Arne Bjørke Gjuvsland

The Duroc-derived reference genome Sscrofa11.1 has provided a critical foundation for pig genomics, providing a high-quality reference genome for accurate variant detection and comparative genomics but does not capture breed-specific variation. Here, we present a near-complete, gap-free genome assembly for the Landrace pig (Landrace_v1, GCA_963921485.1), spanning all 20 chromosomes and totaling 2.6 Gb, including 176 Mb of sequence absent from Sscrofa11.1. Comparative analyses with recently published high-quality pig genomes reveal a conserved centromere organization across breeds, accompanied by substantial variation in repeat composition and length, and identify a pig specific pattern of telomere variant repeats across eight pig breeds. The improved resolution of repetitive regions in Landrace_v1 enables more complete reconstruction of complex gene families, including olfactory receptors, and uncovers structural variation at the KIT proto-oncogene receptor tyrosine kinase locus not represented in the Duroc reference. Together, these findings highlight the limitations of single-reference genomes and demonstrate the value of breed-specific assemblies for capturing genomic diversity and improving downstream analyses.

Trio-binning is a robust method for haplotype-separated assembly, providing the most accurate representation of diploid genomes including complex and haplotype-specific variation. Conventional trio-binning methods depend on parental short-read sequences to differentiate offspring reads originating from the maternal and paternal haplotypes. Here, we present a genotype-based trio-binning pipeline (GT-Trio) which reconstructs parent sequences based on phased parental genotypes and uses this as an alternative source of parental information for haplotype assembly. The GT-Trio pipeline was applied to assemble the maternal and paternal haplotypes of three Norwegian Red cattle individuals, using either imputed sequence variants or SNP array genotypes as input. Haplotypes assembled with GT-Trio using sequence variants as parental input demonstrated high phasing accuracy, similar to that achieved with conventional trio-binning. Using lower-density panels of array SNPs as input resulted in a decrease in phasing accuracy, but still substantially improved haplotype resolution compared to trio-free assembly. We show that the use of high-density parental information from short-reads or sequence variants increases the level of fragmentation in repeat-rich and low heterozygosity regions. Using lower-density parental information from array resolves the contiguity in these regions, but at the expense of phasing accuracy. Overall, GT-Trio provides a robust and scalable framework for haplotype assembly without parental sequencing and is available at https://github.com/theahettasch/GT-Trio .

Norwegian Red (NR) cattle are the main dairy breed in Norway, bred according to a broad breeding goal including health and fertility since the 1970s. Genomic studies on NR cattle have relied on the public Hereford reference, thus increasing the risk of missing or misrepresenting NR breed-specific variation. Moreover, the Hereford reference is a pseudohaploid assembly, representing homologous chromosomes in a collapsed manner, which results in loss of haplotype-specific alleles and misrepresentation of complex variants. To develop more refined NR specific resources, we utilised long-read sequencing (PacBio HiFi + ONT) and trio-binning to construct six new haplotype-resolved assemblies representing NR genomes. These six NR2025 assemblies show high completeness (BUSCO: 95.82-98.11%) and contiguity (N50: 73.8-88.5 Mb) and are accurately phased (hamming error rate: 0.46-2.52%). Most autosomes have been assembled into the acrocentric centromere, and mapping of bovine satellite sequences reveal distinct organisational patterns of different satellite units across this highly repetitive region. The NR2025 assemblies provide a valuable resource for identification of novel variants and haplotypes in the NR population, which will enable more accurate association studies of genotype-to-phenotype relations and genomic predictions in NR cattle, ultimately enhancing the efficacy of selection for desirable traits.

