No major safety signals were observed but tolerability issues of diarrhea and odor were common (up to 13%). We hypothesized that genetic variants associated with Lp-PLA2 activity may influence efficacy and tolerability and therefore performed a comprehensive pharmacogenetic analysis of both trials. We genotyped patients within the STABILITY and SOLID-TIMI 52 trials who provided a DNA sample and consent  using genome-wide arrays with exome content and performed imputation using a 1000 Genomes reference panel. We investigated baseline and change from baseline in Lp-PLA2 activity, two efficacy endpoints  as well as tolerability parameters at genome-wide and candidate gene level using a meta-analytic approach. We replicated associations of published loci on baseline Lp-PLA2 activity  and identified three novel loci  using the GWAS-significance threshold P≤5E-08. Review of the PLA2G7 gene (encoding Lp-PLA2) within these datasets identified V279F null allele carriers as well as three other rare exonic null alleles within various ethnic groups, however none of these variants nor any other loci associated with Lp-PLA2 activity at baseline were associated with any of the drug response endpoints. The analysis of darapladib efficacy endpoints, despite low power, identified six low frequency loci with main genotype effect (though with borderline imputation scores) and one common locus  with genotype by treatment interaction effect passing the GWAS-significance threshold. This locus conferred risk in placebo subjects, hazard ratio (HR) 1.22 with 95% confidence interval (CI) 1.11–1.33, but was protective in darapladib subjects, HR 0.79 (95% CI 0.71–0.88). No major loci for tolerability were found. Thus, genetic analysis confirmed and extended the influence of lipoprotein loci on Lp-PLA2 levels, identified some novel null alleles in the PLA2G7 gene, and only identified one potentially efficacious subgroup within these two large clinical trials.Darapladib, a lipoprotein-associated phospholipase A2 (Lp-PLA The maudy [FHS].2 activity levels. Baseline Lp-PLA2 activity levels were associated with primary and secondary endpoints in STABILITY [2 activity levels and darapladib absorption, distribution, metabolism and excretion (ADME). The results reported here represent the one of largest pharmacogenetic (PGx) studies of cardiovascular treatment endpoints reported to date and also contain exome chip variants which have not been included in many PGx evaluations to date. We found genetic variation influencing baseline Lp-PLA2 activity levels, but those variants did not influence outcomes. Despite the large size of the clinical trials and availability of a good pharmacodynamic marker, we found just one common locus that appeared to influence cardiovascular outcomes.We hypothesized that genetic variants may also influence response to darapladib. As the STABILITY and SOLID-TIMI 52 trials were so large , we conducted genotyping ahead of time so that the data would be available contemporaneously with the clinical data upon trial completion. In order to maximize the power we performed meta-analyses of the STABILITY and SOLID-TIMI 52 trials for efficacy, pharmacodynamic and tolerability endpoints. The efficacy analysis was focused on MCE and MI endpoints, as those phenotypes had greater efficacy within STABILITY as compared to others. We also investigated baseline and change from baseline Lp-PLASample size, demographics, and laboratory characteristics of each endpoint by study are presented in Our PGx analysis included various clinical endpoints and analysis populations, as outlined in When we assessed whether the PGx population was representative of the overall clinical study in STABILITY study, a bias was identified in the PGx population resulting in slightly lower hazard ratios in the efficacy endpoint  and the tolerability endpoints . This difference in STABILITY may be explained by the collection of PGx samples at the month 3 study visits, rather than at randomization, thereby missing collection of samples from subjects with early MCE events and those who withdrew from the study due to tolerability issues. PGx sample collection was also delayed in some countries due to late approval of the PGx component to the study. The SOLID-TIMI 52 study, where PGx samples were collected at baseline, did not show this bias.2 activity at baseline for imputed variants with imputation r2 ≥ 0.5 using a linear regression model that included the top 10 principle component (PC) scores as covariates in STABILITY  and SOLID-TIMI 52  separately. Meta-analysis of the ~13.6 million common, low frequency and rare genome-wide variants was carried out to combine the association results from both studies. We detected 578 variants with consistent direction of effect in both STABILITY and SOLID-TIMI 52 studies that surpassed the genome-wide significant P-value threshold of 5E-08 in regions of chromosomes 1, 6 (2 clusters), 9 (2 clusters), 12, 15 and 19. Results for the most significant variant from each chromosomal cluster are summarized in gc) estimates were close to 1, suggesting no inflation due to uncontrolled population structure . The most striking association detected is due to the well-known  variant  [15;18;2 variant . We alsovariants , which tvariants . Two comactivity .2 activity and how this correlates with various baseline lipid and C-reactive-protein levels. The strongest association at the 1p13 locus was for rs12740374, located in the 3’UTR of the CELSR2 gene. This variant was also significantly associated with baseline LDL and HDL levels. Four significant variants were located in the LPA gene. Variant rs10455872 was the most significant and was also associated with baseline LDL levels. Low frequency variant rs57578064 is the most significant in the locus that includes the TOMM5 and FBXO10 genes and has not been previously described  in the APOE gene was the most significant variant, which was also associated with baseline LDL and triglyceride levels.escribed . Anothernificant . The var2 activity revealed two additional loci that surpassed the candidate gene significance threshold of 5E-04, but were not genome-wide significant , had a significant allele by treatment interaction effect (P = 6.03E-9) but marginal main genetic effect in individual treatment arms. Carriers of the minor allele in the placebo groups had an elevated HR 1.22 (95% CI 1.11–1.33), meta-P = 4.30E-5 and in the darapladib groups a reduced HR 0.79 (95% CI 0.71–0.88), meta-P = 2.07E-5. All six associated low frequency variants had MAF<2.5% and many had imputation r2 values between 0.5 and 0.8, which reduces the level of confidence in these results. All genomic control (λgc) estimates were close to 1, suggesting no inflation due to uncontrolled population structure  with MCE or MI in either the darapladib or placebo treatment arm. Also none of the variants that were associated with Lp-PLA2 activity were associated with efficacy endpoints (MCE or MI events).2 activity levels in the darapladib treatment arm at these loci  was the most significant variant associated with baseline and change from baseline Lp-PLA2 activity. Although this variant is almost exclusively observed in East-Asian populations, other common PLA2G7 variants were also significant in this study and mirror observations from the Framingham Heart, CHARGE and JUPITER studies [CELSR2, SCARB1, LPA and APOE [CELSR2, LPA, APOE and LDLR) and likely represent the known positive correlation between Lp-PLA2 and LDL-cholesterol, two HDL-cholesterol associated genes (LPL and SCARB1) were also associated with Lp-PLA2. This is not surprising as HDL particles are known to carry a minority of Lp-PLA2.In the present meta-analysis, we identified eight loci associated with baseline Lp-PLAactivity –23. PLA2 studies –23. Otheand APOE –23. WhilFRMD5 gene variant on chromosome 15 in a region of large copy number variation, some of which encompass the entire gene. Genome-wide significant associations have been reported between FRMD5 variants and triglyceride levels [2 activity. The other two loci were observed on chromosome 9, both rare variants with large effects. Carriage of the A allele of a TOMM5 intronic variant was correlated with a large increase in Lp-PLA2 activity and observed at very low frequency (0.15%). Database review to assess possible impact on gene regulation did not reveal any convincing evidence. It is also located upstream of the FBXO10 gene, for which sub-genomewide associations have been reported with lipid traits and triglyceride levels (dbGAP data entries pha003081 & pha003082 from the NHLBI Family Heart Study study) and lymphocyte counts [OR13C5 and OR13C9 genes and lipid traits, triglyceride levels, stroke and coronary artery calcification [PLA2G7 gene itself is completely independent of lipids. None of these eight loci were associated with change from baseline Lp-PLA2 activity after adjusting for baseline Lp-PLA2 activity, nor were they linked to efficacy in terms of the MCE and MI endpoints.Additionally, three novel loci were found, one of which is represented by an intronic e levels , but note counts . For thefication –42 [r2 bins did not identify any significant deviation from 1 for lower frequent (MAF<1%) or less well imputed variants (r2<0.8). Ideally, these variants should be directly genotyped to confirm, but that is not feasible at the present time.The genome-wide meta-analysis did not reveal any high confidence effects of common genetic variants on darapladib tolerability, even though the study was relatively well powered to detect these effects. Our ability to identify efficacy variants though was dramatically reduced by the low treatment effects observed in STABILITY and no treatment effect observed in the SOLID-TIMI 52 clinical trials, leading to low power for the PGx analysis. Nevertheless, six low frequency (MAF<2.5%) loci associated with the MCE or MI endpoints suggested a marginally improved outcome with the common allele following treatment with darapladib when compared to placebo (see  = 0.063). AssessmAnalysis of darapladib tolerability endpoints was challenging due to uncertainty around the validity of the more heterogeneous and subjective nature of the diarrhea and odor endpoints, as well as the high prevalence of diarrhea in the placebo arm for both trials. In order to reduce the non-specificity of the diarrhea phenotype and to focus on diarrhea related to darapladib drug treatment, we limited the time window and ethnicity (reduce heterogeneity) as well as excluded metformin users. Initial meta-analysis was performed using a >0.1% minor allele frequency threshold. However, as this generated a lot of noise in the results and complicated the interpretation of the data the minor allele frequency threshold was raised to 0.5% to alleviate these problems. Most of the loci associated with diarrhea were represented by variants with frequencies ranging from 0.6 to 1.5% and no association was