TEAC. Specifically, TAATEAC was significantly (57%) higher in organic than conventional Vidiano grapes, while there was no significant effect of production system on TAATEAC in Kotsifali and Villana grapes. As expected from previous studies, the TAC was substantially higher in red Kotsifali grapes. Redundancy analysis (RDA) identified grape variety as the only strong explanatory variable/driver for yield, TPC, TAA, and TAC of table grapes, and positive associations were detected between the variety Vidiano and both TPC and TAATEAC. All other explanatory variables included in the RDA  explained only a small proportion of the additional variation.The antioxidants found in grapes and wine have been linked to health benefits in humans, but may be affected by agronomic parameters, grape type/variety, and processing. Here, we report results of a farm survey which investigated the effects of production system  and grape variety on fruit yield, total antioxidant activity/capacity  and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assays), and total concentrations of total phenolics (TPC) and anthocyanins (TAC) in grapes of one red  and two white (Villana and/or Vidiano) traditional Cretan grape varieties. Analysis of variance (ANOVA) results showed that grape variety choice had a more substantial effect on TPC, TAA, and TAC than primary production protocols, and significant interactions were identified between production system and grape variety choice for TAA Table grapes and wine are a rich source of phytochemicals with antioxidant activity, including polyphenols and anthocyanins ,2, and bConsumer perception that organic foods have a higher sensory and nutritional quality has been a major driver for the increase in demand for organic fruit and vegetables, including grapes ,9. Some Recent systematic reviews and meta-analyses reported that organic production methods result in lower yields, but higher concentrations of phytochemical/antioxidants and higher antioxidant activity in crops, and this was primarily linked to a lower and/or more balanced supply of nitrogen when organic instead of mineral N-fertilisers are used ,12. HoweA recent United Kingdom (UK) retail survey confirmed that there are substantial differences in phenolic concentrations and antioxidant activity/capacity -6-sulfonic acid (TEAC/ABTS) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) methods) among grape types (white vs. red vs. black), but that significant differences in nutritional quality parameters between organic and conventional table grapes could only be detected for specific white, red, and black varieties . AlthougThere are, to our knowledge, virtually no studies that compared antioxidant/phenolic levels in indigenous, traditional Mediterranean grape varieties/landraces, and the effects of organic and conventional production methods on their nutritional quality. Although the production of many traditional varieties has declined substantially over the last 50 years, they are thought to be more adapted/resistant to local abiotic  and biotic  stress factors ,16,17. IThe main objective of the farm survey-based study reported here was, therefore, to identify the effects of production system  on the fruit yield and the nutritional composition of one red  and two white (Vidiano and Villana) indigenous, traditional Cretan grape varieties  that areThe farm survey was based on collecting grape samples and soil/agronomic/vineyard data immediately before harvest on farms (vineyards) in the Heraklion region, which is a main grape production region of Crete, Greece. Grapes were collected in two successive growing seasons (2013/2014 and 2014/2015) with climatic conditions typical for the region . The aveOn all participating farms, data on (a) grape yields  and (b) soil texture  were recorded by investigators. A range of other vineyard and agronomic parameters were recorded via questionnaires with farmers or farm managers (see Dry matter (DM)  and sugaFolin–Ciocalteau (FC) phenol reagent, gallic acid, potassium persulfate, and radical scavenging assay reagents , 2,2-diphenyl-1-picrylhydrazyl (DPPH), and 2,2-azinobis-(3-ethylbenzothiazoline)-6-sulfonic acid (ABTS)) were purchased from Sigma-Aldrich . Sodium carbonate (SC), methanol, hydrochloric acid , sodium chloride, sodium dihydrogen phosphate, sodium hydrogen phosphate, potassium chloride, sodium acetate, formic acid, acetonitrile, and methanol (MeOH HPLC grade) were supplied by Fisher Scientific .Total phenolic content was determined using the Folin–Ciocalteau (FC) colorimetric assay method ; see HasGrape samples were extracted according to Tassoni, Tango, and Ferri (2013) . Half a −1 of sample fresh weight (FW).Extracts from each grape sample were analysed in triplicate  and the mean of the three replicates was used for statistical analyses. Gallic acid (GA) solution was used as the standard for calibration curve calculations. Results were expressed as mg GA equivalent·kgGrape samples were extracted as previously described  and totaIt should be pointed out that results obtained with the DPPH and TEAC/ABTS assays can differ ,25. For Undiluted extracts from each grape sample were analysed in triplicate , and the mean of the three replicates was used for statistical analyses.Grape samples were extracted as previously described , and totGrape samples were diluted with pH buffers, and dilution factors of 5 and 10 were used for white and red grape samples, respectively. Each diluted grape sample was analysed in triplicate , and the mean of the three replicates was used for statistical analyses.520) and 700nm (A700). Final absorbance was calculated according to the formula A = (A520nm − A700nm) pH 1.0 − (A520nm − A700nm) pH 4.5. Two of the most common anthocyanin pigments  were used as equivalents in the calculation. Results were expressed as mg·kg−1 of sample fresh weight (FW) (for grape samples) or mg·L−1 (for wine samples).The diluted grape samples were then used for spectrophotometric analyses (using a UV–visible light (Vis) spectrophotometer ), and absorbance was assessed at 520 nm   with sliTM 4 μm Hydro-RP 80 Å  column, fitted with a C18 guard column ) at a temperature of 25 °C. The Shimadzu HPLC system  was equipped with LabSolution software, a DGU-20A3R degasser, two LC-20AD pumps, an SIL-20AC HT autosampler, an SPD-M20A diode array detector, and a CTO-20AC column oven. The detector was set to an acquisition range of 190–700 nm.Analyses and separation of individual anthocyanin components were performed using a Phenomenex, Synergi−1. The gradient programme for the mobile phases (A:B) was as follows: 0.02 min (10:90), 5 min (10:90), 15 min (25:75), 20 min (31:69), 25 min (40:60), 35 min (50:50), 45 min (100:0), 50 min (10:90), and 55 min (10:90). An injection volume of 50 μL was used for all samples, and quantification was performed at 520 nm.Water/formic acid/acetonitrile (A) (87:10:3) and water/formic acid/acetonitrile (B) (40:10:50) were used as the mobile phase with a flow rate of 0.8 mL·minO-glucoside in the range of 50 to 0.05 µg·mL−1.Identification was based on peak relative retention times and elution order of chromatograms obtained by Kammerer et al. (2004) . Individv/v). Each sample was analysed with an individual LC–MS experiment using a Thermo RSLC Nano LC  coupled to a Sciex 6600 mass spectrometer  Mobile phases were made as follows; loading buffer 4% (v/v) acetonitrile with 0.1% (v/v) TFA, buffer A (4% acetonitrile 0.1% formic acid (FA)), and buffer B (80% acetonitrile 0.1% FA). Separation was carried out using a linear gradient from 4–80% Buffer B over 40 min. This was followed by a 10 min wash at 90% Buffer B, and then a column equilibration at 4% Buffer B to return the column to original starting conditions. Next, 5 μL samples (1/10 dilution) were loaded onto the 300 μm C18 trap column for desalting before being resolved on a 23 cm 75 μm ID home-packed analytical column containing Dr Maisch 3 μm particle size stationary phase. Analytes were injected online into the mass spectrometer, which acquired data in a data-dependant format. Survey scans were performed over an m/z range of 400–1200. From each survey, the 30 most intense ions were selected for MS/MS; charge states +1 to +5 were considered for MS/MS. Precursors were fragmented with a rolling collision energy, as a function of the charge state of the peptide ion. The total cycle time was 1.7 s.LC–MS analyses to confirm the identity of anthocyanins identified by HPLC analysis was carried out by the Newcastle University Protein and Proteome Analysis (NUPPA) laboratory based on previously described methods ,28,29. Am/z anthocyanin values, and respective MS/MS spectra for relevant m/z were exported and compared with previously published data [Data were visualised using Analyst v2.2 (Sciex). Extracted ion chromatograms, hed data ,28,29.nlme package in R [multcomp package in R. A linear mixed-effects model was used for the Tukey contrasts, containing a treatment main effect, with three levels, with the random error term specified as described above. The relationships between soil, orchard, and agronomic factors (recorded on participating farms via structured questionnaires) and table grape yield and quality parameters were investigated by redundancy analysis (RDA) using the CANOCO 5 software [The effects and interactions between factors on measured parameters were assessed by analysis of variance (ANOVA) derived from linear mixed-effects (LME) models  by usingage in R . The hiesoftware . AutomatDue to the small number of yield and quality response variables available for RDA, the number of explanatory variables/drivers was restricted to nine. In the biplot derived from RDA shown in DPPH and TAATEAC, (d) total anthocyanin concentration (TAC) assessed by two different equivalences , and (e) anthocyanin profiles  and one red variety  were collected from 13 organic and 13 conventional grape orchards  in two cprofiles .