Identifying risk variants for embryo aneuploidy using ultra-low coverage whole-genome sequencing from preimplantation genetic testing.

Aneuploidy frequently arises during human meiosis and is the primary cause of early miscarriage and in vitro fertilization (IVF) failure. Individuals undergoing IVF exhibit significant variability in aneuploidy rates, although the exact genetic causes of the variability in aneuploid egg production remain unclear. Preimplantation genetic testing for aneuploidy (PGT-A) using next-generation sequencing is a standard test for identifying and selecting IVF-derived euploid embryos. The wealth of embryo aneuploidy data and ultra-low coverage whole-genome sequencing (ulc-WGS) data from PGT-A have the potential to discover variants in parental genomes that are associated with aneuploidy risk in their embryos. Using ulc-WGS data from ∼10,000 PGT-A biopsies, we imputed genotype likelihoods of genetic variants in embryo genomes. We then used the imputed variants and embryo aneuploidy calls to perform a genome-wide association study of aneuploidy incidence. Finally, we carried out functional evaluation of the identified candidate gene in a mouse oocyte system. We identified one locus on chromosome 3 that is significantly associated with meiotic aneuploidy risk. One candidate gene, CCDC66, encompassed by this locus, is involved in chromosome segregation during meiosis. Using mouse oocytes, we showed that CCDC66 regulates meiotic progression and chromosome segregation fidelity, especially in older mice. Our work extended the research utility of PGT-A ulc-WGS data by allowing robust association testing and improved the understanding of the genetic contribution to maternal meiotic aneuploidy risk. Importantly, we introduce a generalizable method that has potential to be leveraged for similar association studies that use ulc-WGS data.

Cost-effective genomic prediction of critical economic traits in sturgeons through low-coverage sequencing.

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.

The construction of a haplotype reference panel using extremely low coverage whole genome sequences and its application in genome-wide association studies and genomic prediction in Duroc pigs.

Extremely low coverage whole genome sequencing (lcWGS) is an economical technique to obtain high-density single nucleotide polymorphisms (SNPs). Here, we explored the feasibility of constructing a haplotype reference panel (lcHRP) using lcWGS and evaluated the effects of lcHRP through a genome-wide association study (GWAS) and genomic prediction in pigs. A total of 297 and 974 Duroc pigs were genotyped using lcWGS and a 50 K SNP array, respectively. We obtained 19,306,498 SNPs using lcWGS with an accuracy of 0.984. With the help of lcHRP, the accuracy of imputation from the SNP array to lcWGS was 0.922. Compared to the SNP array findings, those from the imputation-based GWAS identified more signals across four traits. With the integration of the top 1% imputation-based GWAS findings as genomic features, the accuracies of genomic prediction was improved by 6.0% to 13.2%. This study showed the great potential of lcWGS in pigs' molecular breeding.

Highly precise breakpoint detection of chromosome balanced translocation in chronic myelogenous leukaemia: Case series.

Chronic myelogenous leukaemia (CML) has a special phenomenon of chromosome translocation, which is called Philadelphia chromosome translocation. However, the detailed connection of this structure is troublesome and expensive to be identified. Low-coverage whole genome sequencing (LCWGS) could not only detect the previously unknown chromosomal translocation, but also provide the breakpoint candidate small region (with an accuracy of ±200 bases). Importantly, the sequencing cost of LCWGS is about US$300. Then, with the Sanger DNA sequencing, the precise breakpoint can be determined at a single base level. In our project, with LCWGS, BCR and ABL1 are successfully identified to be disrupted in three CML patients (at chr22:23,632,356 and chr9:133,590,450; chr22:23,633,748 and chr9:133,635,781; chr22: 23,631,831 and chr9:133,598,513, respectively). Due to the reconnection after chromosome breakage, classical fusion gene (BCR::ABL1) was found in bone marrow and peripheral blood. The precise breakpoints were helpful to investigate the pathogenic mechanism of CML and could better guide the classification of CML subtypes. This LCWGS method is universal and can be used to detect all diseases related to chromosome variation, such as solid tumours, liquid tumours and birth defects.

