44 Accuracy of Genomic Predictions over Time in Broilers

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 28-28
Author(s):  
Jorge Hidalgo ◽  
Daniela Lourenco ◽  
Shogo Tsuruta ◽  
Yutaka Masuda ◽  
Vivian Breen ◽  
...  
Keyword(s):  
Feed Efficiency ◽  
Growth Trait ◽  
Genomic Data ◽  
First Year ◽  
Linear Regression Method ◽  
Genomic Information ◽  
Training Population ◽  
Over Time ◽  
Genomic Predictions ◽  
Training Sets

Abstract The objectives of this research were to investigate trends for accuracy of genomic predictions over time in a broiler population accumulating data, and to test if data from distant generations are useful in maintaining the accuracy of genomic predictions in selection candidates. The data contained 820k phenotypes for a growth trait (GROW), 200k for two feed efficiency traits (FE1 and FE2), and 42k for a dissection trait (DT). The pedigree included 1.2M animals across 7 years, over 100k from the last 4 years were genotyped. Accuracy was calculated by the linear regression method. Before genotypes became available for training populations, accuracy was nearly stable despite the accumulation of phenotypes and pedigrees. When the first year of genomic data was included in the training population, accuracy increased 56, 77, 39, and 111% for GROW, FE1, FE2, and DT, respectively. With genomic information, the accuracies increased every year except the last one, when they declined for GROW and FE2. The decay of accuracy over time was evaluated in progeny, grand-progeny, and great-grand-progeny of training populations. Without genotypes, the average decline in accuracy across traits was 41% from progeny to grand-progeny, and 19% from grand-progeny to great-grand-progeny. Whit genotypes, the average decline across traits was 14% from progeny to grand-progeny, and 2% from grand-progeny to great-grand-progeny. The accuracies in the last 3 generations were the same when the training population included 5 or 2 years of data, and a marginal decrease was observed when the training population included only 1 year of data. Training sets including genomic information provided an increased accuracy and persistence of genomic predictions compared to training sets without genomic data. The two most recent years of data were enough to maintain the accuracy of predictions in selection candidates.

scholarly journals Investigating the persistence of accuracy of genomic predictions over time in broilers

2021 ◽  
Author(s):  
Jorge Hidalgo ◽  
Daniela Lourenco ◽  
Shogo Tsuruta ◽  
Yutaka Masuda ◽  
Vivian Breen ◽  
...  
Keyword(s):  
Second Generation ◽  
Genomic Data ◽  
Third Generation ◽  
Genomic Information ◽  
Training Population ◽  
The Third ◽  
Three Generations ◽  
One Year ◽  
Over Time ◽  
Genomic Predictions

Abstract Accuracy of genomic predictions is an important component of the selection response. The objectives of this research were: 1) to investigate trends for prediction accuracies over time in a broiler population of accumulated phenotypes, genotypes, and pedigrees; 2) to test if data from distant generations are useful to maintain prediction accuracies in selection candidates. The data contained 820K phenotypes for a growth trait (GT), 200K for two feed efficiency traits (FE1 and FE2), and 42K for a carcass yield trait (CY). The pedigree included 1,252,619 birds hatched over seven years, of which 154,318 from the last four years were genotyped. Training populations were constructed adding one year of data sequentially, persistency of accuracy over time was evaluated using predictions from birds hatched in the three generations following or in the years after the training populations. In the first generation, before genotypes became available for the training populations (first three years of data), accuracies remained almost stable with successive additions of phenotypes and pedigree to the accumulated dataset. The inclusion of one year of genotypes in addition to four years of phenotypes and pedigree in the training population led to increases in accuracy of 54% for GT, 76% for FE1, 110% for CY, and 38% for FE2; on average, 74% of the increase was due to genomics. Prediction accuracies declined faster without than with genomic information in the training populations. When genotypes were unavailable, the average decline in prediction accuracy across traits was 41% from the first to the second generation of validation, and 51% from the second to the third generation of validation. When genotypes were available, the average decline across traits was 14% from the first to the second generation of validation, and 3% from the second to the third generation of validation. Prediction accuracies in the last three generations were the same when the training population included five or two years of data, and a decrease of ~7% was observed when the training population included only one year of data. Training sets including genomic information provided an increase in accuracy and persistence of genomic predictions compared to training sets without genomic data. The two most recent years of pedigree, phenotypic and genomic data were sufficient to maintain prediction accuracies in selection candidates. Similar conclusions were obtained using validation populations per year.

