N = 289,038) principally in East Asians and meta-analysis in East Asians and Europeans. We report 19 new genetic loci and ancestry-specific BP variants, conforming to a common ancestry-specific variant association model. At 10 unique loci, distinct non-rare ancestry-specific variants colocalize within the same linkage disequilibrium block despite the significantly discordant effects for the proxy shared variants between the ethnic groups. The genome-wide transethnic correlation of causal-variant effect-sizes is 0.898 and 0.851 for systolic and diastolic BP, respectively. Some of the ancestry-specific association signals are also influenced by a selective sweep. Our results provide new evidence for the role of common ancestry-specific variants and natural selection in ethnic differences in complex traits such as BP.Blood pressure (BP) is a major risk factor for cardiovascular disease and more than 200 genetic loci associated with BP are known. Here, we perform a multi-stage genome-wide association study for BP (max  Blood pressure (BP) is a major risk factor for cardiovascular disease and more than 200 genetic loci associated with BP are known. Here, the authors perform discovery GWAS for BP in East Asians and meta-analysis in East Asians and Europeans and report ancestry-specific BP SNPs and selection signals. An individual’s risk for high blood pressure is determined by genetic, environmental and demographic factors and their interaction. Genome-wide association studies (GWASs) and/or large-scale analyses by gene-centric (or exome) variation arrays have identified over 200 genetic loci influencing blood pressure in predominantly European-descent populations (henceforth referred to as Europeans)8. The prevalence of high blood pressure is increased in people of East Asian ancestry, contributing to their increased risk of stroke9. The reasons for such ethnic differences remain to be clarified from the viewpoint of genetic susceptibility as well as lifestyle. Although the recent progression of GWAS in East Asians allows us to make a preliminary comparison of association signals between the populations11, the sample sizes of GWAS in East Asians have been generally much smaller than those in Europeans and under-powered for the comprehensive interethnic comparison at a genome-wide scale. Therefore, large-scale genome-wide association data in both ethnic groups are required for systematic, genome-wide interethnic comparison.High blood pressure is a major risk factor for cardiovascular disorders such as coronary heart disease and stroke. Approximately 10 million deaths each year can be attributed to high blood pressure globallyN = 289,038), to seek both transethnic and ancestry-specific genetic effects for five blood pressure phenotypes: systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), mean arterial pressure (MAP), and hypertension. We then seek interethnic genetic heterogeneity of GWAS results between East Asians and Europeans, followed by examination of natural selection as a potential mechanism underlying the ethnic differences in genetic susceptibility for blood pressure as well as other complex traits. We report ancestry-specific blood pressure variants and selection signals in this study.Here, we perform a multi-stage GWAS with a discovery sample of 130,777 East Asian individuals and follow-up meta-analyses involving East Asians and Europeans  and stage 2 (follow-up), and a replication study , indicating no residual confounding biases such as population stratification . Genomic control λGC was used as a correction factor in the other studies. Quantile−quantile plots for each of the five blood pressure traits are presented in Supplementary Fig. P value < 1.6×10–5 against any blood pressure phenotype in East Asians. This set of 13,003 SNPs  from the International Consortium on Blood Pressure (ICBP) GWAS 3 and the International Genomics of Blood Pressure (iGEN-BP) Consortium 10; there was no overlap in samples between the two data sets. Sentinel SNPs  that (i) reached P < 5×10–8 in combined meta-analysis of stages 1 and 2 and (ii) showed evidence of support (P < 0.05) in the stage 2 meta-analysis alone are reported as novel loci in this study. We identified 19 previously unreported loci; 15 loci in East Asian-specific analyses and 4 additional loci in the transethnic meta-analysis 14 plus East Asians from the China Kadoorie Biobank 15, we examined associations at our list of 19 sentinel SNPs. With the exception of four SNPs, 15 sentinel SNPs showed significant (P < 0.00263 = 0.05/19) blood pressure association with the concordant direction of allelic effects   were coding or associated with gene expression and other traits. At three loci, the sentinel SNPs were nonsynonymous, and 4 of 19 novel loci contained SNPs (in LD of r2 > 0.80 with the top eVariant) associated with expression quantitative trait loci (eQTLs) in at least one tissue in the Genotype-Tissue Expression (GTEx) database (Supplementary Tables MTX1 and rs3825942 at LOXL1) were nonsynonymous and associated with eQTLs. Furthermore, seven sentinel SNPs and/or their proxy SNPs (r2 ≥ 0.95) were previously reported to be significantly associated with non-blood pressure traits  have been reported to associate with body mass index and type 2 diabetes16. In our study, rs11642015 was significantly associated with SBP, MAP, and PP (P = 1.9×10–12–1.3×10–9) with consistent reproducibility in both stages of East Asian analyses  and European  GWAS results in the subsequent analysis of interethnic heterogeneity. We defined interethnic heterogeneity as heterogeneity of genetic  impact on SBP between the ethnic groups. Using GWAS data sets, we compared the genetic impact at transethnic SNPs and detected a total of eight interethnic heterogeneity loci—two significant (P < 5×10–8) and six suggestive (5×10–8 ≤ P < 1×10–6) loci . Seven of the eight loci with interethnic heterogeneity were annotated to the previously reported blood pressure loci; sentinel blood pressure SNPs at half of them  were found to be in LD (r2 ≥ 0.2) with the SNPs showing some evidence for interethnic heterogeneity. The two loci with significant interethnic heterogeneity were on 12q24 and 10q21 and both contained multiple association signals  and East Asians (near rs671 at ALDH2), had been identified19. We found that both of the signals were responsible for the discordant direction of allelic effects on 12q24  arose from a haplotype shared between ethnic groups, containing multiple transethnic SNPs. The discordant direction of effects for the shared haplotypes could be explained by alternation of effects attributable to the derived alleles of rs4590817 (decreasing in Europeans) and rs145193831 (increasing in East Asians)  blood pressure loci , we examined interethnic comparability of genetic impact on blood pressure at 48 loci   heterogeneity between two ethnic groups. At 8 of 11 loci, we found that distinct common ancestry-specific variants colocalized within the same LD block and that the direction of effects for the proxy shared SNPs was discordant between the ethnic groups, similar to 12q24 and 10q21. On 5q14, for instance, a genome-wide significant association of rs112862634 with SBP, DBP, and MAP was detected in East Asians of this study (Supplementary Data 8. It turned out that rs112862634 was in strong LD (East Asian LD r2 = 0.95) with a haplo-SNP (rs6882046) at this locus and distinct common ancestry-specific variants with mutually inverted genetic effects—European-specific rs10059921 (MAF = 0.09 in EUR) and East Asian-specific rs78245349 (MAF = 0.46 in EAS)—did colocalize in this region , for which the most significant interethnic heterogeneity of genetic impact was detected , a haplo-SNP could not be selected , nine sentinel SNPs (3.7%) showed significant (Phetero < 2.1×10–4) interethnic heterogeneity . On 10q21 near C10orf107, a DBP association signal was previously reported at rs153044021, which we found to be in LD (European LD r2 = 0.48) with an ancestry-specific SNP at the locus, rs4590817, aforementioned  with an ancestry-specific SNP at the locus, rs12258967. These indicated that interethnic heterogeneities identified for non-rare transethnic variants on 10q21 and 10p12 were the cases for which common ancestry-specific variants were actually responsible.In addition to the ancestry-specific loci, we investigated interethnic heterogeneity at non-rare (MAF ≥ 0.01 in both ethnic groups) blood pressure loci that might be shared between the ethnic groups; 382 tested loci were either previously reported or newly identified in the present study . The estimated proportion was 2.5% each in group 1 and group 2a, where the C10orf107 and CACNB2 loci