Exact p-values for large-scale single step genome-wide association, with an application for birth weight in American Angus

Aguilar, Ignácio; Legarra, Andrés; Cardoso, F. F.; Masuda, Yutaka; Lourenco, Daniela; Misztal, I.

doi:10.1101/555243

Cited by 10 publications

(8 citation statements)

References 47 publications

(78 reference statements)

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“…The population structure of the lines with genomic information was assessed by a principal component analysis of the marker variation. These analyses were performed with preGSf90 (Aguilar et al., 2014).…”

Section: Methodsmentioning

confidence: 99%

Consolidating 23 years of historical data from an irrigated subtropical rice breeding program in Uruguay

et al. 2023

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Breeding programs generate vast amount of data which are often scattered in separate files. This hinders the application of modern breeding tools such as multienvironment analyses and genomic selection. This research work describes the process of consolidating 23 years of phenotypic, pedigree, and genomic records from the Uruguayan national rice (Oryza sativa L.) breeding program, and the features and structure of the resulting database. Using a custom-made R code, we gathered all the available data from 1997 to 2020 corresponding to field trials, blast disease evaluation nurseries, laboratory analyses of milling and cooking quality, pedigree information, and genomic information for selected advanced breeding lines, and organized it into a relational database. Records of 996 trials in 12 locations over a span of 23 years, 91,636 field plots with information on 14 phenotypic variables, pedigree for 19,447 genotypes, and genomic information regarding 61,260 single nucleotide polymorphism (SNP) markers for 965 genotypes were recovered. The dataset is structured in trials, phenotypes, lines, genomic information, and SNP tables in an easy-to-access relational database, which will be a valuable resource for rice breeding.

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Section: Methodsmentioning

confidence: 99%

Consolidating 23 years of historical data from an irrigated subtropical rice breeding program in Uruguay

et al. 2023

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“…The Pearson correlation coefficients between the direct SNP effects estimated from NEI and indirect SNP effects estimated from its composition traits were estimated. The SNP effect (

\overset{a}{true}

) for the NEI and its composition traits were estimated using the POSTGSF90 software (version 1.73; Aguilar et al, 2014). The formula for the

\overset{a}{true}

is the same as that described by Wang et al (2012) but without iteration.…”

Section: Methodsmentioning

confidence: 99%

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation

Chen

Atashi

Mota

et al. 2023

J Animal Breeding Genetics

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Nitrogen (N) use efficiency (NUE) is an economically important trait for dairy cows. Recently, we proposed a new N efficiency index (NEI), that simultaneously considers both NUE and N pollution. This study aimed to validate the genomic prediction for NEI and its composition traits and investigate the relationship between SNP effects estimated directly from NEI and indirectly from its composition traits. The NEI composition included genomic estimated breeding value of N intake (NINT), milk true protein N (MTPN) and milk urea N yield. The edited data were 132,899 records on 52,064 cows distributed in 773 herds. The pedigree contained 122,368 animals. Genotypic data of 566,294 SNP was available for 4514 individuals. A total of 4413 cows (including 181 genotyped) and 56 bulls (including 32 genotyped) were selected as the validation populations. The linear regression method was used to validate the genomic prediction of NEI and its composition traits using best linear unbiased prediction (BLUP) and single‐step genomic BLUP (ssGBLUP). The mean theoretical accuracies of validation populations obtained from ssGBLUP were higher than those obtained from BLUP for both NEI and its composition traits, ranging from 0.57 (MTPN) to 0.72 (NINT). The highest mean prediction accuracies for NEI and its composition traits were observed for the genotyped cows estimated under ssGBLUP, ranging from 0.48 (MTPN) to 0.66 (NINT). Furthermore, the SNP effects estimated from NEI composition traits, multiplied by the relative weight were the same as those estimated directly from NEI. This study preliminary showed that genomic prediction can be used for NEI, however, we acknowledge the need for further validation of this result in a larger dataset. Moreover, the SNP effects of NEI can be indirectly calculated using the SNP effects estimated from its composition traits. This study provided a basis for adding genomic information to establish NEI as part of future routine genomic evaluation programs.