scholarly journals The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs

2020 ◽  
Vol 52 (1) ◽  
Author(s):  
Amir Aliakbari ◽  
Emilie Delpuech ◽  
Yann Labrune ◽  
Juliette Riquet ◽  
Hélène Gilbert
Keyword(s):  
Feed Intake ◽  
Genomic Prediction ◽  
Feed Efficiency ◽  
Prediction Accuracy ◽  
Target Line ◽  
The Other ◽  
Training Set ◽  
The Impact ◽  
Genomic Predictions ◽  
Training Sets

Abstract Background Most genomic predictions use a unique population that is split into a training and a validation set. However, genomic prediction using genetically heterogeneous training sets could provide more flexibility when constructing the training sets in small populations. The aim of our study was to investigate the potential of genomic prediction of feed efficiency related traits using training sets that combine animals from two different, but genetically-related lines. We compared realized prediction accuracy and prediction bias for different training set compositions for five production traits. Results Genomic breeding values (GEBV) were predicted using the single-step genomic best linear unbiased prediction method in six scenarios applied iteratively to two genetically-related lines (i.e. 12 scenarios). The objective for all scenarios was to predict GEBV of pigs in the last three generations (~ 400 pigs, G7 to G9) of a given line. For each line, a control scenario was set up with a training set that included only animals from that line (target line). For all traits, adding more animals from the other line to the training set did not increase prediction accuracy compared to the control scenario. A small decrease in prediction accuracies was found for average daily gain, backfat thickness, and daily feed intake as the number of animals from the target line decreased in the training set. Including more animals from the other line did not decrease prediction accuracy for feed conversion ratio and residual feed intake, which were both highly affected by selection within lines. However, prediction biases were systematic for these cases and might be reduced with bivariate analyses. Conclusions Our results show that genomic prediction using a training set that includes animals from genetically-related lines can be as accurate as genomic prediction using a training set from the target population. With combined reference sets, accuracy increased for traits that were highly affected by selection. Our results provide insights into the design of reference populations, especially to initiate genomic selection in small-sized lines, for which the number of historical samples is small and that are developed simultaneously. This applies especially to poultry and pig breeding and to other crossbreeding schemes.

scholarly journals 25 Determining stability of genomic predictivity in future generations in commercial pig populations

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 21-21
Author(s):  
Mary Kate Hollifield ◽  
Daniela Lourenco ◽  
Jeremy Howard ◽  
Yijian Huang ◽  
Ignacy Misztal
Keyword(s):  
Selection Pressure ◽  
Predictive Ability ◽  
Single Step ◽  
First Year ◽  
Training Population ◽  
Data Set ◽  
Validation Population ◽  
Related Individuals ◽  
Second Year ◽  
Over Time

Abstract Genomic predictivity is expected to decay over time as predictions are evaluated to more distant generations. More data increases predictive ability; however, data from distant ancestors may not add a significant amount of value compared to the data from closely related individuals. The objective of this study was to evaluate the decay in genomic predictivity over time and to compare the magnitude of decay when including ancestral data versus data from 2 and 3 most recent generations for body weight at off-test (BW). The data set included 211,812 phenotypic records. The pedigree included 406,983 animals from 2001 to 2020, of which 55,118 were genotyped. A single-trait model was used with all ancestral data and sliding subsets of two- and three-generation intervals. Single-step GBLUP was used to calculate GEBVs. Predictive abilities were calculated by the correlation between GEBVs and adjusted phenotypes. Validation populations consisted of single generations succeeding the training population and continued for all generations available. Predictive ability was slightly higher, with all ancestral data in the training population compared to three- and two-generation intervals. The decay of predictivity was similar when comparing the three training population subsets. The average predictivity for the validation population immediately following the training population was 0.40 for 2016, 0.39 for 2017, 0.35 for 2018, and 0.29 for 2019. Predictive ability reached a maximum in the year 2017 (0.45) for the ancestral training population, 2017 (0.43) for the 3-year training population, and 2016 (0.38) for the 2-year training population. The average decay of predictive ability from the first year after the training population to the second year was -0.08. Realized predictivity is affected by selection pressure. The drop in predictive ability suggests declining heritability. With more data and with consistent selection pressure, predictive abilities should increase.

