Toward better understanding of artifacts in variant calling from high-coverage samples

Faster genetic mapping of complex traits in C. elegans

Caenorhabditis elegans is a tractable model system that enables the identification of genetic determinants that underlie phenotypic variation. Over the years, new approaches have been developed to lower the cost of and expedite genetic mapping in this model system. The ce X-QTL approach uses the fog-2 ( q71 ) allele to create obligate outcrossing recombinant populations for selection and sequencing experiments. Here, we tested whether the fog-2 ( q71 ) allele is essential to the ce X-QTL approach by comparing crosses between the N2 and XZ1516 strains using either fog-2 ( q71 ) or fog-2 RNAi knockdown to facilitate outcrossing. The genome-wide allele frequencies of the bulk recombinant populations derived from these two methods were largely similar. These results demonstrate that fog-2 RNAi is a viable alternative for rapidly generating recombinant populations, allowing greater flexibility in experimental design.

Germline Pathogenic DROSHA Variants Are Linked to Pineoblastoma and Wilms Tumor Predisposition

PURPOSE

DROSHA, DGCR8, and DICER1 regulate miRNA biogenesis and are commonly mutated in cancer. Although DGCR8 and DICER1 germline pathogenic variants (GPV) cause autosomal dominant tumor predisposition, no association between DROSHA GPVs and clinical phenotypes has been reported.

EXPERIMENTAL DESIGN

After obtaining informed consent, sequencing was performed on germline and tumor samples from all patients. The occurrence of germline DROSHA GPVs was investigated in large pediatric and adult cancer datasets. The population prevalence of DROSHA GPVs was investigated in the UK Biobank and Geisinger DiscovEHR cohorts.

RESULTS

We describe nine children from eight families with heterozygous DROSHA GPVs and a diagnosis of pineoblastoma (n = 8) or Wilms tumor (n = 1). A somatic second hit in DROSHA was detected in all eight tumors analyzed. All pineoblastoma tumors analyzed were classified as miRNA processing-altered 1 subtype. We estimate the population prevalence of germline DROSHA loss-of-function variants to be 1:3,875 to 1:4,843 but find no evidence for increased adult cancer risk.

CONCLUSIONS

This is the first report of DROSHA-related tumor predisposition. As pineoblastoma and Wilms tumor are also associated with DICER1 GPVs, our results suggest that the tissues of origin for these tumors are uniquely tolerant of general miRNA loss. The miRNA processing-altered 1 pineoblastoma subtype is associated with older age of diagnosis and better outcomes than other subtypes, suggesting DROSHA GPV status may have important clinical and prognostic significance. We suggest that genetic testing for DROSHA GPVs be considered for patients with pineoblastoma, Wilms tumor, or other DICER1-/DGCR8-related conditions and propose surveillance recommendations through research studies for individuals with DROSHA GPVs.

Population History Across Timescales in an Urban Archipelago

Contemporary patterns of genetic variation reflect the cumulative history of a population. Population splitting, migration, and changes in population size leave genomic signals that enable their characterization. Existing methods aimed at reconstructing these features of demographic history are often restricted in their temporal resolution, leaving gaps about how basic evolutionary parameters change over time. To illustrate the prospects for extracting insights about dynamic population histories, we turn to a system that has undergone dramatic changes on both geological and contemporary timescales-an urbanized, near-shore archipelago. Using whole genome sequences, we employed both common and novel summaries of variation to infer the demographic history of three populations of endemic white-footed mice (Peromyscus leucopus) in Massachusetts' Boston Harbor. We find informative contrasts among the inferences drawn from these distinct patterns of diversity. While demographic models that fit the joint site frequency spectrum (jSFS) coincide with the known geological history of the Boston Harbor, patterns of linkage disequilibrium reveal collapses in population size on contemporary timescales that are not recovered by our jSFS-derived models. Historical migration between populations is also absent from best-fitting models for the jSFS, but rare variants show unusual clustering along the genome within individual mice, a novel pattern that is reproduced by simulations of recent migration. Together, our findings indicate that these urban archipelago populations have been shaped by both ancient geological processes and recent human influence. More broadly, our study demonstrates that the temporal resolution of demographic history can be extended by examining multiple facets of genomic variation.

Genetic Basis of Reproductive Isolation in Torrey Pine (Pinus torreyana Parry): Insights From Hybridization and Adaptation

Tree species are often locally adapted to their environments, but the extent to which environmental adaptation contributes to incipient speciation is unclear. One of the rarest pines in the world, Torrey pine (Pinus torreyana Parry), persists naturally across one island and one mainland population in southern California. The two populations are morphologically and genetically differentiated but experience some connectivity, making it an ideal system for assessing the evolution of reproductive isolation. Previous work has found evidence of heterosis in F1 mainland-island hybrids, suggesting genetic rescue could be beneficial in the absence of reproductive barriers. Using ddRADseq and GWAS for a common garden experiment of island, mainland, and F1 individuals, we identified candidate loci for environmentally driven reproductive isolation, their function, and their relationship to fitness proxies. By simulating neutral evolution and admixture between the two populations, we identified loci that exhibited reduced heterozygosity in the F1s, evidence of selection against admixture. SNPs with reduced F1 heterozygosity were enriched for growth and pollination functions, suggesting genetic variants that could be involved in the evolution of reproductive barriers between populations. One locus with reduced F1 heterozygosity exhibited strong associations with growth and reproductive fitness proxies in the common garden, with the mainland allele conferring increased fitness. If this locus experiences divergent selection in the two natural populations, it could promote their reproductive isolation. Finally, although hybridization largely reduced allele fixation in the F1s initially, indicating heterosis is likely due to the masking of deleterious alleles, the emergence of reproductive isolation between populations may diminish the longer-term benefits of genetic rescue in F2 or advanced-generation hybrids. As Torrey pine is a candidate for interpopulation genetic rescue, caution is warranted where longer-term gene flow between diverged populations may result in reduced fitness if barriers have evolved.