seen with common variants (MAF>5%) in any of the tolerability meta-analyses  and therefore any genetic signals from rare variants will not have been detected in this study.Additionally, the different disease background of the STABILITY (chronic heart disease) and SOLID-TIMI 52 (acute coronary syndrome) clinical trials may have had an impact on our ability to see a consistent genetic effect on response to darapladib treatment. Cardiovascular disease trials are extremely heterogeneous with broad endpoints (MACE), which are challenging for genetic studies. It is worth noting that genetic studies for CHD used to predict drug effects tend to have much more discrete set of phenotypes  as compared to the phenotypes in the STABILITY and SOLID-TIMI 52 trials. For the darapladib trials combining stroke and CHD meant a limited genetic overlap in these broad endpoints. Besides the analysis of baseline Lp-PLAFor GWAS, the classical GWAS-significance threshold (P≤5E-08) was applied to declare significant results. This threshold was established at a time when most GWAS involved approximately 1 million independent tests, mostly for common variants with MAF>5%. Given the larger number of both common and low frequency variants (MAF<5%) to be analyzed here, this implies a lack of stringent multiple testing correction such that any associations reaching classical genome wide significance would be in need of further follow-up for validation or replication.2 activity were observed, which, further demonstrated the close relationship between Lp-PLA2 activity and lipoproteins and extends the number of variants that can be included in a gene score for Lp-PLA2, for investigation of other diseases or conditions. These genetic variants associated with baseline Lp-PLA2 activity levels did not however contribute to the interpretation of darapladib treatment response, which seems to correspond with the lack of effect of Lp-PLA2 inhibition on cardiovascular events in the trials. Thus, genetic analysis confirmed and extended the influence of lipoprotein loci on Lp-PLA2 levels, identified some novel null alleles in the PLA2G7 gene, but did not identify any major efficacious subgroups within these two large clinical trials.Meta-analysis of two large darapladib Phase III trials identified only one region with common variants that significantly influenced differential response to darapladib, though the main effects were not statistically significant. No major loci for tolerability were found. Ten variants associated with baseline Lp-PLA2 activity endpoints, as this content also allowed the identification of additional PLA2G7 null allele carriers. Finally, this darapladib meta-analysis represents one of the largest PGx studies ever conducted, providing a large pool of data with comprehensive data mining opportunities, to further the understanding of the mechanism of action of Lp-PLA2 inhibitors and CHD disease genetics.Significant exome variants, included as additional content on the genotyping array, were useful for the baseline and change from baseline Lp-PLASubjects included in our study were recruited as part of two darapladib Phase III trials: STABILITY (STabilization of Atherosclerotic plaque By Initiation of darapLadIb TherapY) and SOLID-TIMI 52 . Clinical study approval was provided by the relevant Ethics Committee of each participating country: Argentina, Australia, Belgium, Brazil, Bulgaria, Canada, Chile, Colombia, Czech Republic, Denmark, Estonia, France, Germany, Greece, Hong Kong, Hungary, India, Israel, Italy, Japan, Korea, Mexico, Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines, Poland, Romania, Russia, Slovakia, South Africa, Spain, Sweden, Taiwan, Thailand, Turkey, Ukraine, United Kingdom, United States. A full list of Ethics Committees and Independent Review Boards used in the STABILITY and SOLID-TIMI 52 trials can be found in All STABILITY subjects had chronic CHD, defined as prior MI, prior coronary revascularization or multivessel CHD without revascularization. In addition, subjects had to fulfill at least one of the following: age ≥60 years; diabetes mellitus requiring pharmacotherapy; HDL-cholesterol <1.03 mmol/l; current or previous (discontinuation within three months of randomization) smoker defined as ≥ 5 cigarettes per day on average; significant renal dysfunction ; or polyvascular disease . Among tAll SOLID-TIMI 52 subjects were recruited based on having experienced acute coronary syndrome  within 30 days of randomization. In addition, subjects had to fulfill at least one of the following: age ≥60 years; history of MI prior to the qualifying event, significant renal dysfunction (estimated glomerular filtration rate ≥30 and <60 mL/min per 1.73 m2), diabetes mellitus requiring pharmacotherapy, or polyvascular disease . Among tThe above study recruitment criteria highlight a considerable difference between the chronic disease phenotype for the STABILITY study and the acute phenotype in the SOLID-TIMI 52 study. See 2 enzyme activity was measured by diaDexus, Inc.  using the PLAC® Test for Lp-PLA2 Activity, both at baseline and post-baseline at the Month 1 treatment visit. At Month 1 most of the effect of Lp-PLA2 inhibition by darapladib was expected to have occurred plus the impact of subject withdrawal was minimal, therefore the maximum number of subjects would be available for evaluation.Baseline values were defined as the last value obtained until and including the date of randomization. Plasma Lp-PLALDL, HDL, hsCRP levels were measured by Quest .2 activity was performed for baseline and change from baseline  in the darapladib treatment arm after one month of treatment.Efficacy analyses were focused on the Major Coronary Events (MCE) and myocardial infarction (MI) endpoints in both the darapladib and placebo treatment arms. The pharmacodynamic analysis of Lp-PLATo evaluate darapladib tolerability, both diarrhea and odor-related endpoints were analyzed in the darapladib treatment arm only. In order to reduce a signal to noise ratio, the diarrhea endpoint was defined as a subset that was most likely associated to treatment with darapladib. Subjects were included based on a) diarrhea experienced in the first 90 days of darapladib treatment (time of onset of diarrhea in placebo arm tended to be later); b) excluding subjects who were also treated with metformin (since diarrhea is a well-known side-effect of metformin treatment); c) including subjects of White race only . Additionally, a stricter diarrhea phenotype was also evaluated, focusing on subjects who experienced moderate and/or severe diarrhea . Individuals were excluded if genotyping call rates were less than 95%, if they had a 3rd degree or higher relationship as determined by kinship coefficient estimates using KING software [−7 in PCA defined White and Korean subgroups. PCA analysis was carried out using the SNPRelate [r2 < 0.2. When a variant had multiple markers on the array the lowest efficiency marker was removed. For markers with minor allele frequency (MAF) less than 0.5% missing genotypes were recalled using zCall [STABILITY: Genotype data for 951,117 markers were generated on the HumanOmniExpressExome-8 v1 array  by Expression Analysis Inc.  for 13,728 samples. Quality control was performed using the toolset PLINK v1.07  and SNP software  or, if tNPRelate  softwareng zCall . After qAdditional STABILITY genotype data was generated by LGC Genomics for 12 candidate variants in 13,752 samples using KASP technology . No markers were excluded and subjects were excluded if they had missing data for >3 markers.−6 in White and Korean subgroups. When a variant had multiple markers on the array the best assay was included using the Affymetrix SNPolisher Best Probeset filter (http://www.affymetrix.com/estore/partners_programs/programs/developer/tools/devnettools.affx).SOLID-TIMI 52: Genotype data for 692,259 markers were generated on the Axiom® Biobank Plus Genotyping Array with custom content array  by BioProcessing Solutions  for 10,619 samples. Quality control was performed using the toolset PLINK v1.07 . IndividTM technology . No markers were excluded and subjects were excluded if they had missing data for >3 markers.Additional SOLID-TIMI 52 genotype data was generated by BPS for 35 candidate variants in 10,054 samples using Fluidigm® Biomark2 activity levels) analyses candidate genes or loci and key variants were selected based on published association data for CVD risk and Lp-PLA2 activity and/or mass and related pathways.For the efficacy and pharmacodynamic (Lp-PLAhttp://www.1000genomes.org/), which contains haplotypes on 1092 samples for ~39.7 million bi-allelic polymorphic markers, by University of Michigan for the STABILITY study and by GSK for the SOLID-TIMI 52 study. Prior to genotype imputation, additional GWAS variants were excluded if Hardy-Weinberg p-values were less than 10−7 in the Principle Component Analysis (PCA)-defined White population or if they showed irreconcilable differences in alleles or allele frequency with reference panel genotypes. Pre-phasing of STABILITY GWAS samples was performed per chromosome using SHAPEIT v2.644 [r2 = -1 plus a comparison of the reference allele frequency data with the imputation allele frequency data). For X chromosome, male and female subjects were phased and imputed separately using the same methods.1000 Genome imputation was performed using the 1000 Genomes Project Reference Panel  and 547,000 (SOLID-TIMI 52) common and low frequency coding variations using GWAS arrays enriched for exome variants were used for imputation of genome-wide variants that were not part of the GWAS arrays, resulting in approximately 13.6M markers post-QC. Imputation data for STABILITY and SOLID-TIMI 52 were kept separately for the statistical analysis.Characteristics of the study population were summarized by number of sample size, mean and standard deviation for continuous variables related to demographics and laboratory measures, percentage for binary variables related to demographics, the use of co-medication and confounding diseases.2 enzyme activity  with time to event outcome for the efficacy endpoints (MCE and MI). A multivariate Cox model that includes treatment, genotype and treatment by genotype interaction and top 10 PCs was used to detect genetic variants associated with efficacy response. A 2-degree-freedom test for both genotype main and genotype by treatment interaction effect was performed. P value, effect size estimates (hazard ratio) and standard errors (SEs) for genotype main effect in treated and placebo arm were obtained in STABILITY and SOLID-TIMI 52 separately. Summary statistics from both trials were meta-analyzed using the inverse variance method using METAL  in each A Cox proportional hazards regression model was used to assess the association of genotype of common variants (MAF>0.5%) with time-to-event outcome for tolerability endpoints (Diarrhea and Odor) in patients treated by