Genetics Selection Evolution

The exponential increase in the number of genotyped animals, combined with the availability of high-density SNP chips has introduced computational challenges for routine genomic evaluations, particularly during the construction of the genomic relationship matrix. Although higher-density SNP panels can facilitate the identification of causal mutations, their use substantially increases computational requirements without a proportional gain in genomic prediction performance. To optimize computational efficiency while maintaining accuracy of genomic predictions, this study compared five SNP selection strategies (i.e., random sampling, random sampling with inclusion of informative SNPs, linkage disequilibrium (LD)-based pruning, a Shannon entropy-based machine learning approach, and [[EQUATION]]-based prioritization) to develop reduced-density panels for Nellore cattle. Using high-density (HD) genotype data comprising 437,650 SNPs from 304,782 animals (after quality control) as reference, three reduced-density panels (25K, 45K, and 65K SNPs) panels were tested across five traits (i.e., Age at first calving, Stayability, Weaning weight, Yearling weight, Muscling) with diverse genetic architectures. Genomic estimated breeding values (GEBVs) derived from these reduced panels were compared to those obtained from the HD reference panel using Pearsons correlations, under both genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) methods. In the GBLUP model, prediction accuracy generally improved with increased marker density. Random selection with and without the informative SNPs consistently yielded the highest accuracies, whereas the [[EQUATION]]-based approach showed the lowest agreement with the HD reference across all densities. In contrast, ssGBLUP demonstrated strong robustness to marker reduction, producing uniformly high correlations {approx}1.00) across all SNP densities and selection strategies. These findings indicate that optimized low-density SNP panels maintain prediction accuracy comparable to HD panels, offering a cost-effective tool for large-scale genomic evaluations.

Variant intolerance methods score the essentiality of genes based on large datasets of genetic variants and have been used in population genomics of humans and model organisms. In this paper, we estimated Residual Variation Intolerance Scores for protein-coding genes and predicted protein domains in cattle. In agreement with results from other species, the most variant-tolerant genes and domains included genes related to olfaction and adaptive immunity, whereas the least-variant tolerant genes and domains included genes involved in fundamental cellular processes. There was a moderate positive correlation with estimates from orthologous human genes. We provide estimates of variant intolerance for cattle may be useful for genomic analyses of deleterious variants and population genomics in cattle.

Social genetic effects can substantially influence phenotypes in animals and plants and the study of them is of interest. Group housing in animals for example, in pigs provide an ideal framework for studying interactions among individuals. In animals, the genetic of social effects has been studied by simplifying the underlying interactions for example, by assuming that all the individuals in the group contribute equally to the social environment. This homogeneous behaviour is not observed in real conditions. Here, we address this issue showing that identification of interacting individuals is feasible in pig behaviour during growth, and we propose a model that leads to more accurate modelling of social genetics. Our approach infers the strength of interaction via time interval between visits of animals to automatic feeders. This measure of strength was then used in a reaction norm model to evaluate the genetic determinism of social effects. We also build a framework to transform variance components estimated for a trait continuously measured, such as feed intake per visit, into variance of total or aggregated trait e.g. during the growth period. Including social effects improves the fitting of the models. Visit-based social interaction was highest at short time intervals, decreasing rapidly and becoming negligible after 80 seconds. Variance components for the aggregated trait the total feed intake support the contribution of the social interactions. Our approach allows more precise understanding of social effects through individual feeding events and a measure of interaction strength, and further translates these into in an aggregated phenotype of interest.

The goat research community benefits from one of the most comprehensive livestock genome assemblies, ARS1. However, the rich diversity of goat breeds suggests that a single individual genome is not enough to represent the species full genomic content. Third-generation sequencing technologies have recently made it possible to generate very high quality assemblies that should enable to elucidate unique and/or complex phenotype determinisms depending on genetic structural variations. In the SeqOccin project, nine goat breeds were selected to represent French goat genetic diversity: Alpine, Boer, Creole, Fosses, Poitevine, Provencale, Pyreneenne, Rove and Saanen. High-molecular-weight DNA was extracted from blood samples and subjected to long read whole-genome sequencing. There reads were used to generate de novo assemblies and were aligned onto the public reference to call structural variants. PacBio HiFi and parental Illumina reads were used to assemble phased haplotypes of the Alpine breed. For the other breeds, PacBio raw (CLR) or ONT reads were generated. Our work demonstrates the feasibility of assembling genomes with a reasonable amount of data and appropriate software packages, delivering one high-quality assembly and 8 standard goat assemblies that pave the way for pangenome construction and structural variant functional studies.

