Biology subjects

Lourenco, D.

Publications and source records attributed to Lourenco, D..

12 recordsLinked to original sources

bioRxiv2025

Integrating Longitudinal Metabolite Profiles Improves Trait Prediction in Pigs in a Trait- and Timepoint-Dependent Manner

BackgroundAccurate prediction of genetic merit is essential for accelerating genetic improvement in pigs, particularly for traits that are costly or difficult to measure directly. This study investigated the potential of integrating individual-level blood serum metabolite profiles sampled at two developmental stages (10-week and 20-week) into genomic prediction models for five economically important traits: average daily feed intake (DFI), feed conversion ratio (FCR), backfat thickness (BF), test daily gain (TDG), and loin depth (LD). Using a BayesC modeling framework, we analyzed 1,637 pigs from a single purebred population with complete phenotype, genotype, and metabolite-profile data. We evaluated seven models, including a genotype-only model (G), metabolite-only models using metabolite-profile data from 10-week (M1), 20-week (M2), or both timepoints (M1+M2), and combined models integrating genotypes with metabolite-profile data (G+M1, G+M2, G+M1+M2). ResultsHeritability estimates were generally low to moderate, ranging from 0.07 to 0.30 for the 10-week metabolite profile and from 0.04 to 0.28 for the 20-week metabolite profile. Prediction accuracy of phenotype consistently improved when metabolite-profile data were integrated into genotype-based models, although the magnitude of improvement varied depending on the trait and the timepoint of metabolite sampling. Prediction accuracy increased from 0.31 (G) to 0.41 (G+M2; G+M1+M2) for DFI, and from 0.27 (G) to 0.33 (G+M2; G+M1+M2) for FCR. The latter two models also delivered the largest gains over G for BF (from 0.41 to 0.45) and TDG (from 0.28 to 0.32). However, LD benefited the most when both 10-week and 20-week metabolite profiles were combined (G+M1+M2: 0.45 compared to G: 0.42). ConclusionsAcross all traits, models combining genotype data with metabolite profiles from one or multiple timepoints achieved the highest or equally high prediction accuracies compared to the genotype-only model, reflecting complementary biological insights captured by metabolite profiles. These findings highlight the potential value of metabolite-profile data as an intermediate omics layer to enhance genomic prediction, particularly when integration strategies are tailored to trait-specific biology and sampling timepoints.

genetics↗

bioRxiv2025

Genetic parameters and genome-wide association analysis of service sire effect on litter size and its relationship with boar semen quality in three terminal sire lines

This study aimed to estimate the genetic parameters for service sire effects on the number born alive (NBA) and its relationship with semen quality traits. Data of 6,416, 23,188, and 48,890 litter size records collected between 2020 and 2024 from three purebred terminal sire lines were analyzed. The number of sows was 3,071, 11,819, and 24,089, with 197, 554, and 891 service sires for the three respective lines. There were 1,424,858, 4,344,630, and 2,146,583 animals in the pedigree of which 67,990, 259,250, and 365,392 were genotyped and imputed up to 50K SNPs. The service sire and dam effects were modeled as additive genetic effects, considering a covariance structure among them, given by the relationship matrix. The model also included fixed effects of contemporary group and parity group with random permanent environmental effects for service sire and dam. Genetic parameters were estimated using the AIREML option in the BLUPF90+ software, and GEBVs were generated by ssGBLUP with the algorithm for proven and young (APY). Heritability for the service sire effect ranged from 0.01 to 0.03, and the heritability for the dam effect ranged from 0.09 to 0.15, with genetic correlations ranging from -0.20 to 0.37. A single-step genome-wide association study (ssGWAS) was also performed, and no strong signals were detected for service sire effects on NBA. Sperm motility (MOT) and total abnormal morphology (ABN_MOR) GEBVs from the three lines were estimated based on about 57,500 to 608,996 ejaculates recorded using the computer-assisted semen analysis (CASA) system from 1,698 to 29,095 boars collected from 2013 to 2025. Heritability estimates ranged from 0.10 to 0.12 for motility and from 0.17 to 0.28 for total abnormal morphology. The correlations between service sire GEBV for NBA and semen quality were low but in the favorable direction. Results suggest that although paternal genetic contributions to litter size were small compared to maternal genetic contributions, selecting on service sire effects on litter size in addition to semen quality traits will improve overall reproductive success.