−1), followed by Kotsifali (14.6 t·ha−1) and Vidiano (12.5 t·ha−1)  in 2014 than 2015, but there was no significant effect of year on grape yields. There were no significant effects of variety and production system on grape yields, dry matter, and sugar content detected . However t·ha−1) .DPPH, and TAATEAC values were significantly higher  in the 2013/2014 season  toward significantly higher TACcyan and TACmal in 2013/2014 , and no significant effect of production system could be detected TEAC were significantly  higher in red Kotsifali than white Villana grapes was also as expected from previous studies . This and the finding that organic white Vidiano grapes had similar TAATEAC when compared to organic red Kotsifali grapes, while conventional Vidiano grapes had significantly lower TAATEAC than conventional Kotsifali grapes, were unexpected (DPPH was significantly (~95%) higher in Kotsifali than both Vidiano and Villana grapes, and that there was no significant difference in TAADPPH between Vidiano and Villana  overall had similar TPC and TAAexpected . It shou Villana .FVillana = 17.1; FVidiano = 5.7; FKotsifali = 5.7) and only significant  explanatory variable/driver for the grape composition parameters assessed  of all other explanatory variables included in the RDA was substantially lower and decreased in the following order: irrigation  > orchard orientation  > production system  > soil type , while vineyard age, plant density, and estimated total nitrogen and potassium inputs explained very little (F ≤ 0.2) of the additional variation  and TAATEAC along the negative axis 1. In contrast, for the variety Villana, RDA identified negative associations with all composition parameters assessed, but a positive association with grape yield along the positive axis 1 (The RDA drivers explained approximately 41% of the variation (32.5% by axis 1 and a further 8.6% by axis 2) associated with the grape composition response variables assessed . There we axis 1 .There were also weak positive associations between grape yield and the use of supplementary irrigation, west-facing orchards, higher N and K inputs, clay soils, older orchards, and conventional crop management along the positive axis 1 . In contA range of previous studies analysed the effects of pedo-climatic conditions, agronomic protocols, grape type/variety, region, and/or vintage on sensory and nutritional quality parameters in table grape and/or wine ,21,33,34However, there is very limited information on the performance of indigenous, traditional grape varieties in organic and conventional production systems. This is, to our knowledge, the first study in which a significant interaction between production systems  and different traditional Cretan grape varieties is reported. The study was based on farms in a traditional Cretan wine-growing region with a very uniform temperature and rainfall pattern, to minimise potential confounding effects of variable environmental background conditions. We recorded a range of orchard  and agronomic  parameters on all farms. This allowed us to investigate possible associations between grape yield and quality parameters and (a) grape variety, (b) production system , and (c) specific agronomic and orchard/site explanatory variables/drivers by RDA.The finding that grape yields of all three varieties were similar in organic and conventional production was unexpected, since a meta-analysis of comparative studies reported that crop yields are higher in conventional compared with organic production .The absence of significant yield differences between organic and conventional production may have been due to low downy mildew disease severity. This is the most important factor affecting grape yields and quality and is more difficult to control in organic farming systems, where the use of synthetic chemical fungicides is prohibited and farmers are only allowed to use relatively inefficient Cu-fungicides ,16. CretSimilarly, the finding of no significant differences in yield between varieties was unexpected, but may also have been due to the relatively low-input  agronomic protocols used in both organic and conventional production in Crete and/or the large variability in yields between orchards observed for the variety Villana, which numerically produced the highest yields.This view is supported by results from RDA, which indicated that (a) variety choice was the strongest explanatory variable/driver for both yield and grape quality parameters, and (b) there were trade-offs between yield and nutritionally relevant antioxidant concentrations and activity. These RDA results are consistent with previous studies, which reported that the intensification  of agronomic practices to increase yields may cause a “dilution effect” resulting a reduction in antioxidant/phytochemical and mineral micro-nutrient concentrations in crops ,35,36. IThe finding of higher phenolic and antioxidant activity in the 2013/2014 season compared with the 2014/2015 season was expected and most likely due to farmers harvesting grapes before the optimum ripening stage in 2015 to avoid fungal infection/spoilage of grapes after the unusually high rainfall in late August . This viDifferences between production years were also reported in a recent table grape retail survey .TEAC activity in organic than conventional Vidiano grapes). These results are consistent with previous studies that compared antioxidant activity and phenolic concentrations in different grape types  and organic and conventional grapes of the same variety  or non-production system-specific agronomic parameters such as the use of supplementary irrigation and total N, P, and K inputs and orchard parameters . However, the study also demonstrated that production system can have substantial effects on grape quality in specific varieties  . Moreovegnoletto . Similargnoletto .The reasons for the variable and sometimes contrasting effects of production systems on antioxidant activity, TPC, and TAC are poorly understood. Results from long-term factorial field experiments with arable crops have suggested that higher nitrogen supply/availability from mineral N-fertilisers results in lower phenolic and flavonoid concentrations in conventional wheat grain and found no evidence for pesticides  used in conventional farming contributing to the differences in antioxidant levels between organic and conventional wheat grain . HoweverTEAC, and TAADPPH between growing season/years was, therefore, not surprising. This is consistent with the results of previous studies that identified not only variety but also geographical location and climatic conditions as important drivers for antioxidant activity and/or concentrations in grapes [There were differences in climatic conditions  between the two growing seasons in which surveys were carried out. The finding of differences in dry matter, sugar content, TPC, TAAn grapes ,42,43. In grapes . HoweverThe production of indigenous, traditional grape varieties in Crete (and other regions of the Mediterranean) declined significantly between the 1960s and early 2000s, but has increased again more recently . The reaTEAC) levels to red grapes of the variety Kotsifali was, therefore, unexpected. Furthermore, both traditional Cretan white grape varieties had relatively high TPC and TAA levels compared with other white grape varieties , we were also able to confirm results of a recent retail survey which suggested that the nutritional composition of grapes is determined by interactions between variety choice and production system  Table 3. SpecifiTEAC) and/or total phenolic levels when compared to many red/black grape varieties examined in this and previous studies [White grapes are known to contain anthocyanins but at very low levels ,46 and a studies ,21,34,44Evidence from a recent retail survey  and the It should be pointed out that, although most previous studies reported in There is increasing evidence that the antioxidant (mostly (poly)phenolic) compounds in food crops have protective effects against a range of chronic diseases, but there is still limited information on the exact physiological mechanism underlying these effects ,3,4. ConHowever, several epidemiological studies have linked the consumption of grapes and grape products to a reduced risk of various chronic diseases including cardiovascular diseases, some cancers, and neurodegenerative diseases ,4. SelecThe finding that antioxidant activity and concentrations were broadly similar in organic and conventional grapes samples may suggest that there is no nutritional benefit of switching to organic table grape consumption. However, to what extent organic table grape consumption may provide health benefits remains unclear from the results reported here. This is mainly because other nutritionally relevant compounds that are known to be affected by production system  were not assessed in this study, and there is limited information in the existing literature on differences in mineral, toxic metal, and pesticide residues between organic and conventional grapes ,11.The main limitation of the study reported here was the inability to obtain detailed information on (a) crop protection protocols and (b) certain non-production system-specific management parameters , (c) foliar disease severity, and (d) processing methods and storage conditions used for wines included in the survey. More detailed recording of these factors in farm surveys and additional assessment of mineral nutrients, toxic metal, and pesticide residues should be considered for future comparative studies of yield and nutritional and sensory quality difference in organic and conventional table grapes.Results reported here suggest that, in regions with relatively extensive grapevine production systems, organic and conventional agronomic protocols produce broadly similar yields and nutritional composition when traditional, local varieties are used. However, the finding that organic grapes of one white grape variety (Vidiano) had higher antioxidant activity than its conventional comparators suggests that production system can have an effect on the nutritional composition for certain varieties.Red and black grapes are often considered to have a higher nutritional value than white grapes due to their higher anthocyanin content. The finding that organically produced Vidiano grapes had similar antioxidant activity and higher phenolic levels than the grapes of the red variety Kotsifali demonstrates the potential for identifying traditional white varieties which compensate for the low anthocyanin content with higher expression of other phenolics.


