods section on "statistical analysis" outlines the most trivial model possible, so I assume that is the approach used throughout the paper. In the absence of this information, I simply tried to use a GLM analysis of the GWAS population samples to estimate (via ML) the within and among line variances across environmental treatments. I found that the among-strain variance accounted for about 1-2% of the total variance observed among individuals, while the condition-by-strain variance explained about 2-10%. Now this is for tens of thousands of observations, so these estimates are for the most part quite significant.We thank the reviewer for taking the time to perform statistical analysis on the GWAS data set. To confirm these results, we performed the same analysis using the raw BIOSORT data and reached the same conclusion as the reviewer. Unfortunately, these data are not what is used in our analyses. In the reviewer’s analysis, one would have to consider each observation  from a population of growing animals in one well of a 96-well plate to be an independent replicate. In that well, the animals have experienced the same preparation procedure, the same bacterial food concentration, and the same environment. For this reason, we use summary statistics for each replicate population of animals grown in the same well and not single animal measurements. Our linkage and GWA mapping experiments only used a single replicate population of animals from each RIAIL or wild isolate, respectively. To make our procedure more clear, we included a “Format of Supplementary Files” section at the end of the manuscript that contains detailed information of each Supplementary File. Additionally, we added more detail to the “Calculation of fitness traits for genetic mappings” section to better describe what traits were used for the mapping and follow-up experiments.However, it is the total among-strain (or strain-by-condition) variance that will determine the power for identifying any particular marker as significant as opposed to the overall heritability. I have little doubt that my quick and dirty look at this is flawed. However, I would expect to see something like this in the paper itself, at least as a supplementary table. Lack of this analysis also does not allow the authors to say anything about effect sizes, which are also notably absent from the paper.dbt-1, and for the fatty acid supplementation experiments to define more of the mechanism for the effect of arsenic on dbt-1 variation.As mentioned above, we have included effect-size calculations  and broad-sense heritability estimates  from the dose response experiment as supplemental data files. We have also reported effect sizes for the chromosome II QTL, for the NILto recapitulate the QTL effect and interval, for the allele-swap to define causality of It is perhaps then not surprising that no results turned up for the single-trait analysis.We are sorry that the reviewer missed our summaries and other supplemental data. Our goal was to keep the analyzed traits  consistent throughout all experiments in the manuscript. To address this comment, we included GWA mapping results for all traits with significant QTL  and included a summary plot of these results .These results show that non-PCA-transformed traits also map to the center of chromosome II and overlap with the QTL identified by linkage mapping. To emphasize this point, it is not just principal component traits that mapped to this QTL; 26 traits from our high-throughput fitness assays mapped to the same position. Our desire to make the traits more understandable led this reviewer down to the unfortunate conclusion that the population-wide differences in arsenic were not significant.The authors therefore turn to a PCA approach, which itself features some fairly unusual aspects in terms of presentation. There appear to be five traits measured on each individual, yet the PCA is based on 67 measurements ("traits"). These are apparently largely summary statistics for individuals from each strain.This assessment is correct. Please refer to our response above for further details regarding our rationale for this approach.The PCA must therefore have been generated via the among-line statistics in the absence of within-line variation, which is fine.We want to reiterate that we have no measure of within-line variation for the GWA mapping dataset. As we describe above, we have made the Materials and methods section more clear in this regard.What seems less fine is that the "traits" used here are actually all derivatives of the original five traits and are mostly descriptions of the within-line distributions. This means that many of the measures are functions of one another . Indeed, I am surprised that the PCA works at all, as the correlation matrix for this set of traits should be singular.e.g. a singular trait correlation matrix results in eigenvalues that approach zero). We therefore used PCA to eliminate the collinearity of the summary statistic traits. As expected from the colinearity of our traits, the PC loadings for the first principal component are very similar to each other. To further supplement this analysis, we included supplementary figures and files of the trait correlations and PC loadings for all experiments.It is true that the traits we used for PCA are linearly codependent, and the determinant of the trait correlation is essentially zero (1.21e-126). However, because PCA is not iterative, in that only one eigen-decomposition step is performed, it does not require independent variables (traits)  is a linear combination of the input traits. In the original manuscript, we showed that PC1 is highly correlated with traits