Tutorial: guidelines for quality filtering of whole-exome and whole-genome sequencing data for population-scale association analyses

Genetic sequencing technologies are powerful tools for identifying rare variants and genes associated with Mendelian and complex traits; indeed, whole-exome and whole-genome sequencing are increasingly popular methods for population-scale genetic studies. However, careful quality control steps should be taken to ensure study accuracy and reproducibility, and sequencing data require extensive quality filtering to delineate true variants from technical artifacts. Although processing standards are harmonized across pipelines to call variants from sequencing reads, there currently exists no standardized pipeline for conducting quality filtering on variant-level datasets for the purpose of population-scale association analysis. In this Tutorial, we discuss key quality control parameters, provide guidelines for conducting quality filtering of samples and variants, and compare commonly used software programs for quality control of samples, variants and genotypes from sequencing data. As sequencing data continue to gain popularity in genetic research, establishing standardized quality control practices is crucial to ensure consistent, reliable and reproducible results across studies.

K-mer-based Approaches to Bridging Pangenomics and Population Genetics

Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes can be challenging for many species, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that k-mers are a very useful but underutilized tool for bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of k-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different k-mer-based measures of genetic variation behave in population genetic simulations according to the choice of k, depth of sequencing coverage, and degree of data compression. Overall, we find that k-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity (π) up to values of about π=0.025 (R2=0.97) for neutrally evolving populations. For populations with even more variation, using shorter k-mers will maintain the scalability up to at least π=0.1. Furthermore, in our simulated populations, k-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of k-mer-based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using k-mers.

Integration of GWAS and transcriptome analysis to identify temperature-dependent genes involved in germination of rapeseed (Brassica napus L.)

Low temperature germination (LTG) is one of crucial agronomic traits for field-grown rapeseed in the Yangtze River Basin, where delayed sowing frequently exposes germination to cold stress. Because of its importance, the genetic basis underlying rapeseed germination under different temperatures has been continuously focused. By long-term field observation, we screened out two cultivars with significantly different LTG performance (JY1621 and JY1605) in field and lab conditions, which therefore were further used for the transcriptome sequencings at three key timepoints under normal and low temperatures. Comparative analysis among multiple groups of differentially expressed genes (DEGs) revealed a set of either early or late temperature response germination (ETRG or LTRG) genes, as well as cold-tolerant (CDT) and temperature-insensitive (TPI) candidate regulators at different germination stages. Furthermore, we performed a genome-wide association study (GWAS) using germination index of 273 rapeseed accessions and identified 24 significant loci associated with germination potential under normal temperatures. Through integrated analysis of transcriptome sequencing and GWAS, we identified a series of candidate genes involved in temperature-dependent germination. Based on the comprehensive analysis, we hypothesized that BnaA3.CYP77A4 and BnaA3.NAC078 could be important candidate genes for LTG due to their expression patterns and haplotype distributions. This study performed the multi-omics analysis on temperature-dependent germination and provided potential genetic loci and candidate genes required for robust germination, which could be further considered for low-temperature germination breeding of rapeseed.

Manipulation of seed coat content for increasing oil content via modulating BnaMYB52 in Brassica napus

Seed oil is synthesized in the embryo, which is surrounded by the seed coat. However, the genetic and molecular mechanisms of how seed coat development affects seed oil content (SOC) remains less studied. Through genome-wide association studies in Brassica napus accessions, we identify BnaA09.MYB52 transcription factor as a candidate gene underlying ZS11_qSCC.A09. Knocking out BnaMYB52 results in a 7.7%-8.2% reduction in seed coat content (SCC) but a 12.3%-13.4% increase in SOC, whereas overexpression of BnaA09.MYB52 leads to the opposite effects. Intriguingly, BnaA09.MYB52 has predominant expression in seed coat during seed development. BnaA09.MYB52 activates the expression of PMEI14 and BAN. Mutant analyses indicate that the SOC is increased by 4.3%-7.7%, while seed coat thickness shows a 12.9%-22.8% reduction in the ban and pmei14 mutants. Thus, our findings identify the molecular mechanism of MYB52-BAN/PMEI14 modules regulating SCC and SOC and provide a potential target for oil content improvement in B. napus.

Genomic consequences of intensive inbreeding in miniature inbred pigs

BACKGROUND

Inbreeding, a central theme in evolutionary and conservation biology, is a crucial practice in breeding to stabilize and enhance the specific traits or to establish inbred lines. It also carries the risk of inbreeding depression, reduced fitness, and increased potential for extinction. Nevertheless, inbreeding has been extensively studied in small and endangered populations but its effects in large domesticated animals are poorly understood. Here, we aim to investigate the genomic consequences of inbreeding in the Banna miniature inbred pig (BN), a breed that has been inbred for over 40 years.

RESULTS

We have sequenced 41 genomes of BN and Diannan miniature pig (DN) at high-coverage (> 31×) and combined them with published whole-genomes of swine to comprehensively investigate the genetic consequences of inbreeding. We find that BN is genetically closely related to DN, which is consistent with breeding records. All families of BN have undergone an extreme bottleneck due to intensive inbreeding, resulting in higher genomic inbreeding coefficients, reduced genetic diversity, and a lower effective population size (Ne) compare to non-inbred pigs. Furthermore, BN and DN exhibit an increased genetic load relative to Asian wild boars. Prolonged inbreeding and bottlenecks have led to some purging of deleterious mutations in BN compared to DN, and a conversion from masked load to realized load.

