The Chubao black-head goat is a novel hybrid breed that combines the advantages of Macheng black goats, such as good reproductive performance, strong adaptability, and resistance to rough feeding, with the superior growth and meat characteristics of Boer goats. Given the substantial economic importance of growth (such as birth weight, body height, body length, and chest circumference across different growth stages) and reproductive traits (particularly average litter size after first parity), the aim of this study was to identify significant SNPs and candidate genes associated with these traits in Chubao black-head goats. Through whole-genome sequencing (with 34 goats at approximately 15× coverage and 466 goats at approximately 1× coverage), genotype imputation, and quality control, 22,665,331 SNPs were identified and subsequently used for genetic analyses. Heritability estimates indicated that growth traits exhibit moderate to high heritability (ranging from 0.297 ± 0.071 to 0.535 ± 0.118), while reproductive traits demonstrated low to moderate heritability (with a value of 0.220 ± 0.108). By performing FarmCPU-based genome-wide association studies, we identified 48 potentially significant SNPs associated with growth traits and 7 with reproductive traits. Additionally, 85 candidate genes (such as COL14A1, ZNF148, and TTC39C) linked to growth traits were identified and enriched in pathways associated with fundamental molecular biological activities such as protein deubiquitination, regulation of mRNA stability, and the MAPK signaling pathway. Furthermore, 10 candidate genes (such as SOHLH2, CCNA2, and SOX7) associated with reproductive traits were identified and enriched in pathways related to specific reproductive processes such as oocyte differentiation, endoderm formation, and progesterone-mediated oocyte maturation. Overall, these findings provide valuable preliminary insights into the molecular mechanisms underlying growth and reproductive traits in Chubao black-head goats. However, further functional validation is needed to effectively use these potential SNPs and candidate genes in improving the breeding of these traits in this breed.

The uncertainty resulting from missing genotypes in low-coverage whole-genome sequencing (LCWGS) data complicates genotype imputation. The aim of this study is to find out an optimal strategy for accurately imputing LCWGS data and assess its effectiveness for genomic prediction (GP) and genome-wide association study (GWAS) on economically important traits of Large White pigs. The LCWGS data of 1 423 Large White pigs were imputed using three different strategies: (1) using the high-coverage whole-genome sequencing (HCWGS) of 30 key progenitors as the reference panel (Ref_LG); (2) mixing HCWGS of key progenitors with LCWGS (Mix_HLG) and (3) self-imputation in LCWGS (Within_LG). Additionally, to compare the imputation effects of LCWGS, we also imputed SNP chip data of 1 423 Large White pigs to the whole-genome sequencing level using the reference panel consisting of key progenitors (Ref_SNP). To evaluate effects of the imputed sequencing data, we compared the accuracies of GP and statistical power of GWAS for four reproductive traits based on the chip data, sequencing data imputed from chip data and LCWGS data using an optimal strategy. The average imputation accuracies of the Within_LG, Ref_LG and Mix_HLG were 0.9893, 0.9899 and 0.9875, respectively, which were higher than that of the Ref_SNP (0.8522). Using the imputed sequencing data from LCWGS with the Ref_LG imputation strategy, the accuracies of GP for four traits improved by approximately 0.31-1.04% compared to the chip data, and by 0.7-1.05% compared to the imputed sequencing data from chip data. Furthermore, by using the sequence data imputed from LCWGS with the Ref_LG, 18 candidate genes were identified to be associated with the four reproductive traits of interest in Large White pigs: total number of piglets born - EPC2, MBD5, ORC4 and ACVR2A; number of piglets born healthy - IKBKE; total litter weight of piglets born alive - HSPA13 and CPA1; gestation length - GTF2H5, ITGAV, NFE2L2, CALCRL, ITGA4, STAT1, HOXD10, MSTN, COL5A2 and STAT4. With the exception of EPC2, ORC4, ACVR2A and MSTN, others represent novel candidates. Our findings can provide a reference for the application of LCWGS data in livestock and poultry.

With the rapid and significant cost reduction of next-generation sequencing, low-coverage whole-genome sequencing (lcWGS), followed by genotype imputation, is becoming a cost-effective alternative to single-nucleotide polymorphism (SNP)-array genotyping. The objectives of this study were 2-fold: (1) construct a haplotype reference panel for genotype imputation from lcWGS data in rainbow trout (Oncorhynchus mykiss); and (2) evaluate the concordance between imputed genotypes and SNP-array genotypes in 2 breeding populations. Medium-coverage (12×) whole-genome sequences were obtained from a total of 410 fish representing 5 breeding populations with various spawning dates. The short-read sequences were mapped to the rainbow trout reference genome, and genetic variants were identified using GATK. After data filtering, 20,434,612 biallelic SNPs were retained. The reference panel was phased with SHAPEIT5 and was used as a reference to impute genotypes from lcWGS data employing GLIMPSE2. A total of 90 fish from the Troutlodge November breeding population were sequenced with an average coverage of 1.3×, and these fish were also genotyped with the Axiom 57K rainbow trout SNP array. The concordance between array-based genotypes and imputed genotypes was 99.1%. After downsampling the coverage to 0.5×, 0.2×, and 0.1×, the concordance between array-based genotypes and imputed genotypes was 98.7, 97.8, and 96.7%, respectively. In the USDA odd-year breeding population, the concordance between array-based genotypes and imputed genotypes was 97.8% for 109 fish downsampled to 0.5× coverage. Therefore, the reference haplotype panel reported in this study can be used to accurately impute genotypes from lcWGS data in rainbow trout breeding populations.