genetics↗

bioRxiv2023

Marker effect p-values for single-step GWAS with the algorithm for proven and young in large genotyped populations

BackgroundAlthough single-step GBLUP (ssGBLUP) is a breeding value method, single-nucleotide polymorphism (SNP) effects can be backsolved from ssGBLUP genomic estimated breeding values (GEBV), and p-values can be obtained as a measure of estimation certainty. This enables single-step genome-wide association studies (ssGWAS). However, obtaining p-values for ssGWAS relies on the inversion of dense matrices, which poses computational limitations in large genotyped populations. In this study, we present an algorithm to approximate p-values for SNP in ssGWAS with many genotyped animals. The approximation relies on the algorithm for proven and young (APY) and submatrices for core animals. To test that, we first compared SNP p-values obtained with an exact inversion using the genomic relationship matrix (G-1) for 50K genotyped animals to those estimated with an exact inversion using [Formula] and those obtained with the proposed approximation based on [Formula]. Then, we compared these results with those obtained with the proposed approximation using 450K genotyped animals. ResultsThe same genomic regions in chromosomes 7 and 20 were identified with p-values obtained with G-1, [Formula], and the approximation based on [Formula] when using 50k genotyped animals and 1.5M in the pedigree. In terms of computational requirements, obtaining p-values with the approximation based on [Formula] represented a reduction of 38 times in wall-clock time and ten times in memory requirement compared to using the exact inversion with [Formula]. When the approximation was applied to a population of 450K genotyped animals and 1.8 in the pedigree, apart from the two genomic regions in chromosomes 7 and 20 previously identified with the smaller genotyped population, two new significant regions on chromosomes 6 and 14 were uncovered, indicating an increase in GWAS detection power when including more genotypes in the analyses. The process of obtaining p-value with the approximation and 450K genotyped individuals took 24.5 wall-clock hours and 87.66 GB of memory, which is expected to increase linearly with the addition of noncore genotyped individuals. ConclusionsWith an algorithm that approximates the prediction error variance of SNP effects based on APY, ssGWAS with p-values for SNP is possible in large genotyped populations. The computational cost of obtaining p-values in ssGWAS is no longer a limitation in extensive populations with many genotyped animals.

genetics↗

bioRxiv2023

Dysregulated Pulmonary Inflammatory Responses Exacerbate the Outcome of Secondary Aspergillosis Following Influenza

Inhalation of airborne conidia of the ubiquitous fungus Aspergillus fumigatus commonly occurs but invasive aspergillosis is rare except in profoundly immunocompromised persons. Severe influenza predisposes patients to invasive pulmonary aspergillosis by mechanisms that are poorly defined. Using a post-influenza aspergillosis model, we found that superinfected mice had 100% mortality when challenged with A. fumigatus conidia on days 2 and 5 (early stages) of influenza A virus infection but 100% survival when challenged on days 8 and 14 (late stages). Influenza-infected mice superinfected with A. fumigatus had increased levels of the pro-inflammatory cytokines and chemokines IL-6, TNF, IFN{beta}, IL-12p70, IL-1, IL-1{beta}, CXCL1, G-CSF, MIP-1, MIP-1{beta}, RANTES and MCP-1. Surprisingly, on histopathological analysis, superinfected mice did not have greater lung inflammation compared with mice infected with influenza alone. Mice infected with influenza had dampened neutrophil recruitment to the lungs following subsequent challenge with A. fumigatus, but only if the fungal challenge was executed during the early stages of influenza infection. However, influenza infection did not have a major effect on neutrophil phagocytosis and killing of A. fumigatus conidia. Moreover, minimal germination of conidia was seen on histopathology even in the superinfected mice. Taken together, our data suggest that the high mortality rate seen in mice during the early stages of influenza-associated pulmonary aspergillosis is multifactorial, with a greater contribution from dysregulated inflammation than microbial growth. ImportanceSevere influenza is a risk factor for fatal invasive pulmonary aspergillosis; however, the mechanistic basis for the lethality is unclear. Utilizing an influenza-associated pulmonary aspergillosis (IAPA) model, we found that mice infected with influenza A virus followed by A. fumigatus had 100% mortality when superinfected during the early stages of influenza but survived at later stages. While superinfected mice had dysregulated pulmonary inflammatory responses compared to controls, they had neither increased inflammation nor extensive fungal growth. Although influenza-infected mice had dampened neutrophil recruitment to the lungs following subsequent challenge with A. fumigatus, influenza did not affect the ability of neutrophils to clear the fungi. Our data suggest that the lethality seen in our model IAPA is multifactorial with dysregulated inflammation being a greater contributor than uncontrollable microbial growth. If confirmed in humans, our findings provide a rationale for clinical studies of adjuvant anti-inflammatory agents in the treatment of IAPA.