CONCLUSIONS

We present a comprehensive analysis to understand and assess the consequences of inbreeding in miniature inbred pigs from a perspective of population genomics. Utilizing genomic measurements proves effective in estimating the consequences of inbreeding, especially when a detailed and accurate historical record of pedigree are lacking. Our results provide valuable resources and a detailed perspective on the genomic impacts of inbreeding, potentially guiding efforts in breeding, breed improvement, and conservation.

Refined variant calling pipeline on RNA-seq data of breast cancer cell lines without matched-normal samples

OBJECTIVE

RNA-seq delivers valuable insights both to transcriptional patterns and mutational landscapes for transcribed genes. However, as tumour cell lines frequently lack their matched-normal counterpart, variant calling without the paired normal sample is still challenging. In order to exclude variants of common genetic variation without a matched-normal control, filtering strategies need to be developed to identify tumour relevant variants in cell lines.

RESULTS

Here, variants of 29 breast cancer cell lines were called on RNA-seq data via HaplotypeCaller. Low read depth sites, RNA-edit sites, and low complexity regions in coding regions were excluded. Common variants were filtered using 1000 genomes, gnomAD, and dbSNP data. Starting from hundred thousands of single nucleotide variants and small insertions and deletions, about thousand variants remained after filtering for each sample. Extracted variants were validated against the Catalogue of Somatic Mutations in Cancer (COSMIC) for 10 cell lines included in both data sets. Approximately half of the COSMIC variants were successfully called. Importantly, missing variants could mainly be attributed to sites with low read depth. Moreover, filtered variants also included all 10 cancer gene census COSMIC variants, a condensed hallmark variant set.

Whole genome sequencing in early onset advanced heart failure

The genetic contributions to early onset heart failure (HF) are incompletely understood. Genetic testing in advanced HF patients undergoing heart transplantation (HTx) may yield clinical benefits, but data is limited. We performed deep-coverage whole genome sequencing (WGS) in 102 Swedish HTx recipients. Gene lists were compiled through a systematic literature review. Variants were prioritized for pathogenicity and classified manually. We also compared polygenic HF risk scores to a population-based cohort. We found a pathogenic (LP/P) variant in 34 individuals (34%). Testing yield was highest in hypertrophic (63% LP/P carriers), dilated (40%) and arrhythmogenic right ventricular (33%) cardiomyopathy and lower in ischemic cardiomyopathy (10%). A family history was more common in LP/P variant carriers than in non-carriers but was present in less than half of carriers (44% vs 13%, P < 0.001), whereas age was similar. Polygenic risk scores were similar in HTx recipients and the population cohort. In conclusion, we observed a high prevalence of pathogenic cardiomyopathy gene variants in individuals with early-onset advanced HF, which could not accurately be ruled out by family history and age. In contrast, we did not observe higher polygenic risk scores in early onset advanced HF cases than in the general population.

Translating pharmacogenomic sequencing data into drug response predictions-How to interpret variants of unknown significance

The rapid development of sequencing technologies during the past 20 years has provided a variety of methods and tools to interrogate human genomic variations at the population level. Pharmacogenes are well known to be highly polymorphic and a plethora of pharmacogenomic variants has been identified in population sequencing data. However, so far only a small number of these variants have been functionally characterized regarding their impact on drug efficacy and toxicity and the significance of the vast majority remains unknown. It is therefore of high importance to develop tools and frameworks to accurately infer the effects of pharmacogenomic variants and, eventually, aggregate the effect of individual variations into personalized drug response predictions. To address this challenge, we here first describe the technological advances, including sequencing methods and accompanying bioinformatic processing pipelines that have enabled reliable variant identification. Subsequently, we highlight advances in computational algorithms for pharmacogenomic variant interpretation and discuss the added value of emerging strategies, such as machine learning and the integrative use of omics techniques that have the potential to further contribute to the refinement of personalized pharmacological response predictions. Lastly, we provide an overview of experimental and clinical approaches to validate in silico predictions. We conclude that the iterative feedback between computational predictions and experimental validations is likely to rapidly improve the accuracy of pharmacogenomic prediction models, which might soon allow for an incorporation of the entire pharmacogenetic profile into personalized response predictions.

Unprecedented female mutation bias in the aye-aye, a highly unusual lemur from Madagascar

Every mammal studied to date has been found to have a male mutation bias: male parents transmit more de novo mutations to offspring than female parents, contributing increasingly more mutations with age. Although male-biased mutation has been studied for more than 75 years, its causes are still debated. One obstacle to understanding this pattern is its near universality-without variation in mutation bias, it is difficult to find an underlying cause. Here, we present new data on multiple pedigrees from two primate species: aye-ayes (Daubentonia madagascariensis), a member of the strepsirrhine primates, and olive baboons (Papio anubis). In stark contrast to the pattern found across mammals, we find a much larger effect of maternal age than paternal age on mutation rates in the aye-aye. In addition, older aye-aye mothers transmit substantially more mutations than older fathers. We carry out both computational and experimental validation of our results, contrasting them with results from baboons and other primates using the same methodologies. Further, we analyze a set of DNA repair and replication genes to identify candidate mutations that may be responsible for the change in mutation bias observed in aye-ayes. Our results demonstrate that mutation bias is not an immutable trait, but rather one that can evolve between closely related species. Further work on aye-ayes (and possibly other lemuriform primates) should help to explain the molecular basis for sex-biased mutation.