Background: The yak (Bos grunniens) is an essential livestock species in high-altitude alpine ecosystems, serving key roles in meat, milk, and wool production as well as ecological maintenance. However, traditional yak breeding systems face several limitations, including fragmented herd data management, inefficient phenotypic collection, missing pedigree records, and high technical barriers to applying genomic technologies. These factors constrain the widespread application of modern genetic evaluation methods in yak breeding programs. Description: We developed the Yak Genomic Evaluation Platform (YGEP, https://yakbreeding.swun.edu.cn/en/), an integrated digital system for yak genetic selection and herd management. YGEP integrates three core analytical modules: pedigree reconstruction, genotype imputation and genomic breeding value estimation. Benchmarking on 706 yaks showed that GBLUP achieved a prediction accuracy of r = 0.96 for cannon bone circumference (CCB), and KBeagle reached 84.0% imputation accuracy with the combined reference panel. Conclusion: YGEP bridges the gap between advanced genomic analytics and practical yak breeding. By integrating pedigree reconstruction, genotype imputation, and GEBV estimation into a user-friendly cloud platform, YGEP effectively eliminates technical and data bottlenecks. Overall, it provides an accessible, scalable, and standardized solution to advance digital herd management and precision genetic improvement in alpine livestock.

We propose a stochastic epidemic model to describe the spread of avian influenza in a network of poultry farms. We consider three main paths of infection transmission between poultry farms: from nearby farms, mainly through airborne diffusion; from farms belonging to the same company, through shared veterinarians, forage providers and similar; from undetected small farms or wildlife. From the parameter estimation, based on a modified Expectation-Maximization algorithm, we infer that approximately 63% of the farms were infected from nearby ones, with an infection force declining with distance; of these, more than one third belonged to the same company of the estimated infector. About 20% were infected from premises belonging to the same company but farther away than the distance threshold of 2 km; the remaining ones from wildlife or unidentified sources. These estimates have been validated by a comparison with genetic data, available for a subset of the farms: the genetic distance between two farms identified, with high probability, as an infector-infectee pair is much lower than between random pairs. These results may help in the implementation of tailored prevention and control measures for future outbreaks.

Skeletal dysplasias can occur in animals, including humans, and are attributed to both environmental and genetic causes. As our understanding of the distinguishing phenotypes of different skeletal dysplasias improves, alongside elucidation of the mechanisms and genetic networks that contribute to skeletal development, identifying and controlling genetic mutations in livestock populations has become increasingly possible, including approaches beyond selective breeding alone. Within Scotland an 'achondroplasia' syndrome that was first described in the South Country Cheviot breed of sheep (OMIA 000004-9940), has continued to occur periodically within the same geographical locations, despite culling of affected lambs over many decades. Affected lambs exhibit rounded heads, short legs, a loss of hooves, abnormal stance and short twisted tails. As the condition has been observed in flocks and as progeny from specific ram and ewe matings, and cases have been co-twinned to normal siblings, this suggests a syndrome caused by a recessive genetic condition rather than environmental factors. Through homozygosity mapping and whole genome sequence analyses, we identified a 10 bp deletion in the open reading frame of the PRICKLE1 gene that encodes prickle planar cell polarity protein 1 and is essential for normal vertebrate embryonic development. We identified additional phenotypes present in affected lambs, including a loss of phalanges, neural tube defects, vertebral and articular chondrocyte defects, hair orientation differences and polycystic kidneys, consistent with a loss of PRICKLE1 function based on comparisons to mouse mutant lines. These findings enabled the development of a simple DNA test for the identified PRICKLE1 mutation that was made commercially available to facilitate the elimination of this genetic disease from sheep flocks.