were counted in group 2a  loci, we estimated the proportion of loci showing significant interethnic heterogeneity within the total blood pressure loci tested (18. We first estimated the SNP-based heritability (h2) of SBP and DBP  for East Asians and 0.086 (SE 0.009) for Europeans and lower than a previously reported UK Biobank estimate of 0.156 (SE 0.004)22 calculated by the moment-matching method in Europeans. This discrepancy was likely due to the methodological differences in SNP-based heritability analyses between the studies but does not appear to affect genetic-correlation estimates themselves23. Also, the h2 of DBP was almost comparable between the ethnic groups in this study. Then, we found that the genetic correlations in SBP and DBP were 0.898 (SE 0.040) and 0.851 (SE 0.046) respectively, and significantly different from 1 (P = 0.005 for SBP and P = 0.0007 for DBP). This indicated that the allele-substitution effect-sizes differed significantly between the two ethnic groups despite the reportedly substantial genetic overlap in blood pressure traits  based on heritability parameters (see details in Supplementary Methods) via modeling, computing and random sampling Fig. , 13. SimWe extended the interethnic analyses to other complex traits such as plasma lipid level, anthropometric measurement, and type 2 diabetes using published GWAS summary statistics of relatively large number of samples  but no significant association signal was detectable in another  due to low allele frequency (MAF < 0.05)  Fig. . Thus, t24  increase in the incidence of recent selection signals at the ancestry-specific loci, given that a total of 405 distinct genomic regions were identified to show evidence of positive selection across 14 populations worldwide24.Among a list of ancestry-specific loci for multiple traits, we identified evidence of a positive selection at five unique loci using a highly sensitive algorithm, haploPS24 Fig. . For bloans Fig. . In thisans Fig. . There wN = 289,038)  plus East Asians   appear to be specific to East Asians; i.e., in Europeans the corresponding SNPs were monomorphic and no significant association signals were detectable in the vicinity  that are assumed to be restricted to a limited number of loci. Apart from these models, we have discovered a new model in which genetic effects for transethnic SNPs that form a shared haplotype at a locus are driven by causal variants that are ancestry-specific but are not rare, which can be called a common ancestry-specific variant association model. We previously reported on 12q24 the East Asian-specific association signal at ALDH2 with blood pressure, which was located near the association signal at SH2B3 identified in Europeans19. We also reported that these two association signals were phylogenetically independent, although a distance between the sentinel SNPs (rs671 and rs3184504) was relatively close (357 kb apart). Moreover, in the present study we have detected a number of transethnic SNPs in the 12q24 region to show highly significant heterogeneity of genetic impact on SBP between the ethnic groups , where inverted genetic effects are attributable to each of the ancestry-specific sentinel SNPs; a similar situation was also observed on 10q21 to reproduce this phenomenon , we have found significant interethnic heterogeneity of genetic impact at a number of blood pressure loci that are non-rare in both ancestries, with most of them originally reported in Europeans to date. It is assumed that the potential presence of modifier genes and/or gene−environment interactions can contribute to such interethnic heterogeneity but the overall influences and underlying mechanisms remain to be investigated. When combined with ancestry-specific variant associations  participating in GWAS meta-analysis is provided in the Supplementary Methods. Descriptive statistics of the individuals, genotyping arrays, quality control filters, and genotype imputation applied to the individual studies are provided in Supplementary Tables For individuals taking antihypertensive therapies, blood pressure was imputed by adding 15 mmHg and 10 mmHg to SBP and DBP values, respectively. MAP and PP were calculated as MAP = (2 DBP + SBP)/3 and PP = SBP – DBP. In each study, the association of blood pressure  with SNP allele dose was tested using linear regression adjusted for age, sex, and any study-specific covariates. Hypertensive cases were defined as follows: (i) SBP ≥ 160 mmHg and/or DBP ≥ 100 mmHg and/or on antihypertensive treatment and (ii) age of onset ≤65 years. Normotensive controls were defined as follows: (i) SBP < 130 mmHg and DBP < 85 mmHg and not on antihypertensive treatment and (ii) age ≥50 years. In each study, the association of a dichotomous trait of hypertension status with SNP allele dose was tested using logistic regression adjusted for sex and any study-specific covariates. The effect-sizes and standard errors estimated in linear and logistic regressions were used in subsequent meta-analysis.R2 < 0.5 or MAF < 0.01. If a SNP from a study did not fit the quality standards, we regarded it as missing from that study for the purpose of meta-analysis. Results for an SNP that failed to pass the quality control filters in a given study were pooled among the other contributing studies. To detect studies with inflated GWAS significance, which can be caused by confounding biases such as population stratification, we computed the genomic control lambda (λGC)29 and the intercept of LD Score regression13. A study showing a score of >1.1 for both measures was regarded as inflated. Since the LD Score regression intercept was shown to be a more powerful and accurate correction factor estimate than genomic control for GWAS with large sample size13, we used the LD Score regression intercept as a correction factor for GWAS with a sample size of >3000 (BBJ in this study). Otherwise, λGC was used as a correction factor.Before meta-analysis, quality control was applied to each study. SNPs were excluded if they had study-specific call rate < 0.95, imputation quality 30 using the fixed-effects inverse-variance-weighted method. Heterogeneity of effect-sizes was tested using Cochran’s Q statistic. For the stage-1 of GWAS, there were 6.2 million SNPs with heterogeneity P > 10–6 and the sample size being at least half of the total. The Q−Q plots and Manhattan plots are shown in Supplementary Figs. Genome-wide association and replication studies were carried out in the multistage approach. The discovery stage stage 1) of GWAS was carried out in a total of 130,777 East Asian individuals from five studies . Second, we sought further replication from two European GWAS data sets: the International Consortium of Blood Pressure (ICBP) 3 and the International Genomics of Blood Pressure (iGEN-BP) Consortium 10. This gave a total of N = 158,261 independent follow-up samples for the GWAS analysis. Combined meta-analyses of stages 1+2 data were carried out for East Asians alone  as well as across the two ancestral population groups . We used P < 5×10–8 to denote genome-wide significance in the combined (stages 1+2) meta-analyses. Additionally, the sentinel SNPs with P < 5×10–8 were subjected to lookups in European plus East Asian samples, including large-scale data sets for blood pressure (SBP and DBP) GWAS of the UK Biobank 14, which are publicly available via 10.1038/s41588-018-0144-6, and the China Kadoorie Biobank 15.In the follow-up stage (stage 2) of GWAS, we considered for follow-up any SNPs with P < 5×10–8) in the combined meta-analysis (stages 1+2) for any of the five blood pressure traits; (ii) the sentinel SNP showed evidence of support (P < 0.05) in the GWAS stage-2 alone for association with the most significantly associated blood pressure trait from the combined meta-analysis; (iii) the sentinel SNP showed further evidence of support (P < 0.00263 = 0.05/19) in association results for either SBP or DBP of lookup variants (n = 19 in this study); and (iv) the sentinel SNP had concordant directions of effect across the discovery and replication stages.In the present study, an association signal was declared to be validated if it satisfied all four of the following criteria: (i) the sentinel SNP was genome-wide significant  from previously reported blood pressure SNPs at the time of analysis.We reported novel loci in a unified way across the blood pressure traits. For each trait, we listed SNPs reaching genome-wide significance and filtered them by regarding two SNPs at most 500 kb apart to belong to the same locus. A blood pressure locus was defined as a chromosomal region, where a group of significant SNPs are localized ≤500 kb to the adjacent ones. For each