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“…The method of ssGBLUP relies on combining information from the pedigree in A with relationships in the genomic relationship matrix ( G ) by building the combined H matrix (Christensen & Lund, 2010; Legarra et al, 2009). The required inverse ( H −1 ) was derived with the ‘PreGSF90’ software (Aguilar et al, 2014) from the ‘BLUPF90’ family of programmes. G was scaled based on A 22 so that mean(diag( G )) = mean(diag( A 22 )) and mean(offdiag( G )) = mean(offdiag( A 22 )) with scaling parameters of α = 0.95 and β = 0.05:

H^{- 1} goodbreak= A^{- 1} goodbreak+ (][, \begin{matrix} 0 & 0 \\ 0 & {(0.95 G + 0.05 {bold-italicA}_{22})}^{- 1} goodbreak- A_{22}^{- 1} \end{matrix})

where A is the numerator relationship matrix, A 22 is the block of A for genotyped animals and G is the genomic relationship matrix, calculated according to VanRaden (2008) as:

bold-italicG goodbreak= \frac{()(, bold-italicM goodbreak- 2 bold-italicP) {(M - 2 P)}^{'}}{2 \sum_{j} ()(, {bold-italicp}_{j}) ()(, 1 goodbreak- p_{j})}

where M is the genotypic matrix of 0/1/2 B‐allele marker counts with dimensions animals by markers and P is the corresponding matrix ( m × n ) where the j th column of P is a vector of m replicates of the allele frequency

p_{j}

within all the animals.…”

Section: Methodsmentioning

confidence: 99%

Including genomic information in the genetic evaluation of production and reproduction traits in South African Merino sheep

Nel,

Gurman,

Swan

et al. 2023

J Animal Breeding Genetics

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Genomic selection (GS) has become common in sheep breeding programmes in Australia, New Zealand, France and Ireland but requires validation in South Africa (SA). This study aimed to compare the predictive ability, bias and dispersion of pedigree BLUP (ABLUP) and single‐step genomic BLUP (ssGBLUP) for production and reproduction traits in South African Merinos. Animals in this study originated from five research and five commercial Merino flocks and included between 54,072 and 79,100 production records for weaning weight (WW), yearling weight (YW), fibre diameter (FD), clean fleece weight (CFW) and staple length (SL). For reproduction traits, the dataset included 58,744 repeated records from 17,268 ewes for the number of lambs born (NLB), number of lambs weaned (NLW) and the total weight weaned (TWW). The single‐step relationship matrix, H, was calculated using PreGS90 software combining 2811 animals with medium density (~50 K) genotypes and the pedigree of 88,600 animals. Assessment was based on single‐trait analysis using ASREML V4.2 software. The accuracy of prediction was evaluated according to the ‘LR‐method’ by a cross‐validation design. Validation candidates were assigned according to Scenario I: born after a certain time point; and Scenario II: born in a particular flock. In Scenario I, the genotyping rate for the reference population between 2006 and the 2013 cut‐off point approached 7% of animals with phenotypes and 20% of their sires. For reproduction traits, about 20% of ewes born between 2006 and 2011 cut‐off were genotyped, along with 15% of their sires. Genotyping rates in the validation set of Scenario I were 3.7% (production) and 11.4% (reproduction) for candidates and 35% of their sires. Sires were allowed to have progeny in both the reference and validation set. In Scenario I, ssGBLUP increased the accuracy of prediction for all traits except NLB, ranging between +8% (0.62–0.67) for FD and +44% (0.36–0.52) for WW. This showed a promising gain in accuracy despite a modestly sized reference population. In the ‘across flock validation’ (Scenario II), overall accuracy was lower, but with greater differences between ABLUP and ssGBLUP ranging between +17% (0.12–0.14) for TWW and +117% (0.18–0.39) for WW. There was little indication of severe bias, but some traits were prone to over dispersion and the use of genomic information did not improve this. These results were the first to validate the benefit of genomic information in South African Merinos. However, because production traits are moderately heritable and easy to measure at an early age, future research should be aimed at best exploiting GS methods towards genetic prediction of sex‐limited and/or lowly heritable traits such as NLW. GS methods should be combined with dedicated efforts to increase genetic links between sectors and improved phenotyping by commercial flocks.

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Exact p-values for large-scale single step genome-wide association, with an application for birth weight in American Angus

Cited by 10 publications

References 47 publications

Consolidating 23 years of historical data from an irrigated subtropical rice breeding program in Uruguay

Consolidating 23 years of historical data from an irrigated subtropical rice breeding program in Uruguay

Validating genomic prediction for nitrogen efficiency index and its composition traits of Holstein cows in early lactation

Including genomic information in the genetic evaluation of production and reproduction traits in South African Merino sheep

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