Population history across timescales in an urban archipelago

Contemporary patterns of genetic variation reflect the cumulative history of a population. Population splitting, migration, and changes in population size leave genomic signals that enable their characterization. Existing methods aimed at reconstructing these features of demographic history are often restricted in their temporal resolution, leaving gaps about how basic evolutionary parameters change over time. To illustrate the prospects for extracting insights about dynamic population histories, we turn to a system that has undergone dramatic changes on both geological and contemporary timescales - an urbanized, near-shore archipelago. Using whole genome sequences, we employed both common and novel summaries of variation to infer the demographic history of three populations of endemic white-footed mice (Peromyscus leucopus) in Massachusetts' Boston Harbor. We find informative contrasts among the inferences drawn from these distinct patterns of diversity. While demographic models that fit the joint site frequency spectrum (jSFS) coincide with the known geological history of the Boston Harbor, patterns of linkage disequilibrium reveal collapses in population size on contemporary timescales that are not recovered by our candidate models. Historical migration between populations is also absent from best-fitting models for the jSFS, but rare variants show unusual clustering along the genome within individual mice, a pattern that is reproduced by simulations of recent migration. Together, our findings indicate that these urban archipelago populations have been shaped by both ancient geological processes and recent human influence. More broadly, our study demonstrates that the temporal resolution of demographic history can be extended by examining multiple facets of genomic variation.

Phenotypic and Complete Reference Whole Genome Sequence Analyses of Two Paenibacillus spp. Isolates from a Gray Wolf (Canis lupus) Gastrointestinal Tract

Inflammatory bowel disease (IBD) is increasing among mammals around the world, and domestic dogs are no exception. There is no approved cure for canine IBD with limited treatment options. Novel probiotic bacteria discovery from free-ranging animals for the treatment of IBD in domestic pets can likely yield promising probiotic candidates. Consequently, the overall aim was to isolate bacteria from free-ranging animals that could potentially be utilized as novel probiotics. Two bacteria identified as unique Paenibacillus spp. strains by small ribosomal RNA (16S) gene sequencing were isolated from the gastrointestinal tract of a North American Gray Wolf (Canis lupus). The bacteria were typed as Gram-variable, and both were catalase/oxidase positive as well as sensitive to commonly used antibiotics. The bacteria digested complex carbohydrates and lipids by standard assays. The isolated bacteria also inhibited the growth of Staphylococcus aureus and Micrococcus luteus. The whole genome sequence (WGS) length of bacterial isolate ClWae17B was 6,939,193 bp, while ClWae19 was 7,032,512 bp, both similar in size to other Paenibacillus spp. The genomes of both bacteria encoded enzymes involved with the metabolism of complex starches and lipids, such as lyases and pectinases, along with encoding antimicrobials such as lanthipeptides, lasso peptides, and cyclic-lactone-autoinducers. No pernicious virulence genes were identified in the WGS of either bacterial isolate. Phylogenetically, the most closely related bacteria based on 16S gene sequences and WGS were P. taichungensis for ClWae17B and P. amylolyticus for ClWae19. WGS analyses and phenotypic assays supported the hypothesis that the isolates described constitute two novel candidate probiotic bacteria for potential use in dogs.

Dominant negative variants in ITPR3 impair T cell Ca2+ dynamics causing combined immunodeficiency

The importance of calcium (Ca2+) as a second messenger in T cell signaling is exemplified by genetic deficiencies of STIM1 and ORAI1, which abolish store-operated Ca2+ entry (SOCE) resulting in combined immunodeficiency (CID). We report five unrelated patients with de novo missense variants in ITPR3, encoding a subunit of the inositol 1,4,5-trisphosphate receptor (IP3R), which forms a Ca2+ channel in the endoplasmic reticulum (ER) membrane responsible for the release of ER Ca2+ required to trigger SOCE, and for Ca2+ transfer to other organelles. The patients presented with CID, abnormal T cell Ca2+ homeostasis, incompletely penetrant ectodermal dysplasia, and multisystem disease. Their predominant T cell immunodeficiency is characterized by significant T cell lymphopenia, defects in late stages of thymic T cell development, and impaired function of peripheral T cells, including inadequate NF-κB- and NFAT-mediated, proliferative, and metabolic responses to activation. Pathogenicity is not due to haploinsufficiency, rather ITPR3 protein variants interfere with IP3R channel function leading to depletion of ER Ca2+ stores and blunted SOCE in T cells.

Genotyping Error Detection and Customised Filtration for SNP Datasets

A major challenge in analysing single-nucleotide polymorphism (SNP) genotype datasets is detecting and filtering errors that bias analyses and misinterpret ecological and evolutionary processes. Here, we present a comprehensive method to estimate and minimise genotyping error rates (deviations from the 'true' genotype) in any SNP datasets using triplicates (three repeats of the same sample) in a four-step filtration pipeline. The approach involves: (1) SNP filtering by missing data; (2) SNP filtering by error rates; (3) sample filtering by missing data and (4) detection of recaptured individuals by using estimated SNP error rates. The modular pipeline is provided in an R script that allows customised adjustments. We demonstrate the applicability of the method using non-invasive sampling from the Asiatic wild ass (Equus hemionus) population in Israel. We genotyped 756 samples using 625 SNPs, of which 255 were triplicates of 85 samples. The average SNP error rate, calculated based on the number of mismatching genotypes across triplicates before filtration, was 0.0034 and was reduced to 0.00174 following filtration. Evaluating genetic distance (GD) and relatedness (r) between triplicates before and after filtration (expected to be at the minimum and maximum respectively) showed a significant reduction in the average GD, from 58.1 to 25.3 (p = 0.0002) and a significant increase in relatedness, from r = 0.98 to r = 0.991 (p = 0.00587). We demonstrate how error rate estimation enhances recapture detection and improves genotype quality.