The sheep research community benefits from a genome assembly that has been refined over the last 15 years. However, the rich diversity of sheep breeds suggests that a single individual genome is not enough to represent the species full genomic content. Third-generation sequencing technologies have recently made it possible to generate high quality assemblies that should enable to elucidate unique and/or complex phenotype determinisms depending on genetic structural variations. In the SeqOccin project, twelve breeds were selected to represent (mostly French) sheep genetic diversity: Basco-Bearnaise, Black Belly, Blanche du Massif Central, Corse, Ile de France, Lacaune, Manech Tete Noire, Noire du Velay, Ouessant, Rambouillet, Vendeen, Soay. High-molecular-weight DNA was extracted from blood samples and subjected to long read whole-genome sequencing. The reads were used to generate de novo assemblies and were aligned onto the public reference to call structural variants. PacBio HiFi and parental Illumina reads were used to assemble phased haplotypes of the Lacaune breed. For the other breeds, PacBio raw (CLR) or ONT reads were generated. Our work demonstrates the feasibility of assembling genomes with a reasonable amount of data and appropriate software packages, delivering one high-quality assembly and 11 standard sheep assemblies that pave the way for pangenome construction and structural variant functional studies.

Allelic heterogeneity is a term from genetics which means that alleles that differ in primary sequence can have a similar phenotypic outcome. In other words, they are functional equivalents, and they naturally appear through convergent evolution under selection. Current GWAS has trouble detecting these instances, as allelic heterogeneity leads to signal dilution in these analyses, often leading to a LOD score that stays below detection thresholds. In this paper, we show a method that can overcome this problem by bundling haplotypes into Artificial Combined Markers. The created marker matrix can then easily be used in existing GWAS software.

Circadian feeding rhythmicity reflects the distribution of feed intake across the daily 24-hour cycle. Regular feeding-fasting cycles play an important role in regulating metabolism and maintaining health, but endogenous and environmental factors can disrupt the normal circadian pattern of feed intake. Here, we quantified hourly feed intake in 3,470 Swiss Landrace and 15,181 Swiss Large White pigs using 40.5 million records from automated feeding systems. We used the proportion of days exhibiting a significant feeding rhythm after wavelet analysis to investigate variation in circadian feeding organization. Circadian feeding rhythmicity was influenced by sex and age, with higher rhythmicity observed in females and older animals. Variance components analysis revealed a strong additive genetic contribution and a high heritability (0.53-0.57). Pigs with higher circadian feeding rhythmicity had reduced nocturnal intake, improved feed conversion ratio, and lower day-to-day variability in feed intake, suggesting improved feed efficiency and resilience. Genome-wide association testing of 15.7 and 23.1 million imputed sequence variants identified a QTL on chromosome 4 explaining 5.3% and 2.9% of the phenotypic variance of circadian feeding rhythmicity in Swiss Landrace and Swiss Large White pigs, respectively. The QTL overlaps OPRK1 and NPBWR1, two genes implicated in the regulation of feeding behavior, reward and energy homeostasis. Collectively, our findings establish circadian feeding behavior as a heritable trait in pigs and provide a basis to further investigate the genetic regulation of feeding rhythms in mammals and explore its use in animal breeding programs to improve feed efficiency and resilience.

Background Identifying deleterious DNA changes underpins efforts to improve animal health, welfare, and sustainable breeding. In cattle, current variant prioritization focuses on coding changes, uses single annotation types, and gives limited resolution in non-coding sequence. Results We developed BovCADD (bovine Combined Annotation-Dependent Depletion), a nucleotide-level deleteriousness score for substitutions in Bos taurus and Bos indicus, combining evolutionary constraint, sequence context, epigenetic and regulatory annotations, and gene and protein features. A logistic regression model trained on 41.9 million high-frequency derived alleles from about 3,700 cattle, contrasted with context-matched simulated variants, scored all 8.1 billion possible substitutions. BovCADD distinguished known pathogenic variants from background variation, discriminated among variants within the same consequence class, and scored intronic and intergenic sites. Aggregating scores identified genes carrying rare deleterious variation and revealed elevated genetic load at trait-relevant loci and in bottlenecked, intensively selected populations. Conclusions BovCADD provides the first genome-wide, nucleotide-resolution measure of deleteriousness in cattle, extending variant interpretation to non-coding sequences and linking variant-level prioritization to population-level patterns of mutational burden. Precomputed scores for all substitutions are publicly available.