darapladib, including allelic dosage assuming an additive genetic model and top 10 PCs as covariates to account for population structure. One degree-freedom P value, effect size estimates (hazard ratio) and SEs for genotype main effect in the darapladib treatment arm were calculated in STABILITY and SOLID-TIMI 52 separately. Summary statistics for the darapladib treatment arm from both trials were meta-analyzed using the inverse variance method using METAL .The X chromosome was imputed and analyzed by sex for each trait in STABILITY and SOLID-TIMI 52 separately. As more than 70% of patients were males in both studies, meta-analysis of all endpoints were conducted in males based on summary statistics from both trials using the inverse variance method in METAL .The Bonferroni corrected multiple test threshold for declaring statistical significance was calculated as P≤5.0E-04 for the candidate gene analysis (0.05/ number of variants tested). For GWAS, the classical GWAS-significance threshold (P≤5E-08) was applied to declare significant results. This threshold was established at a time when most GWAS involved approximately 1 million independent tests, mostly for common variants with MAF>5%. Given the larger number of both common and low frequency variants (MAF<5%) to be analyzed here, this implies a lack of stringent multiple testing correction such that any associations reaching classical genome wide significance would be in need of further follow-up for validation or replication. Due to the exploratory nature of the genetic analyses, no further correction was applied to adjust for the number of endpoints tested.S1 TextDetails on differences in reporting of odor events and functional insights for variants associated with efficacy and tolerability endpoints.(DOCX)Click here for additional data file.S1 Fig2 activity in both placebo and darapladib arms  and Change from Baseline in darapladib arm  at alpha = 5E-08; b) efficacy endpoints in both placebo and darapladib arms  at alpha = 0.001 (candidate gene) and 5E-08 (GWAS) tolerability endpoints in darapladib arm only arms  at alpha = 0.001 (candidate gene) and 5E-08 (GWAS).Power for a) Baseline Lp-PLA(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig2 activity at genome-wide (a&b) and candidate gene (c) significance. The dashed line in the region plots indicates the significance threshold.Regional, forest and box plots of mean difference (MD) of novel loci  associated with baseline Lp-PLA(PDF)Click here for additional data file.S4 FigFor MCE: a) main genotype effect P-value in placebo arm, b) main genotype effect P-value in darapladib, c) genotype by treatment interaction P-value, and d) 2df test P-value for both main genotype and genotype by treatment interaction; For MI: e) main genotype effect P-value in placebo arm, f) main genotype effect P-value in darapladib, g) genotype by treatment interaction P-value, and h) 2df test P-value for both main genotype and genotype by treatment interaction.(PDF)Click here for additional data file.S5 FigThe dashed line in the regional plots indicates the significance threshold. a) rs138741635, b) rs192427471, c) rs181937009, d) rs12290663, e) rs147204125.(PDF)Click here for additional data file.S6 FigThe dashed line in the regional plots indicates the significance threshold. a) rs192476688, rs201052613, b) rs117714106.(PDF)Click here for additional data file.S7 Figa) diarrhea in darapladib arm, b) moderate and severe diarrhea in darapladib arm, c) bathroom relate odor events in darapladib arm, d) non-bathroom relate odor events in darapladib arm.(PDF)Click here for additional data file.S8 Fig(PDF)Click here for additional data file.S9 Fig(PDF)Click here for additional data file.S10 FigHighlights the great diversity in country origin which is inherent to large clinical trials.(PDF)Click here for additional data file.S11 FigMain plot shows association P-values (main plot), with effect estimates in top right insert.(PDF)Click here for additional data file.S1 Tabler2: imputation quality.(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Tabler2: imputation quality; EFF: effect size for log hazard ratio for minor allele; trt: darapladib arm; plc: placebo arm; SE = standard error; Direction: effect direction in STABILITY and SOLID-TIMI 52 trials respectively.(XLSX)Click here for additional data file.S5 TableMeta-analysis P-values are indicated in bold.(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.


Despite the importance of relationships between somatic cell score (SCS) and currently selected traits  of Holstein cows, there was a lack of comprehensive literature for it in Iran. Therefore we tried to examine heritabilities and relationships between these traits using a fixed-regression animal model and Bayesian inference. The data set consisted of 1,078,966 test-day observations from 146,765 primiparous daughters of 1930 sires, with calvings from 2002 to 2013.Marginal posterior means of heritability estimates for SCS (0.03 ± 0.002) were distinctly lower than those for milk (0.204 ± 0.006), fat (0.096 ± 0.004) and protein (0.147 ± 0.005) yields. In the case of phenotypic correlations, the relationships between production and SCS were near zero at the beginning of lactation but become increasingly negative as days in milk increased. Although all environmental correlations between production and SCS were negative , slightly antagonistic genetic correlations were found; with posterior mean of relationships ranging from 0.01 ± 0.039 to 0.11 ± 0.036. This genetic opposition was distinctly higher for protein than for fat.Although small, the positive genetic correlations suggest some genetic antagonism between desired increased milk production and reduced SCS . Selection has traditionally focused on production traits. Today it is generally accepted that undesirable genetic associations  exist between production and health disorders, including mastitis –3. ConseThe SCC (concentration of somatic cells) of cow’s milk is an economically important trait in dairy cattle because it can be used both as an indicator of mastitis and as a measure of response to infection . StudiesPossibilities for simultaneous improvement in SCS and yield traits are dependent on the relationship between the traits. Early estimates of the genetic correlation between these traits, especially from the Iran, were few and based on small data sets. Therefore, the objective of the current study is to estimate heritabilities and genetic, phenotypic and environmental relationships between SCS and currently selected traits  on test-day basis using fixed regression animal models (FR-AM) and Bayesian inference in the first parity for Holstein cows.3 cells/mL) to 500 (× 103 cells/mL) SCC; allowable length of lactation was from 5 to 305 d. Test-day SCC were converted to SCS using a base 2 logarithmic function: SCS = log2 (SCC/100) + 3 [A total of 1,078,966 test-day SCC and production traits  records from 146,765 first lactations of Holstein cows recorded from 2002 to 2013 were analyzed. The cows were descendants of 1930 sires (average 76 cows/sire), were distributed over 268 herds (average 547 cows/herd), with age at first calving ranging from 18 to 36 months (in 19 classes). The data were obtained from the Animal Breeding Center of Iran. The test-day yield was limited to ranges of 3 to 75 kg of milk yield, 1.5 to 8 % fact content, 1 to 7 % protein content, and 1 (× 10100) + 3 . Only dayijklmn was the nth test-day record of the mth cow for a trait i ; HYMij was the jth herd-year-month of test-day effect for a trait i; βikq was the qth fixed regression coefficient for a trait i specific to the kth age-season of calving class; δilq was the qth fixed regression coefficient for a trait i specific to the lth herd-year of calving class; q was the number of covariates; zq(t) was a vector of covariates of size q describing the shape of lactation curve of fixed regressions evaluated at t days in milk; αim and ρim, respectively, were the additive genetic and permanent environmental effects of cow m for a trait i; and e was the residual.The analyses were conducted using a multi-trait FR-AM. The model involved the direct additive, permanent environmental and residual random effects. The fixed part of the lactation curve was modeled with a herd by year-month of test-day effect (HYM) and Legendre polynomials with six coefficients for the age-season of calving effect  and with five coefficients for the herd-year of calving effect . The equation presentation of the model is given by:The (co)variance components were estimated by Bayesian inference using the Gibbs sampler of the GIBBS3F90 program . A chainPhenotypic correlations between yield traits and SCS on daily basis are in Fig. Means and posterior standard errors of distributions for variances of random effects are presented in Table Posterior estimates of the genetic correlation between the milk production traits were moderate to high Fig. , with thPosterior estimates of the genetic correlation between SCS and production traits were low and often symmetric Fig. . Means oPhenotypic correlations between SCS and yield traits were all negative, ranging from 0.0 to −0.24, in agreement with the literature finding that yield traits decreased under mastitis conditions. However phenotypic relationships seemed stranger with advancing days in milk; therefore, the effects of SCS on yields of milk, fat and protein were larger in the late than in the beginning of lactation. This result in contrary to results of most studies , 15, whiAcross production traits, milk yield showed the highest heritability estimates (>0.2). In the earlier study of the Iranian population conducted by Kheirabadi et al. , obtaineAs a consequence, standard errors of the estimates of genetic correlations between yield traits were negligible (<0.02). These results imply that yield traits are influenced by similar genes. This corroborates the idea that increasing milk yield by selection would have a positive effect on the other economic traits (referring to fat and protein yields). It seems clear from results in this and other studies , 22, 23 Previous results from studies , 19, 23 As is usually found in the literature, the genetic correlations between SCS and yield traits were positive, reflecting an antagonism between production and mastitis resistance, whereas the phenotypic correlations were negative, reflecting an unfavorable effect of mastitis on production. This emphasizes the need for including udder health traits in the breeding goal. Results of the present study indicate the presence of low additive genetic variation for SCS in this population of Holstein cows. Therefore, a relatively low genetic progress will be expected following selection. Despite the low estimates of heritability for SCS, the utilization of SCS in order to improve the resistance to mastitis, and in consequence, an increase the profitability of production system, is recommended.