Correlating entrance data and first year academic performance of students enrolled in the Integrated Master in Mechanical Engineering at the University of Porto

2021 ◽  
pp. 030641902110156
Author(s):  
TMGP Duarte ◽  
AM Lopes ◽  
LFM da Silva
Keyword(s):  
Academic Performance ◽  
Secondary School ◽  
Undergraduate Students ◽  
Mechanical Engineering ◽  
Institutional Factors ◽  
First Year ◽  
Female Type ◽  
The University ◽  
First Year University ◽  
Over Time

Understanding how the academic performance of first year undergraduate students is influenced by home, personal and institutional factors is fundamental to delineate policies able to mitigate failure. This paper investigates possible correlations between the academic performance of students at the end of high school with their achievements at the end of first year university. Data for students in the Integrated Master in Mechanical Engineering (MIEM) program within the Faculty of Engineering at the University of Porto are analysed for the period 2016/2017 to 2019/2020. The students’ performance is measured by two metrics and the students are structured as a whole and by groups, according to their gender (Male/Female), type of secondary school (Public/Private), living place (Away/Home) and the rank of MIEM in their application list of options (Option 1/Option 2–6). The information is organized statistically and possible correlations between the data are investigated. The analysis reveals limited correlation between the two metrics, meaning that all students may exhibit good or poor results at the end of first year in MIEM, independent of their status at entrance. An unanticipated pattern is exhibited for the group Option 2–6, since it shows that, despite entering into MIEM without top application marks, the students in this group can perform as well as the others. This behavior is consistent over time.

36 Effect of Blending and Tuning Relationship Matrices in Single-step Genomic BLUP

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 19-20
Author(s):  
Taylor M McWhorter ◽  
Andre Garcia ◽  
Matias Bermann ◽  
Andres Legarra ◽  
Ignacio Aguilar ◽  
...  
Keyword(s):  
Single Step ◽  
Fixation Index ◽  
Genomic Information ◽  
Software Suite ◽  
Genetic Base ◽  
Actual Difference ◽  
The Impact ◽  
Genomic Predictions

Abstract Single-step GBLUP (ssGBLUP) relies on the combination of genomic (G) and pedigree relationships for all (A) and genotyped animals (A22). The procedure implemented in the BLUPF90 software suite first involves combining a small percentage of A22 into G (blending) to avoid singularity problems, then an adjustment to account for the fact the genetic base in G and A22 is different (tuning). However, blending before tuning may not reflect the actual difference between pedigree and genomic base because the blended matrix already contains a portion of A22. The objective of this study was to evaluate the impact of tuning before blending on predictivity, bias, and inflation of GEBV, indirect predictions (IP), and SNP effects from ssGBLUP using American Angus and US Holstein data. We used four different scenarios to obtain genomic predictions: BlendFirst_TunedG2, TuneFirst_TunedG2, BlendFirst_TunedG4, and TuneFirst_TunedG4. TunedG2 adjusts mean diagonals and off-diagonals of G to be similar to the ones in A22, whereas TunedG4 adjusts based on the fixation index. Over 6 million growth records were available for Angus and 5.9 million udder depth records for Holsteins. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Predictivity and reliability were obtained for 19,056 and 1,711 validation Angus and Holsteins, respectively. We observed the same predictivity and reliability for GEBV or IP in all four scenarios, ranging from 0.47 to 0.60 for Angus and was 0.67 for Holsteins. Slightly less bias was observed when tuning was done before blending. Correlation of SNP effects between scenarios was > 0.99. Refined tuning before blending had no impact on GEBV and marginally reduced the bias. This option will be implemented in the BLUPF90 software suite.