Asynchronous Transitions from Hepatoblastoma to Carcinoma in High-Risk Pediatric Tumors

Most malignant hepatocellular tumors in children are classified as either hepatoblastoma (HB) or hepatocellular carcinoma (HCC), but some tumors demonstrate features of both HB and HCC1-3. These tumors have been recognized under a provisional diagnostic category by the World Health Organization and are distinguished from HB and HCC by a combination of histological, immunohistochemical, and molecular features4-6. Their outcomes and cellular composition remain an open question7-9. The heterogeneous histological and molecular profiles of hepatoblastomas with carcinoma features (HBCs)4 may result from cells with combined HB and HCC characteristics (HBC cells) or from mixtures of cells displaying either HB or HCC signatures. We used multiomics profiling to show that HBCs are mixtures of HB, HBC, and HCC cell types. HBC cells are more chemoresistant than HB cells, and their chemoresistance-a driver of poor outcomes10-12-is determined by their cell types, genetic alterations, and embryonic differentiation stages. We showed that the prognosis of HBCs is significantly worse than that of HBs. We also showed that HBC cells are derived from HB cells at early hepatoblast differentiation stages, that aberrant activation of WNT-signaling initiates HBC transformation, and that WNT inhibition promotes differentiation and increases sensitivity to chemotherapy. Furthermore, our analysis revealed that each HBC is the product of multiple HB-to-HBC and HBC-to-HCC transitions. Thus, multiomics profiling of HBCs provided key insights into their biology and resolved major questions regarding the etiology of these childhood liver tumors.

A chromosome-scale reference genome of the Banna miniature inbred pig

The Banna miniature inbred pig (BN) is an intensively inbred line for biomedical research and xenotransplantation due to its low individual variation and stable genetic background. Although it is originated from the Diannan miniature pig (DN), substantial genetic changes have actually occurred. However, the lack of a BN reference genome has limited studies on the complete genomic architecture and utilization as a biomedical model. Here, we present a high-quality genome for BN using PacBio HiFi and Hi-C sequencing technologies, with a total length of 2.66 Gb, a scaffold N50 of 143.60 Mb, and 97.59% of the sequences anchored to chromosomes. Its BUSCO score is 96.30%, higher than porcine reference assembly and DN. The genome contains 48.49% of repeats, 19,756 protein-coding genes, and 7,207 non-coding RNAs according to our annotation. The OMArk score shows a proteome completeness and consistency of 99.58% and 93.62%, respectively. These findings indicate that the chromosome-scale genome of BN provides a valuable resource for studying the genetic basis of inbreeding, facilitating further research and clinical applications.

Simulating Genetic Mixing in Strongly Structured Populations of the Threatened Southern Brown Bandicoot (Isoodon obesulus)

Genetic mixing aims to increase the genetic diversity of small or isolated populations, by mitigating genetic drift and inbreeding depression, either by maximally increasing genetic diversity, or minimising the prevalence of recessive, deleterious alleles. However, few studies investigate this beyond a single generation of mixing. Here, we model genetic mixing using captive, low-diversity recipient population of the threatened Southern brown bandicoot (Isoodon obesulus) over 50 generations and compare wild populations across south-eastern Australia as candidate source populations. We first assess genetic differentiation between 12 populations, including the first genomic assessment of three mainland Australian and three Tasmanian populations. We assess genetic diversity in the 12 populations using an individualised autosomal heterozygosity pipeline, using these results to identify a candidate recipient population for genetic mixing simulations. We found that populations fell into four major groups of genetic similarity: Adelaide Hills, western Victoria, eastern Victoria, and Tasmania, but populations within these groups were also distinct, and additional substructure was observed in some populations. Our autosomal heterozygosity pipeline indicated significant variability in mean heterozygosity between populations, identifying one extremely genetically degraded population on Inner Sister Island, Tasmania. Genetic mixing simulations of a low heterozygosity captive population in Victoria suggested the greatest increase in heterozygosity would be reached by using highly differentiated populations as mixing sources. However, when removing populations that may represent taxonomically discrete lineages, neither metrics of differentiation nor heterozygosity was strongly correlated with modelled heterozygosity increase, indicating the value of simulation-based approaches when selecting source populations for population mixing.

Orthopteran Neo-Sex Chromosomes Reveal Dynamics of Recombination Suppression and Evolution of Supergenes

The early evolution of sex chromosomes has remained obscure for more than a century. The Vandiemenella viatica species group of morabine grasshoppers is highly suited for studying the early stages of sex chromosome divergence and degeneration of the Y chromosome. This stems from the fact that neo-XY sex chromosomes have independently evolved multiple times by X-autosome fusions with different autosomes. Here, we generated new chromosome-level assemblies for two chromosomal races representing karyotypes with and without neo-sex chromosomes (P24XY and P24X0), and sequence data of a third chromosomal race with a different neo-XY chromosome system (P25XY). Interestingly, these two neo-XY chromosomal races are formed by different X-autosome fusions (involving chr1 and chrB, respectively), and we found that both neo-Y chromosomes have partly ceased to recombine with their neo-X counterpart. We show that the neo-XY chromosomes have diverged through accumulation of SNPs and structural mutations, and that many neo-Y-linked genes have degenerated since recombination ceased. However, the non-recombining regions of neo-Y chromosomes host non-degenerated genes crucial for sex determination, such as sex-lethal and transformer, alongside genes associated with spermatogenesis, fertility, and reproduction, illustrating their integrative role as a masculinizing supergene. Contrary to expectations, the neo-Y chromosomes showed (slightly) lower density of transposable elements (TEs) compared to other genomic regions. The study reveals the unique dynamics of young sex chromosomes, with evolution of recombination suppression and pronounced decay of (some) neo-sex chromosome genes, and provides a compelling case illustrating how chromosomal fusions and post-fusion mutational processes contribute to the evolution of supergenes.

scMSI: Accurately inferring the sub-clonal Micro-Satellite status by an integrated deconvolution model on length spectrum