Dahlem Red (PD-3) chicken population for direct additive genetic, maternal effects and to estimate the estimated breeding value (EBV), genetic parameters, genetic trends and rate of inbreeding (ΔF) utilizing seven generation’s data. The generation and hatch had significant (P≤0.01) effect on the body weight at 0 day (BW0), 2 (BW2), 4 (BW4) and 6 weeks (BW6) and shank length at six weeks of age (SL6). The average least squares means (LSM) for BW6 and SL6 were 273.93±0.62 g and 53.97±0.05 mm, respectively. All the production traits were significantly (P≤0.01) influenced by generation and hatch. The average LSM for age at sexual maturity (ASM), egg production up to 40 weeks (EP40) and egg mass up to 40 weeks (EM40) were 168.82±0.25 d, 72.60±0.41 eggs and 4.21±0.07 kg, respectively. Model 5 with additive direct, maternal genetic, maternal permanent environmental and residual variance components was the best for BW0, BW2 and BW4 based on the AIC values obtained in WOMBAT. Model 4 was the best model for BW6, SL6, ASM, EP40 and EM40 with additive direct, maternal permanent environmental and residual variance components. Maternal effects were higher during early age, decreased with age, and remained present until 20 weeks of age. The heritability (h2) estimates were low to moderate in magnitude for all the growth traits and ranged from 0.02±0.03 to 0.19±0.03. The maternal heritability was high at hatch (0.35±0.06), decreased gradually until 4th week (0.02±0.01) and ceased afterwards. The heritabilities of EP40 (0.11±0.03) and EM40 (0.12±0.04) were low. The direct additive genetic correlations (ra) between BW2, BW4, BW6 and SL6 were high and positive (P≤ 0.05). The additive genetic and maternal permanent environmental correlation between EP40 and EM40 were high and positive (P≤ 0.05). The EBV of EM40 was significant (P≤ 0.05) with 0.48 kg/generation in PD-3 chicken at the end of the seventh generation. The EBV of EP40 showed an increasing trend with a genetic gain of 1.87 eggs per generation. The average inbreeding coefficient of the population was 0.019 and average ΔF was 0.007 over the last seven generations of selection. The EBV trends for primary and associated traits showed linear trends in the desired direction and negligible inbreeding.Variance and covariance components of growth and production traits were analyzed employing REML animal model to assess the  The per capita availability is about 79 eggs and 3.2 kg chicken meat per annum against the recommended level of 180 eggs and 10.25 kgs meat as per the Indian Council of Medical Research. Backyard poultry farming with improved chicken varieties such as Vanaraja, Gramapriya, Srinidhi, and Rajasree is promoted by the Government as it is one of the proven technologies for poverty alleviation and nutritional security in rural and tribal areas of the country. A wide gap exists between the availability and requirement of chicken products, thus providing an opportunity to expand the poultry farming in the commercial as well as the backyard sectors. Since commercial poultry has inherent difficulties for expansion in rural and tribal areas, the backyard poultry has bright scope in these areas. Promoting the backyard poultry farming among the rural masses is going to increase its share in total poultry production apart from providing healthy animal protein in terms of eggs and meat. Dahlem Red (PD-3) is the female parent of Gramapriya, an improved backyard chicken layer variety propagated across the country. The improvement in egg production, egg mass, growth and other economic traits in the terminal cross is being achieved through the application of selection in the parent lines.Poultry production has grown exponentially in structure and operation from a traditional backyard activity into a major commercial agri based industry over the years. Development of high yielding layer (310–340 eggs) and broiler (2.4–2.6 kg at 6 weeks) varieties together with standardized practices on nutrition, housing, management and disease control [The estimated breeding value (EBV) determines the rate of improvement in the primary trait of selection. Variability in the population is the basis for any genetic improvement program in livestock and poultry. Genetic improvement is determined by the response to selection in the primary as well as other correlated traits of economic importance. Thorough knowledge on the genetic basis, gene action and the environmental effect is essential for planning breeding programs for genetic improvement of the desired traits in poultry. Selection response of trait depends on the population size, gene frequency, mutation, allelic fixation, random drift and physiological limits . The mag2 estimates. The maternal effect is defined as the situation where the phenotype of the offspring is determined not only by the environment and its genotype, but also by the genotype and environment of their mother. A maternal effect is a situation wherein traits are influenced by the genotype and environment of the mother. Maternal effects are important in the development and expression of the economic traits due to genetic or environmental differences between dams or by the combination of genetic or environmental differences [Partition of variance into every possible source and their interaction effects reduces the error variance and minimizes over-weightage to some components, leading to precision in the estimates. Animal model accommodates additional components of variance, which makes the estimates less biased by confounding environmental variation and precise modeling . The traferences , 7. The ferences . Many auferences –16. Someferences , 17 and ferences , 18 wereTherefore, the present study was taken up with an aim to estimate the breeding value, genetic trends of economic traits, genetic  and non-genetic environmental effects and performance of important economic traits in (PD-3 line using Average Information Restricted Maximum Likelihood (AIREML) animal model.The study was carried out at the experimental poultry farm of ICAR-Directorate of Poultry Research, Hyderabad, Telangana, India. Hyderabad is in Deccan plateau placed between 17°23’ N and 78° 28’ E at 500 m from mean sea level. The climate is hot tropical with maximum temperature ranging from 20°C in winter to 45°C in summer.The experiment was approved by the Institutional Animal Ethics Committee (IAEC/DPR/17/4). The manuscript does not contain clinical studies and patient data.Gramapriya, a popular brown egg layer suitable for rural poultry farming developed by ICAR-Directorate of Poultry Research. PD-3 line was developed from Dahlem Red breed. The work on the development of PD-3 line as a female line for production of egg type rural poultry was started in 1998–99. A pedigreed population of this line was produced for the first time (S-0) in 2000–01. After recording the performance for 6 generations, a random mating program using pooled semen for two generations was followed. The variability in the existing population was increased subsequently with the addition of Dahlem Red population from Anand Agriculture University, Anand, Gujarat, India. The base population was stabilized for two generations with random inter se mating and the third generation was considered as the base population with the initiation of pedigreed mating and selection. The population was selected for higher 40 week egg mass (EM40) during the last seven generations.PD-3 population was selected over the generations , 20 for ad-libitum layer starter diet  based on maize-soybean meal up to 6 weeks of age in each generation, with layer grower ration  until 16 weeks of age and with layer breeder ration  until 40 weeks of age. The chicks were vaccinated against Marek’s disease (MD) on the 1st day; Newcastle disease (ND) on the 5th and 14th days; infectious bursal disease (IBD) on the 24th day and fowl pox (FP) on the 42nd day, ND with R2B at the 9th week, infectious coryza at the 12th week and ND, IBD and infectious bronchitis (IB) combined vaccine at the 18th and the 40th weeks of age.About 3500 to 4000 healthy chicks were produced in 7–8 hatches in each generation in a pedigreed mating with 50 sires and 250 dams. These chicks were wing banded and reared on deep litter in an open sided poultry house under standard management. About 600 females and 200 males were housed in individual cages in each generation at 12–14 weeks of age. The chicks were fed nd (BW2), 4th (BW4) and 6th (BW6) week of age; shank length at 6th week (SL6); adult body weight at 20th (BW20) and 40th (BW40) weeks were measured. The body weight was measured to 0.1g accuracy using digital balance, while shank length was measured to the nearest of 0.01mm accuracy using digital Vernier Calipers. Age at sexual maturity (ASM); egg weight at 28 (EW28) and 40 (EW40) weeks; egg production (EP40) and egg mass (EM40) up to 40 weeks of age were recorded. The weight of eggs was recorded using a digital balance to an accuracy of 0.01g. The data were not recorded on BW0 in S-3 and S-7; BW2 in S-6 and hatch 8; BW40 in S-7 generation. The descriptive statistics of growth and production data are presented in Data on growth and production traits of 16,292 PD-3 birds descended from 336 sires and 1076 dams collected over seven generations (S1 to S7) from 2012–13 to 2018–19 were utilized in the study. In each generation, juvenile body weight at 0 day (BW0), 2−4. Univariate animal models were fitted to estimate (co)variance components for all the traits. The six single–trait models used to estimate variance components were defined as follows:Variance and covariance components were estimated by average information restricted maximum likelihood (AIREML) fitting an animal model . Data wey is the vector of records; β, a, m, pe and ε are vectors of fixed, direct additive genetic, maternal additive genetic, permanent environmental effects of the dam, and residual effects, respectively; with association matrices X, Za,Zm and Zpe;A is the numerator relationship matrix between animals; and σam is the covariance between additive direct and maternal genetic effects. Assumptions for variance (V) and covariance (Cov) matrices involving random effects were V(a) = Aσ2a, V(m) = Aσ2m,V(c) = Iσ2c, V(e) = Iσ2e, and Cov = Aσam, where I is an identity matrix and σ2a, σ2m, σ2c and σ2e are additive direct (a), additive maternal (m), maternal permanent environmental (c) and residual variances (e), respectively. The heritability (h2) was calculated as h2 = σ2a/σ2p, where σ2p = σ2a + σ2m + σam + σ2c+ σ2e. The m2, ram and c2 were calculated as m2 = σ2m/ σ2p, ram = σam/ σ2p c2 = σ2c/ σ2p, respectively. The total heritability (h2t), was calculated using the formula h2t = h2 + 0.5m2 + 1.5mramh [Where 1.5mramh . Akaike’1.5mramh . The modA = 0). Significance of maternal permanent environmental covariance was also tested accordingly (COVC = 0). [The best models from the single trait analyses were combined with appropriate (co)variance between random effects in the model for the bivariate analysis. The best model identified for a specific trait using likelihood ratio test was only used for the bivariate analysis. The estimates of genetic parameters i.e., genetic, phenotypic and environmental correlations between different economic traits were obtained by AIREML fitting an animal model. To test the significance of the genetic covariance, the full model was compared with the model in which genetic covariance was zero (COVVC = 0). . SignifiVC = 0). .The EBV obtained from the best single trait model suited for each trait was used to plot the genetic trend. The genetic trend was estimated by regression of the EBV of the females that contributed in each generation for the trait under selection (EM40) and the other production traits under