👤 Saman Hosseini Ashtiani

The reproduction process in domestic animals is one of the most important challenges of animal husbandry. Fertility is an important trait that contributes to herd profitability and can be improved by genomic information. One of the best ways to investigate the association between single nucleotide polymorphisms (SNPs) and phenotypic performance is the genome-wide association study (GWAS). The aim of our study was to identify the genomic regions affecting reproductive traits, interval between first and last insemination (IFL), days open (DO), days from calving to first service (DFS), number of services per conception (NSPC), age at first calving (AFC) and age at first insemination (AFI) using SNP chip data in Iranian Holstein cows. GWAS analysis for all reproductive traits based on the significant-association threshold P < 1 × 10 Our results identified 55 marker-trait associations (MTAs) and 54 different candidate genes associated with reproductive traits. As a result, the SNPs and candidate genes discovered in this study can be used in genomic experiments to improve the reproductive performance of Iranian Holstein dairy cows and provide new information about the genetic architecture of these traits. Show less

Blood-based biomarkers in epilepsy could constitute important research tools advancing neurobiological understanding and valuable clinical tools for better diagnosis and follow-up. An interesting question is whether biomarker patterns could contribute additional understanding compared to individual marker values. We analyzed OLINK proteomics data from a large epilepsy cohort in which we have previously found four differentially expressed proteins (CDH15, PAEP, LTBP3, PHOSPHO1). Using two machine-learning techniques, we identified ten consensus candidate protein biomarkers (CDH15, PAEP, LTBP3, PHOSPHO1, NEFL, SFRP1, TDGF1, DUSP3, WWP2 and DSG3) that contributed to the classification of patients as being seizure-free or not. Six out of the ten consensus proteins were identified as differentially expressed in our previous study (although NEFL and TDGF1 not significantly so after multiple testing correction). The remaining four consensus proteins were newly identified by machine learning and were chosen for detailed analysis. In comparison to the four significantly differentially expressed proteins (CDH15, PAEP, LTBP3, PHOSPHO1), the newly identified consensus proteins (SFRP1, DSG3, DUSP3, and WWP2) and in particular a combination of all eight proteins, outperformed individual proteins in identifying individuals with recent seizures, highlighting the potential of multi-protein profiles. These findings emphasize the need for integrative bioinformatic approaches in epilepsy research and underscore the role of neuroinflammation and immune pathways in epileptogenesis. Our results support the applicability of plasma protein profiling for developing future blood-based tests for epilepsy seizure prediction, diagnosis, and treatment. Further validations in independent cohorts are required to establish these candidate biomarkers in clinical practice. Show less

Our goal of this study was to reconstruct a "genome-scale co-expression network" and find important modules in lung adenocarcinoma so that we could identify the genes involved in lung adenocarcinoma. We integrated gene mutation, GWAS, CGH, array-CGH and SNP array data in order to identify important genes and loci in genome-scale. Afterwards, on the basis of the identified genes a co-expression network was reconstructed from the co-expression data. The reconstructed network was named "genome-scale co-expression network". As the next step, 23 key modules were disclosed through clustering. In this study a number of genes have been identified for the first time to be implicated in lung adenocarcinoma by analyzing the modules. The genes EGFR, PIK3CA, TAF15, XIAP, VAPB, Appl1, Rab5a, ARF4, CLPTM1L, SP4, ZNF124, LPP, FOXP1, SOX18, MSX2, NFE2L2, SMARCC1, TRA2B, CBX3, PRPF6, ATP6V1C1, MYBBP1A, MACF1, GRM2, TBXA2R, PRKAR2A, PTK2, PGF and MYO10 are among the genes that belong to modules 1 and 22. All these genes, being implicated in at least one of the phenomena, namely cell survival, proliferation and metastasis, have an over-expression pattern similar to that of EGFR. In few modules, the genes such as CCNA2 (Cyclin A2), CCNB2 (Cyclin B2), CDK1, CDK5, CDC27, CDCA5, CDCA8, ASPM, BUB1, KIF15, KIF2C, NEK2, NUSAP1, PRC1, SMC4, SYCE2, TFDP1, CDC42 and ARHGEF9 are present that play a crucial role in cell cycle progression. In addition to the mentioned genes, there are some other genes (i.e. DLGAP5, BIRC5, PSMD2, Src, TTK, SENP2, PSMD2, DOK2, FUS and etc.) in the modules. Show less