PurposeWe introduce GO_SCPLOWRAC_SCPLOWNPA, standing for Graph Node-Phenotype Association, a method performing a GWAS-like analysis on a pangenome variation graph (PVG) built using a small number of individual genome sequences, without the need for additional population materials or kinship information for qualitative phenotypes. This method reduces the number of individuals required for association studies and prevents reference bias from variant calling in these types of analyses. BackgroundA PVG represents the multiple alignment of a set of complete genomes. It contains all variations, from single nucleotide polymorphisms (SNPs) to large structural variations (SVs), which are represented as nodes in the graph. By integrating phenotype information within nodes, we can assign a Phenotype Score (PS) to each node in the PVG and identify phenotype-related regions directly within it. These regions represent statistically significant shifts in PS distribution, highlighting their implication in the phenotype. Finally, GO_SCPLOWRAC_SCPLOWNPA provides their positions and scores for further analysis. ResultsThis method was tested using simulated data and two publicly available datasets: the Sub1A gene locus for Oryza sativa in a 13 individuals PVG, and the insertion responsible for the white-headed cattle with a PVG of 24 individuals. Source code of GO_SCPLOWRAC_SCPLOWNPA is available here https://forge.ird.fr/diade/graphgwas/granpa under GNU GPLv3. ConclusionGO_SCPLOWRAC_SCPLOWNPA was able to identify the expected area in two simulated datasets and the responsible loci for these two known traits using only a few dozen complete genomes in these PVGs. While currently limited to qualitative phenotypes, this method opens the way to more efficient ones relying on PVGs and few individuals.

Abstract The gayal (Bos frontalis) is an endangered semi-domesticated bovine species renowned for its high-quality beef. However, its semi-feral lifestyle, ongoing habitat fragmentation, and extensive genetic introgression from sympatric local cattle have led to dramatic population decline and severe erosion of purebred genetic integrity, posing substantial challenges to its conservation and utilization. To address the urgent demand for rapid, non-invasive, and field-compatible germplasm identification, we developed an integrated artificial intelligence (AI) framework that predicts genomic admixture composition from external morphological images. We constructed a comprehensive dataset comprising 6,245 morphological images and matched genomic sequences from 52 gayals maintained at the Yunnan Provincial Gayal Conservation Farms. Following a preliminary evaluation of nine deep learning models, five were incorporated into a anatomical segment-based multi-modal pipeline, among which Inception_V3 delivered the optimal overall performance. To enhance simultaneous extraction of local fine-grained features and global structural information, we further designed an innovative HybridInceptionViT model by integrating the multi-scale Inception module with the Vision Transformer (ViT) framework. This hybrid model significantly outperformed the baseline Inception_V3, boosting the accuracy of phenotype-derived prediction against genomic admixture estimate from 69.69% to 87.87% (absolute error <15%). This study establishes a practical, low-cost "phenotype-to-genotype" tool for rapid on-site gayal germplasm screening, offering a scalable strategy for the conservation and breeding management of endangered livestock, and holds broad application prospects for agricultural and livestock production systems.