study . The EBVThe least squares means (LSM) for body weight up to six weeks of age and SL6 are presented in The LSM for ASM, BW20, BW40, EW28, EW40, EP40 and EM40 are presented in The appropriate model for different economic traits was selected based on the AIC values obtained in WOMBAT . The AIC2) estimates were low to moderate in magnitude for all the growth traits and ranged from 0.02±0.03 to 0.19 ±0.03 (2) at birth was high in magnitude (0.35±0.06), which reduced gradually by the end of the 4th week . The heritability of EP40 and EM40 was low with 0.11±0.03 and 0.12±0.04 h2 estimates, respectively. BW20 and BW40 had very less magnitude of maternal h2, while ASM, BW20, EP40 and EM40 had low magnitude c2. The heritability was high for egg weight (EW28 and EW40) and moderate for adult body weight (EW20 and EW40) in PD-3 line  between BW0 and other juvenile traits was less in magnitude and positive, while maternal genetic (rg) and maternal permanent environmental (rc) correlation were significantly higher; phenotypic correlation (rp) was less in magnitude  positive association. The rc was observed up to 20 weeks of age with significant high correlation coefficients between the body weights and shank length. The rp was higher among BW2, BW4, BW6 and SL6 with a positive association. The ra between BW6 and adult body weights (BW20 and BW40) was high in magnitude.The direct additive genetic correlation (ragnitude . The rg a between ASM and BW20 was negative as heavier birds matured early. ASM and BW40 had a low degree of positive association. ASM had significantly (P≤ 0.05) higher negative correlation  with EP40 and EM40 (a and rp between ASM and egg weights (EW28 and EW40) were of low magnitude and positive in direction. The ra between EP40 and BW40 was negative, while between EP40 and BW20 was positive with less magnitude. EP40 and EM40 had highly significant (P≤ 0.05) positive association with ra, rc and rp with. The EP40 and egg weight (EW28 and EW40) had negative association, while EM40 and egg weight had a positive association for both ra and rp. The EM40 had a positive association with all the production traits, except ASM. The adult body weight (BW20 and BW40) and egg weight (EW28 and EW40) had highly significant (P≤ 0.05) positive ra and low magnitude rp. The BW20 and BW40 had highly significant (p≤ 0.05) positive association for ra and rg and rp components.The correlation coefficients from different components between production traits are presented in and EM40 . The ra The average EBV for BW6 and SL6, the important juvenile traits, was 17.65 g and 1.33 mm, respectively during the S-7 generation. The genetic trend of BW6 and SL6 showed significant (P < 0.05) and linear increment over the generations. The EBV of EM40 (0.48 kg) at the end of seventh generation significantly (P≤ 0.05) increased linearly as a response to selection in PD-3 line. The average genetic and phenotypic response was 0.08 and 0.11 kg per generation for EM40, the primary trait of selection  on body weight and shank length  with an nd and 5th generation where lower egg mass was observed, which was due to the reduced egg production in the generation. However, a significant positive trend was observed for EM40 over the generations (The LSM of ASM significantly (P≤ 0.01) reduced over the generations as a correlated response to the selection for EM40 in PD-3 line. The increased body weight over the generations may have resulted in reduced ASM in a positive direction. Selection for body weight resulted in the reduction of ASM though direct selection was not practiced for this trait . The sigerations .Dahlem Red breed is one of the excellent female parent lines. The inclusion of one of the maternal effects either m, or am or c in the model could be enough to adjust the variation that occurred due to all the maternal effects [Model 5 was appropriate for juvenile body weight  with a, m, c, r and p effects and Model 4 for BW6 and SL6 with a, c, r and p effects. Maternal effects, both m and c were observed up to 20 weeks of age in PD-3 . Jasouri effects . The imp effects , 8 in diModel 4 was the appropriate model for ASM, EP40 and EM40 with effects of a and c, while model 1 was the best for EW28 and EW40 with direct additive genetic effect. Similar to the present findings, maternal effects in addition to the direct additive effect for the egg production and egg weight , 33, 34 2 determined using the animal model was more precise due to partitioning of the variance and covariance into all possible sources. The h2 estimates of low magnitude with high precision and accuracy using the animal model were documented in the literature [2 estimates over the time was due to the activation of various genes responsible for egg production [2 estimates for body weight (0.24–0.47), ASM (0.36), egg number (0.17), egg mass (0.16) and egg weight (0.32–0.43) were observed in Mazandaran chicken [2 estimates was also reported for egg production in Horro chicken from Ethiopia [The estimates of hterature , 8, 29. oduction  and persoduction . Higher  chicken  due to iEthiopia  in commeEthiopia  and in Kt 0.32–0. were obs2 estimates for growth and egg production traits were reported in PD-1 [2estimates in the present study were due to the inclusion of maternal effects in the model, which will aid the breeder in planning the breeding program with reliable prediction.The heritability estimated using the animal model was lesser as compared to the Henderson variance component analysis model. Maternal effects account for only small portion (2–8%) of the variance in chicken, but ignoring these effects results in biasness and over estimation of genetic parameters , 36, 44  in PD-1  from Inda, rc continue to exist up to 20 weeks of age (g between growth traits (Body weight and Shank length) was high in magnitude in PD-3 line in the present study, similar to the reports in PD-1 line [Genetic correlation plays a significant role in the success of the breeding experiment, as selection for one trait improves the performance in other traits as a positive correlated response, whereas negative association depresses the performance , 11. In s of age  indicatiD-1 line  in PB-2 D-1 line ; in MazaD-1 line ; in ThaiD-1 line .a, rc and rp) between ASM and EP40 was significant with a high degree of negative association in PD-3 line. A similar significant inverse relationship between ASM and egg production in chicken was reported by many authors [a) between BW40 and EP40 was negative , which may be due to the ideal breeding strategy followed in the PD-3 line, wherein close relatives were not included in the mating plan. The number of sires and dams utilized and contributed was higher than those required for maintaining effective population size. The inbreeding coefficient of the population at the end of the 7 chicken , 47. TheThe EBV of primary and associated traits was positive with a linear trend in the desired direction indicating the efficacy of selection practiced in PD-3 line with negligible inbreeding over the seven generations. Partitioning of variance into additive, maternal genetic, maternal genetic permanent environmental and residual sources improved the precision of the BV and genetic parameters. The accuracy in EBV and the genetic parameters aids in selecting the suitable breeding strategy for genetic improvement of PD-3 line. The study concluded that the positive genetic gains with respect to economic traits in PD-3 line lead to improvement in terminal crosses intended for backyard poultry farming.S1 Material(PDF)Click here for additional data file.


Association studies have been widely used to search for associations between common genetic variants observations and a given phenotype. However, it is now generally accepted that genes and environment must be examined jointly when estimating phenotypic variance. In this work we consider two types of biological markers: genotypic markers, which characterize an observation in terms of inherited genetic information, and metagenomic marker which are related to the environment. Both types of markers are available in their millions and can be used to characterize any observation uniquely.Our focus is on detecting interactions between groups of genetic and metagenomic markers in order to gain a better understanding of the complex relationship between environment and genome in the expression of a given phenotype.We propose a novel approach for efficiently detecting interactions between complementary datasets in a high-dimensional setting with a reduced computational cost. The method, named SICOMORE, reduces the dimension of the search space by selecting a subset of supervariables in the two complementary datasets. These supervariables are given by a weighted group structure defined on sets of variables at different scales. A Lasso selection is then applied on each type of supervariable to obtain a subset of potential interactions that will be explored via linear model testing.Medicago truncatula and metagenomic markers in its rhizosphere bacterial community.We compare SICOMORE with other approaches in simulations, with varying sample sizes, noise, and numbers of true interactions. SICOMORE exhibits convincing results in terms of recall, as well as competitive performances with respect to running time. The method is also used to detect interaction between genomic markers in An R package is available , along w Association studies are a popular approach for digging out genetic information relating to a given phenotype. To avoid confusion effects (e.g. stratification due to population origin) and improve the diagnostic, it is common practice to integrate environmental data in the analysis. These additional variables are generally few in number, of the order of tens.Medicago truncatula  and metagenomic markers in its rhizosphere bacterial community, but it could be applied in many other contexts.In this paper we propose a generic method for taking thousands or even millions of environmental variables into consideration, with the aim of finding significant interactions between these variables and genetic markers. We illustrate the proposed algorithm on the genome of Genome-Wide Association Studies (GWAS) look for genetic markers linked to a phenotype of interest. Typically, hundreds of thousands of single nucleotide polymorphisms (SNPs) are analyzed with a limited sample size using high-density genotyping arrays. GWAS are a powerful tool for investigating the genetic architecture of complex biological processes and have been successful in identifying hundreds of associated variants. However, they have been able to explain only a small proportion of the phenotypic variations expected from classical linkage analyzes .Some of the missing heritability may be uncovered by taking into account correlations among variables and epistasis ). WhTo reduce the dimension of Problem , the firmentclass2pt{minimH and for h and To keep track of the tree structure, an additional measure may be included to quantify the loss of information between two successive levels. More specifically, for a tree structure of height imonprez  for the mentclass2pt{minimmentclass2pt{minimTo summarize each group of variables the mean, the median, or other quantiles may be used, as well as more sophisticated representations based on eigenvalue decomposition, such as the first factor of a Principal Component Analysis.With the aid of this compressed representation we can uncover relevant interactions using a multiple testing strategy.Compression is a key ingredient in reducing significantly the dimension of Problem . We takeThis step for selecting the supervariables in the two complementary datasets can be subject to instability when setting the amount of selection. The method can