locus, the SNP with the lowest 31. We evaluated top genetic variants (eVariants) in LD (r2 > 0.8) with the sentinel SNPs for evidence of mediation of eQTLs in 14 tissues using the GTEx database, to identify loci that are highly expressed and to highlight specific tissue types that show eQTLs for a large proportion of the loci. Other annotations were applied to all SNPs in LD (r2 > 0.8 in East Asians) with the sentinel SNPs. We used the SNPnexus32 to provide an aggregate set of functional annotations for the SNPs, including gene location, conservation, amino acid substitution impact based on prediction tools, SIFT and PolyPhen. Previously reported association signals with other traits were looked up in the GWAS Catalogue (https://www.ebi.ac.uk/gwas/). We thus identified a list of candidate genes at the 19 novel loci, to which ≥1 line(s) of evidence  could indicate a biological link of the blood pressure SNPs.To prioritize associated SNPs at the novel loci, we took a series of bioinformatics approaches in order to collate functional annotation  in different populations, we can consider both the correlation of allele effect-sizes and the correlation of allelic impact. The latter is defined as  × sqrt, where σ2 = 2 × MAF × (1 − MAF). The genetic impact at non-rare (MAF ≥ 0.01) SNPs were compared between two populations of different ancestries using GWAS data sets available in this study: East Asian samples  and European samples . Heterogeneity of genetic impact was tested using Cochran’s Q statistic. We used genome-wide significance P < 5×10–8 to denote significant SNPs in evaluating the interethnic heterogeneity of genetic impact on SBP.To examine whether genetic variants have the same phenotypic effects in different populations, we used the method for estimating the transethnic genetic correlationP value for interethnic heterogeneity of genetic impact on a lead blood pressure trait within ±500 kb  of and r2 ≥ 0.1 to the sentinel SNP, and (iii) select an alternate ancestry-specific SNP showing the largest genetic impact on blood pressure  in the second ethnic group within ±500 kb of and r2 ≥ 0.1 to the haplo-SNP. A distance of ±500 kb and r2 ≥ 0.1 were set by assuming the limited recombination and LD at the locus. Interethnic differences at the haplo-SNP were considered to be significant at P < 1.5×10–4 ≃ 5×10–8 × [3 Gb/1 Mb]  SNPs that were reported to reach genome-wide significance in either of the ethnic groups, we explored transethnic SNPs forming a haplotype shared between ethnic groups (denoted as haplo-SNPs) and alternate ancestry-specific SNPs in the following three steps: (i) select a sentinel SNP that was associated with blood pressure in the index ethnic group and monomorphic or MAF < 0.01 in the second ethnic group (corresponding to a group-2a SNP described below), (ii) select a haplo-SNP showing the smallest P > 0.05/the number of SNPs located ≤500 kb from the sentinel SNP), considering the possible interethnic differences in genetic architecture or LD structure.As an approach to investigating interethnic comparability of GWAS results for complex traits, we created a list of ancestry-specific loci by using the published GWAS summary statistics  did not show significant association with blood pressure. At C10orf107 and CACNB2, rs4590817 and rs12258967 (group 2a) were examined in addition to rs1530440 and rs1813353 (group 1) respectively, since the former variants were considered to be responsible for the latter association signals. Interethnic heterogeneity of genetic impact was tested using Cochran’s Q statistic, where we used Phetero < 0.05/242 = 2.1×10–4 to denote significant SNPs.We also investigated interethnic heterogeneity of genetic impact on a lead blood pressure trait at non-rare (MAF ≥ 0.01 in both ethnic groups) blood pressure loci previously reported and newly identified (Supplementary Data 18 (https://github.com/brielin/popcorn). Genetic correlation measures the concordance of allele-substitution effects of causal SNPs between two populations. Popcorn is shown to use the entire spectrum of GWAS associations without raw genotype data, while accounting for LD with the use of external reference panels  to avoid filtering correlated SNPs. Popcorn creates unbiased approximations of the genetic correlation and the population-specific heritability. We employed our method modified from Popcorn to estimate SNP-based heritability in two populations of different ancestries and to quantify transethnic genetic correlation using only summary statistics. It used to be assumed that per-SNP heritability should be equally distributed for all SNPs in a chromosomal region, but it has recently become apparent that per-SNP heritability can depend on allele frequency33, and LD-related34 or other functional annotations35. Hence we modified Popcorn by incorporating the dependence of per-SNP heritability on allele frequency and LD-related functional annotations (see details in Supplementary Methods).We modified the method for estimating the transethnic genetic correlation that was implemented in the Popcorn program37, type 2 diabetes39, and anthropometric measurement (BMI41 and height43), for which summary statistics of relatively large  GWAS meta-analysis are available for both East Asians and Europeans at the time of analysis.We estimated SNP-based heritability of complex disease and phenotype traits including blood pressure (SBP and DBP), plasma lipid level Z-statistics divided by the square root of sample size in GWAS. We modeled the effect-size distribution based on the observed heritability parameters. By iterative computing and random sampling, we could obtain one possible instance of true observable effect-size for the significant SNPs under the assumed heritability parameters. For this true effect-size, we computed the expected number of genome-wide significant SNPs (or loci) showing equal to or larger than the given value in a GWAS of a given sample size  based on heritability parameters (see details in Supplementary Methods). Briefly, we first computed the distribution of standardized effect-sizes of SNPs, which are the correlation between the SNP genotype and the phenotype and observable as the 24. HaploPS is a highly sensitive algorithm to locate genomic signatures of positive selection and to allow for the detection of the founder haplotype form that carries the selected allele. HaploPS had successfully identified 405 distinct genomic regions exhibiting evidence of positive selection across 14 populations worldwide. We compared this list of 405 regions with 63 ancestry-specific loci (or the respective sentinel SNPs) identified for complex traits in search of their colocalization.We tested the hypothesis that natural selection could play a role in ancestry-specific association signals of complex traits, by using the findings in the previous HaploPS analysishttps://github.com/fumi-github/Popcorn-t).The source code for SNP-based heritability analysis is publicly available (Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9


Quantitative genetic studies suggest the existence of variation at the genome level that affects the ability of cattle to resist to parasitic diseases. The objective of the current study was to identify regions of the bovine genome that are associated with resistance to endo-parasites.Fasciola hepatica-damaged liver from 18 abattoirs. Deregressed estimated breeding values (EBV) for F. hepatica-damaged liver were generated for genotyped animals with a record for F. hepatica-damaged liver and for genotyped sires with a least one progeny record for F. hepatica-damaged liver; 3702 animals were available. In addition, individual cow records for antibody response to F. hepatica on 6388 genotyped dairy cows, antibody response to Ostertagia ostertagi on 8334 genotyped dairy cows and antibody response to Neospora caninum on 4597 genotyped dairy cows were adjusted for non-genetic effects. Genotypes were imputed to whole-sequence; after edits, 14,190,141 single nucleotide polymorphisms (SNPs) and 16,603,644 SNPs were available for cattle with deregressed EBV for F. hepatica-damaged liver and cows with an antibody response to a parasitic disease, respectively. Association analyses were undertaken using linear regression on one SNP at a time, in which a genomic relationship matrix accounted for the relationships between animals.Individual cattle records were available for F. hepatica-damaged liver were located on Bos taurus autosomes (BTA) 1, 8, 11, 16, 17 and 18; each region included at least one SNP with a p value lower than 10−6. Five SNPs were identified as significant  for antibody response to N. caninum and were located on BTA21 or 25. For antibody