Eusociality Shapes Convergent Patterns of Molecular Evolution across Mitochondrial Genomes of Snapping Shrimps.

Eusociality is a highly conspicuous and ecologically impactful behavioral syndrome that has evolved independently across multiple animal lineages. So far, comparative genomic analyses of advanced sociality have been mostly limited to insects. Here, we study the only clade of animals known to exhibit eusociality in the marine realm-lineages of socially diverse snapping shrimps in the genus Synalpheus. To investigate the molecular impact of sociality, we assembled the mitochondrial genomes of eight Synalpheus species that represent three independent origins of eusociality and analyzed patterns of molecular evolution in protein-coding genes. Synonymous substitution rates are lower and potential signals of relaxed purifying selection are higher in eusocial relative to noneusocial taxa. Our results suggest that mitochondrial genome evolution was shaped by eusociality-linked traits-extended generation times and reduced effective population sizes that are hallmarks of advanced animal societies. This is the first direct evidence of eusociality impacting genome evolution in marine taxa. Our results also strongly support the idea that eusociality can shape genome evolution through profound changes in life history and demography.

Accurate genome-wide genotyping from archival tissue to explore the contribution of common genetic variants to pre-cancer outcomes.

PURPOSE: 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. EXPERIMENTAL DESIGN: 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). RESULTS: 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. CONCLUSIONS: 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.

Validation of low-coverage whole-genome sequencing for mitochondrial DNA variants suggests mitochondrial DNA as a genetic cause of preterm birth.

Preterm birth (PTB), or birth that occurs earlier than 37 weeks of gestational age, is a major contributor to infant mortality and neonatal hospitalization. Mutations in the mitochondrial genome (mtDNA) have been linked to various rare mitochondrial disorders and may be a contributing factor in PTB given that maternal genetic factors have been strongly linked to PTB. However, to date, no study has found a conclusive connection between a particular mtDNA variant and PTB. Given the high mtDNA copy number per cell, an automated pipeline was developed for detecting mtDNA variants using low-coverage whole-genome sequencing (lcWGS) data. The pipeline was first validated against samples of known heteroplasmy, and then applied to 929 samples from a PTB cohort from diverse ethnic backgrounds with an average gestational age of 27.18 weeks (range: 21-30). Our new pipeline successfully identified haplogroups and a large number of mtDNA variants in this large PTB cohort, including 8 samples carrying known pathogenic variants and 47 samples carrying rare mtDNA variants. These results confirm that lcWGS can be utilized to reliably identify mtDNA variants. These mtDNA variants may make a contribution toward preterm birth in a small proportion of live births.

A bioinformatic platform to integrate target capture and whole genome sequences of various read depths for phylogenomics.

The increasing availability of short-read whole genome sequencing (WGS) provides unprecedented opportunities to study ecological and evolutionary processes. Although loci of interest can be extracted from WGS data and combined with target sequence data, this requires suitable bioinformatic workflows. Here, we test different assembly and locus extraction strategies and implement them into secapr, a pipeline that processes short-read data into multilocus alignments for phylogenetics and molecular ecology analyses. We integrate the processing of data from low-coverage WGS (<30×) and target sequence capture into a flexible framework, while optimizing de novo contig assembly and loci extraction. Specifically, we test different assembly strategies by contrasting their ability to recover loci from targeted butterfly protein-coding genes, using four data sets: a WGS data set across different average coverages (10×, 5× and 2×) and a data set for which these loci were enriched prior to sequencing via target sequence capture. Using the resulting de novo contigs, we account for potential errors within contigs and infer phylogenetic trees to evaluate the ability of each assembly strategy to recover species relationships. We demonstrate that choosing multiple sizes of kmer simultaneously for assembly results in the highest yield of extracted loci from de novo assembled contigs, while data sets derived from sequencing read depths as low as 5× recovers the expected species relationships in phylogenetic trees. By making the tested assembly approaches available in the secapr pipeline, we hope to inspire future studies to incorporate complementary data and make an informed choice on the optimal assembly strategy.