associated with animal length , which is a proxy of developmental progression in our fitness assay. In an attempt to further assign biological meaning to the PC1 trait, we included PC1 vs animal length or brood size correlation plots and loadings for all experiments  in the revised manuscript.Unless motivated quite differently, the PCA should be conducted on just the original five traits.We have included additional justification for the use of using PCA throughout the manuscript. Furthermore, we relate the first principal component trait to phenotypes measured using the high-throughput fitness assay.It is also odd that the interpretation for the PCA results is then motivated by a regression of PCA1 on body length . That is exactly what the PCA loading (eigenvector coefficient) provides in the PCA analysis.eLife readers with a more intuitive representation of the correlation between principal components and animal length or brood size traits. Because not every reader will be as keenly aware of the intricacies of PCA as the reviewer, we think that this visualization is helpful. As stated above, we report the PC loadings for all experiments in the updated manuscript.We agree with the reviewer that principal component loadings describe the same thing as what was shown in Supplemental Figure 4 of the original manuscript. However, our goal was to provide general audience If the PCA is to be conducted, then it should be done so in the context of a full analysis, which would include reporting the factor loadings.We have included all of the requested files  and corresponding figures .It seems that nearly all of these features simply measure how big something is  and that including each of these measures into a single composite parameter allows the GWAS to cross a variance threshold that provides the single significant point. The PCA itself does not completely make sense from a pure statistical point of view, and so it appears that this mapping result is the primary motivation for the approach.We hope that the reviewer understands the approach better after the clarifications above and our edits to the manuscript.A final mapping oddity is that the authors report that they use full DNA strain-specific sequencing results to calculate the relatedness matrix, but only use much older RAD marker data to do the actual mapping.C. elegans GWA mapping experiments. We agree that this approach makes less sense with genome-wide sequence data available. We found  that these markers are sufficient to “tag” all strain haplotypes present in 97 wild isolates made available by Andersen et al., 2012. The 86 strains that we phenotyped in the presence of arsenic trioxide were drawn from this same 97 strain population. We reasoned that the same marker set would be sufficient for GWA mapping. However, we updated our GWA mapping approach to alleviate any possible concerns the reviewer might have about our choice of markers. Here is a brief summary that we detail in the Materials and methods section:We appreciate this criticism. The use of the RAD marker data since its original publication in 2012 has been to keep markers consistent among different https://elegansvariation.org/data/release/latest. We removed variants with minor allele frequency less than 5% and then removed highly correlated markers ((r^2) > 0.8). Finally, we kept sites where all strain genotypes were present. This pruned marker set contains 59,241 markers. We used this marker set to construct the relatedness matrix and perform GWA mapping on the 86 phenotyped strains. We have included the resulting marker set in Figure 2—source data 5.We started with the imputed VCF that can be found at The only motivation that I can see for this is to reduce the number of comparisons so that the significance threshold does not become too high. Whatever the motivation, it is not specified by the authors, nor are potential power issues addressed in the paper itself or in the "transparent reporting" document.We are sorry that the reviewer evaluated our analysis and came to this conclusion. As we described, a large number of traits map to the same QTL on chromosome II above a Bonferroni-corrected significance threshold. The updated manuscript has several changes. To improve our mapping techniques, we included genome-wide markers and then used two different methods for setting significance thresholds. The first is the Bonferroni-corrected (BF) threshold as we already described. The BF threshold for the updated marker set is ~ 8.4e-07, corresponding a -log10(p) of ~ 6. In the second method for multiple-testing correction, we performed spectral decomposition of the genotype correlation matrix from the genome-wide marker data set as performed previously. We set all eigenvalues greater than one to one and then summed the all of the eigenvalues to identify the number of independent tests in the marker set. This analysis revealed that there are 500.761 independent tests in our marker set of 59,241 markers, corresponding to a significance threshold of 1e-04 or a -log10(p) of 4. We have included a detailed explanation of this approach in the Materials and methods section.Taken together, the GWAS approach appears to have the hallmarks of "p-hacking." In other words, the primary goal of the analysis is to ensure that there is at least one red dot on Figure 2, such that it supports the conclusions of the QTL mapping.We are convinced that the main drivers of the reviewer reaching this conclusion are (1) a misunderstanding of the input data structure and (2) marker set choice. We have addressed both of these points above. However, the reviewer’s suspicion motivated us to update our mapping approach, and we are thankful