Microsatellite instability (MSI) is an important genomic biomarker for cancer diagnosis and treatment, and sequencing-based approaches are often applied to identify MSI because of its fastness and efficiency. These approaches, however, may fail to identify MSI on one or more sub-clones for certain cancers with a high degree of heterogeneity, leading to erroneous diagnoses and unsuitable treatments. Besides, the computational cost of identifying sub-clonal MSI can be exponentially increased when multiple sub-clones with different length distributions share MSI status. Herein, this paper proposes "scMSI", an accurate and efficient estimation of sub-clonal MSI to identify the microsatellite status. scMSI is an integrative Bayesian method to deconvolute the mixed-length distribution of sub-clones by a novel alternating iterative optimization procedure based on a subtle generative model. During the process of deconvolution, the optimized division of each sub-clone is attained by a heuristic algorithm, aligning with clone proportions that adhere optimally to the sample's clonal structure. To evaluate the performance, 16 patients diagnosed with endometrial cancer, exhibiting positive responses to the treatment despite having negative MSI status based on sequencing-based approaches, were considered. Excitingly, scMSI reported MSI on sub-clones successfully, and the findings matched the conclusions on immunohistochemistry. In addition, testing results on a series of experiments with simulation datasets concerning a variety of impact factors demonstrated the effectiveness and superiority of scMSI in detecting MSI on sub-clones over existing approaches. scMSI provides a new way of detecting MSI for cancers with a high degree of heterogeneity.

Genetic structure, selective characterization and specific molecular identity cards of high-yielding Houdan chickens based on genome-wide SNP

The high-yielding Houdan chicken (GGF) is characterized by high egg production and disease resistance. This study conducted whole genome resequencing of the GGF population and compared it to data from other breeds. Genetic diversity analysis revealed higher observed heterozygosity (Ho), Polymorphism information content (PIC), number of runs of homozygosity (ROH), and inbreeding coefficient (FROH) in GGF. Linkage disequilibrium (LD) decay was slowest in GGF, indicating intensive inbreeding and strong selection. These findings suggest a need for appropriate strategies to enhance genetic diversity conservation in this breed. Population structure analysis demonstrated that GGF was genetically distinct from both the red jungle fowl (RJF) and Chinese indigenous chicken (CIC) populations, highlighting GGF as a unique genetic resource warranting intensive protection and utilization. Selective sweep analysis identified genes under selection in GGF, primarily enriched in signaling pathways related to oocyte meiosis and progesterone-mediated oocyte maturation. Key candidate genes included: CCNE1, SKP1, CDC20, CDK2, ADCY8, RPS6KA6, PPP3CB, PDE3B, HSP90AB1, and AKT3. These findings provide a theoretical foundation for their potential application in poultry breeding. Additionally, this study combined bioinformatics analysis with PCR amplification and Sanger sequencing to identify 4 SNPs that can serve as a molecular identity card (ID) for GGF: SNP1 (Chr2: 136130976), SNP3 (Chr4:11705164), SNP4 (Chr4: 63255588), and SNP5 (Chr24: 3271008). This study provides a scientific basis for effective management and conservation of GGF genetic resources, and establishes a simple, economical, and accurate set of molecular IDs to combat the proliferation of inferior breeds and protect genetic resources.

Virus-modified paraspeckle-like condensates are hubs for viral RNA processing and their formation drives genomic instability

The nucleus is a highly organised yet dynamic environment containing distinct membraneless nuclear bodies. This spatial separation enables a subset of components to be concentrated within biomolecular condensates, allowing efficient and discrete processes to occur which regulate cellular function. One such nuclear body, paraspeckles, are comprised of multiple paraspeckle proteins (PSPs) built around the architectural RNA, NEAT1_2. Paraspeckle function is yet to be fully elucidated but has been implicated in a variety of developmental and disease scenarios. We demonstrate that Kaposi's sarcoma-associated herpesvirus (KSHV) drives formation of structurally distinct paraspeckles with a dramatically increased size and altered protein composition that are required for productive lytic replication. We highlight these virus-modified paraspeckles form adjacent to virus replication centres, potentially functioning as RNA processing hubs for viral transcripts during infection. Notably, we reveal that PSP sequestration into virus-modified paraspeckles result in increased genome instability during both KSHV and Epstein Barr virus (EBV) infection, implicating their formation in virus-mediated tumourigenesis.

Comprehensive analysis of Mycobacterium tuberculosis genomes reveals genetic variations in bacterial virulence

Tuberculosis, a disease caused by Mycobacterium tuberculosis (Mtb), is a significant health problem worldwide. Here, we developed a method to detect large insertions and deletions (indels), which have been generally understudied. Leveraging this framework, we performed a comprehensive analysis of single nucleotide variants and small and large indels across 1,960 Mtb clinical isolates. Comparing the distribution of variants demonstrated that gene disruptive variants are underrepresented in genes essential for bacterial survival. An evolutionary analysis revealed that Mtb genomes are enriched in partially deleterious mutations. Genome-wide association studies identified small and large deletions in eccB2 significantly associated with patient prognosis. Additionally, we unveil significant associations with antibiotic resistance in 23 non-canonical genes. Among these, large indels are primarily found in genetic regions of Rv1216c, Rv1217c, fadD11, and ctpD. This study provides a comprehensive catalog of genetic variations and highlights their potential impact for the future treatment and risk prediction of tuberculosis.

