Accuracy of genomic BLUP when considering a genomic relationship matrix based on the number of the largest eigenvalues: a simulation study.

International audienceAbstractBackgroundThe dimensionality of genomic information is limited by the number of independent chromosome segments (Me), which is a function of the effective population size. This dimensionality can be determined approximately by singular value decomposition of the gene content matrix, by eigenvalue decomposition of the genomic relationship matrix (GRM), or by the number of core animals in the algorithm for proven and young (APY) that maximizes the accuracy of genomic prediction. In the latter, core animals act as proxies to linear combinations of Me. Field studies indicate that a moderate accuracy of genomic selection is achieved with a small dataset, but that further improvement of the accuracy requires much more data. When only one quarter of the optimal number of core animals are used in the APY algorithm, the accuracy of genomic selection is only slightly below the optimal value. This suggests that genomic selection works on clusters of Me.ResultsThe simulation included datasets with different population sizes and amounts of phenotypic information. Computations were done by genomic best linear unbiased prediction (GBLUP) with selected eigenvalues and corresponding eigenvectors of the GRM set to zero. About four eigenvalues in the GRM explained 10% of the genomic variation, and less than 2% of the total eigenvalues explained 50% of the genomic variation. With limited phenotypic information, the accuracy of GBLUP was close to the peak where most of the smallest eigenvalues were set to zero. With a large amount of phenotypic information, accuracy increased as smaller eigenvalues were added.ConclusionsA small amount of phenotypic data is sufficient to estimate only the effects of the largest eigenvalues and the associated eigenvectors that contain a large fraction of the genomic information, and a very large amount of data is required to estimate the remaining eigenvalues that account for a limited amount of genomic information. Core animals in the APY algorithm act as proxies of almost the same number of eigenvalues. By using an eigenvalues-based approach, it was possible to explain why the moderate accuracy of genomic selection based on small datasets only increases slowly as more data are added

Genomic investigation of milk production in Italian buffalo

The aim of this study was to test the feasibility of genomic selection in the Italian Mediterranean water buffalo, which is farmed mainly in the south Italy for milk, and mozzarella, production. A total of 498 animals were genotyped at 49,164 loci. Test day records (80,417) of milk (MY), fat (FY) and protein (PY) yields from 4127 cows, born between 1975 and 2009, were analysed in a three-trait model. Cows born in 2008 and 2009 with phenotypes and genotypes were selected as validation animals (n = 50). Variance components (VC) were estimated with BLUP and ssGBLUP. Heritabilities for BLUP were 0.25 ± 0.02 (MY), 0.16 ± 0.01 (FY) and 0.25 ± 0.01 (PY). Breeding values were computed using BLUP and ssGBLUP, using VC estimated from BLUP. ssGBLUP was applied in five scenarios, each with a different number of genotypes available: (A) bulls (35); (B) validation cows (50); (C) bulls and validation cows (85); (D) all genotyped cows (463); (E) all genotypes (498). Model validation was performed using the LR method: correlation, accuracy, dispersion, and bias statistics were calculated. Average correlations were 0.71 ± 0.02 and 0.82 ± 0.01 for BLUP and ssGBLUP-E, respectively. Accuracies were also higher in ssGBLUP-E (0.75 ± 0.03) compared to BLUP (0.57 ± 0.03). The best dispersions (i.e. closer to 1) were found for ssGBLUP-C. The use of genotypes only for the 35 bulls did not change the validation values compared to BLUP. Results of the present study, even if based on small number of animals, showed that the inclusion of genotypes of females can improve breeding values accuracy in the Italian Buffalo.Highlights The genotypes of males did not improve the predictions. Genotypes of females improve breeding values accuracy. Slight increase in prediction accuracy with weighted ssGBLUP

On the implications of aerosol liquid water and phase separation for modeled organic aerosol mass

Water is an important component of PM2.5 Many traditional SOA species are highly soluble and thus can be considered extractable Water can influence the partitioning of compounds traditionally considered insoluble in models Organic aerosol takes up water according to RH Organic aerosol interacts with inorganic water Deviations in ideality (solubility) must be considered

Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP

[EN] Model-based accuracy, defined as the theoretical correlation between true and estimated breeding value, can be obtained for each individual as a function of its prediction error variance (PEV) and inbreeding coefficient F, in BLUP, GBLUP and SSGBLUP genetic evaluations. However, for computational convenience, inbreeding is often ignored in two places. First, in the computation of reliability = 1-PEV/(1 + F). Second, in the set-up, using Henderson's rules, of the inverse of the pedigree-based relationship matrix A. Both approximations have an effect in the computation of model-based accuracy and result in wrong values. In this work, first we present a reminder of the theory and extend it to SSGBLUP. Second, we quantify the error of ignoring inbreeding with real data in three scenarios: BLUP evaluation and SSGBLUP in Uruguayan dairy cattle, and BLUP evaluations in a line of rabbit closed for >40 generations with steady increase of inbreeding up to an average of 0.30. We show that ignoring inbreeding in the set-up of the A-inverse is equivalent to assume that non-inbred animals are actually inbred. This results in an increase of apparent PEV that is negligible for dairy cattle but considerable for rabbit. Ignoring inbreeding in reliability = 1-PEV/(1 + F) leads to underestimation of reliability for BLUP evaluations, and this underestimation is very large for rabbit. For SSGBLUP in dairy cattle, it leads to both underestimation and overestimation of reliability, both for genotyped and non-genotyped animals. We strongly recommend to include inbreeding both in the set-up of A-inverse and in the computation of reliability from PEVs.FEDER; INRA; Universidad Nacional de Lomas de Zamora; European Unions' Horizon 2020 Research & Innovation Programme, Grant/Award Number: No772787Aguilar, I.; Fernandez, EN.; Blasco Mateu, A.; Ravagnolo, O.; Legarra, A. (2020). Effects of ignoring inbreeding in model-based accuracy for BLUP and SSGBLUP. Journal of Animal Breeding and Genetics. 137(4):356-364. https://doi.org/10.1111/jbg.12470S3563641374Bijma, P. (2012). Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations. Journal of Animal Breeding and Genetics, 129(5), 345-358. doi:10.1111/j.1439-0388.2012.00991.xChristensen, O. F., Madsen, P., Nielsen, B., Ostersen, T., & Su, G. (2012). Single-step methods for genomic evaluation in pigs. Animal, 6(10), 1565-1571. doi:10.1017/s1751731112000742Colleau, J.-J., Palhière, I., Rodríguez-Ramilo, S. T., & Legarra, A. (2017). A fast indirect method to compute functions of genomic relationships concerning genotyped and ungenotyped individuals, for diversity management. Genetics Selection Evolution, 49(1). doi:10.1186/s12711-017-0363-9Edel, C., Pimentel, E. C. G., Erbe, M., Emmerling, R., & Götz, K.-U. (2019). Short communication: Calculating analytical reliabilities for single-step predictions. Journal of Dairy Science, 102(4), 3259-3265. doi:10.3168/jds.2018-15707Fernández, E. N., Sánchez, J. P., Martínez, R., Legarra, A., & Baselga, M. (2017). Role of inbreeding depression, non-inbred dominance deviations and random year-season effect in genetic trends for prolificacy in closed rabbit lines. Journal of Animal Breeding and Genetics, 134(6), 441-452. doi:10.1111/jbg.12284Golden, B. L., Brinks, J. S., & Bourdon, R. M. (1991). A performance programmed method for computing inbreeding coefficients from large data sets for use in mixed-model analyses. Journal of Animal Science, 69(9), 3564-3573. doi:10.2527/1991.6993564xGroeneveld E. Kovac M. &Wang T.(1990).PEST a general purpose BLUP package for multivariate prediction and estimation. Proceedings of the 4th World Congress on Genetics Applied to Livestock Production Edinburgh 13 488–491.Henderson, C. R. (1975). Best Linear Unbiased Estimation and Prediction under a Selection Model. Biometrics, 31(2), 423. doi:10.2307/2529430Henderson, C. R. (1976). A Simple Method for Computing the Inverse of a Numerator Relationship Matrix Used in Prediction of Breeding Values. Biometrics, 32(1), 69. doi:10.2307/2529339Legarra, A., Aguilar, I., & Colleau, J. J. (2020). Short communication: Methods to compute genomic inbreeding for ungenotyped individuals. Journal of Dairy Science, 103(4), 3363-3367. doi:10.3168/jds.2019-17750Legarra, A., Aguilar, I., & Misztal, I. (2009). A relationship matrix including full pedigree and genomic information. Journal of Dairy Science, 92(9), 4656-4663. doi:10.3168/jds.2009-2061Legarra A. Lourenco D. A. L. &Vitezica Z. G.(2018).Bases for genomic prediction. Retrieved fromhttp://genoweb.toulouse.inra.fr/~alegarra/Masuda, Y., Aguilar, I., Tsuruta, S., & Misztal, I. (2015). Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements1,2. Journal of Animal Science, 93(10), 4670-4674. doi:10.2527/jas.2015-9395Matilainen, K., Strandén, I., Aamand, G. P., & Mäntysaari, E. A. (2018). Single step genomic evaluation for female fertility in Nordic Red dairy cattle. Journal of Animal Breeding and Genetics, 135(5), 337-348. doi:10.1111/jbg.12353Mehrabani-Yeganeh, H., Gibson, J. P., & Schaeffer, L. R. (2000). Including coefficients of inbreeding in BLUP evaluation and its effect on response to selection. Journal of Animal Breeding and Genetics, 117(3), 145-151. doi:10.1046/j.1439-0388.2000.00241.xMeyer, K. (2007). WOMBAT—A tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University SCIENCE B, 8(11), 815-821. doi:10.1631/jzus.2007.b0815Misztal, I., & Wiggans, G. R. (1988). Approximation of Prediction Error Variance in Large-Scale Animal Models. Journal of Dairy Science, 71, 27-32. doi:10.1016/s0022-0302(88)79976-2Mrode, R. A., & Thompson, R. (Eds.). (2005). Linear models for the prediction of animal breeding values. doi:10.1079/9780851990002.0000Pryce, J. E., Gonzalez-Recio, O., Nieuwhof, G., Wales, W. J., Coffey, M. P., Hayes, B. J., & Goddard, M. E. (2015). Hot topic: Definition and implementation of a breeding value for feed efficiency in dairy cows. Journal of Dairy Science, 98(10), 7340-7350. doi:10.3168/jds.2015-9621Sargolzaei, M., Chesnais, J. P., & Schenkel, F. S. (2014). A new approach for efficient genotype imputation using information from relatives. BMC Genomics, 15(1), 478. doi:10.1186/1471-2164-15-478Strandén, I., Matilainen, K., Aamand, G. P., & Mäntysaari, E. A. (2017). Solving efficiently large single-step genomic best linear unbiased prediction models. Journal of Animal Breeding and Genetics, 134(3), 264-274. doi:10.1111/jbg.12257Ten Napel J. Vandenplas J. Lidauer M. Stranden I. Taskinen M. Mäntysaari E. Veerkamp R. F.(2017).MiXBLUP user‐friendly software for large genetic evaluation systems–Manual V2. Retrived from:https://www.mixblup.eu/documents/Manual%20MiXBLUP%202.1_June%202017_V2.pdfTier B. Schneeberger M. Hammond K. &Fuchs W. C.(1991).Determining the accuracy of estimated breeding values in multiple trait animal models. Proceedings of the 9th AAABG Conference 239–242Van Vleck, L. D. (1993). Variance of prediction error with mixed model equations when relationships are ignored. Theoretical and Applied Genetics, 85(5), 545-549. doi:10.1007/bf00220912VanRaden, P. M. (2008). Efficient Methods to Compute Genomic Predictions. Journal of Dairy Science, 91(11), 4414-4423. doi:10.3168/jds.2007-0980Xiang, T., Christensen, O. F., & Legarra, A. (2017). Technical note: Genomic evaluation for crossbred performance in a single-step approach with metafounders1. Journal of Animal Science, 95(4), 1472-1480. doi:10.2527/jas.2016.115