Interaction of Cottonwood Clones with Site and Planting Year

1973 ◽  
Vol 3 (3) ◽  
pp. 329-332 ◽  
Author(s):  
C. A. Mohn ◽  
W. K. Randall
Keyword(s):  
First Year ◽  
Diameter Growth ◽  
Mississippi Valley ◽  
Lower Mississippi Valley ◽  
Large Clone ◽  
Over Time

Height and diameter growth to age three and the number of first year branches were analyzed for 25 cottonwood clones grown in six plantations in central Mississippi. Plantations were on two contrasting sites and planted in three consecutive years. Results showed small clone × planting year interactions and large clone × site interactions for all parameters. In the lower Mississippi Valley, therefore, emphasis should be placed on testing over a range of sites rather than replicating over time.

scholarly journals Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires

2022 ◽  
Author(s):  
Gabriel Soares Campos ◽  
Fernando Flores Cardoso ◽  
Claudia Cristina Gulias Gomes ◽  
Robert Domingues ◽  
Luciana Correia de Almeida Regitano ◽  
...  
Keyword(s):  
Reference Population ◽  
Single Step ◽  
Linear Regression Method ◽  
Effective Population ◽  
Genetic Progress ◽  
Angus Cattle ◽  
Improvement Programs ◽  
Snp Panels ◽  
Estimate Variance ◽  
Genomic Predictions

Abstract Genomic prediction has become the new standard for genetic improvement programs, and currently, there is a desire to implement this technology for the evaluation of Angus cattle in Brazil. Thus, the main objective of this study was to assess the feasibility of evaluating young Brazilian Angus (BA) bulls and heifers for 12 routinely recorded traits using single-step genomic BLUP (ssGBLUP) with and without genotypes from American Angus (AA) sires. The second objective was to obtain estimates of effective population size (Ne) and linkage disequilibrium (LD) in the Brazilian Angus population. The dataset contained phenotypic information for up to 277,661 animals belonging to the Promebo® breeding program, pedigree for 362,900, of which 1,386 were genotyped for 50k, 77k, and 150k SNP panels. After imputation and quality control, 61,666 SNP were available for the analyses. In addition, genotypes from 332 American Angus (AA) sires widely used in Brazil were retrieved from the AA Association database to be used for genomic predictions. Bivariate animal models were used to estimate variance components, traditional EBV, and genomic EBV (GEBV). Validation was carried out with the linear regression method (LR) using young-genotyped animals born between 2013 and 2015 without phenotypes in the reduced dataset and with records in the complete dataset. Validation animals were further split into progeny of BA and AA sires to evaluate if their progenies would benefit by including genotypes from AA sires. The Ne was 254 based on pedigree and 197 based on LD, and the average LD (±SD) and distance between adjacent SNPs across all chromosomes was 0.27 (±0.27) and 40743.68 bp, respectively. Prediction accuracies with ssGBLUP outperformed BLUP for all traits, improving accuracies by, on average, 16% for BA young bulls and heifers. The GEBV prediction accuracies ranged from 0.37 (total maternal for weaning weight and tick count) to 0.54 (yearling precocity) across all traits, and dispersion (LR coefficients) fluctuated between 0.92 and 1.06. Inclusion of genotyped sires from the AA improved GEBV accuracies by 2%, on average, compared to using only the BA reference population. Our study indicated that genomic information could help to improve GEBV accuracies and hence genetic progress in the Brazilian Angus population. The inclusion of genotypes from American Angus sires heavily used in Brazil just marginally increased the GEBV accuracies for selection candidates.

A Longitudinal Investigation of Dropout from College in the United States

2002 ◽  
Vol 4 (2) ◽  
pp. 173-201 ◽  
Author(s):  
Terry T. Ishitani ◽  
Stephen L. DesJardins
Keyword(s):  
United States ◽  
College Students ◽  
Financial Aid ◽  
The United States ◽  
Dropout Rates ◽  
First Year ◽  
Student Financial Aid ◽  
Student Dropout ◽  
Longitudinal Investigation ◽  
Over Time

This study investigates the dropout behavior of college students in the United States. Previous attrition studies have typically focused on dropout at specific points in time, such as the first year of enrollment. In this study we examine the timing of dropout over a five-year period and find that factors that affect student dropout often have effects that change over time. For instance, the results demonstrate that students who receive financial aid generally have lower dropout rates than non-aided students. But of special interest is our findings that dropout rates vary depending on the amount and timing of student financial aid.

Sign in / Sign up

Export Citation Format

Share Document