response to F. hepatica and O. ostertagi, six and nine quantitative trait loci (QTL) regions that included at least one SNP with a p value lower than 10−6 were identified, respectively. Gene set enrichment analysis revealed a significant association between functional annotations related to the olfactory system and QTL that were suggestively associated with endo-parasite phenotypes.Genomic regions for N. caninum.A number of novel genomic regions were suggestively associated with endo-parasite phenotypes across the bovine genome and two genomic regions on BTA21 and 25 were associated with antibody response to The online version of this article (10.1186/s12711-019-0457-7) contains supplementary material, which is available to authorized users. Fasciola hepatica. In addition, parasitic diseases are associated with significant economic losses in both dairy and beef cattle [F. hepatica in cattle and proposed that selection and breeding animals resistant to F. hepatica would complement current control strategies for F. hepatica  for F. hepatica in the top 10% versus the bottom 10%; the mean EBV reliability of those animals was 0.18. It is hypothesised that the inclusion of genomic information within these evaluations will improve the reliability of the EBV for F. hepatica and thus improve the prediction of whether or not an animal will have F. hepatica-damaged liver.Parasitic diseases are highly prevalent worldwide in cattle production systems  and thisf cattle –7. Detaif cattle . Using pf cattle  documentatments; ). Genetistertagi , 11 and  caninum , 12. Sin caninum , the acc caninum  was limiBos taurus autosomes (BTA) 9 and 19 harboured quantitative trait loci (QTL) associated with nematode burden in 768 Dutch dairy cows that were selected to be in the top 10% and bottom 10% of their respective sire family (n = 12) for faecal egg count (FEC). Using 305 Aberdeen Angus calves genotyped for 190 microsatellite markers only, Kim et al. [Few studies have attempted to locate genomic regions associated with endo-parasitic diseases in cattle. Using a genotyping panel of only 153 microsatellite markers, Coppieters et al.  documentm et al.  reportedThe objective of our study was to identify single nucleotide polymorphisms (SNPs) associated with three endo-parasitic diseases in cattle using imputed whole-genome sequence data in a large multi-breed population. Results from this study will improve our understanding of the underlying genetic mechanisms of resistance in cattle to parasitic diseases. This could contribute to enhance the development of further control strategies for parasitic diseases, such as vaccine development, and to improve diagnostics of parasitic diseases. In addition, our results provide information that would be useful in future studies that attempt to increase the accuracy of genetic evaluations, through the exploration of targeted genomic regions.F. hepatica-damaged liver, and (2) serological testing of individual cows in 68 dairy herds for endo-parasites.The phenotypic data analysed in this study originated from two sources: (1) national abattoir cattle data containing records for F. hepatica-damaged liver were available from dairy and beef cattle that were slaughtered in 18 Irish abattoirs between February 2012 and February 2018. Cattle livers were diagnosed as either “liver exhibits F. hepatica damage and live F. hepatica observed in the liver” or “liver exhibits F. hepatica damage without the identifiable presence of live F. hepatica”. Animals were considered to have no F. hepatica-damaged liver if none of the above diagnostic elements were detected and were slaughtered in the same abattoir and at the same date than an animal diagnosed with a F. hepatica-damaged liver. Twomey et al. [F. hepatica-damaged liver phenotypes, and also the procedures and criteria imposed to maximise the likelihood that only animals exposed to the parasite were included in the analysis based on F. hepatica-damaged liver information available from their herd-mates. In summary, only animals with no herd movement after 90 days of age were retained. Animals were considered as exposed if they had resided in a herd 100 days prior to the slaughter of a herd-mate with live F. hepatica present in the liver. If the slaughtered herd-mate had a F. hepatica-damaged liver with no observable live F. hepatica, an additional criterion was imposed, i.e. as well as having resided in the herd 100 days prior to the slaughter of the slaughtered herd-mate, animals also had to be born within 100 days of the birth of the slaughtered herd-mate, to be considered as exposed.Individual animal records for F. hepatica was dichotomized; animals were considered either infected  or not infected with F. hepatica . The final dataset consisted of 187,584 animals with a F. hepatica-damaged liver record, either positive or negative.For the purposes of our study, liver damage caused by F. hepatica-damaged liver were estimated with an animal linear mixed model in the MIX99 genetic evaluation software suite [F. hepatica-damaged liver [F. hepatica-damaged liver  or were sires that had at least one progeny record for F. hepatica-damaged liver (n = 5359). The effective record contribution (ERC) for each animal was estimated and used as the weighting factor for the deregression of EBV. Deregression was undertaken using the Secant method in the MiX99 software [Breeding values and associated reliabilities for the binary trait of re suite . The moded liver . Fixed eF. hepatica using the Svanovir F. hepatica-Ab ELISA kit, to O. ostertagi using the Svanovir O. ostertagi-Ab ELISA kit, and to N. caninum using the Svanovir Neospora-Ab ELISA kit . ELISA tests for all blood samples were carried out by the same commercial laboratory . Antibody responses to F. hepatica and O. ostertagi were expressed as optical density ratio (ODR), and antibody response to N. caninum was expressed as percent positivity (PP). Data edits were as described by Twomey et al. [F. hepatica was transformed using the natural logarithm to approximate a normally distributed variable, whereas a reciprocal transformation was used to normalize the positively skewed PP for N. caninum. Records were removed if, at blood sampling, the cow resided in a herd different to that in which it had been present at 90 days of age. Only animals with a record for antibody response to F. hepatica were retained if they resided in a herd that had at least five cows positive for antibody response to F. hepatica  and a within-herd prevalence higher than 5% with a positive antibody response to F. hepatica on the day of the blood test. Since the majority of grazing cattle are exposed to O. ostertagi [O. ostertagi. For antibody response to N. caninum, we retained only the records as positive for cows that resided in a herd with a within-herd cow prevalence of more than 1%  for antibody response to N. caninum. In addition, the likelihood that progeny from a cow infected with N. caninum will also be infected with N. caninum [N. caninum (86 cows were discarded). The final dataset consisted of 6892 records for antibody response to F. hepatica, 9260 records for antibody response to O. ostertagi and 5289 records for antibody response to N. caninum.Blood samples were collected in autumn 2016 from 10,879 dairy cows as part of a cross-sectional seroprevalence study of 68 Irish dairy herds. All blood samples were tested separately for the presence of antibodies to n losses  and, thustertagi , 20 and  caninum  is high,F. hepatica, O. ostertagi and N. caninum was adjusted for non-genetic effects. Genetic and non-genetic effects for antibody response to each of the three parasites were estimated using the MIX99 software suite [F. hepatica, 0.003 for antibody response to O. ostertagi and 0.001 for antibody response to N. caninum, while the residual variance was 0.734 for F. hepatica, 0.034 for O. ostertagi and 0.010 for N. caninum, which resulted in heritability values of 0.12, 0.08 and 0.09, respectively. For each cow, phenotypes for the three parasites were adjusted for fixed effects (estimated with the linear mixed model) from the cow’s respective phenotype. Only animals that had both an adjusted phenotype and an available genotype were retained for further analysis; 6388 animals remained for F. hepatica, 8334 animals remained for O. ostertagi and 4597 animals remained for N. caninum.Each observed animal phenotype for the continuous traits for antibody response to re suite . The stare suite . Fixed ey et al.  for the F. hepatica-damaged liver, 87% of the animals were genotyped using either the Illumina bovine High-Density BeadChip  or the International Dairy and Beef version 1 , version 2  or version 3  genotyping