Genomic signatures of selection can reveal loci underlying adaptation and disease resistance in livestock populations, but such analyses in water buffalo (Bubalus bubalis) have historically been constrained by the absence of a chromosome-level, species-native reference genome for SNP array data. We re-analyzed genotype data from 85 Nili-Ravi buffalo (Axiom Buffalo Genotyping 90K array, originally positioned using bovine (Bos taurus, UMD3.1) proxy coordinates, by performing a full coordinate liftover to the buffalo-native UOA_WB_1 assembly using an independently published SNP remapping resource. Following quality control (51,209 markers retained), haplotype phasing, and genome-wide integrated haplotype score (iHS) and Wrights Fst (case/control) selection scans, we evaluated 14 classical bovine-tuberculosis (bTB) candidate genes and identified six additional genes with putative immune function through an unbiased genome-wide screen. None of the 14 classical candidates (including SLC11A1, the Toll-like receptors, and IFNG) reached genome-wide significance in either scan. In contrast, six novel loci TNFSF18, IL2RB, TNFRSF19, IRF2, IL15, and CD28 showed significant iHS or Fst signals, four of which (TNFSF18, IL2RB, IL15, CD28) converge functionally on T-cell costimulation and cytokine receptor signaling (KEGG pathways map04660 and map04060, Bos taurus proxy annotation). Using extended haplotype homozygosity (EHH) decay, haplotype furcation structure, and per-marker haplotype counts as three independent lines of corroborating evidence, we classified these six genes into confidence tiers: TNFSF18 and IL2RB showed the strongest, most balanced support, while CD28 and IL15 signals were driven by very few haplotypes (3 and 5 of 30, respectively) and should be interpreted cautiously pending replication. These findings suggest that adaptive, cell-mediated immune signaling rather than the innate/macrophage-centred mechanisms emphasized by existing bTB candidate gene panels may be a more productive avenue for future selection studies in Nili-Ravi buffalo, while underscoring the value of buffalo-native coordinate systems for accurate genomic inference in this species.

Brook trout (Salvelinus fontinalis) exhibit strain-level variation in growth performance, environmental tolerance, and survival, yet the biochemical mechanisms underlying these differences remain poorly understood. We developed and applied a ratiometric biochemical framework integrating the pentose-phosphate pathway (PPP) and glutathione metabolism to characterize strain-specific hepatic metabolic organization in brook trout. Five strains reared under standardized conditions differed significantly in hepatic soluble protein density, glutathione pool size, total NADP(H) concentration, and activities of glucose-6-phosphate dehydrogenase (G6PDH), glutathione reductase (GR), and transketolase (TKT). These differences were not uniformly coordinated across pathways, demonstrating that metabolic phenotype cannot be inferred from individual biomarkers alone. Derived ratios describing oxidative-to-non-oxidative PPP capacity (G6PDH/TKT) and glutathione buffering relative to recycling capacity ((GSH+GSSG)/GR) resolved distinct patterns of metabolic allocation among strains. Despite shared ancestry, the Temiscamie (TEM) strain and its domestic x TEM hybrid (TXD) exhibited markedly divergent metabolic phenotypes, demonstrating that closely related strains can differ substantially in hepatic metabolic organization. Together, these findings identify relative allocation among interconnected metabolic pathways as an axis of physiologic diversity and establish a ratiometric approach for comparing metabolic organization across populations and species. Graphical abstractHepatic metabolic phenotypes of brook trout strains were characterized by integrating pentose phosphate pathway enzyme capacities, glutathione metabolism, NADP(H) availability, and soluble protein into a ratiometric framework. Ratios distinguish investment in oxidative versus non-oxidative PPP capacity (G6PDH/TKT), antioxidant buffering versus glutathione recycling capacity (total glutathione/GR), and hepatic protein density (soluble protein/liver mass), revealing distinct metabolic organization among strains. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=88 SRC="FIGDIR/small/743818v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1694676org.highwire.dtl.DTLVardef@90f2d4org.highwire.dtl.DTLVardef@365327org.highwire.dtl.DTLVardef@8d56ca_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA ratiometric framework was developed to characterize hepatic metabolic organization in brook trout C_LIO_LIGlutathione buffering and recycling capacity distinguish alternative redox phenotypes C_LIO_LIInvestment in oxidative and non-oxidative PPP capacity varies independently among strains C_LIO_LIG6PDH/TKT and total glutathione (GSH+GSSG)/GR reveal distinct metabolic phenotypes C_LIO_LIRatiometric indices provide a framework for interpreting redox metabolism and carbon allocation C_LI