Drosophila melanogaster Genetic Reference Panel.The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for prediction of complex traits as well as estimation of population genetic parameters. Improved inference and prediction accuracy of GBLUP may be achieved by identifying genomic regions enriched for causal genetic variants. We aimed at searching for patterns in GBLUP-derived single-marker statistics, by including them in genetic marker set tests, that could reveal associations between a set of genetic markers (genomic feature) and a complex trait. GBLUP-derived set tests proved to be powerful for detecting genomic features, here defined by gene ontology (GO) terms, enriched for causal variants affecting a quantitative trait in a population with low degree of relatedness. Different set test approaches were compared using simulated data illustrating the impact of trait- and genomic feature-specific factors on detection power. We extended the most powerful single trait set test, covariance association test (CVAT), to a multiple trait setting. The multiple trait CVAT (MT-CVAT) identified functionally relevant GO categories associated with the quantitative trait, chill coma recovery time, in the unrelated, sequenced inbred lines of the  GBLUP is a “black box” modelling approach fitting fixed and random effects simultaneous, utilizing the genetic relationship between individuals based on the correlation structure among genetic markers. Typically, GBLUP ignores prior biological information. Although models ignoring the underlying biology can serve as useful tools for prediction of genetic values or phenotypes, models utilizing known biological mechanisms provide a functional tool for testing our understanding of those mechanisms, and potentially improve inference and prediction accuracy.The genomic best linear unbiased prediction (GBLUP) model has proven to be useful for estimation of population genetic parameters (e.g. heritability) as well as prediction of complex traits7. Such knowledge could be utilized to build models that quantify the joint effect of a set of markers located in a genomic feature, i.e. genomic regions defined by e.g. genes, biological pathways, sequence annotation or other external evidence10. Improved inference and prediction accuray of GBLUP may be achieved by identifying genomic regions enriched for causal genetic variants.It appears that markers associated with trait variation are not uniformly distributed throughout the genome, but enriched in genes that are connected in biological pathways16. Applying the genomic feature best linear unbiased prediction (GFBLUP) model to the Drosophila Genetic Reference Panel (DGRP)18, we have previously demonstrated, that GFBLUP models can increase prediction accuracy for quantitative traits15. These results were further supported by simulation studies illustrating the impact of trait- and genomic feature-specific factors on prediction accuracy15. The GFBLUP model approach is, however, computationally intensive. An alternative approach is to search for patterns in GBLUP-derived single-marker statistics that can reveal associations between a genomic feature and a complex trait. We have previously evaluated a number of GBLUP-derived set tests on a binary outcome (i.e. disease trait) using high-density single nucleotide polymorphisms (SNPs) from genotyping arrays19. These GBLUP-derived set tests proved to be computationally fast and powerful compared to existing set test approaches19.The GBLUP approach can be modified in several ways to utilize genomic features as prior information. One approach is to extend the traditional GBLUP model to include additional genomic effects based on genetic markers located within a genomic featureet al.19, and applied it to whole genome sequence data contrary to the genotypes derived from SNP arrays as previously shown19. Whole genome sequence data greatly exacerbate the true genomic signal to non-causal marker noise problem and may influence the power of set tests. Extending GBLUP-derived set tests could potentially increase detection power and contribute to a better understanding of complex traits’ underlying genetic architecture. First, multiple feature sets can be fitted in the model (e.g. a GFBLUP model), such as grouping markers based on their minor allele frequency20 or prior QTL information16. By fitting multiple feature sets, genetic effects are estimated based on a mixture of normal distributions enabling further differential shrinkage of single marker effects across feature sets. Second, a multiple trait GBLUP model22 can be fitted. This can potentially increase the accuracy of the total genomic value22 and thereby the single marker effect, which in turn will lead to more accurate test statistics for genetic marker sets, thereby increasing detection power of the set test.Here, we evaluated GBLUP-derived set tests on a quantitative trait as opposed to the binary outcome in the study of Rohde The aim of the study was to evaluate and compare genetic marker set tests derived from GBLUP on a quantitative trait using whole genome sequence data. Different set tests were evaluated and compared using simulated data generated from DGRP genotypes, focussing on factors specific to genomic features  that influence the power of set tests to detect genomic features affecting the trait phenotype. Furthermore, we investigated whether the results obtained using the GBLUP-derived set tests can be used to develop more accurate GFBLUP prediction models. Finally, we derived a multiple trait GBLUP set test (MT-CVAT) and used it to identify genomic features associated with a quantitative trait phenotype, chill coma recovery time (CCRT), in the unrelated, sequenced inbred lines of the DGRP.In the following a range of different GBLUP-derived set test approaches will be described in detail. The general procedure is to obtain single marker effects based on a standard GBLUP model, from which it is possible to compute and evaluate a test statistic for a set of genetic markers, measuring the degree of association between the genomic feature and the complex trait phenotype. This includes the statistical model and the underlying assumptions, test statistics for the set of genetic markers, and statistical procedures for assessing the statistical significance of the observed test statistic under a specific null hypothesis.The GBLUP-derived set test approach is based on two steps: First a standard linear mixed model is fitted, and then a test statistic for the marker set is computed.y is the vector of phenotypic observations, X and Z are design matrices for the fixed and random effects, b is a vector of fixed effects, g is the vector of genomic values captured by all genetic markers, and e is the vector of residuals. The random genomic values and the residuals were assumed to be independent normally distributed values described as follows:GBLUP is based on a linear mixed model including only one random genomic effect:G is constructed23 using all genetic markers as follows: G = WW′/m, where W is the centered and scaled genotype matrix, and m is the total number of markers. Each column vector of W was calculated as follows: i is the minor allele frequency of the ith genetic marker and ai is the ith column vector of the allele count matrix, A which contains the genotypes coded as 0, 1 or 2 counting the number of the minor allele.The additive genomic relationship matrix Single marker effects and the (co)variance of the single marker effects can be computed as:25 for GBLUP where Cgg is the part of this equation system that corresponds to the total genomic value.In this expression, the (co)variance of the predicted genomic value e residual degrees of freedom. The residual degrees of freedom dfe is computed as tr(I − H), which is equivalent to n − tr(H) where n is the total number of phenotypic observations and tr(H) represents the degrees of freedom occupied by the penalised fit (e.g. the linear mixed model fit). The hat matrix H transforms y into Assessing association of individual markers is based on a single marker test statistic such as the t-statistic and a threshold for this statistic.The set test statistics for the feature set can be computed in a number of ways. Below is described four different approaches all derived from the GBLUP model.first set test statistic is the covariance association test (CVAT)19, which considers the covariance between the total genomic effect for all markers The f and mr respectively.In this expression f columns in W at random.The distribution of this set test statistic under the competitive null hypothesis (genomic feature comprises randomly sampled markers) is unknown and an empirical distribution is required. An empirical distribution for the competitive null hypothesis can be obtained by sampling msecond set test statistic considered is a commonly used score based approach. It is derived from the first derivative of the likelihood as is Rao’s score test26. A key difference compared to Rao’s score test is that only the quadratic term in the first derivative form the basis of this test statistic29 from an argument that this is the only part that involves the data30. The score based approach used here is thus equivalent to the sequence kernel association test (SKAT)28. The score statistic can therefore be written as:b and the phenotypic covariance matrix V are estimated under a null model. The purpose of the null model is to adjust for environmental non-genetic factors, and for genetic factors not part of the genomic feature, including population structure. Several alternative null models can be used in the score test approach. If the GBLUP model is used as the null model the genomic effects can either be defined as f columns in W. It is also possible to derive an approximate distribution using the Satterthwaite’s procedure of moment matching to approximate the null distribution of TScore by a Gamma distribution29. The two parameters in the approximate distribution are calculated by matching the first and second moments (mean and variance) with those of the score set test statistic.The third test statistic is based on the sum of the test statistic for all genetic markers belonging to the same genomic feature such as:i represents the i’th single variant test statistic, e.g. marker effects , t0 is an arbitrary chosen threshold for the single marker test statistics, and I is an indicator function that takes the value one if the argument  and mf . Alternatively, the statistical significance of the Tcount statistic can be assessed using a χ2 test for independence31 or by obtaining an empirical distribution under a specific null hypothesis.The The CVAT is a flexible set test approach which can be extended in a number of ways facilitating further investigation of the underlying genetic architecture of complex traits. E.g. it can be decomposed at different levels of a hierarchy of gene sets, genes and markers; it can be derived from a model with multiple genetic components; or it can be derived from multiple trait models.First, the CVAT statistic can be decomposed at different levels of a hierarchical genomic feature classification scheme, such as decomposing the covariance between the total genomic value and the genomic value defined by a genomic feature at the pathway level (e.g. group of genes) into the contribution from individual genes Second, the CVAT statistic can be derived from a GFBLUP model with multiple genetic components19. The total genomic values in the GBLUP model are assumed to be drawn from the same distribution 20 or genetic markers known a priori to have large effects (e.g. discovered in previous GWAS). Such prior information can be used by fitting multiple genetic components in the linear mixed model:gi is the vector of genetic values captured by the i’th genetic marker set. The random genetic effects and residuals were assumed to be independent and distributed as The notation is similar to the GBLUP model presented above except Third, the CVAT statistic can be derived from a multiple trait GBLUP model (or GFBLUP model)22. This can be important if we have records on correlated traits, for example a high heritability trait (or a trait with many observations) correlated with a low heritability trait (or a trait with few observations). In such a situation using a multiple trait model is likely to increase the accuray of the predicted total genetic value and single marker effects for the low heritability trait which in turn will increase the power of the set test. This becomes highly relevant for borrowing information across traits or same trait recorded in different breeds or study populations. The linear mixed model for multiple traits (2 traits in this example) can be expressed as:y1 and y2 are vectors of phenotypes for trait 1 and 2, respectively. X1 and X2 are design matrices for the fixed effects and b1 and b2 are the vectors of these fixed effects. Z1 and Z2 are design matrices for the random effects, g1 and g2 are vectors of total genetic values and e1 and e2 are vectors of residuals for trait 1 and 2.The notation is similar to the GBLUP model presented above except that CVAT can be used to identify features associated with the covariance between total genetic values in different traits expressed as:The random genetic effects, which consider the covariance between the total genomic effect for all markers 33 as implemented in the software DMU. In this procedure, matrices were not full rank due to centering of the observed genotypes, which necessitated a generalized inverse of the genomic relationship matrices.Estimates of the variance components  should be retained. The empirical distribution of the test statistics was therefore obtained using the circular permutation procedure as described in Cabrera http://psoerensen.github.io/qgg/. This includes fitting a series of linear mixed models, estimating variance components using methods such as REML, computing the test statistic for the set of genetic markers, and testing the statistical significance of the observed test statistic under a specific null hypothesis. Example scripts and data sets are provided for illustrating the GBLUP model derived set test approaches. For specific experimental design with replicated phenotypes within line such as DGRP it is more efficient to use the AI-REML procedure33 implemented in DMU32. The AI-REML function in the qgg package provides an R interface to the DMU which can be downloaded from http://dmu.agrsci.dk/DMU/. The CVAT approach can also be derived from the REML procedures implemented in existing software packages commonly used in genomics such as GCTA36, LDAK37, DISSECT38 and MTG239.The GBLUP-derived set test approaches described above were implemented in the R package qgg, which is available at 2), proportion of genomic variance explained by causal SNPs in the genomic feature (dilution), genome distribution of causal SNPs  and the number of phenotypic records for each genotype (Nrep). For each data set and replicate we estimated variance components for the GBLUP and GFBLUP models using AI-REML and applied the different set tests (not including the extensions to CVAT).To compare the different set test approaches described above, and to investigate different factors that might influence the power to detect causal sets of SNPs, a series of phenotypic simulations were established. The factors varied in the simulations should imitate different genetic architectures and included genomic heritability  with minor allele frequencies ≥0.05 and for which the Phred scaled variant quality was greater than 500 and the genotype call rate was ≥0.8, resulting in a total of 1,725,755 SNPs.The simulations were based on the real genotype DGRP data set of 205 lines. Genotypes were originally obtained from whole genome sequences using an integrative genotyping procedureC1, contained 100 SNPs and was used as the causal SNP set in the genomic feature that explained 10%, 20%, 30%, or 50% of the genomic variance. The second subset, C2, contained 900 SNPs and explained the remaining genomic variance. To mimic relevant genetic scenarios, the genome distribution of the causal SNPs in the genomic feature was simulated using two different causal models: a random and a cluster model. The cluster model simulates the situation in which multiple causal SNPs occur in a limited number of genes, whereas in the random model single causal SNPs occur in a larger number of genes. The main difference is that the genomic variance is associated with a smaller genome region in the cluster model compared to the random model. For the clustered causal model, the 100 causal SNPs in C1 were chosen from 20 randomly selected genome regions spanning 50 SNPs each, and the remaining 900 SNPs in C2 were randomly selected from the complete SNP set (excluding the SNPs in C1). For the random causal model, the SNPs in C1 and C2 were randomly selected from the complete SNP set. To investigate the effects of non-causal SNPs within the causal sets, we added an increasing number of non-causal SNPs , to the causal set C1, in a process referred to as dilution. To determine the false-positive rate, 50 non-causal SNP sets of varying sizes  were sampled, none of which were contained in the causal sets of SNPs.In all scenarios, there were 1,000 causal SNPs, which were divided into two subsets. The first subset, G1 and G2 are the genomic relationship matrices for causal SNPs in C1 and C2, respectively. Z is a design matrix linking DGRP lines to individual phenotypes. The total phenotypic variance C1rep of 5, 10, and 50. Increasing the number of replicates per line decreases the variance of the phenotypic value for each line. Combining these factors yielded a total of 1,440 individual simulated data sets [3 (Nrep) × 3 (h2) × 4  that are correctly identified, i.e. the ratio between the number of identified causal sets and the number of sets that should have been identified, thus, the sum of TP and false negatives (FN). Contrary, the precision p is the proportion of positives that truly are positives, i.e. the proportion of true causal sets of all sets identified, thus, the sum of TP and false positives (FP). The F1 score can take values between 0 and 1, with maximum performance at the value of 1. The F1 score was calculated for each test staticstic under each combination of factors, using a p-value cut-off of 0.05 for a positive detection of a genomic feature.To measure the performance of the different test statistics we used the F15. The predictive ability of the GFBLUP model was assessed using a cross validation procedure15. In the GFBLUP model the total genomic value is We investigated whether the results obtained using the GBLUP derived set tests can be used to develop more accurate GFBLUP prediction models. The GFBLUP model is an extension of the traditional GBLUP model, where an additional genomic effect (defined by the genomic feature) is included in the linear mixed modelCVAT was computed. The relationship between this test statistic and the predictive ability of incorporating these GO terms as features in the GFBLUP model15 was considered.We applied the GBLUP-derived CVAT on CCRT measured in the DGRP. The CVAT test statistic was chosen based on its good performance in the simulation studies (see first section of Results). Individual genes and gene ontology (GO) terms defined genetic marker sets (genomic features) for which TDrosophila melanogaster Genetic Reference Panel (DGRP)18. All data can be accessed via the website: http://dgrp2.gnets.ncsu.edu/. The DGRP consists of 205 inbred lines obtained by 20 generations of full-sib mating from the offspring of single wild-caught females collected from the Raleigh, NC, USA population, and which have full genome sequence data available18. All flies were reared under standard culture conditions . The DGRP is polymorphic for common inversions and Wolbachia pipientis infection status18. These factors were included in the models described below as fixed effects.The phenotypic and genotypic data originate from the 41. There were two replicates of ~50 flies/sex/line .Chill coma recovery time (CCRT) for 159 DGRP lines was measured by transferring three to seven day old flies without anesthesia to empty vials, and placing them on ice for three hours. Flies were transferred to room temperature, and the time it took for each individual to right itself and stand on its legs was recorded18. All analyses were based on segregating biallelic single nucleotide polymorphisms (SNPs) with minor allele frequencies ≥0.05 and for which the Phred scaled variant quality was greater than 500 and the genotype call rate was ≥0.8, for a total of 1,725,755 SNPs distributed on six chromosome arms .Genotypes were obtained from whole genome sequences using an integrative genotyping procedure42 using the BioConductor package ‘org.Dm.eg.db’ v. 2.1443. Only GO terms with at least 10 directly evidenced genes were used in the analyses. SNPs were mapped to FlyBase genes using the v5.49 annotations of the D. melanogaster reference genome44. Only the 963,235 SNPs located within genes (i.e. within open reading frames) were used for the genomic feature. In total the markers were annotated to 10,517 genes and 1,117 GO terms. A total of 1,725,755 markers were used in all analyses, and the number of markers linked to a single GO term ranged from 23–163,938.Genomic features were defined at gene-level and GO level. Genes grouped according to a specific GO term were considered a genomic feature. Genes were linked to the ‘Biological Processes’ (BP), ‘Molecular Function’ (MF), and ‘Cellular Component’ (CC) GO termsy1 and y2 are vectors of phenotypes for trait 1  and 2 , X1 and X2 are design matrices for fixed effects of inversion karyotypes and Wolbachia infection status and b1 and b2 are the vectors of these fixed effects. Z1 and Z2 are design matrices linking observations to genomic values, g1 and g2 are vectors of total genetic values. Q1 and Q2 are design matrices for replicate within line effects, l1 and l2 the vectors of replicate within line effects, and e1 and e2 are vectors of residuals for trait 1 and 2.We applied CVAT to the CCRT data, and considered CCRT in males and females as two different, but correlated traits. The multiple trait CVAT analysis was based on phenotypic records of the quantitative trait adjusted for relevant factors using the following multi-trait linear mixed model:The random genetic effects,CVAT, was computed using the vectors of total genomic values in males and females, g1 and g2 from the multiple trait analyses. The within trait CVAT test statistics were computed as The CVAT test statistic, TThe single trait CVAT was done by analysing phenotypes for males and females separately using the same fixed and random factors as in the multiple trait model presented above.An empirical distribution of these test statistics was based on a competitive null hypothesis using the permutation procedure described earlier. Two competive null hypotheses were used to test if the observed test statistics of the genomic feature differs from the test statistics obtained by randomly sampling genetic markers from (a) exclusively genic regions or (b) the whole genome (i.e. genic and intergenic regions). Thus