Seasonal analysis of submicron aerosol in Old Delhi using high-resolution aerosol mass spectrometry: chemical characterisation, source apportionment and new marker identification

We present the first real-time composition of submicron particulate matter (PM1) in Old Delhi using high-resolution aerosol mass spectrometry (HR-AMS). Old Delhi is one of the most polluted locations in the world, and PM1 concentrations reached ∼ 750 µg m−3 during the most polluted period, the post-monsoon period, where PM1 increased by 188 % over the pre-monsoon period. Sulfate contributes the largest inorganic PM1 mass fraction during the pre-monsoon (24 %) and monsoon (24 %) periods, with nitrate contributing most during the post-monsoon period (8 %). The organics dominate the mass fraction (54 %–68 %) throughout the three periods, and, using positive matrix factorisation (PMF) to perform source apportionment analysis of organic mass, two burning-related factors were found to contribute the most (35 %) to the post-monsoon increase. The first PMF factor, semi-volatility biomass burning organic aerosol (SVBBOA), shows a high correlation with Earth observation fire counts in surrounding states, which links its origin to crop residue burning. The second is a solid fuel OA (SFOA) factor with links to local open burning due to its high composition of polyaromatic hydrocarbons (PAHs) and novel AMS-measured marker species for polychlorinated dibenzodioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs). Two traffic factors were resolved: one hydrocarbon-like OA (HOA) factor and another nitrogen-rich HOA (NHOA) factor. The N compounds within NHOA were mainly nitrile species which have not previously been identified within AMS measurements. Their PAH composition suggests that NHOA is linked to diesel and HOA to compressed natural gas and petrol. These factors combined make the largest relative contribution to primary PM1 mass during the pre-monsoon and monsoon periods while contributing the second highest in the post-monsoon period. A cooking OA (COA) factor shows strong links to the secondary factor, semi-volatility oxygenated OA (SVOOA). Correlations with co-located volatile organic compound (VOC) measurements and AMS-measured organic nitrogen oxides (OrgNO) suggest SVOOA is formed from aged COA. It is also found that a significant increase in chloride concentrations (522 %) from pre-monsoon to post-monsoon correlates well with SVBBOA and SFOA, suggesting that crop residue burning and open waste burning are responsible. A reduction in traffic emissions would effectively reduce concentrations across most of the year. In order to reduce the post-monsoon peak, sources such as funeral pyres, solid waste burning and crop residue burning should be considered when developing new air quality policy