Low-coverage whole-genome sequencing (LCS) offers a cost-effective alternative for sturgeon breeding, especially given the lack of SNP chips and the high costs associated with whole-genome sequencing. In this study, the efficiency of LCS for genotype imputation and genomic prediction was assessed in 643 sequenced Russian sturgeons (∼13.68×). The results showed that using BaseVar+STITCH at a sequencing depth of 2× with a sample size larger than 300 resulted in the highest genotyping accuracy. In addition, when the sequencing depth reached 0.5× and SNP density was reduced to 50 K through linkage disequilibrium pruning, the prediction accuracy was comparable to that of whole sequencing depth. Furthermore, an incremental feature selection method has the potential to improve prediction accuracy. This study suggests that the combination of LCS and imputation can be a cost-effective strategy, contributing to the genetic improvement of economic traits and promoting genetic gains in aquaculture species.

BACKGROUND: Plasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection.
METHODS: One hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19-9 (CA19-9) was explored to improve model performance.
RESULTS: The stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved.
CONCLUSIONS: Our model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

The selection and breeding of good meat rabbit breeds are fundamental to their industrial development, and genomic selection (GS) can employ genomic information to make up for the shortcomings of traditional phenotype-based breeding methods. For the practical implementation of GS in meat rabbit breeding, it is necessary to assess different marker densities and GS models. Here, we obtained low-coverage whole-genome sequencing (lcWGS) data from 1515 meat rabbits (including parent herd and half-sibling offspring). The specific objectives were (1) to derive a baseline for heritability estimates and genomic predictions based on randomly selected marker densities and (2) to assess the accuracy of genomic predictions for single- and multiple-trait linear mixed models. We found that a marker density of 50 K can be used as a baseline for heritability estimation and genomic prediction. For GS, the multi-trait genomic best linear unbiased prediction (GBLUP) model results in more accurate predictions for virtually all traits compared to the single-trait model, with improvements greater than 15% for all of them, which may be attributed to the use of information on genetically related traits. In addition, we discovered a positive correlation between the performance of the multi-trait GBLUP and the genetic correlation between the traits. We anticipate that this approach will provide solutions for GS, as well as optimize breeding programs, in meat rabbits.

Wolbachia (Hertig 1936) (Rickettsiales: Ehrlichiaceae) has emerged as a valuable biocontrol tool in the fight against dengue by suppressing the transmission of the virus through mosquitoes. Monitoring the dynamics of Wolbachia is crucial for evaluating the effectiveness of release programs. Mitochondrial (mtDNA) markers serve as important tools for molecular tracking of infected mitochondrial backgrounds over time but require an understanding of the variation in release sites. In this study, we investigated the mitochondrial lineages of Aedes aegypti (Linnaeus 1762) in Jeddah, Saudi Arabia, which is a prospective release site for the \"wAlbBQ\" Wolbachia-infected strain of this mosquito species. We employed a combination of comprehensive mitogenomic analysis (including all protein-coding genes) and mtDNA marker analysis (cox1 and nad5) using data collected from Jeddah. We combined our mitogenome and mtDNA marker data with those from previous studies to place mitochondrial variation in Saudi Arabia into a broader global context. Our findings revealed the presence of 4 subclades that can be broadly categorized into 2 major mitochondrial lineages. Ae. aegypti mosquitoes from Jeddah belonged to both major lineages. Whilst mitogenomic data offered a higher resolution for distinguishing Jeddah mosquitoes from the wAlbBQ strain, the combination of cox1 and nad5 mtDNA markers alone proved to be sufficient. This study provides the first important characterization of Ae. aegypti mitochondrial lineages in Saudi Arabia and offers essential baseline information for planning future molecular monitoring efforts during the release of Wolbachia-infected mosquitoes.