be improved further in terms of model consistency by using resampling techniques , 33, 47.t-test for instance) on each parameter For the purpose of feature selection the relevant interactions may be uncovered separately by considering each selected group  Problem , in thatWe present some numerical simulations to assess SICOMORE’s ability to uncover relevant interactions. We compare our approach with two other methods, namely MLGL  and glinThe ability to recover relevant interactions will be observed on different configurations with respect to the sample sizes, the noise, and the number of true interactions.The impact of the weighting scheme will be shown with two versions of our approach, using both weighted and unweighted supervariables.The impact of the compression scheme will be compared to MLGL using the same structure but with the initial variables.Finally, a dedicated simulation sketches the running times necessary for each method to reach convergence when the dimension of one of the matrices grows. To allow the comparison of SICOMORE with MLGL or glinternet, the dimensions of the simulated matrices have been kept between a few hundred and a few thousand.These numerical simulations are designed to study several aspects of SICOMORE:Genomic data. To obtain a matrix oftware  , 60, whientclass1pt{minimaMetagenomic data. The data matrix mentclass2pt{minimFor all simulations we used a fixed value of tatistic  to idententclass1pt{minimaTo generate the phenotype, we considered a data structure for which the data to regress was generated using supervariables according a linear model with interactions of the form:I blocks of the coefficients of We considered In accordance with the outline given in the preamble of Section It is worth mentioning that SICOMORE is an approach that draws on the work of Park et al.  and MLGLGrimonprez  defines The model of interactions is fitted with weights on the groups defined by the expanded representation of the two hierarchies using the initial variables, as illustrated in Fig.all variables. This approach is not well suited in a high-dimensional setting and still less in a model of interactions. But, as we explained at the beginning of "It should be noted that here MLGL is not being evaluated in a context for which it was intended, since MLGL examines the different levels of a hierarchical structure using Lim and Hastie  introducIt fits a hierarchical group Lasso model, with constraints on the main and interactions effects, as specified in "Since this method can only work at the level of variables, we needed to include a group structure into the analysis, and so we decided to fit the glinternet model on the compressed variables and to constrain the model to only fit the interaction terms between the supervariables of the two matrices To ensure that our comparison of SICOMORE was fair, we considered two options, namely GLtree and GLgap. The GLtree option works on the unweighted compressed representations of the two hierarchies Fig. c and thuFor each run we evaluated the quality of the variable selection using Precision and Recall. More precisely, we compared the true interaction matrix For all possible true positive corresponds to a significant p-value on a true causal interaction, a false positive to a significant p-value on a noise interaction, and a false negative to a non-significant p-value on a true causal interaction.Here, a For the three tested methods we corrected for multiple testing by controlling the family-wise error rate with the Holm-Bonferroni method. Even though it is known to be stringent, we chose the Holm-Bonferroni method to adjust for multiple testing because the number of hypothesis tests that needed to be performed for our simulation was quite low. In a high-dimensional context, for example in analyzing real microarray data, the Benjamini-Hochberg method would be preferable for controlling the false discovery rate.The performances of the different methods in uncovering true causal interactions are shown in Fig. The Recall results show that MLGL and In order to reduce the computation time required to run our algorithm, we chose to restrict the search space. It is limited to the area of the tree where the jumps in the hierarchy are the largest, and the number of groups to be evaluated is arbitrarily set to five times the number of initial features. This reduces the number of variables to be fitted in the Lasso regression but does not affect performance regarding Recall and Precision.We compared the computational performance of our method with the two others by varying the number of variables in We can conclude from the results presented in Table Medicago truncatula and the metagenome (16S rRNA gene sequencing) of its rhizosphere bacterial community. We were seeking to identify significant interactions in order to better understand the effect of both the plant genome and the rhizosphere bacterial microbial community on plant growth.For an implementation of our algorithm on real data we chose to study the interactions between the genome of Total Dry Biomass (TDB).Root Total Dry Biomass Ratio (RTDBR).mg of Specific Nitrogen Uptake (SNU) expressed as In addition to the phenotypic measurement, the rhizosphere of each accession was also analyzed to determine the bacterial diversity and composition  were grown in a controlled environment and phenotyped for several traits related to the plant growth and nutritional strategy:http://www.medicagohapmap.org, were genotyped with a DNA microarray chip, giving a total of 6 372 968 SNPs after 3% MAF, multiallele SNP exclusion and minimum count (100) filtering. The missing values were imputed using the snp.imputation function from the R package snpStats [A total of 15617 different bacterial OTUs were found in the rhizosphere of the plants. The different OTUs were pooled according to their taxonomic affiliation at the genus level, and a total of 329 genera were thus analyzed. The 155 sequenced accessions, extracted from snpStats . Given thttp://www.medicagohapmap.org. A Singular Enrichment Analysis was conducted using an exact Fisher test with the R package topGO [http://www.medicagogenome.org.The positions of SNPs inside or in the vicinity of genes (± 2Kb) were extracted from context files downloaded from ge topGO  and GO tAggregating function: For the genomic and the metagenomic data, we defined the mean value of the group as supervariable.Clustering algorithm: For the metagenomic data we used a hierarchical clustering using Ward’s distance as the measure of similarity. For the genomic data we used a spatially constrained hierarchical clustering algorithm that integrates the linkage disequilibrium as the measure of dissimilarity.Stability selection: The parameters of the function stabs in SICOMORE for the metagenomic data were fixed to p-values Search space: For computational reasons we chose to run some analyses chromosome by chromosome. Correction for multiple testing was done by controlling the false discovery rate . Since wRegarding the running time for the application, for about 2M SNPs and 329 bacterial genera, the algorithm was able to perform the analysis in 250 min (® Xeon® CPU E7-4870 @ 2.40GHz) and 2.5 Gb of memory.The algorithm requires several hyper-parameters to be chosen in order to run properly:No significant interactions were found for this phenotype.p-value p-value of p-value of p-value of p-value of Rhizobiales family, which is known to contribute to N nutrition of Medicago truncatula. Plant disease resistance genes play a major role in the plant immune system that was induced during pathogenic plant-microbial interactions but also during mutualistic plant-microbe interactions [For RTDBR, four interactions were significant at entclass1pt{minimaractions .Table 2RNone of the 39 bacterial genera identified was affiliated to genera known as plant pathogens. However, several of the bacterial genera identified were affiliated to genera known as plant symbiont or plant growth promoting bacteria. We could hypothesize that bacteria affiliated to these genera could be in positive interaction with the plant and induced some defense response.p-value of p-value of p-value of For the SNU, we retrieved 157 698 significant SNPs and 5 476 genes from the three significant interactions, as shown in Table Rhizobium-Legume symbiosis [Rhizobiales family, other OTUs were affiliated to bacteria genera harboring functional traits relating to the N cycle, such as nitrogen fixation, nitrate reduction to ammonium, and denitrification, which can contribute to plant nitrogen nutrition.The two first classes were partly redundant and mainly composed of LRR receptor kinase genes, known to be involved in plant innate immunity. The term “regulation of anion channel activity” was linked to other significant terms related to regulation to ion/anion transport. The “lignin biosynthesis” process included genes involved in lignin biosynthesis such as 8 caffeic acid O-methyltransferase genes, 3 cinnamyl alcohol dehydrogenase-like protein or 2 shikimate O-hydroxycinnamoyltransferase, which serve as building blocks in the formation of plant lignin . The colymbiosis  and pathymbiosis . In addiymbiosis , 28. ForAltogether, the mathematical method proposed here could support some biological hypothesis that need to be validated using other biological approaches combining plant mutant affected by these genes and simplified bacteria community defined on the genera identified.The detection of interaction effects in a high-dimensional setting remains a difficult problem because multiple testing is onerous and because effects are small in terms of their significance. In this work, we proposed SICOMORE, a method that reduces the dimension of the search space by selecting a subset of compressed variables obtained from the biological characteristics of complementary datasets.Our approach has demonstrated its ability to uncover interaction effects with a high statistical power. In our simulations, where sample sizes, noise, and the number of true interactions all varied, SICOMORE always exhibited stronger recall than both MLGL and glinternet. SICOMORE combines the strengths of different methods in a powerful single algorithm. SICOMORE is also significantly more efficient than the others in terms of computation time.Medicago truncatula and its rhizosphere, which are linked to the Root Total Dry Biomass Ratio as well as its Specific Nitrogen Uptake.SICOMORE was able to detect interactions between the genome of SICOMORE can be applied to any kind of numerical data, as long as an underlying hierarchical or group structure is available . In particular, our method can handle shotgun sequencing as well as other omics data, or even clinical follow-up, which often takes the form of categorical data that can be easily structured.The compression scheme used in SICOMORE means that the model of interactions can easily be extended to The R package already incorporates these two possibilities.Although our approach as presented here concerns the detection of interactions between genomic and metagenomic markers, it should be noted that two major extensions are available. p-values are adjusted. One way around this problem might be to use post-hoc inference for multiple comparisons [Given these interesting results and possible extensions, there are other aspects that may be interesting to address in future works, with a view to improving SICOMORE further in terms of model consistency. Although the Lasso procedure is relevant for dimension reduction purposes, it may induce some biases in the multiple testing procedure used afterwards, since the variable selection step is performed before the parisons . These kAdditional file 1. Supplemental data for the Medicago truncatula example.