Survival through networks: the 'grip' of the administrative links in the Russian post-Soviet context

© 2014 Taylor & Francis. Based on an analysis of the post-Soviet transformation experience of four defence sector organizations in a Russian region where the defence sector occupies a substantial part of the local economy, this article develops a typology of network relationships: Grooved Inter-relationship Patterns (Gr’ip) networks and Fluid Inter-relationship Patterns (Fl’ip) networks. This typology can be applied to a range of transition/emerging market and low system trust contexts. Gr’ip networks, in this case, represent the persisting legacy of the Soviet command-administrative system. Fl’ip networks are here an attempt by the defence companies to link into the civilian supply chains of a developing market economy. This article argues that Gr’ip networks had and still have a crucial role to play in Russian enterprises’ survival and development

NOx and O3 above a tropical rainforest: an analysis with a global and box model

A cross-platform field campaign, OP3, was conducted in the state of Sabah in Malaysian Borneo between April and July of 2008. Among the suite of observations recorded, the campaign included measurements of NOx and O3 – crucial outputs of any model chemistry mechanism. We describe the measurements of these species made from both the ground site and aircraft. We then use the output from two resolutions of the chemistry transport model p-TOMCAT to illustrate the ability of a global model chemical mechanism to capture the chemistry at the rainforest site. The basic model performance is good for NOx and poor for ozone. A box model containing the same chemical mechanism is used to explore the results of the global model in more depth and make comparisons between the two. Without some parameterization of the nighttime boundary layer – free troposphere mixing (i.e. the use of a dilution parameter), the box model does not reproduce the observations, pointing to the importance of adequately representing physical processes for comparisons with surface measurements. We conclude with a discussion of box model budget calculations of chemical reaction fluxes, deposition and mixing, and compare these results to output from p-TOMCAT. These show the same chemical mechanism behaves similarly in both models, but that emissions and advection play particularly strong roles in influencing the comparison to surface measurements

New times, new politics: history and memory during the final years of the CPGB

This article examines the relationship between collective memory, historical interpretation and political identity. It focuses on the dissolution of the Communist Party of Great Britain (CPGB) as constructed through collective narrative memory, and on Marxist interpretations of history. The divisions within the party and the wider Marxist community, stretching from 1956 until 1991, were often framed around questions of historical interpretation. The events of 1989–1991 created an historical and mnemonic crisis for CPGB members who struggled to reconcile their past identities with their present situation. Unlike the outward-facing revisionism of other political parties, this was an intensely personal affair. The solution for many was to emphasise the need to find new ways to progress socialist aims, without relying on a discredited grand narrative. In contrast, other Communist parties, such as the Communist Party of Britain, which had been established (or ‘re-established’) in 1988, fared rather better. By adhering to the international party line of renewal and continued struggle, the party was able to hold its narrative together, condemning the excesses of totalitarian regimes, while reaffirming the need for international class struggle

A note on mate allocation for dominance handling in genomic selection

Estimation of non-additive genetic effects in animal breeding is important because it increases the accuracy of breeding value prediction and the value of mate allocation procedures. With the advent of genomic selection these ideas should be revisited. The objective of this study was to quantify the efficiency of including dominance effects and practising mating allocation under a whole-genome evaluation scenario. Four strategies of selection, carried out during five generations, were compared by simulation techniques. In the first scenario (MS), individuals were selected based on their own phenotypic information. In the second (GSA), they were selected based on the prediction generated by the Bayes A method of whole-genome evaluation under an additive model. In the third (GSD), the model was expanded to include dominance effects. These three scenarios used random mating to construct future generations, whereas in the fourth one (GSD + MA), matings were optimized by simulated annealing. The advantage of GSD over GSA ranges from 9 to 14% of the expected response and, in addition, using mate allocation (GSD + MA) provides an additional response ranging from 6% to 22%. However, mate selection can improve the expected genetic response over random mating only in the first generation of selection. Furthermore, the efficiency of genomic selection is eroded after a few generations of selection, thus, a continued collection of phenotypic data and re-evaluation will be required