for this input.The result of our updated GWA mapping pipeline resulted in 26 of the 64 traits that we used as input for PCA to map to the center of chromosome II. As for the PC1 trait, the p-value for the peak marker of the chromosome II QTL is ~1.48e-07, which is nearly an order of magnitude greater than the Bonferroni-corrected significance threshold and three orders of magnitude greater than the spectral decomposition threshold described above. If we were to take multiple testing correction to the extreme and set the number of tests to be 64 traits tested * 60,000 markers we end up with ~ 4 million tests (BF threshold: ~1.3e-8) and 33,000 tests (decomposition threshold: ~1.5e-6). While the PC1 QTL peak p-value does not quite pass the BF significance threshold in this extreme case, it does pass the decomposition threshold by an order of magnitude. We note that some of the other traits do pass this extreme threshold  However, as the reviewer pointed out, we do not have 64 independent traits because they are all summary statistics of five traits quantified by the BIOSORT. This observation is supported by our observation that four principal components capture >90% of the total variance in the GWAS phenotype data set. If we were to perform multiple testing correction across these four traits, which we note are still not fully independent, the BF threshold is ~2.1e-07, which is nearly two times higher than the p-value for the chromosome II QTL peak marker obtained from mapping the PC1 trait, making our mappings significant.I am not saying that this is in fact what the authors have done; simply that one gets that impression because of the statistical gymnastics that it seems to take to get here, coupled with a realistic assessment of what is actually possible to map for these traits in these populations.Again, we are sorry that the reviewer reached this conclusion. We hope our explanations and re-analyses are sufficiently convincing.This view is further supported by the singular and assertive nature of the narrative, in which limitations and alternative explanations are not entertained. I fully appreciate that this is the nature of the style of presentation in top tier journals such as eLife, in which a clear narrative coupled to a specific mechanism seems to be a requirement for publication. However, eLife's new data standards seem to be at odds with this cultural tradition. They need to figure out how they actually want to have this work. I do feel for the authors that they may be caught between an editorial expectation for clarity and the views of this specific reviewer. And I may be entirely wrong in this, which makes this specific form of publication cycle more awkward than a traditional feedback and response pattern around concerns such as this.dbt-1 locus contributes to differential arsenic toxicity in the C. elegans population. We have provided all raw and processed data, included a description of data processing and mapping procedures, and expanded our reported traits to emphasize that this QTL is highly significant. The reviewer’s comments have improved the overall clarity of the manuscript and results. We are grateful for the input.Our main goal was to describe the results of our analyses that clearly show variation in the Similar concerns exist in the QTL mapping experiment, although the results appear to be clearer here. By my calculations the among strain variance for the RIAILs tends to be around 1-2% and the strain-by-condition variance tends to be 2-8%.The phenotyping of the RIAILs used for linkage mapping was performed in the same manner as we described above for the wild isolates used for GWA mapping. Specifically, we did not quantify replicates of the RIAIL phenotypes, instead we relied on the replication of RIAIL genotypes being derived from two parental strains. This precludes proper analysis of variance for this particular data set, but we are able to estimate variance components based on genetic relatedness across the genome, as we describe below.Analysis of the genetic variance here is approach to the interpretation of the results, especially from the broader worm natural variation point of view, which I recognize is not a point of emphasis in the majority of the paper. Nevertheless, estimates of QTL effect sizes will help the reader interpret both these results and the interpretation of the population genetic and other functional explanations later in the paper.It is true that our main emphasis was not focused on a more quantitative genetic analysis, including genetic variance in our dataset. However, to address the reviewers concerns thoroughly, we added the following analyses:1) We have provided effect size estimates for the dose response experiment for all measured and principal component traits. We report different measures of effect size including η2, partial η2, ω2, partial ω2, and Cohen’s F. Additionally, we included broad-sense heritability estimates from this experiment. – Supplementary Files 2-3 and Figure 1—figure supplement 3.2) We have included genomic heritability estimates of all BIOSORT and principal component traits used for linkage mapping. We provide two estimates of narrow- and broad-sense heritability using a linear mixed-effect model with the equation y=Xβ+Zu+ϵ. The estimates differ in their formulation for the strain additive relatedness matrix, realized relatedness matrices correct for allele frequencies and the expectation matrix does not. For both estimates, the Hadamard product of the additive relatedness matrix was used to calculate the epistatic relatedness matrix .3) We have provided effect size estimates for the QTL identified by linkage mapping , including discussion