Ask about this productRelated genes to: MCPH1 antibody
- Gene:
- MCPH1 NIH gene
- Name:
- microcephalin 1
- Previous symbol:
- -
- Synonyms:
- FLJ12847, BRIT1
- Chromosome:
- 8p23.1
- Locus Type:
- gene with protein product
- Date approved:
- 1998-02-11
- Date modifiied:
- 2014-11-18
Related products to: MCPH1 antibody
Related articles to: MCPH1 antibody
- Hexi cattle are a local cattle population endemic to the Hexi Corridor in Gansu Province, China, and exhibit strong adaptability to the region's arid continental environment. However, comprehensive genomic investigations of this population are still lacking. In the present study, we integrated population genomic analyses with a genome-wide association study (GWAS) to dissect the genetic architecture of Hexi cattle. Whole-genome resequencing data were generated for 264 Hexi cattle, and public genomic datasets from six representative cattle breeds were obtained from the NCBI database for comparative analysis. Multiple analytical approaches-including principal component analysis (PCA), linkage disequilibrium (LD) decay analysis, neighbor-joining (NJ) phylogenetic tree construction, and ADMIXTURE analysis-were adopted to evaluate population structure and evolutionary relationships. A mixed linear model was then used to identify significant SNPs associated with five major body conformation traits in six-month-old cattle: body weight (BW), withers height (WH), hip height (HH), heart girth (HG), and abdominal girth (AG). Our results confirm the admixed nature of Hexi cattle, whose genome is derived primarily from Simmental cattle and secondarily from Mongolian cattle. A total of 69 trait-associated significant SNPs were identified and functionally annotated. Specifically, , and were linked to BW; and to WH; to HH; , , and to HG; and , , , , , , and M18 to AG. This study deepens our understanding of the genetic basis of growth traits in Hexi cattle and offers valuable molecular resources for future selective breeding, genetic improvement, and long-term conservation of this indigenous cattle population. - Source: PubMed
Publication date: 2026/07/16
Wang XinluMa BinWang ZhichengLiu YichengMa XiaomingChu MinLa YongfuGuo XianYan PingWang LeiLiang Chunnian - - Source: PubMed
Peng GuangYim Eun-KyoungDai HuiJackson Andrew Pvan der Burgt InekePan Mei-RenHu RuozhenLi KaiyiLin Shiaw-Yih - - Source: PubMed
Bhattacharya NilanjanaMukherjee NupurSingh Ratnesh KSinha SatyabrataAlam NeyazRoy AnupRoychoudhury SusantaPanda Chinmay Kumar - Mitochondrial dynamics and autophagy are associated with esophageal squamous cell carcinoma (ESCC) progression, but their combined mechanisms are unclear. This study aimed to evaluate the prognostic significance of mitochondrial dynamics-related genes (MDRGs) and mitochondrial autophagy-related genes (MARGs) in ESCC by constructing a risk model. - Source: PubMed
Publication date: 2026/04/27
Lin HuiZhuang Feng-NianChen Wei-JieWei Wen-WeiGao Peng-QiangWang Pei-YuanWang FengShi Shan - Representation learning is an emerging paradigm for deriving phenotypes from complex measurements (e.g., imaging) for genetic discovery. However, the learning dynamics of deep neural networks, especially the evolution of representations during training, while of interest in representation learning, were insufficiently investigated in the context of genetic discovery. In this study, using a 3D convolutional autoencoder trained on T1-weighted brain MRIs UK Biobank participants, we show that its learning trajectory forms an epoch-stratified landscape of brain morphology heritability. Different training epochs capture distinct genetic architectures at comparable heritability levels. Overall, ensembling across informative checkpoints identifies more genomic risk loci than the conventional single-checkpoint approach. Interpretability analysis reveals that epoch-specific loci, including MAPT and MCPH1, map onto biologically coherent and distinct neuroanatomical signatures, identified at different stages of the training process. Our results establish learning dynamics as a novel axis for genetic discovery using unsupervised deep learning and have practical implications for any architecture that saves multiple checkpoints during training. - Source: PubMed
Publication date: 2026/04/28
Saiful Islam Sheikh MuhammadXia TianZhao XingzhongXie ZiqianZhi Degui