panels. In the dataset of adjusted phenotypes for antibody response to the three different phenotypes, most of the animals were genotyped on the IDBv1 , IDBv2  or IDBv3  genotyping panel. All animals had a call rate higher than 90% and only autosomal SNPs, SNPs with a reported chromosomal position, and SNPs with a call rate higher than 90% were retained for each panel.Genotypes were available from six different genotyping panels, and the number of animals genotyped per panel is in Additional file Bos taurus animals from Run6.0 of the 1000 Bulls Genomes Project. All variants in the reference population were called using SAMtools, and genotype calls were improved using the Beagle software to provide a consensus SNP density across all animals. Details of the alignment to the UMD 3.1 Bos taurus assembly, variant calling and quality controls that were completed within the multi-breed sequence reference population are as described by Daetwyler et al. [All animals were imputed to HD using a two-step approach with the FImpute2 software ; first, r et al. . In totar et al. ; versionr et al. .Regions with poor WGS imputation accuracy, perhaps due to local mis-assemblies or mis-orientated contigs, were identified by using a larger dataset of 147,309 verified parent-progeny relationships. Mendelian errors, defined as the proportion of opposing homozygotes in a parent-progeny pair, were estimated for each relationship and the subsequent Mendelian error rate per SNP was calculated. To accurately identify genomic regions with poor imputation, the R package GenWin  which fi−6). After these edits, 14,190,141 SNPs remained for animals with a deregressed EBV for F. hepatica-damaged liver, whereas 16,603,644 SNPs remained for cows with an antibody response to a parasitic disease.Further quality control edits were also imposed on the imputed sequence genotypes, including the removal of SNPs with a minor allele frequency (MAF) lower than 0.002 and of SNPs that deviated from Hardy–Weinberg equation  on the UMD 3.1 genome build was carried out by focusing on the defined QTL regions. In addition, protein-coding genes, which were identified either as overlapping with or located within QTL regions, were subject to enrichment analysis on The Database for Annotation, Visualization and Integrated Discovery (DAVID) v. 6.8. Functional annotations (Gene Ontology (GO) Biological Process, GO Cellular Component, GO Molecular Function and Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome Pathways) were assigned to genes using the functional annotation tool. P values were calculated by EASE (an adoption of the Fisher Exact test to measure the gene-enrichment in annotation terms) to identify functional annotations that were associated with traits of interest.Gene search using Ensembl  was equal to 4.53 (ranging from 1.01 to 277.69) in the dataset of F. hepatica-damaged liver are shown on the Manhattan plot in Fig. F. hepatica-damaged liver. Details of the most strongly associated SNPs  in each QTL region for F. hepatica-damaged liver are in Table −6  and was classified as an intronic variant located in the FTO gene. The frequency of the favourable allele for each of the six SNPs with a p value lower than 10−6 ranged from 0.002 to 0.998 (Table −5) and were located across 23 chromosomes.The associations between each SNP and F. hepatica-damaged liver ; the SNP with the strongest association in this region was rs475893299 . In the QTL region between 93.61 and 93.79 Mb on BTA11, 18 SNPs were suggestively associated with F. hepatica-damaged liver; all the genes located in this region encoded olfactory receptors ; in addition, the only QTL region associated with antibody response to F. hepatica that overlapped with a QTL associated with F. hepatica-damaged liver was on BTA11 between 93.74 and 93.89 Mb.Forty-eight QTL regions were suggestively associated with F. hepatica, O. ostertagi, and N. caninum are shown on the Manhattan plots in Fig. −6) in each QTL region for antibody response to F. hepatica, O. ostertagi, and N. caninum are in Tables The associations between each SNP and antibody response to F. hepatica. Fifty-seven SNPs had a p value lower than 10−6 and were located on BTA6 (1 SNP), BTA14 (1 SNP), BTA15 (43 SNPs), BTA18 (1 SNP) and BTA20 (11 SNPs). The most highly associated SNP was rs801151638 , which was classified as an intergenic variant on BTA14  and 37 QTL regions were identified for antibody response to F. hepatica; the largest QTL was 7,593,750 bp long (between 25.26 and 32.86 Mb) on BTA19;  with flanking SNPs, thus nine of the 37 detected QTL regions harboured only one SNP. Fifty SNPs were suggestively associated with antibody response to F. hepatica in the QTL region between 80.81 and 81.30 Mb on BTA16, which contained 4268 tested SNPs (average LD between 50 suggestive SNPs was 0.99); SNP rs434571591 had the strongest association with antibody response to F. hepatica . Five protein-coding genes were located between 80.8 and 81.30 Mb on BTA16, namely NR5A2, ENSBTAG00000047428, KIF14, DDX59, and CAMSAP2. In the QTL region between 23.52 and 23.58 Mb on BTA15, 43 of the 709 tested SNPs were detected as suggestively associated with antibody response to F. hepatica; no gene was identified in, or overlapped with, this QTL region.We did not detect any SNP that was significantly associated  with antibody response to , BTA15 4 SNPs, BTO. ostertagi , but nine SNPs were detected for antibody response to O. ostertagi with a p value lower than 10−6  and 25 (3 SNPs), respectively. In the QTL region between 13.04 and 14.10 Mb on BTA25, one of the three SNPs was classified as an intronic variant in the PARN gene and the other two as downstream variants on the micro-RNA gene, bta-mir-365-1. SNP rs133449464, that was classified as an intronic variant in the PARN gene, was the most strongly associated  with antibody response to N. caninium  with any other SNP in the flanking region of 5 Mb; the largest of the 41 QTL regions was 6,332,498 bp long (between 106.93 and 113.26 Mb) on BTA1 , between 106.93 and 113.26 Mb on BTA1 (66 SNPs) and between 57.83 and 58.95 Mb on BTA14 (30 SNPs).In total, 248 suggestive and significant SNPs were associated with antibody response to D r2 > 0. with anyF. hepatica-damaged liver, DAVID identified 97 for the gene set enrichment analysis. The most strongly associated pathways and gene ontology (GO) sets with F. hepatica-damaged liver are in Additional file F. hepatica-damaged liver and included 11 genes from the gene set. The GO set with the strongest association  with F. hepatica-damaged liver was the biological process of ion transmembrane transport, which included four genes from the gene set.Of the 102 protein-coding genes, which either overlapped with, or were within QTL regions of suggestive SNPs for F. hepatica, 333 genes for antibody response to O. ostertagi, and 429 genes for antibody response to N. caninum. In total, DAVID recognised 307, 289, and 373 genes for antibody response to F. hepatica, O. ostertagi and N. caninum, respectively. The top five ranked pathways for antibody response to F. hepatica, O. ostertagi, and N. caninum are in Additional file F. hepatica, O. ostertagi, and N. caninum are in Additional file Gene sets submitted to DAVID consisted of 324 genes for antibody response to F. hepatica. The top ranked pathway and GO set for antibody response to F. hepatica was R-BTA-2142712  and antibody response to O. ostertagi; 60 genes were included in this pathway and were also identified in QTL regions associated with antibody response to O. ostertagi  associated with antibody response to N. caninum, 19 pathways and 23 GO sets were identified as significant . The alpha-linolenic acid metabolism pathway was associated with antibody response to N. caninum with a p value lower than 2.70 × 10−8  whole-genome sequence in association analyses in cattle, we carried out an additional analysis in which only 50 k and HD SNPs were used . Thus, one or more QTL regions on BTA19 may be associated with resistance to parasitic diseases.A limited number of previous studies have attempted to locate genomic regions associated with endo-parasitic diseases in cattle, but none used antibody response or liver damage phenotypes to identify regions of the bovine genome associated with parasitic diseases. In their analysis of 768 Dutch dairy cows using 153 microsatellite markers, Coppieters et al.  documentF. hepatica-damaged liver, and this region overlaps with one of the QTL (64.19 to 66.89 Mb) identified by Kim et al. [GRIA1, was previously reported to be associated with ovulation rate in 639 Japanese Black cattle [GRIA1 had no influence on ovulation rate in a study of 139 crossbred beef cows. In