immunology↗

bioRxiv2023

SNP profile for quantitative trait nucleotide in populations with small effective size and its impact on mapping and genomic predictions

In animal populations, increasing the SNP density by incorporating sequence information only marginally increases prediction accuracies. To find out why, we used statistical models and simulations to investigate the profile or distribution of SNP around Quantitative Trait Nucleotides (QTN) in populations with small effective population size (Ne). A QTN profile created by averaging SNP solutions around each QTN was similar to the shape of expected pairwise linkage disequilibrium (PLD) based on Ne and genetic distance between SNP, with a distinct peak for the QTN. Populations with smaller Ne showed lower but wider QTN profiles; however, adding more genotyped individuals with phenotypes dragged the profile closer to the QTN; the QTN profile was higher and narrower for populations with larger compared to smaller Ne. Assuming the PLD curve for the QTN profile, 80% of the additive genetic variance explained by each QTN is contained in 8 "Stam" segments (one segment = 1/4Ne Morgans), corresponding to 1.6 Mb in cattle, and 5 Mb in pigs and broiler chickens. With such large segments, identifying QTN is difficult even if all of them are in the data and the assumed genetic architecture is simplistic. Additional complexity in QTN detection arises from confounding of QTN profiles with signals due to relationships, overlapping profiles with closely-spaced QTN, and spurious signals due to imputation errors. However, small Ne allows for accurate prediction with large data even without QTN identification because QTN are accounted for by QTN profiles if SNP density is sufficient to saturate the segments.

genetics↗

bioRxiv2022

Using large-scale whole-genome sequence data for single-step genomic predictions in maternal and terminal pig lines

BackgroundWhole-genome sequence (WGS) data harbor causative variants that may not be present in the regular SNP chip data. The objective of this study was to investigate the impact of using preselected variants from WGS for single-step genomic predictions in maternal and terminal pig lines with up to 1.8k sequenced and 104k imputed sequenced animals per line. MethodsTwo maternal and four terminal lines were investigated for eight and seven traits, respectively. The number of sequenced animals ranged from 1,365 to 1,491 in maternal lines and 381 to 1,865 in terminal lines. Imputation occurred within each line, and the number of animals imputed to sequence ranged from 66k to 76k in maternal lines and 29k to 104k in terminal lines. Two preselected SNP sets were generated based on genome-wide association study (GWAS). Top40k included the SNP with the lowest p-value in each of 40k genomic windows; ChipPlusSign included significant variants integrated into the regular porcine SNP chip. Single-step genomic predictions with equal or different SNP variances using those SNP sets were compared to the regular porcine SNP chip. ResultsIn maternal lines, ChipPlusSign, and Top40k showed, on average, 0.62%, and 4.9% increased accuracy compared to the regular porcine SNP chip. The greatest changes were for fertility traits with Top40k, where the initial accuracy based on the SNP chip was low. However, for terminal lines, Top40k resulted in a loss of accuracy of 1% on average. Only ChipPlusSign provided a positive, albeit small, gain (0.85%). Assigning different variances for SNP slightly improved accuracies when using variances obtained from BayesR; however, the increase was inconsistent across the lines and traits. ConclusionsThe benefit of using sequence data depends on the line, size of the genotyped population, and how the WGS variants are preselected. When WGS is available on hundreds of thousands of animals, the advantage of sequence data is present but limited in maternal and terminal pig lines.