empirical distributions were obtained by either sampling genetic markers randomly from gene regions or the whole genome.CVAT, was generally more powerful (i.e. highest F1 score) than other set test statistics, across all scenarios , three number of replicates (Nrep), and 4 levels of proportion of additive genetic variance explained by causal SNPs ) improves, as heritability and number of replicates increase, such that it’s power reaches levels comparable to the score based set test statistic  with each other  and 0.01 (0.52). This was also the case for TScore and TCount showing a correlation of 0.74 at a p-value cut-off of 0.05 and 0.48 at p-values less than 0.01.The p-values of set test statistics Ther Fig. . The figCVAT, TScore, and TSum) were all highly correlated to the predictive ability of the GFBLUP model , even without dilution. Impact of dilution was less severe when the proportion of genomic variance explained by genomic feature was highest (rep = 50 replicates within line and a genomic heritability of 50%. The level of genomic heritability (h2) was positively correlated with power .Here we present the results for the GBLUP-derived CVAT set test statistic Fig. . We focuSince the simulation study suggested that CVAT was the most powerful set test statistic, we applied CVAT and its extensions to CCRT data.a) and (b) respectively). Furthermore, the association of GO terms with CCRT was highly correlated between males and females  and (d)) under both the gene based or whole genome null hypothesis (correlation = 0.95 and 0.98 respectively).Initially we fitted a series of linear mixed models (GBLUP or GFBLUP) to determine the final model to be used in the subsequent CVAT analyses of the CCRT trait in DGRP. Models were fitted for single and multiple traits, including one or two features (in this case genes and inter-genic regions) and considering gene based and genome based null hypotheses. Trait heritability for CCRT estimated using a multiple trait GBLUP model was 0.42 for males and 0.48 for females. The genetic correlation between males and females was 0.97. Partitioning genomic variance into genes and inter-genic regions, using a two-component genomic feature model, did not significantly improve the model fit . The empirical distribution of the CVAT test statistic was determined under two null hypotheses: One involving random sampling of genetic markers from gene regions (gene based), and one based on random sampling of genetic markers from the whole genome (whole genome based). Under the gene based null hypothesis GO terms were slightly more significant compared to the whole genome based null hypothesis both in the case of females and males  and (f)). Therefore, results for the CCRT trait reported here are from a one-component, two trait model using a gene based null hypothesis.The significance of Tall markers in males, and the genomic effect for markers in the feature for females and vice versa (MT-CVAT across traits). Eight GO terms for females and nine GO terms for males were significantly associated with CCRT. Males and females shared all but one of the most significant GO terms . The GO term “ATP-dependent DNA helicase activity”  for females being only slightly above the 0.001 p-value cut-off. The across trait  MT-CVAT set test results showed similar patterns as the within trait CVAT set test.Several GO terms were significantly associated to CCRT in both males and females  was significantly associated with CCRT and that this gene is part of all but one of the significant GO terms , ultrabithorax (FBgn0003944), and extra macrochaetae (FBgn0000575) contributed to the significance of the GO term “Midgut development” (GO:0007494), and the genes mago nashi (FBgn0002736) and roughoid (FBgn0003295) contributed to the significance of the GO term “Epidermal growth factor receptor signaling pathway” (GO:0007173). Finally, we found that the genes Chd3 (FBgn0023395) and helicase 89B (FBgn0022787) contributed to the significance of the GO term “ATP-dependent DNA helicase activity” (GO:0004003).Considering the overlap of SNPs between GO terms, further investigations were required to better understand the biological relevance of the CVAT results obtained at the GO term level. Therefore, we applied the CVAT set test at the individual gene level using only the genes that were part of the significantly associated GO terms. This enabled us to identify a number of genes that were associated with CCRT Fig. . In partrms Fig. . In addiWe demonstrated that GBLUP-derived set tests are powerful for detecting genomic features enriched for causal variants affecting a quantitative trait in populations with a low degree of linkage disequilibrium. The different set tests were compared using simulated data generated from DGRP genotypes further illustrating the impact of trait- and genomic feature-specific factors on detection power. These set tests provide a formal statistical modeling framework for borrowing and evaluating information across a wide range of experimental studies that may help provide novel insights into genetic and biological mechanisms underlying complex traits. The methods are computationally fast allowing us to rapidly analyze many different classes of genomic features. This will help to discover genomic features enriched for causal variants that can be used to develop more accurate predictions using GFBLUP models. GBLUP-derived set tests are based on a flexible linear mixed modelling framework that allows us to adjust for other known genetic and non-genetic factors, while using existing standard software. Importantly, the GBLUP models can be extended in several ways that potentially can increase detection power.28 , and that both CVAT and SKAT outperformed the methods based on summing the number of single marker statistics in the feature. Both CVAT and SKAT are fast and powerful methods to identify genomic features enriched for causal variants and thereby contribute to develop more accurate prediction models. One advantage of the CVAT approach is that it builds on a flexible linear mixed modelling framework that can be extended in several ways that potentially can increase detection power. Extensions include the consideration of different levels of a hierarchical feature, multiple genetic components having different genomic value distributions and a multiple trait GBLUP.Several GBLUP-derived set tests were compared in terms of statistical power to detect genomic features enriched for causal variants. Despite GBLUP being considered a “black box” modeling approach we showed that it is possible to derive powerful set tests from it. In particular, in all scenarios evaluated we showed that the covariance association test (CVAT) had similar power to a commonly used score based approachCount) appear to have lower power compared to test statistics based on summing the squared single marker statistics (TSum). This may in part be explained by the simulated genetic architecture which were enriched for causal variants with small to moderate effects. In general, methods based on a count test statistic are likely to have high power to detect association if the genomic feature harbours genetic markers with large effects, but it will not detect a genomic feature with many genetic markers having small to moderate effects45. Our results show that in such cases, it is more powerful to use a test statistic, such as the mean or sum of the single marker statistics for the genomic feature.Set tests based on counting test statistics . Furthermore, the genetic architecture of the causal variants  also influenced power. The increased detection power and resistance towards dilution in the case where the true causal SNPs are clustered in smaller genomic regions is likely due to larger effect size of individual markers in these regions. Not surprisingly, power is increased if the trait is highly heritable and the number of phenotypic records available is high. These patterns were consistent across the different set tests and are factors that need to be considered in the analyses of real data.Drosophila melanogaster Genetic Reference Panel. The population consist of 205 largely unrelated lines with a low degree of linkage disequlibrium across their genomes. Thus, our results suggest that GBLUP-derived set tests may have high power in situations where individuals are largely unrelated such as human study populations. In a population of highly related individuals the general genomic relationship will be a good approximation of the genomic relationship at the true causal variants2. This will lead to more accurate estimates of overall genomic value. On the other hand due to extensive linkage disequilibrium it may be difficult to accurately estimate single marker effects and this will in turn influence the feature set test statistic. Therefore more research is required to understand the influence of genetic relatedness and degree of linkage disequilibrium on detection power of the GBLUP derived set tests.We compared the GBLUP derived set tests based on genotypes obtained from the sequenced inbred lines of the self-contained null hypothesis48. The self-contained null hypothesis states that the genomic feature, by it self, does not display any association to the phenotypic trait. This is usually done by testing whether the variance component or the test statistics for the genomic feature are zero. The self-contained may be preferable over a competitive, as it has more power in general46, and the interpretation is simpler, as it determines whether there is association or not. On the other hand the competitive null hypothesis is perhaps more biologically relevant as it is in agreement with the infinitesimal model50 which is a commonly used genetic model assuming that there are many causal variants each with small to moderate effects underlying the complex trait.In this study we compared the set tests using a competitive null hypothesis. The competitive null hypothesis states that the degree of association within a genomic feature is equal to that of a random set of genetic markers. An alternative is the 20 or prior QTL information16. By fitting multiple feature sets genetic effects are estimated based on a mixture of normal distributions enabling further differential shrinkage of single marker effects across feature sets. Second, further shrinkage of single marker effects within features may be achieved by using a weighted genomic relationship matrix51 for each feature set. Third, a multiple trait GBLUP model22 can be fitted. This can increase the accuracy of the overall genomic effect22 and thereby the single marker effect which in turn will lead to a more accurate test statistic for the genetic marker set. Fourth, in animal and plant populations with extended pedigrees we might use information on inviduals without genotype information51 to increase accuracy of the overall genomic value. We are currently investigating these extensions hypothesizing that they, in some situations, may lead to increased power of the GBLUP-derived set test.The GBLUP-derived set test modeling framework can be extended in several ways that potentially can increase detection power. First, multiple feature sets can be fitted in the model (e.g. a GFBLUP model), such as grouping markers based on their minor allele frequency55. In particular Bayesian mixture models such as BayesB56, BayesR57 or Bayesian Lasso models58 are relevant alternative methods. For these methods it is also possible to derive test statistics that quantify the joint effect of the markers in the feature set. Furthermore, they also allow for differential shrinkage of marker effects within feature sets and can be used to fit multiple feature sets. More investigations are required to compare these methods to the GBLUP-derived set test and investigate to what extend these methods will increase detection power.The GBLUP and GFBLUP models used in this study can also be implemented using Bayesian methodsAlthough the main objective of this paper was to compare different GBLUP derived set tests, we would like to discuss, albeit very condensed, the plausible biological relevance of our results.59, (b) indirectly mediating circadian rhythm, through actin regulation60 as well as (c) contributing to ion homeostasis by regulating K+ channel cell surface expression61.CCRT was strongly associated with the GO terms ‘Rho protein signal transduction’ (GO:0007266) and ‘Rho GTPase activator activity’ (GO:0005100) in both males and females. Rho genes’ functional relevance, with regards to CCRT, has been implied by their involvement in (a) intracellular signal transduction pathways63. Insect cold resistance is directly related to maintenance of water and ion homeostasis66. In the fall field cricket, Gryllus pennsylvanicus, the midgut has been shown to be the most sensitive site for the exchange of ions and water during cold exposure67. In the midgut cold exposure caused rising Na+ levels causing a disruption in water homeostasis ultimately leading to an increased K+concentration in the hemolymph65. It is ultimately this increased K+ concentration that causes an electrophysiological failure of the neuromuscular system and subsequent chill-coma70.