New euchromatic variant dup(11)(p15.3p15.1) transmitted through two generations defined by low coverage whole genome sequencing.

We report on a 14-year old boy, his father, and his paternal uncle, all three carriers of a duplication of chromosomal region 11p15.3-p15.1. The aberration was transmitted by the grandmother, who is carrier of a balanced insertion 46,XX,ins(14;11)(q32.1;p15.3p15.1). In order to determine the precise molecular basis of this structural variant, we performed low-coverage whole genome sequencing on the boy's father. This approach allowed precise determination of the genomic breakpoints and revealed a duplication of 6.9 Mb, centromeric to the Beckwith-Wiedemann/Silver-Russell syndrome critical region in 11p15.5, that inserted in inverse orientation into 14q32.12 (according to HGVS nomenclature: NC_000014.8:g.92871000_92871001ins[NC_000011.9:g.12250642_19165928inv;T]). To our knowledge, this is the first report of a duplication of 11p15.3-p15.1 involving more than 40 genes and transmitted through two generations without apparent clinical effects.

Non-invasive prediction of fetal growth restriction by whole-genome promoter profiling of maternal plasma DNA: a nested case-control study.

OBJECTIVE: To predict fetal growth restriction (FGR) by whole-genome promoter profiling of maternal plasma. DESIGN: Nested case-control study. SETTING: Hospital-based. POPULATION OR SAMPLE: 810 pregnancies: 162 FGR cases and 648 controls. METHODS: We identified gene promoters with a nucleosome footprint that differed between FGR cases and controls based on maternal plasma cell-free DNA (cfDNA) nucleosome profiling. Optimal classifiers were developed using support vector machine (SVM) and logistic regression (LR) models. MAIN OUTCOME MEASURES: Genes with differential coverages in promoter regions through the low-coverage whole-genome sequencing data analysis among FGR cases and controls. Receiver operating characteristic (ROC) analysis (area under the curve [AUC], accuracy, sensitivity and specificity) was used to evaluate the performance of classifiers. RESULTS: Through the low-coverage whole-genome sequencing data analysis of FGR cases and controls, genes with significantly differential DNA coverage at promoter regions (-1000 to +1000 bp of transcription start sites) were identified. The non-invasive 'FGR classifier 1' (CFGR 1) had the highest classification performance (AUC, 0.803; 95% CI 0.767-0.839; accuracy, 83.2%) was developed based on 14 genes with differential promoter coverage using a support vector machine. CONCLUSIONS: A promising FGR prediction method was successfully developed for assessing the risk of FGR at an early gestational age based on maternal plasma cfDNA nucleosome profiling. TWEETABLE ABSTRACT: A promising FGR prediction method was successfully developed, based on maternal plasma cfDNA nucleosome profiling.

TRPV6 variants confer susceptibility to chronic pancreatitis in the Chinese population.

Chronic pancreatitis (CP) is a progressive fibroinflammatory syndrome of the pancreatic tissue caused by genetic and environmental factors. Previously reported susceptibility genes in CP explain less than half of the apparent heritability. To uncover novel pathogenic mechanisms, we initially performed low-coverage whole-genome sequencing on 464 Chinese CP patients and 504 controls. The transient receptor potential cation channel, Subfamily V, Member 6 (TRPV6) gene was found to be significantly associated with CP after a burden test of aggregated rare nonsynonymous variants with a combined annotation dependent depletion score > 20 (p = .020). In the replication stage, we analyzed the entire coding sequence and exon/intron boundaries of the TPRV6 gene by Sanger sequencing in another 205 patients with CP and 105 controls. Integration of the findings from the two stages resulted in the identification of 25 TRPV6 variants: 1 rare nonsense variant, 20 rare missense variants, and 4 common missense variants. Loss-of-function variants, as determined by intracellular Ca2+ concentration in transfected HEK293T cells, were significantly overrepresented in patients as compared to controls (9/669 [1.35%] vs. 1/609 [0.16%]; odds ratio = 8.29; p = .022). This study provides evidence suggesting that TRPV6 is a novel susceptibility gene for CP.