humans, GRIA1 has been linked to the neurological disease schizophrenia [GRIA1 was involved in two pathways, which were over-represented for F. hepatica-damaged liver and are linked to the neurology of the animal, amyotrophic lateral sclerosis, and neuroactive ligand-receptor interaction. Although ectopic F. hepatica infection has been reported to cause neurological diseases in humans [In a study on 584 Angus cattle, Kim et al.  reportedm et al. . One of k cattle . Howeverk cattle  reportedophrenia , asparagophrenia , and migophrenia . In our n humans , 44, theF. hepatica within the region previously reported by Kim et al. [UBE2 K, N4BP2, PDS5A, and RHOH) and one RNA gene (SnRNA) that were located within this region on BTA6. The RHOH gene likely plays a role in the determination of the antibody response to F. hepatica, since it is involved in the activation of mast cells, which are known to increase in numbers in response to F. hepatica infection [O. ostertagi or N. caninum.Kim et al.  also docm et al. . We founnfection . There wnfection  with FECF. hepatica, Alvarez Rojas et al. [F. hepatica. This was expected since arachidonic acid, which is a polyunsaturated omega-6 fatty acid, is documented as suppressing the Th1 immune response in mice [F. hepatica infection [In a study based on only four sheep that were experimentally infected with s et al.  identifi in mice , 48 thatnfection . Furthernfection , as wellnfection .F. hepatica, since Twomey et al. [F. hepatica-damaged liver and antibody response to F. hepatica .In our study, we also expected that there would be very little overlap between QTL regions associated to the two phenotypes for y et al.  found onN. caninum on BTA21 and 25 had not been reported to be associated with endo-parasitic diseases in cattle. The favourable alleles for the most significant SNP on BTA21 and 25 had a frequency of only 0.003 and 0.005, respectively, which suggests that, if this frequency increased, more genetic gain is potentially possible. However, when we analysed exclusively the 3038 purebred Holstein–Friesians in our study, the allele frequency of the most significant SNP on BTA21 and 25 dropped to 0.002 and 0.001, respectively and these SNPs were not associated with antibody response to N. caninum . Nevertheless, the most significant SNP on BTA25 in the multi-breed analysis was an intronic variant located in the PARN gene, which encodes an enzyme (PARN) that has a role in early foetal development [PARN has been linked to embryo loss, since the level of PARN mRNA was lower in persistent follicles (‘ageing’ follicles associated with embryo loss) than in growing follicles [N. caninum infection in females can result in abortion or embryo loss [N. caninum is supported by the association between PARN and embryo loss.To date, the significant regions associated with antibody response to elopment . In cowsollicles . N. caniryo loss  and in rryo loss , thus thFTO gene on BTA18, was associated with F. hepatica-damaged liver but the frequency of its favourable allele (0.988) was very high, thus, the potential gains for phenotypic improvement in resistance are very limited; in fact, the allele is fixed in the two British breeds of the 301 purebred Aberdeen Angus and 164 purebred Herefords included in our study. Analysis of the 1248 purebred Holstein–Friesians  in our study revealed that this SNP rs384464701 had a p value of 3.91 × 10−4 and the frequency of its favourable allele was 0.003. FTO is known to have a strong association with human obesity [FTO has a role in the metabolic regulation of the liver. In cattle, variants of the FTO gene have previously been associated with both growth and carcass traits [FTO may be involved in energy homeostasis. However, Twomey et al. [F. hepatica-damage liver had little or no genetic association with milk production and carcass merit in cattle.We identified that SNP rs384464701, an intronic variant in the  obesity . Bravard obesity  suggestes traits , 57 as ws traits ; taken tO. ostertagi, we detected a SNP at 1,746,600 bp located on BTA3 in the GPA33 gene that was associated with a p value of 7.74 × 10−7; there were 17 other suggestive SNPs identified in this region between 0.95 and 6.39 Mb on BTA3. Although, the frequency of the favourable allele (< 0.004) was low for all these suggestive SNPs, GPA33 is a likely candidate gene associated with antibody response to O. ostertagi. Indeed, in a study on mice, Williams et al. [GPA33 was important for the intestinal barrier function, and thus it may have a role in the defence of gastrointestinal nematodes such as O. ostertagi.Regarding the antibody response to s et al.  reportedResearch on animal resistance to disease focuses mainly on the immune response , 61. NevF. hepatica-damaged liver and antibody response to F. hepatica, included a large number of olfactory receptor genes. This suggests that a proportion of the genetic variation observed for parasitic diseases may be linked to the ability of cattle to avoid infective larvae. Nevertheless, since genes in these suggestive regions were used in the gene set enrichment analysis, further research is needed to support the concept that cattle avoid infection through odour cues.Our findings suggest that cattle use odour cues as part of an avoidance strategy for the three endo-parasites studied here, since at least one pathway and GO set involved in the olfactory system were significant  for each of the parasite phenotypes. In addition, the only region on the bovine genome, that contained QTL that were suggestively associated to N. caninum; the associated favourable alleles had a low frequency in the studied population, which suggests that large genetic gain could be achieved.We identified a large number of QTL regions that were associated with the endo-parasite phenotypes investigated in our study. We observed strong associations between genomic regions on BTA21 and 25 with antibody response to Additional file 1: Table S1. Number of animals with an Illumina Bovine High-Density BeadChip (HD), Illumina Bovine50 beadchip (50 k) and Low-Density BeadChip (LD) and with an International Dairy and Beef version 1 (V1), version 2 (V2) and version 3 (V3) genotype in the current study for deregressed EBV for F. hepatica-damaged liver, as well as for the adjusted phenotype of antibody response to F. hepatica, O. ostertagi and N. caninum.Additional file 2: Table S2. Chromosome number, start of quantitative trait locus (QTL) region, end of QTL region and number of single nucleotide polymorphisms (SNPs) with a p value < 1 × 10−5 for each QTL region identified as suggestively associated with F. hepatica-damaged liver.Additional file 3: Table S3. Chromosome number, start of quantitative trait locus (QTL) region, end of QTL region and number of single nucleotide polymorphisms (SNPs) with a p value < 1 × 10−5 for each QTL region identified as suggestively associated with antibody response to F. hepatica.Additional file 4: Table S4. Chromosome number, start of quantitative trait locus (QTL) region, end of QTL region and number of single nucleotide polymorphisms (SNPs) with a p value < 1 × 10−5 for each QTL region identified as suggestively associated with antibody response to O. ostertagi.Additional file 5: Table S5. Chromosome number, start of quantitative trait locus (QTL) region, end of QTL region and number of single nucleotide polymorphisms (SNPs) with a p value < 1 × 10−5 for each QTL region identified as suggestively associated with antibody response to N. caninium.Additional file 6: Table S6. Name, type, p value and genes for the top 5 ranked pathways and gene ontology (GO) sets for F. hepatica-damaged liver based on the EASE p value (an adoption of the Fisher Exact test to measure the gene-enrichment in annotation terms).Additional file 7: Table S7. Name, source, p value and number of genes for the top 5 ranked pathways for antibody response to F. hepatica, O. ostertagi and N. caninum based on the EASE p value (an adoption of the Fisher Exact test to measure the gene-enrichment in annotation terms).Additional file 8: Table S8. Name, type, p value and genes for the top 5 ranked gene ontology (GO) sets for antibody response to F. hepatica, O. ostertagi and N. caninum based on the EASE p value (an adoption of the Fisher Exact test to measure the gene-enrichment in annotation terms).Additional file 9: Figure S1. Manhattan plot showing -log10 of association between each SNP effect and F. hepatica-damaged liver for (a) 50 k data and (b) HD data. The blue line is the threshold for suggestive SNPs.