Best practices for germline variant and DNA methylation analysis of second- and third-generation sequencing data

This comprehensive review provides insights and suggested strategies for the analysis of germline variants using second- and third-generation sequencing technologies (SGS and TGS). It addresses the critical stages of data processing, starting from alignment and preprocessing to quality control, variant calling, and the removal of artifacts. The document emphasized the importance of meticulous data handling, highlighting advanced methodologies for annotating variants and identifying structural variations and methylated DNA sites. Special attention is given to the inspection of problematic variants, a step that is crucial for ensuring the accuracy of the analysis, particularly in clinical settings where genetic diagnostics can inform patient care. Additionally, the document covers the use of various bioinformatics tools and software that enhance the precision and reliability of these analyses. It outlines best practices for the annotation of variants, including considerations for problematic genetic alterations such as those in the human leukocyte antigen region, runs of homozygosity, and mitochondrial DNA alterations. The document also explores the complexities associated with identifying structural variants and copy number variations, underscoring the challenges posed by these large-scale genomic alterations. The objective is to offer a comprehensive framework for researchers and clinicians, ensuring that genetic analyses conducted with SGS and TGS are both accurate and reproducible. By following these best practices, the document aims to increase the diagnostic accuracy for hereditary diseases, facilitating early diagnosis, prevention, and personalized treatment strategies. This review serves as a valuable resource for both novices and experts in the field, providing insights into the latest advancements and methodologies in genetic analysis. It also aims to encourage the adoption of these practices in diverse research and clinical contexts, promoting consistency and reliability across studies.

Generative haplotype prediction outperforms statistical methods for small variant detection in next-generation sequencing data

MOTIVATION

Detection of germline variants in next-generation sequencing data is an essential component of modern genomics analysis. Variant detection tools typically rely on statistical algorithms such as de Bruijn graphs or Hidden Markov models, and are often coupled with heuristic techniques and thresholds to maximize accuracy. Despite significant progress in recent years, current methods still generate thousands of false-positive detections in a typical human whole genome, creating a significant manual review burden.

RESULTS

We introduce a new approach that replaces the handcrafted statistical techniques of previous methods with a single deep generative model. Using a standard transformer-based encoder and double-decoder architecture, our model learns to construct diploid germline haplotypes in a generative fashion identical to modern large language models. We train our model on 37 whole genome sequences from Genome-in-a-Bottle samples, and demonstrate that our method learns to produce accurate haplotypes with correct phase and genotype for all classes of small variants. We compare our method, called Jenever, to FreeBayes, GATK HaplotypeCaller, Clair3, and DeepVariant, and demonstrate that our method has superior overall accuracy compared to other methods. At F1-maximizing quality thresholds, our model delivers the highest sensitivity, precision, and the fewest genotyping errors for insertion and deletion variants. For single nucleotide variants, our model demonstrates the highest sensitivity but at somewhat lower precision, and achieves the highest overall F1 score among all callers we tested.

AVAILABILITY AND IMPLEMENTATION

Jenever is implemented as a python-based command line tool. Source code is available at https://github.com/ARUP-NGS/jenever/.

Identification of heat stress-related genomic regions by genome-wide association study in Solanum tuberosum

The climate crisis impairs yield and quality of crucial crops like potatoes. We investigated the effects of heat stress on five morpho-physiological parameters in a diverse panel of 178 potato cultivars under glasshouse conditions. Overall, heat stress increased shoot elongation and green fresh weight, but reduced tuber yield, starch content and harvest index. Genomic information was obtained from 258 tetraploid and three diploid cultivars by a genotyping-by-sequencing approach using methylation-sensitive restriction enzymes. This resulted in an enrichment of sequences in gene-rich regions. Population structure analyses using genetic distances and hierarchical clustering revealed strong kinship but weak overall population structure cultivars. A genome-wide association study (GWAS) was conducted with a subset of 20 K stringently filtered SNPs to identify quantitative trait loci (QTL) linked to heat tolerance. We identified 67 QTL and established haploblock boundaries to narrow down the number of candidate genes. Additionally, GO-enrichment analyses provided insights into gene functions. Heritability and genomic prediction were conducted to assess the usability of the collected data for selecting breeding material. The detected QTL might be exploited in marker-assisted selection to develop heat-resilient potato cultivars.

Somatic mutation rates scale with time not growth rate in long-lived tropical trees

The rates of appearance of new mutations play a central role in evolution. However, mutational processes in natural environments and their relationship with growth rates are largely unknown, particular in tropical ecosystems with high biodiversity. Here, we examined the somatic mutation landscapes of two tropical trees, Shorea laevis (slow-growing) and S. leprosula (fast-growing), in central Borneo, Indonesia. Using newly constructed genomes, we identified a greater number of somatic mutations in tropical trees than in temperate trees. In both species, we observed a linear increase in the number of somatic mutations with physical distance between branches. However, we found that the rate of somatic mutation accumulation per meter of growth was 3.7-fold higher in S. laevis than in S. leprosula. This difference in the somatic mutation rate was scaled with the slower growth rate of S. laevis compared to S. leprosula, resulting in a constant somatic mutation rate per year between the two species. We also found that somatic mutations are neutral within an individual, but those mutations transmitted to the next generation are subject to purifying selection. These findings suggest that somatic mutations accumulate with absolute time and older trees have a greater contribution towards generating genetic variation.

Exploring nanopore direct sequencing performance of forensic STRs, SNPs, InDels, and DNA methylation markers in a single assay

INTRODUCTION

The field of forensic DNA analysis has undergone rapid advancements in recent decades. The integration of massively parallel sequencing (MPS) has notably expanded the forensic toolkit, moving beyond identity matching to predicting phenotypic traits and biogeographical ancestry. This shift is of particular significance in cases where conventional DNA profiling fails to identify a single suspect. Supplementing forensic analyses with estimated biological age may be valuable but involves a complex and time-consuming DNA methylation analysis. This study explores and validates the performance of a comprehensive forensic third-generation sequencing assay utilizing Oxford Nanopore Technologies (ONT) in an adaptive and direct sequencing approach. We incorporated the most widely used forensic markers, i.e., STRs, SNPs, InDels, mitochondrial DNA (mtDNA), and two methylation-based clock classifiers, thereby combining forensic genetic and epigenetic analysis in one single workflow.