“Midgut development” (GO:0007494) was also among the high-ranking GO terms for association with CCRT. It is well established that the midgut of insects is an important site for the exchange of ions with the hemolymphcrossveinless-c gene (FBgn0086901), was highly significant and is a part of both Rho protein signal transduction and Rho GTPase activator activity GO terms42. Crossveinless-c is an important regulator of Rho GTPase activity71. The Rho-family of GTPases are in turn associated with the direct regulation of the actin cytoskeleton72. Chilling has been shown to disrupt cytoskeletal organization in primary embryonic cultures of Drosophila cells73. Interestingly, diapausing mosquitos (Culex pipens) have greater abundance of polymerized actin at muscle fiber intersections in the midgut74. Thus, regulation of cytoskeletal function may be implicated as an important component of cold acclimation.There was a substantial overlap among the SNPs associated with each of the top-ranking GO terms. In order to zoom in on relevant genes underlying these GO terms, the CVAT set test was also applied at the individual gene level of the significant GO terms. This enabled us to identify a number of the genes that was associated to CCRT. In particular, we found that the 75.In general, biological interpretation might be hampered by the definition (or misspecification) of the genomic feature and a potential large overlap in the genetic marker sets between the different genomic feature classes. In the latter case, biological interpretation may be improved by using methods that take the overlap into accountGBLUP-derived set tests are powerful compared to existing methods for detecting genomic features enriched for causal variants in populations with a low degree of linkage disequilibrium. The tests can be implemented using standard BLUP models, and can be extended in several ways that potentially can increase detection power. The methods are computationally fast allowing us to rapidly analyze many different classes of genomic features. This will help to discover genomic features enriched for causal variants that can be used to develop more accurate predictions using GFBLUP models.Supplementary Figure 1


In recent years, substantial effort has gone into disentangling the genetic contribution to individual differences in behaviour . Heritability estimates from twin and family studies, and more recently using whole genome approaches, suggest a substantial genetic component to these traits. However, efforts to identify the genes that influence these traits have had relatively little success. Here, we review current work investigating the heritability of individual differences in behavioural traits and provide an overview of the results from genome-wide association analyses of these traits to date. In addition, we discuss the implications of these findings for the potential applications of Mendelian randomization.This article is part of the theme issue ‘Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'. Early studies relied on a candidate gene approach to identify genes influencing these traits; however, many of these failed to replicate, despite having a plausible biological mechanism. More recent studies have used whole genome approaches to investigate the genetic architecture of behavioural traits. However, unlike for many other complex traits such as height ,2 and scIt has been argued that a small number of factors can be used to account for individual differences in personality –8. AlthoWith recent advances in computing and availability of increasingly rich data sources, there has been a surge in the number of behavioural genetic studies investigating personality and temperament traits. Behavioural genetics has the potential both to quantify genetic influences on these traits and to indirectly quantify environmental influences . Here, w2.To investigate the genetic contribution to a phenotype, it is useful to first estimate its heritability . HeritabVP) that can be attributed to genetic differences for the particular context and timepoint at which it is estimated. This can include the proportion of variance due to additive genetic effects (VA), known as narrow-sense heritability (h2), or the proportion due to all genetic effects (VG), known as broad-sense heritability (H2) [H2 includes both between-loci interactions (epistasis) and within-loci interactions (dominance) effects.Heritability is the proportion of variation in a phenotype (ity (H2) . In addiHeritability can be estimated using a number of methods. These include twin, family and adoption studies, in addition to more recent methods that use data captured by genome-wide arrays. Estimates calculated according to these models can be thought of as ‘true’ heritability. These estimates will incorporate heritability due to variants across the entire genome, including rare variants and those not captured by SNPs included on genotyping platforms. However, twin models, by design, use closely related individuals. These individuals are thus likely to share a great deal of their environment, which could lead to false inflation of heritability estimates.Additional methods estimate heritability using SNP-based approaches. This includes heritability due to variants robustly associated with the phenotype of interest that are identified through genome-wide association study (GWASs) VP) and these comprise additive genetic effects (VA), shared environmental effects (VC) and non-shared environmental effects (VE) [Twin studies have been extensively used to disentangle the role of genetics and environment on human traits . These mcts (VE) . Heritabcts (VE) . Violaticts (VE) .et al. [N = 568) investigating temperament and personality traits, the majority (84%) of published twin correlations were consistent with a simple model containing just additive genetic effects [A recent meta-analysis by Polderman et al.  focusing effects ,19. Howe effects ,21.A recent meta-analysis of personality and temperament traits has combined evidence from twin, family and adoption studies . The autDespite the variation across models of personality structure considered in these studies, there is clear evidence that a proportion of variation in these individual difference traits can be attributed to genetic variation. Vukasović & Bratko  found noALDH2 Mendelian randomization example in section 5).An additional consideration when undertaking genetic analysis is the population under investigation. In this meta-analysis, there is an underrepresentation of Asian populations and no representation of South American or African populations. Performing genetic analyses across different samples can be difficult owing to the possibility of population stratification . Risk alλ) [Allele frequencies can differ across populations, which can cause difficulties in association studies . These dλ)  or perfoλ) .(b)h2), it can be problematic to obtain large enough samples to precisely estimate these effects. With recent advances in computing, and the increasing willingness of many cohorts to combine efforts through large-scale consortia, it has been possible to create increasingly large genotyped samples with data on a wide range of phenotypic measures. Unlike twin studies which rely on assumed levels of genetic correlation between twin pairs, we are now able to measure the degree of genetic similarity between individuals in a dataset and look at the extent to which genetic and phenotypic similarity correlates. This means that it is now possible to calculate heritability estimates using non-related individuals [Although the twin studies discussed previously provide estimates of true narrow-sense heritability  and openness . They also identified substantial genetic correlation (rG) between the neuroticism and extraversion factors  and between neuroticism and openness .A recent study by Power & Pluess  used theAlthough previous studies have estimated SNP heritability estimates for various facets of personality ,37, thiset al. [h2 estimates was increased in this study, which involved a sample size of approximately 59 000 participants.A recent meta-analysis by Lo et al.  estimate3.Despite emerging evidence that individual difference traits are heritable, so far relatively few individual genetic variants associated with these traits have been identified. This is likely due to the polygenic nature of these traits. Under the evolutionary neutral model, most variants with large effects will be rare; the majority of phenotypic variation is likely to be due to common variants of small effect . InitialGWASs use a hypothesis-free method of identifying specific common variants associated with a phenotype. Analyses are performed for each SNP in the dataset, which can be either genotyped or imputed. This assumes that any causal SNPs will be either captured within the analysis or tagged by those that are included. GWAS performs a regression for each variant, with the appropriate method determined by the format of the phenotype of interest. The regression model includes the genotype at that SNP plus any other relevant confounding variables, such as principal components, in order to adjust for population stratification, or other covariates that could account for variation in the phenotype not acting through the SNP of interest.p-value has been calculated which accounts for the likely number of functional variants being tested. This is the widely used p < 0.05 level of ‘statistical significance’ divided by 1 million, an approximation of the number of independent tests carried out across the genome. The accepted level of genome-wide significance is therefore p < 5 × 10−8 [To account for the extensive multiple testing performed by running these analyses across the genome, a Bonferroni corrected 5 × 10−8 . This is(a)a priori based on some underlying knowledge of a plausible biological mechanism associated with a particular locus. In recent years, the associations between the serotoninergic and dopaminergic neurotransmitter systems have gained attention [In a candidate