UNASSIGNED: Endometrial carcinoma (EC) is a disease that predominantly affects peri- and post-menopausal women and its incidence has continued to rise over recent years. Since the gold standard for EC diagnosis-hysteroscopic biopsy-is invasive, expensive, and unsuitable for wide use, there is an urgent need for a non-invasive method that exhibits both high sensitivity and high specificity. We therefore investigated the efficacy of UterCAD (the uterine exfoliated cell chromosomal aneuploidy detector) using tampon-collected specimens for the early detection of EC.
UNASSIGNED: We prospectively recruited 51 patients with a history of abnormal bleeding and who planned to undergo hysteroscopic examination or hysterectomy between March 2020 and January 2021. Before executing an invasive procedure, a tampon was inserted into the patient\'s vagina for 6 h to collect exfoliated cells from the uterine cavity. Total DNA was extracted and low-coverage whole-genome sequencing was performed on an Illumina HiSeq X10, and we analyzed the differences in chromosomal status between women with EC and those bearing benign lesions using UterCAD.
UNASSIGNED: Thirty EC patients-including 26 with endometrioid carcinoma (EEC) and four with uterine serous carcinoma (USC), as well as 14 benign cases-were enrolled in our final analysis. Copy-number variations (CNVs) were detected in tampon specimens collected from 26 EC patients (83.3%), including 21 with EEC (80.7%) and four with USC (100%). In the benign group, only one woman with focal atypical hyperplasia presented with a 10q chromosomal gain (P < 0.001). In the EC group, the most common CNVs were copy gains of 8q (N = 14), 2q (N = 4), and 10q (N = 3); and copy losses of 2q (N = 3) and 17p (N = 2). When we stratified by FIGO stage, the CNV rates in stages IA, IB, and II/III were 83.3% (15/18), 85.7% (6/7), and 80.0% (4/5), respectively. At the optimal cutoff (|Z| ≥ 2.3), UterCAD discriminated 83.3% of EC cases from benign cases, with a specificity of 92.9%.
UNASSIGNED: We initially reported that UterCAD could serve as a non-invasive method for the early detection of EC, especially in the rare and aggressive USC subtype. The use of UterCAD might thus avoid unnecessary invasive procedures and thereby reduce the treatment burden on patients.

Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with nonmalignant and malignant ovarian tumors and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data. Concordance analysis indicated a significant correlation of identified CNV markers between CNV profiles of plasma cfDNA and tissue DNA (Pearson\'s r = 0.64, P = 0.006 for the TCGA cohort and r = 0.51, P = 0.04 for the Dariush cohort). By leveraging these specific CNV markers and machine learning algorithms, we developed robust predictive models showing excellent performance in distinguishing between malignant and nonmalignant ovarian tumors with F1-scores of 0.90 and ranging from 0.75 to 0.99, and prediction accuracy of 0.89 and ranging from 0.66 to 0.98, respectively, as well as between early- and late-stage ovarian tumors with F1-scores of 0.84 and ranging from 0.61 to 1.00, and prediction accuracy of 0.82 and ranging from 0.63 to 0.96 in our institute cohort and other external validation cohorts. Furthermore, we also discovered and validated certain CNV features associated with survival outcomes and platinum-based chemotherapy response in multicenter cohorts. In conclusion, our study demonstrated the clinical utility of CNV profiling in plasma cfDNA using LC-WGS as a cost-effective and accessible liquid biopsy for OV.

The contribution of common genetic variants to pre-cancer progression is understudied due to long follow-up time, rarity of poor outcomes and lack of available germline DNA collection. Alternatively, DNA from diagnostic archival tissue is available, but its somatic nature, limited quantity and suboptimal quality would require an accurate cost-effective genome-wide germline genotyping methodology.
Blood and tissue DNA from 10 individuals were used to benchmark the accuracy of Single Nucleotide Polymorphisms (SNP) genotypes, Polygenic Risk Scores (PRS) or HLA haplotypes using low-coverage whole-genome sequencing (lc-WGS) and genotype imputation. Tissue-derived PRS were further evaluated for 36 breast cancer patients (11.7 years median follow-up time) diagnosed with DCIS and used to model the risk of Breast Cancer Subsequent Events (BCSE).
Tissue-derived germline DNA profiling resulted in accurate genotypes at common SNPs (blood correlation r2 > 0.94) and across 22 disease-related polygenic risk scores (PRS, mean correlation r = 0.93). Imputed Class I and II HLA haplotypes were 96.7% and 82.5% concordant with clinical-grade blood HLA haplotypes, respectively. In DCIS patients, tissue-derived PRS was significantly associated with BCSE (HR = 2, 95% CI 1.2-3.8). The top and bottom decile patients had an estimated 28% and 5% chance of BCSE at 10 years, respectively.
Archival tissue DNA germline profiling using lc-WGS and imputation, represents a cost and resource-effective alternative in the retrospective design of long-term disease genetic studies. Initial results in breast cancer suggest that common risk variants contribute to pre-cancer progression.