genomics↗

bioRxiv2022

Dimensionality of genomic information and its impact on GWA and variant selection: a simulation study

BackgroundIdentifying true-positive variants in genome-wide associations (GWA) depends on several factors, including the number of genotyped individuals. The limited dimensionality of the genomic information may give insights into the optimal number of individuals to use in GWA. This study investigated different discovery set sizes in GWA based on the number of largest eigenvalues explaining a certain proportion of variance in the genomic relationship matrix (G). An additional investigation included the change in accuracy by adding variants, selected based on different set sizes, to the regular SNP chips used for genomic prediction. MethodsSequence data were simulated containing 500k SNP with 200 or 2000 quantitative trait nucleotides (QTN). A regular 50k panel included one every ten simulated SNP. Effective population size (Ne) was 20 and 200. The GWA was performed with the number of genotyped animals equivalent to the number of largest eigenvalues of G (EIG) explaining 50, 60, 70, 80, 90, 95, 98, and 99% of the variance. In addition, the largest discovery set consisted of 30k genotyped animals. Limited or extensive phenotypic information was mimicked by changing the trait heritability. Significant and high effect size SNP were added to the 50k panel and used for single-step GBLUP with and without weights. ResultsUsing the number of genotyped animals corresponding to at least EIG98 enabled the identification of QTN with the largest effect sizes when Ne was large. Smaller populations required more than EIG98. Furthermore, using genotyped animals with higher reliability (i.e., higher trait heritability) helped better identify the most informative QTN. The greatest prediction accuracy was obtained when the significant or the high effect SNP representing twice the number of simulated QTN were added to the 50k panel. Weighting SNP differently did not increase prediction accuracy, mainly because of the size of the genotyped population. ConclusionsAccurately identifying causative variants from sequence data depends on the effective population size and, therefore, the dimensionality of genomic information. This dimensionality can help identify the suitable sample size for GWA and could be considered for variant selection. Even when variants are accurately identified, their inclusion in prediction models has limited implications.

genomics↗

bioRxiv2022

Is single-step genomic REML with the algorithm for proven and young more computationally efficient when less generations of data are present?