gene study, the association between allele frequency at a particular variant and phenotype of an individual is investigated . These vttention –50; howeAlthough candidate gene studies were once the norm for behavioural genetic studies, many findings failed to replicate leaving the literature awash with false positive results. A recent study investigated findings from the largest schizophrenia GWAS to date and found no evidence that variants in the most-studied candidate genes were more associated with the disorder than non-candidate genes . Candida(b)(i)GWASs have been successful in identifying loci associated with many complex traits, for example a recent GWAS of schizophrenia identified 108 loci robustly associated with the disorder , while oN = 123 132 to 260 861) [A number of early GWASs focused on the FFM ,54–57; h260 861) . This stThe majority of genome-wide analyses of individual differences have been performed in samples of European ancestry. However, studies have also investigated the genetic architecture of these factors within a Korean sample ,55. Thes(ii)N = 449 484). This study reports finding 136 independent genetic variants associated with neuroticism levels [In addition to studies looking at each of these five domains, other studies have focused on a particular trait. Several studies have focused on neuroticism, with the largest study to date including data from UK Biobank, 23andMe and the Genetics of Personality Consortium N = 9 484. ThOther studies have focused on extraversion, although the samples for these have been smaller and to date have yielded few robust findings ,59. HoweExcitement-seeking and temperament were also investigated using genome-wide association analyses; however, these also comprised relatively small samples and did not find robust evidence of variants associated with these traits –62.With the availability of large samples such as UK Biobank, there have been recent GWAS successes for other individual difference traits such as depressive symptoms. Progress in identifying genetic variants associated with depression had previously been slow, but in recent years, three large GWASs of major depressive disorder have been published ,63,64. T4.CHRNA5-A3-B4 on chromosome 15, which is robustly associated with heaviness of smoking. This association was first reported in candidate gene studies [CHRNA5-A3-B4 locus has also been identified in GWASs of lung cancer [CHRNA5-A3-B4 locus is moderated through smoking status and mediated through the resulting tobacco exposure among smokers [With recent advances in computing and the large number of cohorts willing to pool resources, there has been a rapid growth in the number of published GWASs and publicly available summary statistics. These studies have led to the identification of a vast number of genetic variants associated with disease outcomes in addition to health and lifestyle behaviours. Genome-wide association analyses have made a vast contribution to our understanding of the biological mechanisms unpinning many complex traits –3,65–67. studies  and late studies  and stud studies –73. Subs studies . While tg cancer  and chrog cancer . In the  smokers ,71.For individual personality and temperament traits, it may also be possible to identify the effects of modifiable risk factors by dissecting the results of newly published genome-wide analyses. It is likely that any effects, acting either through biological mechanisms or via modifiable exposures, are small, and large sample sizes will be required to detect these effects. With the recent release of the full UK Biobank sample, in addition to the increasingly collaborative direction in which genetic research appears to be heading, it seems likely that future analyses of these individual difference traits will be undertaken on greater samples, thus increasing power. Despite the fact that samples may not currently be powered to detect variants associated with modifiable risk factors at a genome-wide level, it is possible to interrogate the summary statistics relating to these analyses and investigate for enrichment of variants and networks that are known to relate to exposures of interest.GWASs of other individual difference traits such as reproductive outcomes (measured by age at first birth and number of children ever born)  and educOnce we have estimated the heritability of a trait, we know that there is a genetic component to this phenotype, although further work needs to be carried out to determine which genes influence this. If heritability has been calculated for multiple traits, then we know that there is a genetic component to each of these; however, we do not know if these are influenced by the same genes. Genetic correlation is a method to quantify how much genetic overlap there is between traits. It provides an estimate of the additive genetic effect shared between pairs of traits. It is possible to estimate genetic correlation using either publicly available summary statistics or individual-level data. The vast number of published GWASs means that effect sizes are available for a wide range of traits on specific variants, and we can use the correlation between these to assess the genetic overlap between traits. Although these do not provide information on the causality of any potential relationship, they shed light on the amount of shared genetic architecture across traits. Any overlap here could be due to pleiotropy (genetic effects on multiple traits), shared biological mechanisms between traits or a causal relationship from one trait to another, but the direction of this cannot be ascertained from these approaches.et al. investigated genetic overlap between neuroticism and several mental health outcomes, anthropometric and health-related traits using previously published summary statistics. Strong evidence of genetic correlation was found for several of these outcomes, with the greatest correlations observed with anxiety, depression and subjective well-being. There was also evidence of moderate–low genetic correlation between neuroticism and a number of other outcomes including schizophrenia, attention deficit hyperactivity disorder (ADHD), anorexia nervosa, educational attainment and height [There is a well-documented association between personality domains and a range of health behaviours and physical and mental health outcomes ,79–85. Rd height .5.Once genetic correlation between traits has been established, it may be possible to disentangle the nature of the relationship using techniques such as Mendelian randomization (MR). MR is a method of investigating causality using observational data. Genetic variants are used as proxies, or instrumental variables, for a modifiable risk factor of interest. The principles and assumptions underlying MR have been described in detail elsewhere ,11,86,87For cases where there are strong genetic instruments for both the exposure and outcome, bidirectional MR methods can be used to provide stronger evidence of the direction of effect. Assuming that the instruments are equally as strong for the exposure and outcome, if we were to observe strong evidence of an association in one direction, but not in the other, this would strengthen our confidence that this is the true direction of effect. However, MR alone is not enough to provide definitive evidence of causality, and evidence from different approaches should be triangulated. MR estimates should be compared with those from other methods to investigate whether results are consistent across different approaches and to build a more complete picture of the true effect.To date, there have been relatively few MR studies carried out using individual differences in personality, in part due to the lack of strong instruments for these traits. However, with the publication of increasingly large GWASs for these traits, we expect instrument strength to increase and MR analyses to become more feasible. MR has provided insights into the causal effects of other individual difference traits, such as educational attainment, depression and anxiety ,89. A reALDH2*2 genotype is found almost exclusively in Asian populations and can be used as a strong instrument for alcohol use in these samples. This study found evidence of a causal role for alcohol in adolescent aggression and attention problems.As mentioned previously, allele frequencies can differ across populations, and this information can be useful when designing MR analyses. A recent study investigating the role of alcohol use in adolescent internalizing and externalizing problems was performed within a sample of Chinese adolescents . The ALDIn addition, MR can provide insights into evolutionary developmental hypotheses as it allows us to look at varying levels of genetic vulnerability to a trait, rather than focusing on an individual's phenotype. It seems likely that particular traits would be susceptible to selection; for example, individuals with a diagnosis of schizophrenia tend to have lower reproductive success ,92. Howe6.An alternative to increasing sample size is to refine the phenotype being investigated. Reducing the noise in a phenotype measure may lead to a clearer genetic signal, and thus, lower sample numbers would be required to detect this. Attempts to refine the phenotype are useful in terms of improving statistical power, but could limit the utility of any findings if results are not generalizable to the whole population. Therefore, there is a trade-off between statistical power and clinical utility. Ideally, studies combining the large sample sizes of resources, such as UK Biobank, with the detailed phenotyping of smaller cohort studies would provide the greatest power to detect genetic effects, if such effects exist. Longitudinal cohorts have the potential to contribute to this; measurement error will be reduced for longitudinally defined phenotypes, resulting in increased power.Although there is ongoing discussion about the best model of individual differences in personality, the factor structure suggested by Eysenck and others is a useful starting point when investigating the genetic architecture of these traits –7. Work To date, the majority of studies investigating underlying genetics of behavioural traits have focused on samples of European ancestry. Individual molecular genetic effects are likely to be of small size, and large samples will be required to identify them. There is a finite number of samples available with relevant phenotypes and genetic data available; therefore, in order to maximize power and sample size, combining samples across populations is likely to play an important role in future genetic studies. Analytical approaches that can account for underlying population stratification will be required.7.Despite ongoing debate about the structure of individual differences in personality, there is clear evidence of a genetic component to these traits. Consistent heritability estimates have been calculated for a number of domains using both twin studies and whole genome approaches. Increasingly large GWASs have been published which identify a number of genetic variants for individual differences in behavioural traits. To date, the largest study of a personality trait focuses on neuroticism, which has identified 136 variants of small effect. As the number of samples with both genetic and phenotypic data increases, it is likely that more such evidence will emerge. This has the potential both to provide insights into potential biological mechanisms and to generate instruments for use in future MR analyses investigating causal consequences of these individual differences in personality.