Supplemental Digital Content is available in the text. High blood pressure (BP) is a risk factor for cardiovascular morbidity and mortality. While BP is regulated by the function of kidney, vasculature, and sympathetic nervous system, recent experimental data suggest that immune cells may play a role in hypertension.We studied the relationship between major white blood cell types and blood pressure in the UK Biobank population and used Mendelian randomization (MR) analyses using the ≈750 000 UK-Biobank/International Consortium of Blood Pressure-Genome-Wide Association Studies to examine which leukocyte populations may be causally linked to BP.P<10–50). Using 121 single nucleotide polymorphisms in MR, implemented through the inverse-variance weighted approach, we identified a potential causal relationship of lymphocyte count with systolic BP and diastolic BP , which was directionally concordant to the observational findings. These inverse-variance weighted estimates were consistent with other robust MR methods. The exclusion of rs3184504 SNP in the SH2B3 locus attenuated the magnitude of the signal in some of the MR analyses. MR in the reverse direction found evidence of positive effects of BP indices on counts of monocytes, neutrophils, and eosinophils but not lymphocytes or basophils. Subsequent MR testing of lymphocyte count in the context of genetic correlation with renal function or resting and postexercise heart rate demonstrated a positive association of lymphocyte count with urine albumin-to-creatinine ratio.A positive association between quintiles of lymphocyte, monocyte, and neutrophil counts, and increased systolic BP, diastolic BP, and pulse pressure was observed (eg, adjusted systolic BP mean±SE for 1st versus 5th quintile respectively: 140.13±0.08 versus 141.62±0.07 mm Hg for lymphocyte, 139.51±0.08 versus 141.84±0.07 mm Hg for monocyte, and 137.96±0.08 versus 142.71±0.07 mm Hg for neutrophil counts; all Observational and genetic analyses demonstrate a concordant, positive and potentially causal relationship of lymphocyte count with systolic BP and diastolic BP. This cross-sectional and genetic study found potentially causal, positive effects of total blood lymphocyte count with regards to blood pressure.Among mechanisms that might mediate this relationship, we found evidence that blood lymphocyte count might influence albuminuria.This study may also support a reverse, potentially causal positive effect of blood pressure indices on blood neutrophil, monocyte, and eosinophil counts.High blood lymphocyte count may play a causal role in the development of hypertension.5 For example, immune cells infiltrating heart,6 perivascular adipose tissue, or kidneys contribute to dysfunction of these organs and can mediate high BP.2RAG1–/– mice7 or rats,8 lacking functional T and B lymphocytes, are protected from experimental hypertension, and transfer of T lymphocytes restores the hypertensive phenotype.7 Moreover, targeting B cells,9 monocytes, and neutrophils as well as targeting inflammasome13 shows their contribution to vessel remodeling and hypertension. While most data originate from experimental models, the relationships between white blood cells and hypertension remains poorly defined in humans.High blood pressure (BP) causes cardiovascular disease, including coronary heart disease (CHD) and stroke. While classical BP regulation is controlled by the function of kidney, vasculature and sympathetic nervous system, recent experimental data suggest that immune cells may play an important role in the development of hypertension.15 This creates an opportunity to test genetic, potentially causal relationships between these and other, clinically relevant cardiovascular traits using the Mendelian randomization (MR) approach. In brief, MR exploits characteristics of the human genome—principally its random allocation and nonmodifiable nature—to make potentially causal deductions on the relationship of an exposure on a trait or disease.16 Indeed, previous studies using such genetic approaches identified evidence in support of a causal relationship between white blood cell parameters such as lymphocytes and risk of CHD, making the exploration of the role of white blood cells in altering BP of particular interest.15Well-powered, genome-wide association studies (GWAS) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with BP-related traits or circulating blood cell indices.In the present study, we studied the relationship between major white blood cell types and BP in the UK Biobank (UKB) population and used MR analysis using data generated from large GWAS to examine whether leukocyte subpopulations may be causally linked to BP.http://www.ukbiobank.ac.uk/). Data concerning SNPs used in MR analyses, linking white blood cell count and BP indices, are available in the Excel file in the Data Supplement. GWAS summary statistics of the CKDGen (Chronic Kidney Disease Genetics) Consortium (http://ckdgen.imbi.uni-freiburg.de/), white blood cell counts (http://www.bloodcellgenetics.org/), heart rate (HR) phenotypes  and BP indices  are available online. The ICBP GWAS summary statistics can be assessed through the ICBP Consortium.UKB data are available on application for data access . All participants gave written informed consent before enrollment in the study, which was conducted in accordance with the principles of the Declaration of Helsinki. Using self-reported, ethnic background information, we selected 442 604 white British participants of the UK Biobank study to assess the effect of quintiles of circulating monocyte, lymphocyte, eosinophil, neutrophil, and basophil counts on systolic BP (SBP), diastolic BP (DBP), and pulse pressure (PP).17 Whereas blood cell indices often correlate with each other (Table I in the Data Supplement), these 5 subpopulations were among 13, out of 36, blood cell indices selected by Astle et al as relatively independent and approximated all other white blood cell indices including absolute and percentage counts .18UKB recruited 502 639 participants (37 to 73 years of age) from 22 assessment centers across the United Kingdom between 2007 and 2010. Baseline biological measurements were recorded, and touchscreen questionnaires were administered, as described elsewhere.Data Supplement for details regarding BP measurement procedure) was calculated and used in final analyses, and individuals with only 1 reading available (9.2% for SBP or DBP) were excluded from further analyses, leaving 384 721 individuals with available white blood cell counts for final analysis (Table). Repeated blood pressure measurements were highly correlated.19The average of 2 automated, sitting BP readings . We also performed separate sensitivity analyses after exclusion of individuals reporting BP-lowering medication use, using the same quintile boundaries as compared with the whole cohort. White blood cells were measured on fresh samples as an absolute number per unit volume, and their component leukocytes  as absolute measures and proportions of the overall white blood cells; all using an automated, clinically validated, Coulter LH 750. Calibration and quality control were performed in line with the manufacturer’s recommendations.18 Further details of these measurements can be found in the UK Biobank online showcase and protocol (http://www.ukbiobank.ac.uk). While quintiles of monocyte, lymphocyte, eosinophil, and neutrophil counts contained, on average, 76 944±4588 subjects, quintiles of basophil count were less balanced, with 96 897 subjects , 31 792, 128 937, 55 910, and 77 185 individuals in the 1st, 2nd, 3rd, 4th, and 5th quintiles, respectively .BP values from individuals on BP-lowering medications were adjusted in the same way as compared with GWAS on BP indices in the UKB and ICBPP<8.31×10–9) in analysis of UKB and INTERVAL studies comprising 173 480 individuals.15 Final analyses included common  and uncorrelated 22 SNPs available in GWAS on outcome variables. Palindromic SNPs with a minor allele frequency>40% in the exposure GWAS, as well as SNPs from the major histocompatibility complex region , which likely possess pleiotropic effects, were excluded from analyses.We selected SNPs that associated with the level of circulating monocyte, lymphocyte, eosinophil, basophil, or neutrophil counts at genome-wide significance ,16 a sensitivity analysis was performed using GWAS estimates derived solely from the ICBP consortium comprising 299 024 individuals.14 Of note, GWAS analysis on BP indices was adjusted for body mass index,14 while GWAS analysis on blood cell counts was adjusted for log-height and log-weight, which implicitly adjusted for body mass index.15 Therefore both GWAS of exposures and outcomes adjusted for adiposity-related traits, potentially limiting confounding caused by collider bias.23Our primary analysis examined whether there exists a relation between 5 selected blood cell indices and SBP, DBP, or PP on the basis of genetic effect estimates published by Evangelou et al in meta-analysis of 2 independent GWAS, the UKB and ICBP, comprising 757 601 individuals.24 increase in HR from resting level to peak exercise level,25 estimated glomerular filtration rate assessed in individuals of European ancestry,26 and urine albumin-to-creatinine ratio (UACR) derived from 2 different studies, that is, the CKDGen Consortium, and meta-analysis of the UK Biobank and CKDGen consortium.28 All participants, included in the individual studies within the CKDGen and ICBP consortia, provided written informed consent and studies were approved by their local research ethics committees and institutional review boards as applicable.28A secondary analysis aimed to identify an intermediate phenotype, which may mediate the causal effects, if present, of lymphocyte count on BP. Therefore, using published GWAS summary data, we selected traits related to kidney and heart function that have previously demonstrated a causal link to BP or hypertension through genetic analyses. This included resting HR,14 Among these, we selected 883 independent SNPs ,22 and associated with SBP, DBP, or PP at P<5×10–8 in the meta-analysis of the UKB and ICBP consortia,14 and used these as instrumental variables (IVs) in an MR analysis on 5 selected blood cell indices.15 Palindromic SNPs with a minor allele frequency >40% in the exposure GWAS, as well as SNPs from the major histocompatibility complex region, were excluded from these analyses.We considered 885 SNPs replicated in 1- or 2-stage analyses in a GWAS on BP indices by Evangelou et al.29 or using quantile regression using quantreg package in R (version 3.6.2) with the 3rd quintile set as a reference. To model the continuous relationship between blood cell counts and medication-adjusted BP indices quantile regression, the generalized additive model using mgcv package (version 1.8–31),30 and general linear model analyses in R were used. Cubic regression spline smooth was applied to cell count parameters and generalized additive model analyses were also adjusted for body mass index, age, age squared, sex, smoking status, and alcohol intake frequency.General linear model in SPSS (version 25.0) was used to test the association of quintiles of cell counts on SBP, DBP, and PP level while adjusting for sex, age, age squared, body mass index, smoking status , and alcohol intake frequency . Quintile-specific estimated marginal means were reported and compared using analysis of variance. To verify the observed associations, we performed similar analyses with additional adjustment for salt intake  method, which identifies and excludes SNPs that most likely display pleiotropic effects.32 Leave-one-out sensitivity analyses were performed using TwoSampleMR package in R.33 False discovery rate–based correction was applied to P values derived from MR analyses.Inverse-variance weighted (IVW) analyses were performed using MendelianRandomization package in R.Table II in the Data Supplement).An association between quintiles of all cell types and SBP, DBP, and PP was observed Figure . While tTable III in the Data Supplement), after exclusion of individuals on BP-lowering medication , or after additional adjustment for salt intake . Generalized additive model analyses identified evidence (P<5×10–4) of smooth terms of all analyzed blood cell counts on SBP, DBP, and PP. In particular, count of monocytes and neutrophils