Fertility traits measured early in life define the reproductive potential of heifers. Knowledge of genetics and biology can help devise genomic selection methods to improve heifer fertility. In this study, we used ~2400 Brahman cattle to perform GWAS and multi-trait meta-analysis to determine genomic regions associated with heifer fertility. Heifer traits measured were pregnancy at first mating opportunity (PREG1, a binary trait), first conception score (FCS, score 1 to 3) and rebreeding score (REB, score 1 to 3.5). The heritability estimates were 0.17 (0.03) for PREG1, 0.11 (0.05) for FCS and 0.28 (0.05) for REB. The three traits were highly genetically correlated (0.75–0.83) as expected. Meta-analysis was performed using SNP effects estimated for each of the three traits, adjusted for standard error. We identified 1359 significant SNPs (p-value < 9.9 × 10⁻⁶ at FDR < 0.0001) in the multi-trait meta-analysis. Genomic regions of 0.5 Mb around each significant SNP from the meta-analysis were annotated to create a list of 2560 positional candidate genes. The most significant SNP was in the vicinity of a genomic region on chromosome 8, encompassing the genes SLC44A1, FSD1L, FKTN, TAL2 and TMEM38B. The genomic region in humans that contains homologs of these genes is associated with age at puberty in girls. Top significant SNPs pointed to additional fertility-related genes, again within a 0.5 Mb region, including ESR2, ITPR1, GNG2, RGS9BP, ANKRD27, TDRD12, GRM1, MTHFD1, PTGDR and NTNG1. Functional pathway enrichment analysis resulted in many positional candidate genes relating to known fertility pathways, including GnRH signaling, estrogen signaling, progesterone mediated oocyte maturation, cAMP signaling, calcium signaling, glutamatergic signaling, focal adhesion, PI3K-AKT signaling and ovarian steroidogenesis pathway. The comparison of results from this study with previous transcriptomics and proteomics studies on puberty of the same cattle breed (Brahman) but in a different population identified 392 genes in common from which some genes—BRAF, GABRA2, GABR1B, GAD1, FSHR, CNGA3, PDE10A, SNAP25, ESR2, GRIA2, ORAI1, EGFR, CHRNA5, VDAC2, ACVR2B, ORAI3, CYP11A1, GRIN2A, ATP2B3, CAMK2A, PLA2G, CAMK2D and MAPK3—are also part of the above-mentioned pathways. The biological functions of the positional candidate genes and their annotation to known pathways allowed integrating the results into a bigger picture of molecular mechanisms related to puberty in the hypothalamus–pituitary–ovarian axis. A reasonable number of genes, common between previous puberty studies and this study on early reproductive traits, corroborates the proposed molecular mechanisms. This study identified the polymorphism associated with early reproductive traits, and candidate genes that provided a visualization of the proposed mechanisms, coordinating the hypothalamic, pituitary, and ovarian functions for reproductive performance in Brahman cattle.

Genome-wide association studies (GWAS) are employed to identify genomic regions and candidate genes associated with several traits. The aim of this study was to perform a GWAS to identify causative variants and genes associated with milk yield, frame, and udder conformation traits in Gir dairy cattle. Body conformation traits were classified as "frame," and "udder" traits for this study. After genotyping imputation and quality control 42,105 polymorphisms were available for analyses and 24,489 cows with pedigree information had phenotypes. First, P-value was calculated based on the variance of the prediction error of the SNP-effects on the first iteration. After that, 2 more iterations were performed to carry out the weighted single-step genome-wide association methodology, performed using genomic moving windows defined based on linkage disequilibrium. The significant SNPs and top 10 windows explaining the highest percentage of additive genetic variance were selected and used for QTL and gene annotation. The variants identified in our work overlapped with QTLs from the animal QTL database on chromosomes 1 to 23, except for chromosome 4. The Gir breed is less studied than the Holstein breed and as such the animal QTL database is biased to Holstein results. Hence it is noteworthy that our GWAS had similarities with previously described QTLs. These previously known QTLs were related to milk yield, body height, rump angle, udder width, and udder depth. In total, 5 genes were annotated. Of these genes, FAM13A and CMSS1 had been previously related to bone and carcass weight in cattle.

Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix-partial correlation information theory (AWM-PCIT) approach. Beyond a simple list of candidate genes, AWM-PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility-associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with 'regulation of neurotransmitter secretion' and 'chromatin formation'. Our network recapitulates some genes previously implicated in another network built with lower-density genotypes. Sequence-level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes-KDM5C, LRRK2, and PME-was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further.

Purpose of research: Describe prescribing patterns in Australian Vietnam veterans, identify CYP2C19-metabolised medications using established pharmacogenetic (PGx) resources, characterise CYP2C19 profiles of veterans and assess the potential clinical impact of these medications. Major findings: Among 283 veterans with CYP2C19 profiles, 256 reported current medications use, with a mean prescribed medication of 5.4. Of these, 89 veterans (34.7%) were prescribed at least one medication with CYP2C19 PGx recommendation. Notably, 52 veterans (58.4%) had CYP2C19 profiles that may be at risk of therapeutic failure or adverse effects. Prescribed medications also included six CYP2C19 inhibitors and one inducer, with potential to induce phenoconversion and impact drug metabolism. Conclusion: Veterans experienced high levels of polypharmacy and frequently carried CYP2C19 phenotypes associated with increased risk of therapeutic failure or adverse effects. The presence of CYP2C19 inhibitors and inducers raises the potential for phenoconversion, where CYP2C19 profiles may be placed at risk. Given their similarities to the broader older population, these findings suggest that both groups may benefit from pre-emptive PGx testing. Furthermore, ongoing monitoring of drug–gene and drug–drug interactions remains essential to optimise medication safety and efficacy in these high-risk individuals.

The University of Queensland's institutional repository, UQ eSpace, aims to create global visibility and accessibility of UQ’s scholarly research.

The University of Queensland's institutional repository, UQ eSpace, aims to create global visibility and accessibility of UQ’s scholarly research.