EU has reclassified the sika deer (Cervus nippon) as an undesirable invasive species based on reports that hybridization with the indigenous red deer (C. elaphus) may produce fertile offspring. Since sika-derived DNA previosuly introduced into the red deer population (introgression) cannot be removed, the crucial question is whether new (F1) hybridisation occur. To address this, we analysed the chromosomes in 56 sika and 22 red deer. All red deer had a chromosome number 2n=68. In contrast, the chromosome number in sika ranged from 64 to 67, due to the variable presence of two sika-specific Robertsonian translocations (ROB1,ROB2). In the free-ranging sika population in Jutland, >90% of the sika deer were homozygote for at least one of these ROBs, excluding that they could be F1-hybrids. Moreover, ROB2 was in Hardy-Weinberg equilibrium, further supporting the absence of gene flow between the two species. In contrast, ROB1 was in Hardy-Weinberg disequilibrium, suggesting negative fitness of heterozygotes, including potential F1-hybrids. In Jaegersborg Deer Park, the eight examined sika deer had the same genotype (absence of ROB1, homozygosity of ROB2), supporting that it is a founder population which may have been isolated for [~]100 years. Again, none of these can be F1-hybrids due to the homozygosity of ROB2. We conclude that F1-hybridisation between sika and red deer either does not occur or occur very rarely in Denmark. The study establish the Danish sika-populations as unique models for adressing important biological questions: What underlies the absence of hybridisation? Why are ROBs frequent in sika deer but not in the closely related red deer? How fast do new species/subspecies develop in isolated founder populations? Which factors determine, that some ROBs have little heterozygous effects, whereas others are selected against, with implications for the role of ROBs as genetic barriers promoting speciation, and for fertility problems in some human ROB carriers.

Although many complex phenotypes and diseases are influenced by shared genetic and environmental factors, risk prediction methods typically rely on genetic information from a single trait, leaving a rich source of predictive information largely unexploited. Phenotypic correlations can potentially be used to improve the accuracy of polygenic scores (PGS), but the conditions under which correlated traits meaningfully enhance prediction remain poorly understood. Here, we develop a general theoretical and simulation framework that quantifies the extent to which correlated "helper" traits improve predictive accuracy and identifies the factors that determine the magnitude of these gains. We show that helper traits can substantially improve predictive accuracy, with the magnitude of these gains governed by baseline model performance, genetic and environmental correlations, and the heritability of the target and helper traits, providing principled guidance for helper-trait selection. Paradoxically, when the target trait is itself weakly heritable, helper traits need not be highly heritable to substantially improve the accuracy of PGS, because low-heritability traits can still capture non-redundant environmental factors shared with the target trait. We empirically evaluated the use of helper traits by developing PGS models to predict type 2 diabetes using data from the UK Biobank. Helper traits substantially improved predictive accuracy relative to a single-trait PGS (AUC-ROC = 0.907 versus 0.677) and achieved performance comparable to models that use HbA1c (AUC-ROC = 0.889), the current clinical gold-standard biomarker. Our results establish a general theoretical and practical framework for exploiting correlated traits to improve polygenic prediction, provide principled guidance for selecting informative helper traits, and demonstrate how shared genetic and environmental architecture can be leveraged to substantially increase predictive accuracy. Furthermore, we developed an interactive web application to estimate the expected gain in accuracy from candidate helper traits using empirically measurable quantities.