The G-WIZ code is freely available for download at https://github.com/jonaspatronjp/GWIZ-Rscript/.To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations  are in a publicly accessible database called GWAS-ROCS ( A genome-wide association study (GWAS) is a comprehensive genetic analysis of the association between certain observable traits and specific genetic variations in the form of Single Nucleotide Polymorphisms (SNPs). The appeal of genome wide association (GWA) studies is that they provide a relatively facile approach for detecting potential genetic contributors to common, complex diseases (such as diabetes) or phenotypes (such as body mass index or hair color) using a simple case-control study model. The first GWA study was performed in 2005 . This ea−2 and 1 x 10−584. The p-values in a GWA study report the likelihood of the odd-ratios between two different alleles being statistically different than one. The typical threshold of significance for most published GWA studies is p = 5 x 10−8. The average odds ratio for a statistically significant SNP is 1.33 with very few SNPs having an odds ratio above 3.0 Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: No**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The manuscript presents a statistical method and an R package for approximating the AUC of reported SNPs when only the summary level data of the validation dataset are available. The manuscript is well written, and it is easy to follow.I have the following major concerns.-----------------------------------------On page 7&8, it seems authors first simulate the SNP profile and then assign the healthy/disease statuses. Shouldn’t it simulate the SNP profile given the healthy/disease statues? A detailed explanation is needed for this part.-----------------------------------------On page 10, what is the variance inflation factor? How was it used to determine if there is multicollinearity?-----------------------------------------On page 14, what are odd/even SNPs?-----------------------------------------On page 15, authors claimed that “This was important because the SNP inheritance model in published GWA studies may vary from SNP to SNP, and is not typically disclosed. Thus, for each of the 7 WTCCC disease studies we calculated the true AUROC under these four SNP inheritance schemes.” I do not see the point to have four schemes using logistic and ridge regression. The scheme is automatically handled when the model is being fitted. Why do we need to restrict the inheritance scheme explicitly?-----------------------------------------In Table 2, some AUROCs are close to 0.5. Does it mean the report SNPs has little or no contribution to the classification accuracy? Do we have any explanation that low p-value SNPs have little discrimination power?-----------------------------------------On page 18, there is a comparison of AUROCs between GWAS-derived vs. non-GWAS-derived risk factors. How were those AUROCs of non-GWAS-derived risk factors calculated? Did they use the same logistic or ridge regression models? If not, the comparison may not be fair. The difference may be caused by the models not the risk factors.-----------------------------------------Will the authors release the developed tool as open source? The impact will be limited if the research community cannot verify and apply the computational method.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool,  30 Sep 2019Sept. 29, 2019Dear Dr. Devaney,Please find attached our revised manuscript (PONE-D-19-19050) entitled: “Assessing the performance of genome-wide association studies for predicting disease risk” by Jonas Patron, Arnau Serra-Cayuela, Beomsoo Han, Carin Li and myself. We wish to thank the reviewer for their comments and suggestions. We have tried our best to address all the reviewer’s comments by modifying the paper and have provided detailed responses or described our modifications to the manuscript in the attached pages. We have also produced both a clean version and marked-up version of the manuscript with all changes indicated in red so that the edits or modifications can be more easily seen. For Reviewer #1:1. On page 7&8, it seems authors first simulate the SNP profile and then assign the healthy/disease statuses. Shouldn’t it simulate the SNP profile given the healthy/disease statuses? A detailed explanation is needed for this part.Response: We appreciate the reviewer’s concerns and apologize for the confusion. Indeed, we simulate the SNP profiles given the healthy/disease status of the cohort. Note from equations (1)-(4) on pg. 7&8 we start by determining or inputting the number of cases and controls. That is, we always start by knowing the healthy/disease status of the individuals in the cohort, we just don’t know the SNP profile of said individuals. Next, by knowing the risk allele frequency in the controls and the odds ratio between the cases and controls we calculate the risk allele frequency in the cases. Once we know the risk allele frequency in both the case and control groups, we appropriately assign the SNP profiles to each group. We have added portions of this text to the manuscript to explain the process in more detail.2. On page 10, what is the variance inflation factor? How was it used to determine if there is multicollinearity?Response: Given a regression model with multiple variables, the variance inflation factor is the quotient of the variance from a model which regresses one of the predictor variables against all the others. Multicollinearity was determined to exist when at least two variables showed inflated coefficients, as quantified by the calculation of the variance inflation factor being equal to infinity. We also tried other variance inflation factor cutoff values, however because the differences in the AUC estimates were so small (<0.009) and because standard logistic regression is more easily interpretable than its ridge regression counterpart, we found it appropriate to restrict the use of ridge regression only to models with extreme (i.e. divergent) variance inflation factor estimates. We have added portions of this text to the manuscript to explain this concept in more detail.3. On page 14, what are odd/even SNPs?Response: The characterization of SNPs as odd or even is simply referring to their position in a list—those found at odd numbered positions in the list are “odd SNPs” and those that are found in even numbered positions are called “even” SNPs. It is a simple scheme to split the SNPs in an approximately 50-50 fashion. Consider Table R1 (see below), which is a table with SNPs. The 1st, 3rd, and 5th SNPs would be the odd SNPs and the 2nd and 4th SNPs would be the even SNPs.Table R1Accession Control size Case size Risk allele freq OR Odd/Evenrs7903146 2598 2309 0.3 1.36 oddrs5219 2598 2309 0.36 1.25 evenrs10811661 2598 2309 0.85 1.21 oddrs1801282 2598 2309 0.87 1.21 evenrs2641348 2598 2309 0.11 1.15 odd4. On page 15, authors claimed that “This was important because the SNP inheritance model in published GWA studies may vary from SNP to SNP, and is not typically disclosed. Thus, for each of the 7 WTCCC disease studies we calculated the true AUROC under these four SNP inheritance schemes.” I do not see the point to have four schemes using logistic and ridge regression. The scheme is automatically handled when the model is being fitted. Why do we need to restrict the inheritance scheme explicitly?Response: This is an excellent point. What were doing was conducting two different and independent modeling steps. In the first step, we were trying to calculate the true AUROC of a given WTCCC disease study. To do this we had to convert the raw GWAS data into dummy variables to which we could apply logistic regression. Take Table R2 (below) for example. In this example, there is one individual and we have information about four of his/her SNPs. To run a regression analysis the information in columns “1st Allele” and “2nd Allele” must be converted to a single number (either a 0 or 1) known as the dummy variable. Then, in the second step we would run our logistic/ridge regression to estimate the AUROC . Note that in this simple example a change in the choice of the SNP inheritance scheme produces three distinct results: , , and . As a result, when we run our standard regression techniques we could expect to produce 3 different AUROCs, all of which can be interpreted as the “true” AUROC.Table R2SNP Risk Allele 1st Allele 2nd Allele Dominant Inheritance Recessive Inheritance Dominant (odd) – Recessive (even) #1 A T A 1 0 1#2 A A A 1 1 1#3 T A A 0 0 0#4 C C G 1 0 0As such, to rigorously test GWIZ’s AUROC predictions, we had to ensure that we could accurately predict the AUROC which resulted from any one of these inheritance schemes. In our test set we could restrict the inheritance scheme explicitly, however in practice, given a set of odds ratios we cannot necessarily know which inheritance scheme produced them.5. In Table 2, some AUROCs are close to 0.5. Does it mean the report SNPs has little or no contribution to the classification accuracy? Do we have any explanation that low p-value SNPs have little discrimination power?Response: With regard to the first question, yes, when the AUROCs are close to 0.5 it does indicate that the reported SNPs has/have little or no contribution to the classification accuracy. With regard to the second question, the size of the p-value and its influence on the AUROC is primarily affected by the associated SNP’s odds ratio and allele frequency. In other words, low p-values can arise from statistically significant effects of small magnitude. In GWA studies, the ‘effect’ we are looking for is that of differences in allele frequencies between case and control groups. And the magnitude of the effect in question is measured by the odds ratios. A SNP with a p-value of 10-80 but with an odds ratio close to 1  will have a very small effect on the AUROC. A low odds ratio often arises from a very low SNP/allele frequency. A SNP with an odds ratio of 3 but a p-value of just 10-10 will have a large effect on the AUROC. 