of how much of the total additive variance is explained by the identified QTL.4) We have included presentation of the QTL effect size identified by GWAS in the manuscript, as well as discussion of variance partitioning .5) We also present how much of the parental phenotypic difference can be explained by the NILs and genome-edited allele-replacement strains.The gem of the work is the allele-specific transformation that clearly demonstrates that DBT-1 has a significant effect on arsenic resistance. I do not know why these transformations were done only on the parental lines and not the NILs, although this work can be technically demanding. The latter transformations would be just a little more convincing in proving that this is actually the causal locus from the mapping experiments and not simply "a locus" that influences arsenic resistance, since there is plenty of residual difference among the parents in the transformants. However, the totality of the evidence definitely supports this interpretation, and this work is well beyond the standard in the field on this account.We agree with the reviewer that the allele-specific replacements clearly demonstrate that DBT-1 variation has a significant effect on arsenic resistance. We did not find it necessary to introduce the allele-replacements in the NIL genetic background because these genome-edited strains completely recapitulated the NIL effect. However, we realize that, because we did not include more discussion of effect size recapitulation by the NIL and allele-swap strains, it may not be immediately obvious that the DBT-1(C78S) allele completely recapitulated the QTL effect identified via linkage mapping. Specifically, the NILs and allele-swap strains account for ~64-92% of the parental difference in the presence of arsenic trioxide and the chromosome II QTL, which explains ~33% of the phenotypic variation in the RIAIL population, accounts for ~61.7% of the total phenotypic variance explained by genetic factors (H2~ 0.53 for PC1). The discrepancy between the mapping and follow-up experiments is within error of the heritability estimate. We included discussion of these points in the revised manuscript.I do not understand why there are no error bars on the selection experiments reported in Figure 5. Nor are there any statistical tests associated with these results that I can see. I found the raw data for these results impossible to interpret (Data File 23). Some quite important appears to be missing here, making this section impossible to interpret from a rigor and reproducibility point of view, much less in terms of scientific interpretation. I must be missing something here, so I apologize if it should be more obvious to me.We updated this figure to include error bars, and we calculated significance associated with differences in read counts across all replicates using Fisher’s exact test . We also updated the description of the data file corresponding to these read data to make it more easily interpretable for future readers.C. elegans chromosomes (like the center of chromosome 2), so clear signs of global selective sweeps.While I appreciate the desire to posit an adaptive significance on the identified alleles, we know that the central regions of dbt-1 has no signature of selection as indicated by Tajima’s D, and we did not explore this possibility further.It is our understanding that chromosomes I, IV, V, and X have undergone selective sweeps, not chromosome II. Nevertheless, we explicitly state that the genomic region surrounding It seems just, if not more, likely that this allele is simply along for the ride in a linked haplotype and does not in fact have any functional significance in natural populations. It is all well and good to suggest that more work is needed to explore the environmental circumstances that might generate selection, but it is somewhat disingenuous in this species to not discuss the potential for neutral processes in structuring this variation.We have added to the Discussion section to address the possibility of no adaptive advantage associated with this allele.As noted above, in general, caveats and alternative explanations are not a feature of this work, which tells a singular story from a very particular point of view. This does not make that view incorrect, but it does not seem to me to be the most rigorous way to present what is a truly impressive amount of work on a very difficult question.We added alternative explanations throughout the manuscript as described in our responses to the previous comments from this reviewer. We hope that these additions have shown the breadth of our analyses that led to an interesting discovery.Reviewer #2:[…] There are one major and one minor issue that should be addressed before publication.C. elegans, but with increased cell proliferation in 293 cells treated with arsenic. The authors suggest that the increased cell proliferation is due to the induction of BCAA levels. However this is not supported by the data using 293 cells. In C. elegans, the cysteine variant is associated with the induction of BCAA levels with the CB4856 background . With the N2 background, the induction trend is observed but not significant different . But the N2-DBT-1(S78) without arsenic has only two data sets, which might reduce the power of statistical analysis.1) Major: The cysteine variant is associated with increased arsenic sensitivity in p-value = 0.164721, n=3; N2 DBT-1(C78): Tukey HSD p-value = 0.003747, n=6) and C15 ratios . We note that the C17ISO/C17SC ratio is not significantly different when comparing the CB4856 and CB4856 allele swap strain, but the direction of effect matches our other observations. Furthermore, we