Sequence variant in the CDC42BPB gene is potentially associated with Mullerian duct anomalies.

AIM: Mullerian duct anomalies (MDA) are common female genital tract malformations. Genetic and environmental factors are important causes of MDA in women. Although many genes and mutations have been found to be associated with the pathogenesis of MDA, in most cases, the genetic pathogenic factors of MDA are still unknown. METHODS: We first analyzed the three sisters using low coverage whole-genome sequencing. Then whole-exome sequencing was carried out in each patient. The identified sequence variant was confirmed by Sanger sequencing. In silico pathogenicity analysis and conservative analysis of the mutation site were also performed. Protein structural modeling was used to analyze the effect of the mutated amino acid. RESULTS: We first analyzed the three sisters with septate uterus using low coverage whole-genome sequencing, but no possible pathogenic copy number variation was found. Then whole-exome sequencing was performed on the three sisters, and a rare homozygous variant, CDC42BPB:c.2012G>A:p.R671Q, was identified. All three patients were found with this variant. Sanger sequencing validated that this variant was segregated within the family. In silico pathogenicity analysis and conservative analysis of the mutation site suggested that the variant might be damaging. Protein structural analysis suggested that R671Q might weaken the electrostatic potential of this region, which may be a significant regulation target or protein interaction surface of CDC42BPB. CONCLUSION: We demonstrated that CDC42BPB genetic variant might be potentially associated with the pathogenesis of MDA.

Development and characterization of genomic microsatellite markers in the tree species, Rhodoleia championii, R. parvipetala, and R. forrestii (Hamamelidaceae).

Rhodoleia Champion ex Hooker is one of the most primitive relict genera of Hamamelidaceae, a key family exploited to understand the origin and early evolution of flowering plants. Genomic simple sequence repeats (SSRs) were developed for R. championii to perform genetic diversity, phylogeographical structure or even systematic evolution studies of the genus. Among the 278,743 contigs (105,758,242 bps) de novo assembled from the low-coverage whole genome sequencing of R. championii, a total of 9106 SSRs were detected in 8370 contigs, and SSR primer pairs were successfully designed for 6677 SSRs. Among the 110 selected primer pairs, 41 were amplified successfully in the preliminary test of SSR screening. Further amplification of these 41 primer pairs across the 122 individuals collected from six populations of the three Rhodoleia species showed that 32 and 40 SSR markers can be amplified in Vietnam and Jinping populations of R. parvipetala, 41, 33, and 41 SSR markers in Boluo, Hongkong and Xinyi populations of R. championii, 25 SSR markers in Fugong population of R. forrestii, and 20 SSR markers demonstrated to be polymorphic across the three species. Genetic analysis for these 20 polymorphic SSRs showed that Allele number (A) ranged from four to 13 and polymorphic information content (PIC) ranged from 0.479 to 0.876 across the three species. At the population level, observed heterozygosity (HO) ranged from 0.000 to 1.000, and expected heterozygosity (HE) ranged from 0.091 to 0.851. In the present study, we provided the first whole-genome sequencing database for the species R. championii, identified ample SSR loci with designed primers, and revealed that 20 of the 110 selected SSRs were polymorphic across three Rhodoleia species. These provide valuable resources for future studies on genetic study, species delimitation, phylogeography, and conservation of this genus.

A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas.