Efficient computing techniques allow the estimation of variance components for virtually any traditional dataset. When genomic information is available, variance components can be estimated using genomic REML (GREML). If only a portion of the animals have genotypes, single-step GREML (ssGREML) is the method of choice. The genomic relationship matrix (G) used in both cases is dense, limiting computations depending on the number of genotyped animals. The algorithm for proven and young (APY) can be used to create a sparse inverse of G[Formula] with close to linear memory and computing requirements. In ssGREML, the inverse of the realized relationship matrix (H-1) also includes the inverse of the pedigree relationship matrix, which can be dense with long pedigree, but sparser with short. The main purpose of this study was to investigate whether costs of ssGREML can be reduced using APY with truncated pedigree and phenotypes. We also investigated the impact of truncation on variance components estimation when different numbers of core animals are used in APY. Simulations included 150K animals from 10 generations, with selection. Phenotypes (h2 = 0.3) were available for all animals in generations 1-9. A total of 30K animals in generations 8 and 9, and 15K validation animals in generation 10 were genotyped for 52,890 SNP. Average information REML and ssGREML with G-1 and [Formula] using 1K, 5K, 9K, and 14K core animals were compared. Variance components are impacted when the core group in APY represents the number of eigenvalues explaining a small fraction of the total variation in G. The most time-consuming operation was the inversion, with more than 50% of the total time. Next, numerical factorization consumed nearly 30% of the total computing time. On average, a 7% decrease in the computing time for ordering was observed by removing each generation of data. APY can be successfully applied to create the inverse of the genomic relationship matrix used in ssGREML for estimating variance components. To ensure reliable variance component estimation, it is important to use a core size that corresponds to the number of largest eigenvalues explaining around 98% of total variation in G. When APY is used, pedigrees can be truncated to increase the sparsity of H and slightly reduce computing time for ordering and symbolic factorization, with no impact on the estimates. Lay SummaryThe estimation of variance components is computationally expensive under large-scale genetic evaluations due to several inversions of the coefficient matrix. Variance components are used as parameters for estimating breeding values in mixed model equations (MME). However, resulting breeding values are not Best Linear Unbiased Predictions (BLUP) unless the variance components approach the true parameters. The increasing availability of genomic data requires the development of new methods for improving the efficiency of variance component estimations. Therefore, this study aimed to reduce the costs of single-step genomic REML (ssGREML) with the Algorithm for Proven and Young (APY) for estimating variance components with truncated pedigree and phenotypes. In addition, we investigated the influence of truncation on variance components and genetic parameter estimates. Under APY, the size of the core group influences the similarity of breeding values and their reliability compared to the full genomic matrix. In this study, we found that to ensure reliable variance component estimation it is required to consider a core size that corresponds to the number of largest eigenvalues explaining around 98% of the total variation in G to avoid biased parameters. In terms of costs, the use of APY slightly decreased the time for ordering and symbolic factorization with no impact on estimations. Teaser TextEstimation of variance components is becoming computationally challenging due to the increasing size of genomic information. We investigated the impacts of using the algorithm for proven and young (APY) in genetic evaluations. The use of APY has no impact on variance components and genetic parameters estimation.

genomics↗

bioRxiv2021

Detecting effective starting point of genomic selection by divergent trends from BLUP and ssGBLUP in pigs, beef cattle, and broilers

Genomic selection has been adopted nationally and internationally in different livestock and plant species. However, understanding whether genomic selection has been effective or not is an essential question for both industry and academia. Once genomic evaluation started being used, estimation of breeding values with pedigree BLUP became biased because this method does not consider selection using genomic information. Hence, the effective start point of genomic selection can be detected in two possible ways including the divergence of genetic trends and Realized Mendelian sampling (RMS) trends obtained with BLUP and Single-step genomic BLUP (ssGBLUP). This study aimed to find the start date of genomic selection for a set of economically important traits in three livestock species by comparing trends obtained using BLUP and ssGBLUP. For this purpose, three datasets comprised a pig dataset with 117k genotypes and 1.3M animals in pedigree, Angus cattle dataset consisted of ~842k genotypes and 11.5M animals in pedigree, and a purebred broiler chicken dataset included ~154k genotypes and 1.3M birds in pedigree were used. The genetic trends for pigs diverged for the genotyped animals born in 2014 for average daily gain and backfat. In beef cattle, the trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with little diverging for birth weight. In broiler chickens, the genetic trends estimated by ssGBLUP and BLUP diverged at breeding cycle 6 for two out of three production traits. The RMS trends for the genotyped pigs diverged for animals born in 2014, more for average daily gain than for backfat. In beef cattle, the RMS trends started diverging in 2009 for weaning weight and in 2016 for postweaning gain, with a trivial trend for birth weight. In broiler chickens, the RMS trends from ssGBLUP and BLUP diverged strongly for two production traits at breeding cycle 6, with a slight divergence for another trait. Divergence of the genetic trends from ssGBLUP and BLUP indicates onset of the genomic selection. Presence of trends for RMS indicates selective genotyping, with or without the genomic selection. The onset of genomic selection and genotyping strategies agree with industry practices across the 3 species. In summary, the effective start of genomic selection can be detected by the divergence between genetic and RMS trends from BLUP and ssGBLUP.

genomics↗

bioRxiv2020

PATRIOT: A pipeline for tracing identical-by-descent chromosome segments to improve genomic prediction in self-pollinating crop species