show that there are DBT-1 allele-specific differences in C15ISO and C17ISO for all strains in control conditions (CB4856-C15ISO DBT-1(C78): Tukey HSD p-value = 0.0036201, n=3; N2-C15ISO DBT-1(C78): Tukey HSD p-value = 0.0265059, n=6; CB4856-C17ISO DBT-1(C78): Tukey HSD p-value = 0.0086572, n=3; N2-C17ISO DBT-1(C78): Tukey HSD p-value = 0.0022501, n=6).Importantly, the DBT-1 allele-specific differences in the fatty acid ratio and ISO measurements were not driven by differences in straight-chain fatty acids.We thank the reviewer for this observation. We thought that, in the context of the CB4856 and CB4856 allele swap result, the conclusion was solid as is. However, we performed the L1 larval stage metabolite profiling experiment at higher replication to address the reviewers’ point. For this experiment, we only included the N2 and N2 allele swap strains because these are the relevant strains to make the conclusion that there is a DBT-1 allele-specific effect on branched-chain fatty acid production. We acquired an additional six independent paired 100 µM arsenic trioxide-control replicates to strengthen our conclusions. The results of this experiment are described in the “Arsenic trioxide inhibits the DBT-1 C78 allele” section of the manuscript and shown in Figure 4B and Figure 4—figure supplements 2-4. The results from this experiment support the conclusion that strains with the DBT-1(C78) have higher branched/straight-chain ratios relative to strains with the DBT-1(S78) for the C17 (CB4856 DBT-1(C78): Tukey HSD In addition to increasing the replication of the L1 assay, we quantified the effects of the DBT-1(C78S) allele in young adult animals to see if the conclusions held true across different developmental stages. For this experiment, we included six independent replicates for the N2, CB4856, and allele-swap strains. In contrast to the results at the L1 developmental stage, we did not observe the same effect of the DBT-1(C78S) allele at the young adult life stage. The results of this experiment are shown in Figure 4—figure supplements 5.Taken together, these results suggest that the DBT-1(C78) allele produces more branched chain fatty acids than the DBT-1(S78) allele, but this effect is dependent on the developmental stage of the animals.2) Minor: To avoid unnecessary confusion, the authors might consider changing sample labeling in Figure 4C to be consistent with other figure panels. N2 DBT-1 (S78) instead of N2 DBT-1 (C78S), CB4856 DBT-1 (C78) instead of CB4856 DBT-1 (S78C).We updated Figure 4C to make the genotypes clearer.[Editors' note: the evaluation of the revised submission follows.]Reviewer 1 in particular remains unconvinced regarding the justification for the PCA. Reviewer 2 notes that it has not been demonstrated that the increased cell proliferation in HEK293 cells is due to increased BCAA levels. More details are in the reviews below.Reviewer #1:[…] With that preamble, I appreciate that the authors have clarified a great deal of the analysis, particularly the use of line means in the quantitative genetic analysis. The revision remains as the initial submission: a comprehensive analysis of natural variation in arsenic sensitivity. The overall conclusions are still well supported. Nevertheless, I find that the multivariate approach for the mapping analysis is not well justified. This need not be blown out of proportion, but must still be pointed out.1) The authors have now greatly clarified the conversion of the raw observations into the mapping approach. This was a bit opaque before, but it is now clear that this is a line-means analysis, which is perfectly appropriate for mapping and there newly reported results show that there is substantial among-line variation (H2 for their traits). My original point relative to inferences within natural populations remains, however. These line-based differences actually explain very little about the susceptibility of individual worms to arsenic, which is largely due to random effects. The experimental design concerns discussed here mean that the value that I calculated are actually upper bound to the actual broad sense heritability in the population. This may or may not be important for the problem at hand, but it is important for the interpretation of these results from the standpoint of worm populations.C. elegans is a selfing hermaphrodite with little diversity at the local level, most populations are nearly clonal. Our approach does not address the influences on individual animals but does address what a nearly genetically identical population would encounter in the wild. We added some details to the Results about how this analysis might not be applied easily to natural populations at the individual level.Thank you for your comment about the revision. We added substantial explanations, additional data, and a data dictionary to explain our approach. It is a line-means analysis. Because 2) In the first version of the paper, the authors reported that they did not identify any significant peaks for the single trait analysis so they performed the mapping on the PCA. This is what was meant by "no results turned up for the single-trait analysis". The reviewer did not miss the summaries and supplementary materials. It did not mean that no results were presented, which is something quite different. The current manuscript has been modified to simply gloss over these issues and go directly to the PCA. While the authors "respectfully disagree" with criticisms of the PCA approach, they do not address the issues. The approach is statistically, mathematically, and biologically invalid. There is simply no justification for throwing summary statistics that are functions