The objective of this study was to examine the effects of different methods for handling missing pedigree data on biases, stability, relative increase in accuracy, and genetic trends using national data from Norwegian Red (NRF) cattle. The dataset comprised 8,402,773 milk yield records from 3,896,116 NRF cows, a pedigree with 4,957,544 animals, and a genomic dataset from 170,293 animals with 121,741 SNPs. Missing parents were modelled using three approaches: unknown parent groups (UPG), metafounders (MF), and “Q‐Q⁺” methods. The UPG method is routinely used for genetic evaluations of NRF cattle by including 52 fixed UPG in the pedigree. In the MF method, two MF were defined: MF14 and MF52, with MF treated as random effects. The MF14 included 6 MF defined by birth year intervals for NRF breed and 8 MF defined by breed origins for other breeds. The MF52 classification included all the 52 UPG as MF considering relationships among them. The “Q‐Q⁺” approach corrects for the combined effects of UPG and “J factor” in non‐genotyped animals while avoiding such corrections in genotyped animals. The three approaches, combined with different G matrices (G_rtn matrix constructed with a 0.5 allele frequency (AF) and 10% weight (w) on A, G₀₅ constructed using AF = 0.5 and w = 0.0, and G_cal constructed with observed AF and w = 0.0), led to eight ssGBLUP models being tested. This included one UPG model (using G_rtn), four MF models (MF14 and MF52 using G_rtn or G₀₅), and three Q‐Q+ models (using G_cal, G₀₅, or G_rtn). The models were evaluated through cross‐validation by masking the phenotypes of 5000 genotyped young cows. Results showed that the Q‐Q⁺ models using the G_cal or G₀₅ matrix had significantly (p < 0.05) lower level biases and higher genetic trends than all other models. MF models with 14 or 52 groups using G₀₅ were second best for level bias and performed similarly or slightly better than Q‐Q+ models regarding inflation bias and stability. Increasing the number of MF from 14 to 52 had minimal effects on biases but significantly improved stability and genetic trend estimates. Models with G_rtn had slightly higher gain in accuracy from adding phenotypic data (2.01%) than G₀₅ (1.18%), but pedigree‐based models showed the highest improvement in accuracy due to adding phenotypic (26%) or genomic (47%) data to the partial dataset. Overall, all models with G₀₅ showed the least bias (with a small standard error) and most stable predictions, while models using G_rtn introduced biases and instability. Thus, the Q‐Q⁺ and MF models combined with G₀₅ and Q‐Q⁺ with G_cal are recommended for their improved validation results and genetic trends.

Meiotic crossovers are essential for proper chromosome segregation, and provide an important mechanism for adaptation through linking beneficial alleles and purging deleterious mutations. However, crossovers can also break apart beneficial alleles and are themselves a source of new mutations within the genome. The rate and distribution of crossovers shows huge variation both within and between chromosomes, individuals and species, yet the molecular and evolutionary causes and consequences of this variation remain poorly understood. A key step in understanding this variation is to understand the genetic architecture of how many crossovers occur, where they occur, and how they interfere, as this allows us to identify the degree to which these factors are governed by common or distinct genetic processes. Here, we investigate individual variation in crossover count, crossover interference (ν), and crossover positioning measured as both intra-chromosomal allelic shuffling and distance to telomere (Mb), in a large genotyped breeding population of domestic pigs. Using measures from 82,474 gametes from 4,704 mothers and 271 fathers, we show that crossover traits are heritable within each sex (h ² = 0.03 - 0.11), with the exception of male crossover interference. Crossover count and interference have a strongly shared genetic architecture in females, mostly driven by variants at RNF212 . Female crossover positioning is mediated by variants at MEI4 , PRDM9 , and SYCP2 . We also identify tentative associations at genomic regions corresponding to CTCF and REC114/REC8/CCNB1IP1 (crossover count), and ZCWPW1 and ZCWPW2 (crossover positioning). Our results show that crossover count and crossover positioning in female pigs have the capacity to evolve somewhat independently in our dataset.