White lupin (Lupinus albus L.) is a cool-season grain legume with seed crude protein of 33-47%, competitive with soybean (Glycine max L.) meal. It also fixes nitrogen and mobilizes soil phosphorus. Because soybean is a summer crop, white lupin can occupy Southeastern winter fields as a complementary protein source. Breeding for seed protein is limited by the cost and throughput of reference phenotyping. To determine how each is best deployed, we compared the utility of near-infrared spectroscopy (NIRS)-based phenomic selection with genomic selection based on 246,847 SNPs from low-pass, whole genome sequencing in a panel of Auburn University breeding lines and USDA National Plant Germplasm System germplasm. A handheld NIR calibration against Dumas reference protein reached screening-grade accuracy (R2 = 0.81). Under common cross-validation, phenomic predictive ability was 0.93 and genomic was 0.12. The low genomic value was consistent with moderate heritability (H2 = 0.33) and strong genotype-by-year interaction. Beyond predictive ability, NIRS recovered superior accessions the strictest selection intensity, and 40 to 60 reference assays sufficed to calibrate the model. Handheld NIRS is a low-cost tool for protein calibration and early-generation screening, while genomic prediction remains suited to parental selection, together supporting a complementary strategy for legume breeding Plain Language SummarySoybean meal is the main protein source for livestock and fish farms in the United States. Because soybean is a summer crop, many Southeastern fields sit idle or grow low-value cover crops in winter. White lupin, a cool-season legume whose seeds are as protein-rich as soybean meal, makes a good complementary winter crop: it yields high-protein grain while serving as a cover crop that fixes nitrogen and frees up soil phosphorus for later crops. In our early-stage lupin breeding program, measuring seed protein by standard lab methods is slow and costly. We built a calibration that lets a handheld scanner estimate protein from light, and compared it with predicting protein from the plants DNA. The scanner gave accurate, low-cost protein screening from only about 40-60 lab tests, while DNA-based prediction remains suited to guiding parent selection. Used together, these tools offer breeders a practical path to develop high-protein white lupin. Core ideasO_LIHandheld NIRS provides screening-grade prediction of white lupin seed crude protein. C_LIO_LISpectra carried more usable protein signal than markers by measuring seed chemistry directly. C_LIO_LINIRS and genomic prediction serve different stages of a white lupin breeding program. C_LIO_LIAbout 40 to 60 reference assays sufficed to calibrate NIRS to near-full accuracy. C_LI

The linkage disequilibrium (LD) or correlation of alleles at different loci is a fundamental statistic in population genetics. This work presents a new estimator for the standardised LD measure r^2 between a pair of loci. The Random Probe (RP) estimator involves generating a set of random dummy loci, before calculating the r^2 between each synthetic locus and the two focal loci using one of the existing estimators (even it is known to be biased). The correlation between the two vectors of r^2 is the LD estimate. Computer simulations show promising results, with the RP estimator at least as unbiased as the current Ragsdale and Gravel estimator in most common scenarios. In the more challenging scenarios with skewed allele frequencies and small sample size, RP is preferred by having notably reduced bias, and that the bias is less sensitive to sample size and underlying LD, while maintaining mean squared error comparable to existing methods. The new estimator will most benefit applications relying on accurate measures of LD. Beyond LD, this study stimulates further discussions on whether RP estimator can be generalised to other genetic summary statistics or measures of relatedness.

Parental qualities can influence the development and fitness of their offspring via genetic and non-genetic effects. Although these effects are often linked to parental phenotypes, the effect of parental genetic variation linked with relevant phenotypes is less well understood. We performed full factorial crosses based on parental genotypes for an age-at-maturity-related gene, vgll3, to investigate how the parental genotypes influence Atlantic salmon (Salmo salar) offspring survival, growth, and development in their early life. Beyond the connection with age at maturity, the additional association between vgll3 and body condition in Atlantic salmon offers a potential pathway by which the maternal vgll3 genotype could influence offspring early life fitness. Combined with measurements of maternal phenotype and egg characteristics, the crossing design therefore allowed us to disentangle the maternal and paternal genetic and non-genetic contributions to variation in offspring survival and phenotypic traits. The phenotypic traits measured were hatching length and yolk sac area, growth, and yolk sac consumption and conversion efficiency. Parental vgll3 genotype did not influence the majority of our measured egg traits or alevin traits except for a genetic effect of paternal vgll3 genotype on offspring survival, whereby the paternal late maturation allele was associated with higher survival. Maternal effects were strongest for survival and for traits associated with hatching and weaker for alevin growth and yolk sac usage. Paternal effects on the measured alevin traits were negligible. The results from our study demonstrate that both maternal and paternal effects have the potential to influence offspring early life fitness traits.