was associated with SBP in a dose-dependent manner , while positive effects of lymphocytes on SBP could be observed above the 2nd quintile of lymphocyte count . In addition, generalized additive model analyses confirmed protective effect of higher eosinophil count on all BP indices .Of note, the 1st quintile of eosinophil count was associated with higher SBP and PP compared with any other quintile .We next aimed to investigate causal relationships between major white blood cell counts and BP indices using 121, 87, 146 , 126 , and 50 uncorrelated SNPs used as IVs for analysis of total lymphocyte, neutrophil, monocyte, eosinophil, and basophil counts, respectively (see Table IV in the Data Supplement). In particular, after false discovery rate correction for multiple testing, IVW and MR-PRESSO methods demonstrated a potential causal relationship between lymphocyte count and both BP indices , with similar findings when using robust MR approaches, including MR-Egger and weighted median methods . Of note, using ICBP-only data (with no participant overlap), we demonstrated consistent, positive effects between total lymphocyte count and both SBP and DBP using all 4 analytical methods (Table V in the Data Supplement).We identified positive, potential causal relationships between lymphocyte count with SBP and DBP, concordant in all analytic approaches , and this finding was replicated using BP estimates derived from the ICBP consortium (Table V in the Data Supplement). Of note, MR-Egger found relatively strong evidence for pleiotropy (Tables IV and V in the Data Supplement), and causal effects were in the opposite direction when compared with the observational findings , could be replicated using ICBP-only data at a nominal threshold for significance (Table V in the Data Supplement).All MR analytic approaches demonstrated a relationship between total eosinophil count and higher DBP level . Exclusion of these variants from MR analyses on lymphocyte count (rs3184504) or eosinophil count (rs653178) attenuated the causal estimates  with BP indices and lymphocyte or eosinophil counts, as well as leave-one-out sensitivity analyses, identified a single variant in the Table VI in the Data Supplement; see Excel file in the Data Supplement for a list of all SNPs used as IVs). Of interest, MR analyses supported the existence of positive effects of SBP and DBP on monocyte and eosinophil count, while positive effects of SBP and PP were identified for neutrophil count . Inspection of scatter plots, visualizing the effects of all individual IVs on cell counts and BP indices, as well as leave-one-out sensitivity analyses, found that while rs3184504 SNP in SH2B3 inflated the IVW causal estimates for neutrophil, monocyte, and eosinophil counts, the estimates remained robust to exclusion of this particular SNP .To further understand a source of observational associations between BP and blood cell counts, we used BP-associated variants as IVs to explore the relationship of BP traits with blood cell traits in the reverse direction. We found no evidence of association between any of the BP index and lymphocyte or basophil count . No association between the level of circulating lymphocytes and resting HR, increase in HR from resting level to peak exercise level, or estimated glomerular filtration rate was identified (Table VII in the Data Supplement). Inspection of scatter plots, visualizing effects of all IVs on lymphocyte count and UACR, as well as leave-one-out sensitivity analyses, found that IVW causal estimates remained robust to exclusion of any single SNP from MR analysis .Primary observational association and genetic analyses demonstrated concordant effect directions of lymphocyte count on SBP and DBP. Thus, we tested lymphocyte counts in the context of genetic correlation with kidney function and heart rate parameters. An evidence of positive association was found between the level of circulating lymphocytes and UACR using MR-PRESSO and IVW, but not other methods , which investigated the use of canakinumab, an IL-1β blocker, in cardiovascular disease prevention, demonstrated that although the studied drug was profoundly neutropenic,42 Moreover, circulating lymphocytes have been associated with microvascular remodeling in hypertension.43 At the same time, we have recently observed relations between monocyte subpopulations and hypertension.44 Our present data, which include bidirectional assessment of genetic association between BP and blood cell counts, help to interpret these and may indicate that increases in monocytes with increasing blood pressure, are, similarly to neutrophils, most likely a consequence rather than a cause of hypertension.Our data are important as they provide mechanistic context to observational associations that earlier studies have reported, indicating a relationship between peripheral blood lymphocytes and hypertension. In our previous studies, we have identified that hypertensive patients are characterized by increased central memory, Th17 as well as IFN-γ producing immunosenescent or effector CD8+ cells.29 Using available, large-scale GWAS, we aimed to identify a target organ, whose function is causal to BP and might be affected by lymphocyte count. Our analysis of phenotypes related to kidney, and sympathetic nervous system function, demonstrated that genetically defined, higher total lymphocyte count related to a higher UACR using MR-PRESSO and IVW analyses, yet the associations were weaker using other analytic approaches. On the other hand, associations between lymphocyte count and UACR could be observed using UACR data derived from 2 different studies,28 which may support a causal link between lymphocyte count and albuminuria. Furthermore, this may indicate that lymphocytes affect BP via UACR, though it might be as well that lymphocytes affect UACR through independent causal pathways that include BP. Of note, recent studies indicate that a bidirectional association between albuminuria and BP exists.45 Moreover, whereas genetic liability to albuminuria associates with hypertension, stroke, and heart failure, it does not associate with chronic kidney disease,45 which may explain the lack of a causal relationship between lymphocyte count and estimated glomerular filtration rate in the present study. As a consequence, it has been proposed that albuminuria may have tubular origins, caused by sodium retention in the distal tubule.46The mechanism by which white blood cells, and especially lymphocytes, may cause hypertension remains unclear. Experimental animal data suggest that the effects of lymphocytes might be mediated by modulating vascular function, sympathetic outflow, and hypertension as well as renal sodium reabsorption and salt handling by antigen presenting cells.Table VIII in the Data Supplement). Many genes, whose function may be affected by selected SNPs, have been investigated in the context of BP regulation in vivo, and previous studies linking genetics of hypertension to lymphocyte biology focused on SH2B3 as key driver for hypertension (Table VIII in the Data Supplement). For example, an in vivo study demonstrated exacerbated hypertension, vascular dysfunction, and infiltration of IFNγ-producing CD8+ T cells in response to angiotensin II in Sh2b3 knockout animals as compared with wild type mice.47 SNP rs3184504 is a missense mutation within SH2B3 and leads to an R262W amino acid change in Lnk,48 which is a negative regulator of hematopoiesis and TNFα signaling in endothelial cells and may play a role in integrin signaling.49 Of interest, the SH2B3 locus has been associated with various cardiovascular-related traits and diseases, including CHD,50 and its effect on blood pressure may be at least partially mediated by influencing β-2-microglobulin levels in humans.51 This suggests a potentially pleiotropic effect of SH2B3 on BP level. Of importance, sensitivity analyses performed in the present study identified rs3184504 SNP in SH2B3 as a potential pleiotropic outlier, and its exclusion led to attenuation of the MR relationships calculated by all analytic approaches. However, after exclusion of rs3184504 SNP, evidence of potential causal effect of lymphocyte count on BP was retained in IVW, weighted median or MR-PRESSO analytic approaches.Other than the analyses of the genetic correlation between lymphocyte count and UACR, important biological information can be obtained while selecting genes in proximity to SNPs that associate with both lymphocyte count and BP indices in the concordant direction .53 For example, horizontal pleiotropy, the phenomenon where genetic variants independently associate with traits other than the ones under investigation, can lead to confounding of the MR estimates. The susceptibility of the estimates derived from MR to such horizontal pleiotropy can be investigated through the application of sensitivity analyses that are more robust to such types of pleiotropy, as performed in the present study with MR-Egger and weighted median approaches.16 Although none of these methods, when used individually, entirely protects the findings from MR analyses from the influence of potential pleiotropic effects, a consistency of effect estimate derived across multiple sensitivity analyses adds confidence to the plausibility of the presence of a true underlying causal effect. Other forms of confounding in MR exist, such as population structure.53 In the present analysis, GWAS used in the identification of white blood cell counts and BP indices used methodology that take into account cryptic relatedness and population stratification,15 which should minimize the potential for such sources of confounding. A further vulnerability is the potential for overfitting in the context of having used the same dataset for discovery  and MR.54 Additionally, we used the ICBP GWAS on BP indices,14 characterized by no individuals overlapping with the GWAS on white blood cell counts, to confirm the lack of overfitting of the effects of genetically defined blood lymphocyte count on SBP and DBP levels.The interpretation of estimates derived from MR can be challenging.In contrast with MR analyses, our observational results are likely to be more susceptible to errors induced by confounding or reverse causation phenomena. While the associations of white blood cell counts with BP seem to be independent of commonly used potential confounders such as sex, age, body mass index, salt/alcohol intake, or smoking habits, we cannot exclude the existence of residual confounding by variables influencing blood cell count and BP association. The reverse-causation phenomenon is, by definition, not feasible to address in the context of cross-sectional data, thus motivating the need for either prospective studies with comprehensive follow-up or approaches were reverse causation cannot be a phenomenon, such as MR approaches or randomized controlled trials.In summary, the present study identified evidence in support of a potential causal link between elevated lymphocyte count and higher BP. The molecular mechanism of this association is possibly independent of the classical regulatory mechanisms related to kidney function or heart rate but might involve pathways related to albuminuria. Furthermore, results of reverse MR analyses may support potential causal effects of increased BP on higher levels of blood neutrophil, monocyte, and eosinophil but not lymphocyte or basophil counts.This research has been conducted using the UK Biobank Resource under application number 50282.This work was funded by the European Research Council , ERA-CVD/PLAQUEFIGHT/5/2018 and British Heart Foundation (RE/13/5/30177). M.S. is supported by the National Science Center, Poland (grant no. 2016/22/E/NZ4/00610). M.V.H. works in a unit that receives funding from the UK Medical Research Council and is supported by a British Heart Foundation Intermediate Clinical Research Fellowship (FS/18/23/33512) and the National Institute for Health Research Oxford Biomedical Research Center. M.T. is supported by British Heart Foundation grants PG/17/35/33001 and PG/19/16/34270, and Kidney Research UK grant RP_017_20180302. R.B.S. has received funding from the ERC under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 648131), and German Center for Cardiovascular Research (81Z1710103). P.M. is supported by the British Heart Foundation grant PG/19/84/34771.None.


