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Sheep farming has long been an important part of the rural economy and culture of the United Kingdom. It has always aimed to increase production efficiency to guarantee a plentiful supply of food and to keep producers profitable. However, feed costs have risen dramatically in recent years and continue to increase, posing a significant threat to producer profitability. In sheep production systems, feed accounts for more than 63% of total production costs. As a result, increasing productivity while using less feed and lower feed costs is becoming an increasingly tough dilemma for sheep producers. This piques farmers' interest in identifying and breeding animals with improved FE, which can aid in lowering overall production costs by employing genetic advancements for FE. Because metabolites are most closely related to biological phenotypes, identifying the metabolite biomarkers for FE can assist in distinguishing between feed-efficient and feed-inefficient animals. In doing so, it is also crucial to consider features such as carcass and meat quality, which may be affected positively or negatively by selecting feed efficient animals. The goal of this project will be to use metabolomics to identify metabolite markers associated with FE, carcass, and meat quality and to examine their biological backgrounds using big data deposited in the AFBI's NIFAB database. Secondly, utilising GWAS and post-GWAS analyses, relevant SNP markers in genomic datasets will be discovered. Finally, integrated genomics and metabolomics studies will be conducted to better understand the molecular basis of the genes and metabolites linked to FE, carcass, and meat quality parameters. The study will advise sheep farmers on the possibilities offered by genomics and metabolomics in selecting animals for breeding, as well as increasing farm profit potential by making FE selection more attainable at the farm level, making it easier to include as a breeding objective in ruminant systems.FE: Feed efficiency, AFBI: Agri-food & Biosciences Institute, NIFAB: Northern Ireland Farm Animal Biobank, SNP: Single Nucleotide polymorphisms, GWAS: Genome-Wide Association Studies

Supplementary Table 1: Traits described in the studies returned by the search criteria and how they were scored and where they were undertaken Supplementary Table 2: Behavioural traits which were considered to have low accuracy heritabilities (SE>0.05) Supplementary Table 3: Gene associations reported in studies returned by the search criteria Supplementary Table 4: Single Nucleotide Polymorphisms (SNP) which were found to have associations with behavioural traits in studies returned by the search criteria Supplementary Table 5: Relationship between behavioural traits and production traits reported

Depression is a polygenic trait that causes extensive periods of disability. Previous genetic studies have identified common risk variants which have progressively increased in number with increasing sample sizes of the respective studies. Here, we conduct a genome-wide association study in 322,580 UK Biobank participants for three depression-related phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. We identify 17 independent loci that are significantly associated (P 5 × 10

Depression is a polygenic trait that causes extensive periods of disability. Previous genetic studies have identified common risk variants which have progressively increased in number with increasing sample sizes of the respective studies. Here, we conduct a genome-wide association study in 322,580 UK Biobank participants for three depression-related phenotypes: broad depression, probable major depressive disorder (MDD), and International Classification of Diseases (ICD, version 9 or 10)-coded MDD. We identify 17 independent loci that are significantly associated (P, The UK Biobank provides data for three depression-related phenotypes. Here, Howard et al. perform a genome-association study for broad depression, probable major depressive disorder (MDD) and hospital record-coded MDD in up to 322,580 UK Biobank participants which highlights excitatory synaptic pathways.

We propose a novel approach to analyze genomic data that incorporates haplotype information for detecting rare variants within a regional heritability mapping framework. The performance of our approach was tested in a simulation study based on human genotypes. The phenotypes were simulated by generating regional variance using either SNP(s) or haplotype(s). Regional genomic relationship matrices, constructed with either a SNP-based or a haplotype-based estimator, were employed to estimate the regional variance. The results from the study show that haplotype heritability mapping captures the regional effect, with its relative performance decreasing with increasing analysis window size. The SNP-based regional mapping approach often misses the effect of causal haplotype(s); however, it has a greater power to detect simulated SNP-based-variants. Heritability estimates suggest that the haplotype heritability mapping estimates the simulated regional heritability accurately for all phenotypes and analysis windows. However, the SNP-based analysis overestimates the regional heritability and performs less well than our haplotype-based approach for the simulated rare haplotype-based-variant. We conclude that haplotype heritability mapping is a useful tool to capture the effect of rare variants, and explain a proportion of the missing heritability.

Background: \ud \ud Major depressive disorder (MDD) is the second largest cause of global disease burden. It has an estimated heritability of 37%, but published genome-wide association studies have so far identified few risk loci. Haplotype-block-based regional heritability mapping (HRHM) estimates the localized genetic variance explained by common variants within haplotype blocks, integrating the effects of multiple variants, and may be more powerful for identifying MDD-associated genomic regions.\ud \ud Methods: \ud \ud We applied HRHM to Generation Scotland: The Scottish Family Health Study, a large family- and population-based Scottish cohort (N = 19,896). Single-single nucleotide polymorphism (SNP) and haplotype-based association tests were used to localize the association signal within the regions identified by HRHM. Functional prediction was used to investigate the effect of MDD-associated SNPs within the regions.\ud \ud Results: \ud \ud A haplotype block across a 24-kb region within the TOX2 gene reached genome-wide significance in HRHM. Single-SNP- and haplotype-based association tests demonstrated that five of nine genotyped SNPs and two haplotypes within this block were significantly associated with MDD. The expression of TOX2 and a brain-specific long noncoding RNA RP1-269M15.3 in frontal cortex and nucleus accumbens basal ganglia, respectively, were significantly regulated by MDD-associated SNPs within this region. Both the regional heritability and single-SNP associations within this block were replicated in the UK–Ireland group of the most recent release of the Psychiatric Genomics Consortium (PGC), the PGC2–MDD (Major Depression Dataset). The SNP association was also replicated in a depressive symptom sample that shares some individuals with the PGC2–MDD.\ud \ud Conclusions: \ud \ud This study highlights the value of HRHM for MDD and provides an important target within TOX2 for further functional studies.