of one another into a PCA except to try to minimize variance. Showing that an arbitrary and invalid PCA is correlated with a biological measure begs the question as why one would not simply use the measure itself. Yes, it is possible to perform a PCA on a singular covariance matrix, but the outcomes tend to be highly unstable in the face of sampling variance. It is the eigenvectors that are the elements that are subject to interpretation. It is still the case that nothing about this analysis makes sense except that it yields a result that is consistent with the other, more convincing parts of the paper. It still feels that the main goal here was to derive a trait that yielded a significant result. The non-significant pileups of the other traits could have been subjected to joint probability test to achieve a similar goal in a much more straightforward and interpretable fashion.https://doi.org/10.6084/m9.figshare.7458932.v2). In our revision, we attempted to justify the use of PCA again as a data cleaning method to show that the correlated summary statistics were loaded into the first PC. After the reviewer’s latest comments and discussions with mathematicians, we understand the limitations of using PCA to “clean” up correlated summary statistics as opposed to looking for relationships among seemingly unrelated traits. We would like to point out again that, in our original revision, 26 of 64 statistical summary traits, which relate directly to the biology of C. elegans , map significantly (i.e. different lasers/PMTs or counts on the COPAS BIOSORT), as inputs for PCA. As mentioned above, these four traits are correlated, so it is clear that PC1 is capturing overall arsenic-induced toxicity, and this PC mapped to the center of chromosome II. We use this PC trait across all figures to keep consistency across all analyses. It is important to note that this PC is also highly correlated with biologically meaningful traits like population animal length and optical density. The overlap of association and linkage mapping experiments led us to the interval tested using NILs and containing the allele-replacement validated gene dbt-1. Of the independent traits that we used as inputs for the PCA, only the brood size trait alone did not map to the center of chromosome II. We also include the analysis and results of these four independently quantified traits for all follow-up experiments. We would like to emphasize that this new analysis does not change any of the conclusions of the manuscript, but we hope these final changes address the reviewer’s remaining concerns regarding the PCA approach.We are sorry about this issue. As the reviewer points out, the PCA approach was used in the first version of the paper to reduce the noise from the 67 individual trait measures. As we stated before, the approach was not used to yield a result “consistent with the other, more convincing parts of the paper”. The original genome-wide association mappings in the first version of the manuscript used the RAD-sequencing marker set and a Bonferroni-corrected p-value threshold on only two traits . For these two traits, we did not map a QTL to the center of chromosome II, which we hypothesized was caused by noisy traits measured from single replicates of a small number of wild strains. However, other traits did map to this chromosome II region as we presented in the resubmission. For this reason, we used the PCA approach to “clean” up the correlated traits and performed the mappings to find a QTL on chromosome II. It was not performed after the linkage mappings, NIL experiments, and allele-replacement tests. As we described previously, the overlap of these two mapping approaches is what inspired us to perform the follow-up experiments. In the revision (and thanks to the reviewer’s comments), we updated our mapping algorithm, the marker data set, added a new significance threshold, and included the mapping results of all of the traits used as inputs to PCA. These changes enabled significant GWA mappings (Bonferroni correction) for 26 of 64 of the summary BIOSORT-measured traits and the major PC to the same position on the center of chromosome II . All traficantly  to the s3) The genomic approach and markers used in the GWAS is now much clearer.dbt-1. These results show that natural variation in arsenic responses across C. elegans wild strains maps by linkage mapping to the center of chromosome II  The selection experiments in the human cell lines now contain error bars and significance tests, which is obviously a good thing. These effects are obvious not particularly large and selection among human cells seems like it could be subject to a number of complicating interpretations, but the result is well consistent with the overall results and interpretation.Thank you for these comments.Reviewer #2:C. elegans with more biological replicates and stronger statistical power. However, my comments on BCAA changes in 293 cells have not been addressed. To demonstrate the evolutionary conservation, the authors showed that the cysteine variant is associated with increased cell proliferation in 293 cells treated with arsenic. They suggest that this increased cell proliferation is due to the induction of BCAA levels. However this is not supported by the data using 293 cells.In the revised manuscript, the authors have addressed my concern on BCAA changes in This error was ours and should have been avoided. During the editing process between different software packages, the offending sentence was not removed in the submitted revision. We have removed this sentence and also tightened the arguments in human cells . We hope that these changes have alleviated this concern.