The lowering genotyping cost is ushering in a wider interest and adoption of genomic prediction and selection in plant breeding programs worldwide. However, improper conflation of historical and recent linkage disequilibrium between markers and genes restricts high accuracy of genomic prediction (GP). Multiple ancestors may share a common haplotype surrounding a gene, without sharing the same allele of that gene. This prevents parsing out genetic effects associated with the underlying allele of that gene among the set of ancestral haplotypes. We present Parental Allele Tracing, Recombination Identification, and Optimal predicTion (i.e., PATRIOT) approach that utilizes marker data to allow for a rapid identification of lines carrying specific alleles, increases the accuracy of genomic relatedness and diversity estimates, and improves genomic prediction. Leveraging identity by descent, PATRIOT showed an improvement in GP accuracy by 16.6% compared to the traditional rrBLUP method. This approach will help to increase the rate of genetic gain and allow available information to be more effectively utilized within breeding programs.

genomics↗

bioRxiv2019

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

BACKGROUNDSingle Step GBLUP (SSGBLUP) is the most comprehensive method for genomic prediction. Point estimates of marker effects from SSGBLUP are often used for Genome Wide Association Studies (GWAS) without a formal framework of hypothesis testing. Our objective was to implement p-values for GWAS studies in the ssGBLUP framework, showing algorithms, computational procedures, and an application to a large beef cattle population. METHODSP-values were obtained based on the prediction error (co)variance for SNP, which uses the inverse of the coefficient matrix and formulas to compute SNP effects. RESULTSComputation of p-values took a negligible time for a dataset with almost 2 million animals in the pedigree and 1424 genotyped sires, and no inflation was observed. The SNP passing the Bonferroni threshold of 5.9 in the -log10 scale were the same as those that explained the highest proportion of additive genetic variance, but the latter was penalized (as GWAS signal) by low allele frequency. CONCLUSIONThe exact p-value for SSGWAS is a very general and efficient strategy for QTL detection and testing. It can be used in complex data sets such as used in animal breeding, where only a proportion of pedigreed animals are genotyped.

genomics↗

bioRxiv2018

Genome-wide association analysis with a 50K transcribed gene SNP-chip identifies QTL affecting muscle yield in rainbow trout

Detection of coding/functional SNPs that change the biological function of a gene may lead to identification of putative causative alleles within QTL regions and discovery of genetic markers with large effects on phenotypes. Two bioinformatics pipelines, GATK and SAMtools, were used to identify ~21K transcribed SNPs with allelic imbalances associated with important aquaculture production traits including body weight, muscle yield, muscle fat content, shear force, and whiteness in addition to resistance/susceptibility to bacterial cold-water disease (BCWD). SNPs were identified from pooled RNA-Seq data collected from ~620 fish, representing 98 families from growth- and 54 families from BCWD-selected lines with divergent phenotypes. In addition, ~29K transcribed SNPs without allelic-imbalances were strategically added to build a 50K Affymetrix SNP-chip. SNPs selected included two SNPs per gene from 14K genes and ~5K non-synonymous SNPs. The SNP-chip was used to genotype 1728 fish. The average SNP calling-rate for samples passing quality control (QC; 1,641 fish) was [≥] 98.5%. Genome-wide association (GWA) study on 878 fish (representing 197 families from 2 consecutive generations) with muscle yield phenotypes and genotyped for 35K polymorphic markers (passing QC) identified several QTL regions explaining together up to 28.40% of the additive genetic variance for muscle yield in this rainbow trout population. The most significant QTLs were on chromosomes 14 and 16 with 12.71% and 10.49% of the genetic variance, respectively. Many of the annotated genes in the QTL regions were previously reported as important regulators of muscle development and cell signaling. No major QTLs were identified in a previous GWA study using a 57K genomic SNP chip on the same fish population. These results indicate improved detection power of the transcribed gene SNP-chip in the target trait and population, allowing identification of large-effect QTLs for important traits in rainbow trout.

genomics↗