In this paper, we review and demonstrate how population phenomena can inflate genotype-phenotype associations. Heritability, genetic correlation, and genetic associations estimated from samples of unrelated individuals are often perceived as confirmation that genotype causes the phenotype(s). However, these estimates can arise from indirect mechanisms due to population phenomena including population stratification, dynastic effects, and assortative mating. We introduce these, describe how they can bias or inflate genotype-phenotype associations, and demonstrate methods that can be used to assess their presence. Using data on educational achievement and parental socioeconomic position as an exemplar, we demonstrate that both heritability and genetic correlation may be biased estimates of the causal contribution of genotype. These results highlight the limitations of genotype-phenotype estimates obtained from samples of unrelated individuals. Use of these methods in combination with family-based designs may offer researchers greater opportunities to explore the mechanisms driving genotype-phenotype associations and identify factors underlying bias in estimates. Genotype-phenotype associations inferred from genetic data can be used to provide insight into the genetic architecture of complex traits, interrogate causal and noncausal associations between different phenotypes, and create phenotypic predictors .Box 11. Variant substitution effectsVariant substitution effects can be thought of as the  change in an individual’s phenotype that would occur as a result of changing that individual’s genotype from conception . They can be estimated for a single phenotype (univariate genetic association) or pairs of phenotypes . In most cases, this type of effect is the target of any genotype-phenotype association analysis.2. Population stratificationPopulation stratification refers to confounding introduced to associations between population structure and phenotype by systematic differences in allele frequencies across subpopulations. This arises from ancestry differences due to nonrandom mating and subsequent genetic drift of allele frequencies between subpopulation groups, historically caused by geographic and physical boundaries. If phenotypes also differ systematically between subpopulations, population stratification can lead to genotype-phenotype associations despite no causal relationship between the genotype and the phenotype.3. Dynastic effectsFurther to influencing offspring phenotype through genetic inheritance, parental genotype can indirectly influence offspring phenotype through its expression in the parental phenotype. Where this occurs, offspring may inherit both phenotype-associated SNPs and phenotype-associated environments from parents, leading to biased genetic associations  heritability, which represents the total genetic component of a trait estimated from variation in all measured SNPs; genetic correlation, which represents the correlation in effects of all measured SNPs on two or more phenotypes; and genetic associations, which represent how a phenotype is influenced by a specific SNP. SNP heritability and genetic correlation are estimated from whole genome data with methods such as genomic-relatedness-based restricted maximum likelihood (GREML)  . That thWhile genetic correlation estimates the correlation between the effects of SNPs on two phenotypes, it provides no information of how important genotype effects for one phenotype are for phenotypic differences in another. Bivariate heritability, which estimates the proportion of phenotypic correlation between two traits that can be attributed to genotype  and SEP  were broadly similar to educational achievement and also exceeded those in previous studies of 29% for cognitive ability (r = 0.56) and SEP (r = 0.43), which induced genetic correlations at education-associated loci of r = 0.18. Heritability estimates of educational achievement were attenuated by roughly half when parental education or SEP was controlled for. This supports bias in heritability estimates due to assortative mating and/or dynastic effects in ALSPAC. We found no strong evidence that our estimates were biased by population stratification as measured by the genetic principal components, but this may reflect the inability of genetic principal components to capture subtle population structure rather than adequately control it . Second, further residual population structure in the ALSPAC genetic relatedness matrix not captured by the principal components could bias our results . For full details of the cohort profile and study design, see (P < 5 × 10–7) were removed. Cryptic relatedness was assessed using an IBD estimate of more than 0.125, which is expected to correspond to roughly 12.5% alleles shared IBD or a relatedness at the first cousin level. Related participants that passed all other quality control thresholds were retained during subsequent phasing and imputation. For the mothers, samples were removed, where they had indeterminate X chromosome heterozygosity or extreme autosomal heterozygosity. After quality control, 9115 participants and 500,527 SNPs for the children, 9048 participants and 526,688 SNPs for the mothers, and 2201 participants and 507,586 SNPs for the fathers  passed these quality control filters.DNA of the ALSPAC children was extracted from blood, cell line, and mouthwash samples and then genotyped using reference panels and subjected to standard quality control approaches. ALSPAC children were genotyped using the Illumina HumanHap550 quad chip genotyping platforms by 23andMe subcontracting the Wellcome Trust Sanger Institute  and the Laboratory Corporation of America . ALSPAC mothers were genotyped using the Illumina Human660W-Quad array at Centre National de Génotypage, and genotypes were called with Illumina GenomeStudio. ALSPAC fathers and some additional mothers were genotyped using the Illumina HumanCoreExome chip genotyping platforms by the ALSPAC laboratory and called using GenomeStudio. All resulting raw genome-wide data were subjected to standard quality control methods in PLINK (v1.07). Individuals were excluded on the basis of gender mismatches, minimal or excessive heterozygosity, disproportionate levels of individual missingness (>3%), and insufficient sample replication (IBD < 0.8). Population stratification was assessed by multidimensional scaling analysis and compared with HapMap II (release 22) European descent (CEU), Han Chinese, Japanese, and Yoruba reference populations; all individuals with non-European ancestry were removed. SNPs with a MAF of <1%, a call rate of <95%, or evidence for violations of Hardy-Weinberg equilibrium . We estimated haplotypes using ShapeIT (v2.r644), which uses relatedness during phasing. The phased haplotypes were then imputed to the Haplotype Reference Consortium  panel of approximately 31,000 phased whole genomes. The HRC panel was phased using ShapeIT v2, and the imputation was performed using the Michigan imputation server. After imputation and filtering on MAF > 0.01 and info > 0.8, there were 7,191,388 SNPs. This gave 8237 eligible children, 8675 eligible mothers, and 1722 eligible fathers with available genotype data after exclusion of related participants using cryptic relatedness measures described previously.We use average fine graded point scores at the three major Key Stages of education in the United Kingdom at ages 11, 14, and 16. Point scores were obtained from the Key Stage 4 (age 16) database of the UK National Pupil Database (NPD) through data linkage to the ALSPAC cohort. The NPD represents the most accurate record of individual educational achievement available in the United Kingdom. The Key Stage 4 database provides a larger sample size than the earlier two Key Stage databases and contains data for each. Fine graded point scores provide a richer measure of a child’s achievement than level bandings and were therefore chosen as the most accurate method of determining academic achievement during compulsory schooling.We use two measures of parental SEP: a binary classification based on the widely used Social Class based on Occupation  of “high” (I and II) versus “low”  social classes and a continuous classification based on the Cambridge Social Stratification Score (CAMSIS). Social Class based on Occupation assumes within-strata social homogeneity with clear boundaries, while CAMSIS provides a more flexible measure that accounts for social heterogeneity.Cognitive ability was measured during the direct assessment at age 8 using the short-form Wechsler Intelligence Scale for Children from verbal, performance, and digit span tests (P < 5 × 10−8) in GWAS  using GREML in the software package GCTA  is the genetic variance of phenotype A, and covg is the genetic covariance between phenotypes A and B. Genetic correlations can indicate that two phenotypes are influenced by the same SNPs . In contrast, the bivariate heritability is the proportion of the phenotypic correlation that can be explained by the genotypes. Genetic correlations and bivariate heritability are likely to differ. For example, two phenotypes may be highly genetically correlated, but if they have low heritability, then the bivariate heritability will be low. Bivariate heritability estimates the proportion of phenotypic correlations that can be explained by genetics. It is estimated asrg is the genetic correlation between phenotypes A and B; A and B, respectively; and rp is the phenotypic correlation between phenotypes A and B. GCTA does not directly estimate coheritability or bivariate heritability terms and therefore cannot be used to estimate SEs. Bivariate heritability estimates were derived from entering the GCTA estimated components into We estimate genetic correlations using bivariate GCTA, running nine sets of analyses between educational achievement at each age and each of linear SEP, binary SEP, and cognitive ability. Genetic correlation quantifies the extent to which SNPs that associate with one phenotype  also associate with another phenotype . It therefore refers to the correlation of all genetic effects across the genome for phenotypes We used data for unrelated participants, as indicated by the ALSPAC genetic relatedness matrices. Population stratification is controlled for by using the first 20 principal components of inferred population structure as covariates in analyses. Continuous variables were inverse normally transformed to have a normal distribution, a requirement of GCTA.