Meiotic recombination is an important evolutionary mechanism that breaks up linkages between loci and creates novel haplotypes for selection to act upon. Understanding the genetic control of variation in recombination rates is therefore of great interest in both natural and domestic breeding populations. In this study, we used pedigree information and medium-density (∼50K) genotyped data in a large cattle (Bos taurus) breeding population in Norway (Norwegian Red cattle) to investigate recombination rate variation between sexes and individual animals. Sex-specific linkage mapping showed higher rates in males than in females (total genetic length of autosomes = 2,492.9 cM in males and 2,308.9 cM in females). However, distribution of recombination along the genome showed little variation between males and females compared with that in other species. The heritability of autosomal crossover count was low but significant in both sexes (h² = 0.04 and 0.09 in males and females, respectively). We identified 2 loci associated with variation in individual crossover counts in female, one close to the candidate gene CEP55 and one close to both MLH3 and NEK9. All 3 genes have been associated with recombination rates in other cattle breeds. Our study contributes to the understanding of how recombination rates are controlled and how they may vary between closely related breeds as well as between species.

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The aim of this study was to compare three methods of genomic prediction: GBLUP, BayesC and BayesGC for genomic prediction of six maternal traits in Landrace sows using a panel of 660 K SNPs. The effects of different priors for the Bayesian methods were also investigated. GBLUP does not take the genetic architecture into account as all SNPs are assumed to have equally sized effects and relies heavily on the relationships between the animals for accurate predictions. Bayesian approaches rely on both fitting SNPs that describe relationships between animals in addition to fitting single SNP effects directly. Both the relationship between the animals and single SNP effects are important for accurate predictions. Maternal traits in sows are often more difficult to record and have lower heritabilities. BayesGC was generally the method with the higher accuracy, although its accuracy was for some traits matched by that of GBLUP and for others by that of BayesC. For piglet mortality within 3 weeks, BayesGC achieved up to 9.2% higher accuracy. For many of the traits, however, the methods did not show significant differences in accuracies.

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species such as salmon. We present SALARECON, a model focusing on energy, amino acid, and nucleotide metabolism that links the Atlantic salmon genome to metabolic fluxes and growth. It performs well in standardized tests and captures expected metabolic (in)capabilities. We show that it can explain observed hypoxic growth in terms of metabolic fluxes and apply it to aquaculture by simulating growth with commercial feed ingredients. Predicted limiting amino acids and feed efficiencies agree with data, and the model suggests that marine feed efficiency can be achieved by supplementing a few amino acids to plant- and insect-based feeds. SALARECON is a high-quality model that makes it possible to simulate Atlantic salmon metabolism and growth. It can be used to explain Atlantic salmon physiology and address key challenges in aquaculture such as development of sustainable feeds.

Backfat is an important trait in pork production, and it has been included in the breeding objectives of genetic companies for decades. Although adipose tissue is a good energy storage, excessive fat results in reduced efficiency and economical losses. A large QTL for backfat thickness on chromosome 5 is still segregating in different commercial pig breeds. We fine mapped this QTL region using a genome-wide association analysis (GWAS) with 133,358 genotyped animals from five commercial populations (Landrace, Pietrain, Large White, Synthetic, and Duroc) imputed to the porcine 660K SNP chip. The lead SNP was located at 5:66103958 (G/A) within the third intron of theCCND2gene, with the G allele associated with more backfat, while the A allele is associated with less backfat. We further phased the QTL region to discover a core haplotype of five SNPs associated with low backfat across three breeds. Linkage disequilibrium analysis using whole-genome sequence data revealed three candidate causal variants within intronic regions and downstream of theCCND2gene, including the lead SNP. We evaluated the association of the lead SNP with the expression of the genes in the QTL region (includingCCND2) in a large cohort of 100 crossbred samples, sequenced in four different tissues (lung, spleen, liver, muscle). Results show that the A allele increases the expression ofCCND2in an additive way in three out of four tissues. Our findings indicate that the causal variant for this QTL region is a regulatory variant within the third intron of theCCND2gene affecting the expression ofCCND2.