6. On page 18, there is a comparison of AUROCs between GWAS-derived vs. non-GWAS-derived risk factors. How were those AUROCs of non-GWAS-derived risk factors calculated? Did they use the same logistic or ridge regression models? If not, the comparison may not be fair. The difference may be caused by the models not the risk factors.Response: The AUROCs of the non-GWAS derived risk factors were calculated using a mixture of different approaches. About 1/3 of the values quoted in this table used logistic regression while the others used either PLS-DA or random forest methods. It is true that the choice of the classification model can affect the performance and the reported AUROC but generally most biostatisticians will test multiple classification models  and will quote the best performing model. Our own experience in using many different classifiers for many other related projects is that the AUROC differences between classifiers are generally quite small (<5%) although rare exceptions do occur. Overall, we believe the AUROC differences accurately reflect the risk factors and that they are not a (significant) function of the choice of classifier. We have added this information to the manuscript.7. Will the authors release the developed tool as open source? The impact will be limited if the research community cannot verify and apply the computational method.https://bitbucket.org/wishartlab/gwiz-rscript/src/master/, and is compatible with a wide variety of UNIX platforms, Mac OS and Windows operating systems. The above link to bitbucket is cited in the original manuscript on page 11, however to reduce confusion in the future we have also mentioned this link in the abstract.Response: The code is freely available for download at In addition to these edits we have also made some small changes to the manuscript by adding a few missing references and elaborating on GWIZ’s comparison to other programs. This led to a small update to Figure 3. Once again, we would like to thank the reviewer for their comments and suggestions. We hope that the explanations we have provided are satisfactory and certainly the changes they suggested have significantly improved the overall clarity and quality of the manuscript. We hope these edits are acceptable and are looking forward to seeing the manuscript published in PLOS ONE soon.Sincerely,David WishartAttachmentResponse-letter-PLOS ONE - DSW.docxSubmitted filename: Click here for additional data file. 4 Nov 2019Assessing the performance of genome-wide association studies for predicting disease riskPONE-D-19-19050R1Dear Dr. David,We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.Within one week, you will receive an e-mail containing information on the amendments required prior to publication. 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The Suwan population is a well-known maize germplasm that has greatly contributed to the development of maize breeding in tropical and subtropical regions, especially in southern China. Inbred lines derived from the Suwan population always contain stronger resistance and extensive adaptability in different environments. To evaluate the genetic character of inbred lines derived from the Suwan population, a panel including 226 inbred line derived from the Suwan population and temperate resources was assembled and genotyped by using MaizeSNP50 BeadChip, which contained 56,110 genome-wide single nucleotide polymorphism (SNP) markers. This panel contained 98 temperate inbred line and 128 lines derived from the Suwan population.The results showed that high genetic diversity was found, with PIC and GD to be 0.67 and 0.60, respectively. In addition, two novel subgroups were identified, with representative inbred lines as HCL645 and Ki32, respectively. One acknowledged heterotic group of Iowa Stiff Stalk Synthetic (SS) was also identified in this study. This study can provide some additional scientific evidence for heterotic group division and use in maize. Additionally, lower linkage disequilibrium (LD) levels and weaker genetic relationships were found, with an average LD level of 41.15 kb that varied from 3.5 to 96 kb. A total of 82.8% of paired relative kinships ranged from 0.05 to 0.28.These results would not only facilitate maize breeding practices in tropical and subtropical regions, but also revealed that this panel can be used in dissecting the genetic basis of complex quantitative traits’ variations by using genome-wide association studies (GWAS).The online version of this article (10.1186/s41065-018-0077-1) contains supplementary material, which is available to authorized users. Zea mays L.) rapidly spread throughout the world after its domestication from teosinte in tropical Mexico [Maize  method, Romay et al.  evaluateIn this study, 226 accessions derived from the Suwan population and temperate resources were selected and genotyped by using the MaizeSNP50 BeadChip, including approximately 56,110 single nucleotide polymorphism (SNP) markers. The objectives were to evaluate the genetic diversity, population stratification, relative kinship, linkage disequilibrium (LD) level, and whether this panel is suitable for GWAS of agronomic traits’ variation.ΔK was observed , with 3.5 kb on Chr.1, and 96 kb on Chr.5, Chr.6 and Chr.10  strategy. These results would provide some additional information for heterotic group division and use. As a result, this may facilitate better maize breeding, especially in tropical and subtropical regions.The maize panel used in this study contained 226 accessions derived from the Suwan population and temperate resources. These contained 98 temperate accessions, with representative lines of B73 and Mo17, which were widely used in maize breeding historically . In addiwww.illumina.com/maizeSNP50). Leaves were collected from the five-leaf stage of maize seedlings. The tip of young and tender maize leaves of five plants were collected as a bulk to extract genomic DNA, according to the modified CTAB procedure [http://www.maizegdb.org/). SNPs with ambiguous physical positions or multiple blast-hits were excluded from the genotyped dataset. After that, 43,980 high-quality SNPs were used in further analysis.This panel was genotyped by using MaizeSNP50 BeadChip, including 56,110 SNPs. Those SNPs were evenly distributed across the maize genome based on the B73 reference sequence .To obtain an in-depth picture of the genetic relationships among 226 inbred lines, the genetic distance between pair-lines was calculated by using 43,980 SNPs. Modified Euclidean was defined as follows: D = 1 - identity by state (IBS) similarity . Cluster2) between vectors of SNP alleles according to the method described by Hill [2 was set to be 0.1 [By using 43,980 SNPs with MAF > 0.05 and missing data < 20%, pair-SNPs’ LD level was evaluated using the squared Pearson correlation coefficient , where Qij is the probability of IBS for random loci from i and j, and Qm is the average probability of IBS for loci from random individuals. Pair-kinship coefficients of 0, 0 to 0.1, and 0.1 to 0.5 indicated weak, intermediate, and strong similarity between accessions, respectively [plu and plv are the frequencies of the uth and vth alleles for marker l, respectively.The pair relationship among the 226 inbred lines was estimated by definition as follows: Fectively . The genectively , with GDectively . The PICAdditional file 1:Table S1. Summary information of the 226 accessions. (XLS 25 kb)