BACKGROUND: Copy Number Alternations (CNAs) is defined as somatic gain or loss of DNA regions. The profiles of CNAs may provide a fingerprint specific to a tumor type or tumor grade. Low-coverage sequencing for reporting CNAs has recently gained interest since successfully translated into clinical applications. Ovarian serous carcinomas can be classified into two largely mutually exclusive grades, low grade and high grade, based on their histologic features. The grade classification based on the genomics may provide valuable clue on how to best manage these patients in clinic. Based on the study of ovarian serous carcinomas, we explore the methodology of combining CNAs reporting from low-coverage sequencing with machine learning techniques to stratify tumor biospecimens of different grades. RESULTS: We have developed a data-driven methodology for tumor classification using the profiles of CNAs reported by low-coverage sequencing. The proposed method called Bag-of-Segments is used to summarize fixed-length CNA features predictive of tumor grades. These features are further processed by machine learning techniques to obtain classification models. High accuracy is obtained for classifying ovarian serous carcinoma into high and low grades based on leave-one-out cross-validation experiments. The models that are weakly influenced by the sequence coverage and the purity of the sample can also be built, which would be of higher relevance for clinical applications. The patterns captured by Bag-of-Segments features correlate with current clinical knowledge: low grade ovarian tumors being related to aneuploidy events associated to mitotic errors while high grade ovarian tumors are induced by DNA repair gene malfunction. CONCLUSIONS: The proposed data-driven method obtains high accuracy with various parametrizations for the ovarian serous carcinoma study, indicating that it has good generalization potential towards other CNA classification problems. This method could be applied to the more difficult task of classifying ovarian serous carcinomas with ambiguous histology or in those with low grade tumor co-existing with high grade tumor. The closer genomic relationship of these tumor samples to low or high grade may provide important clinical value.

Mitochondrial DNA variation in Aedes aegypti (Diptera: Culicidae) mosquitoes from Jeddah, Saudi Arabia.

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.

Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics.

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.

Marker Density and Models to Improve the Accuracy of Genomic Selection for Growth and Slaughter Traits in Meat Rabbits.

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.

Accurate genotype imputation from low-coverage whole-genome sequencing data of rainbow trout.

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 SNP (single nucleotide polymorphism) array genotyping. The objectives of this study were two-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 two breeding populations. Medium-coverage (12x) whole-genome sequences were obtained from a total of 410 fish representing five 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 using GLIMPSE2. A total of 90 fish from the Troutlodge November breeding population were sequenced with an average coverage of 1.3x, 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 0.5x, 0.2x and 0.1x, 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 0.5x 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.

Genomic evidence for domestication selection in three hatchery populations of Chinook salmon, Oncorhynchus tshawytscha.

Fish hatcheries are widely used to enhance fisheries and supplement declining wild populations. However, substantial evidence suggests that hatchery fish are subject to differential selection pressures compared to their wild counterparts. Domestication selection, or adaptation to the hatchery environment, poses a risk to wild populations if traits specific to success in the hatchery environment have a genetic component and there is subsequent introgression between hatchery and wild fish. Few studies have investigated domestication selection in hatcheries on a genomic level, and even fewer have done so in parallel across multiple hatchery-wild population pairs. In this study, we used low-coverage whole-genome sequencing to investigate signals of domestication selection in three separate hatchery populations of Chinook salmon, Oncorhynchus tshawytscha, after approximately seven generations of divergence from their corresponding wild progenitor populations. We sequenced 192 individuals from populations across Southeast Alaska and estimated genotype likelihoods at over six million loci. We discovered a total of 14 outlier peaks displaying high genetic differentiation (F ST) between hatchery-wild pairs, although no peaks were shared across the three comparisons. Peaks were small (53 kb on average) and often displayed elevated absolute genetic divergence (D xy) and linkage disequilibrium, suggesting some level of domestication selection has occurred. Our study provides evidence that domestication selection can lead to genetic differences between hatchery and wild populations in only a few generations. Additionally, our data suggest that population-specific adaptation to hatchery environments likely occurs through different genetic pathways, even for populations with similar standing genetic variation. These results highlight the need to collect paired genotype-phenotype data to understand how domestication may be affecting fitness and to identify potential management practices that may mitigate genetic risks despite multiple pathways of domestication.

Low-coverage whole-genome sequencing for the effective diagnosis of early endometrial cancer: A pilot study.

Background: 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. Methods: 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. Results: 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%. Conclusions: 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.