Biology subjects

Marrella, M.

Publications and source records attributed to Marrella, M..

3 recordsLinked to original sources

bioRxiv2024

Genome-wide association analyses in dairy heifers highlight genes overlapping with mouse and human fertility and human health traits

Heifer Infertility and disease are important challenges in dairy cattle production. We investigated genetic differences between Holstein heifers with varying fertility potential and health. We carried out a genome-wide association analysis comparing heifers that conceived at first insemination against those requiring multiple attempts or failing to become pregnant, as well as heifers culled due to health issues. There were 12 significant SNPs (P<5x10-5) associated with fertility and 35 SNPs associated with health traits. There were 166 significant SNPs when infertile, sub-fertile and animals culled due to health issues were grouped. Two SNPs identified in the analysis of infertility were found near NUFIP1 and within TENM4 genes, both genes are linked to embryonic lethality in mouse knockouts. Follow-up CRISPR-Cas9 mediated disruption of NUFIP1 significantly (P<0.05) reduced in vitro blastocyst development in cattle embryos, while TENM4 editing did not alter in vitro blastocyst development. Additionally, SNPs overlapped with previously identified reproduction-related QTL (CNTN4, DLG2, PARP10, PRICKLE, TMEM150B) or health-related QTL (FAM162A, PARP10). We also identified genes within or near genes previously associated with age at menarche (CADM2, DLG2, FHIT, LSAMP and TENM4) or lung function or pulmonary diseases (ASCC2, BCAS3, BTBD9, CADM2, CNTN4, CPEB4, CTNNA2, DEUP1, DGKH, DLG2, ENOX1, EPHB1, ERC2, ERGIC1, EYA2, FAM162A, FGF18, FHIT, GRID1, KCNIP4, LINGO2, LRMDA, MALRD1, NEBL, PLA2G6, PLXDC2, PRPF18, SLC8A1, TEAD4, TSPAN9) in humans. These results further support genetic components of fertility and health in cattle. The findings also show overlapping genetic architecture between fertility and health traits, with a degree of conservation across mammals. Summary sentenceSeveral genetic variants that influence female fertility and health in cattle were identified, and many genes harboring or near significant polymorphisms are common to equivalent phenotypes in mice and humans.

genomics↗

bioRxiv2022

A multi-omics analysis identifies molecular features associated with heifer fertility in a case-control design including Angus and Holstein cattle

BackgroundInfertility or subfertility is a critical barrier to sustainable cattle production, including in heifers. The development of heifers that do not produce a calf within an optimum window of time is a critical factor for the profitability and sustainability of the cattle industry. The early identification of heifers with optimum fertility using molecular phenotyping is a promising approach to improving sustainability in beef and dairy cattle production. ResultsUsing a high-density single nucleotide polymorphism (SNP) chip, we collected genotypic data from 575,053 SNPs. We also produced quantitative transcriptome data for 12,445 genes (12,105 protein-coding genes, 228 long non-coding RNAs, and 112 pseudogenes) and proteome data for 213 proteins. We identified two SNPs significantly associated with heifer fertility (rs110918927, chr12: 85648422, P = 6.7x10-7; and rs109366560, chr11:37666527, P = 2.6x10-5). We identified two genes with differential transcript abundance (eFDR [≤] 0.002) between the two groups (Fertile and Sub-Fertile): Adipocyte Plasma Membrane Associated Protein (APMAP, 1.16 greater abundance in the Fertile group) and Dynein Axonemal Intermediate Chain 7 (DNAI7, 1.23 greater abundance in the Sub-Fertile group). Our analysis revealed that the protein Alpha-ketoglutarate-dependent dioxygenase FTO was more abundant in the plasma collected from Fertile heifers relative to their Sub-Fertile counterparts (FDR < 0.05). Interestingly, two proteins did not reach the significance threshold in the model accounting for all samples (Apolipoprotein C-II, APOC2 (FDRglmm = 0.06) and Lymphocyte cytosolic protein 1, LCP1 (FDRglmm = 0.06)), but both proteins were less abundant in the plasma of Fertile Holstein heifers (P < 0.05). Lastly, an integrative analysis of the three datasets identified a series of features (SNPs, gene transcripts, and proteins) that can be useful for the discrimination of heifers based on their fertility. When all features were utilized together, 21 out of 22 heifers were classified correctly based on their fertility category. ConclusionsOur multi-omics analyses confirm the complex nature of female fertility. Very importantly, our results also highlight differences in the molecular profile of heifers associated with fertility that transcend the constraints of breed-specific genetic background.

genomics↗

bioRxiv2022

Robust identification of regulatory variants (eQTLs) using a differential expression framework developed for RNA-sequencing

BackgroundA gap currently exists between genetic variants and the underlying cell and tissue biology of a trait, and expression quantitative trait loci (eQTL) studies provide important information to help close that gap. However, two concerns that arise with eQTL analyses using RNA-sequencing data are normalization of data across samples and the data not following a normal distribution. Multiple pipelines have been suggested to address this. For instance, the most recent analysis of the human and farm Genotype-Tissue Expression (GTEx) project proposes using trimmed means of M-values (TMM) to normalize the data followed by an inverse normal transformation. ResultsIn this study, we reasoned that eQTL analysis could be carried out using the same framework used for differential gene expression (DGE), which uses a negative binomial model, a statistical test feasible for count data. Using the GTEx framework, we identified 38 significant eQTLs (P<5x10-8) following the ANOVA model and 15 significant eQTLs (P<5x10-8) following the additive model. Using a differential gene expression framework, we identified 2,471 and nine significant eQTLs (P<5x10-8) following an analytical framework equivalent to the ANOVA and additive model, respectively. When we compared the two approaches, there was no overlap of significant eQTLs between the two frameworks. Because we defined specific contrasts, we identified trans eQTLs that more closely resembled what we expect from genetic variants showing complete dominance between alleles. Yet, these were not identified by the GTEx framework. ConclusionsOur results show that transforming RNA-sequencing data to fit a normal distribution prior to eQTL analysis is not required when the DGE framework is employed, thus this may be more suitable for finding genes whose expression are impacted by genetic variants. Our approach detected biologically relevant variants that otherwise would not have been identified due to data transformation to fit a normal distribution.

genomics↗