METHODS AND RESULTS

In our investigation, DNA from six anonymous individuals was sequenced using the ONT standard adaptive direct sequencing approach, reaching a mean percentage of on-target reads ranging from 6.6 % to 7.7 % per sample. ONT data was compared to standard MPS data and Illumina EPIC DNA methylation profiles. Basecalling employed recommended ONT software packages. TREAT was used for ONT-based analysis of autosomal and Y-chromosome STRs, achieving 90-92 % correct calls depending on allelic read depth thresholds. InDel analyses for two lower-quality samples proved challenging due to inadequate read depth, while the remaining four samples significantly contributed to the observed percentage markers (60.9 %) and correct calls (97.8 %). SNP analysis achieved a 98 % call rate, with only two mismatches and two missed alleles. ONT-generated DNA methylation data demonstrated Pearson's correlation coefficients with EPIC data ranging from 0.67 to 0.97 for Horvath's clock. Additional age-associated markers exhibited Pearson's correlation coefficients with chronological age between 0.14 (ELOVL2) and 0.96 (FHL2) at read depths of <30 and <20, respectively. Despite excluding mtDNA from our targeted sequencing approach, adaptive proof-reading fragments covered the complete mtDNA with an average read depth of 21-72, showing 100 % concordance with reference data.

DISCUSSION

Our exploratory study using ONT adaptive sequencing for conventional forensic and age associated DNA methylation markers showed high sequencing accuracy for a significant number of markers, showcasing ONT as a promising (epi)genetic forensic method. Future studies must address three critical aspects: determining clear quantity and quality measures and detection thresholds for accuracy, optimizing input DNA quantity for forensic casework expectations, and addressing ethical considerations associated with phenotype and ancestry analysis to prevent ethnic biases.

Benchmarking reveals superiority of deep learning variant callers on bacterial nanopore sequence data

Variant calling is fundamental in bacterial genomics, underpinning the identification of disease transmission clusters, the construction of phylogenetic trees, and antimicrobial resistance detection. This study presents a comprehensive benchmarking of variant calling accuracy in bacterial genomes using Oxford Nanopore Technologies (ONT) sequencing data. We evaluated three ONT basecalling models and both simplex (single-strand) and duplex (dual-strand) read types across 14 diverse bacterial species. Our findings reveal that deep learning-based variant callers, particularly Clair3 and DeepVariant, significantly outperform traditional methods and even exceed the accuracy of Illumina sequencing, especially when applied to ONT's super-high accuracy model. ONT's superior performance is attributed to its ability to overcome Illumina's errors, which often arise from difficulties in aligning reads in repetitive and variant-dense genomic regions. Moreover, the use of high-performing variant callers with ONT's super-high accuracy data mitigates ONT's traditional errors in homopolymers. We also investigated the impact of read depth on variant calling, demonstrating that 10× depth of ONT super-accuracy data can achieve precision and recall comparable to, or better than, full-depth Illumina sequencing. These results underscore the potential of ONT sequencing, combined with advanced variant calling algorithms, to replace traditional short-read sequencing methods in bacterial genomics, particularly in resource-limited settings.

Exome sequencing of 20,979 individuals with epilepsy reveals shared and distinct ultra-rare genetic risk across disorder subtypes

Identifying genetic risk factors for highly heterogeneous disorders such as epilepsy remains challenging. Here we present, to our knowledge, the largest whole-exome sequencing study of epilepsy to date, with more than 54,000 human exomes, comprising 20,979 deeply phenotyped patients from multiple genetic ancestry groups with diverse epilepsy subtypes and 33,444 controls, to investigate rare variants that confer disease risk. These analyses implicate seven individual genes, three gene sets and four copy number variants at exome-wide significance. Genes encoding ion channels show strong association with multiple epilepsy subtypes, including epileptic encephalopathies and generalized and focal epilepsies, whereas most other gene discoveries are subtype specific, highlighting distinct genetic contributions to different epilepsies. Combining results from rare single-nucleotide/short insertion and deletion variants, copy number variants and common variants, we offer an expanded view of the genetic architecture of epilepsy, with growing evidence of convergence among different genetic risk loci on the same genes. Top candidate genes are enriched for roles in synaptic transmission and neuronal excitability, particularly postnatally and in the neocortex. We also identify shared rare variant risk between epilepsy and other neurodevelopmental disorders. Our data can be accessed via an interactive browser, hopefully facilitating diagnostic efforts and accelerating the development of follow-up studies.

Heterogeneous patterns of heterozygosity loss in isolated populations of the threatened eastern barred bandicoot (Perameles gunnii)

Identifying and analysing isolated populations is critical for conservation. Isolation can make populations vulnerable to local extinction due to increased genetic drift and inbreeding, both of which should leave imprints of decreased genome-wide heterozygosity. While decreases in heterozygosity among populations are frequently investigated, fewer studies have analysed how heterozygosity varies among individuals, including whether heterozygosity varies geographically along lines of discrete population structure or with continuous patterns analogous to isolation by distance. Here we explore geographical patterns of differentiation and individual heterozygosity in the threatened eastern barred bandicoot (Perameles gunnii) in Tasmania, Australia, using genomic data from 85 samples collected between 2008 and 2011. Our analyses identified two isolated demes undergoing significant genetic drift, and several areas of fine-scale differentiation across Tasmania. We observed discrete genetic structures across geographical barriers and continuous patterns of isolation by distance, with little evidence of recent or historical migration. Using a recently developed analytical pipeline for estimating autosomal heterozygosity, we found individual heterozygosities varied within demes by up to a factor of two, and demes with low-heterozygosity individuals also still contained those with high heterozygosity. Spatial interpolation of heterozygosity scores clarified these patterns and identified the isolated Tasman Peninsula as a location where low-heterozygosity individuals were more common than elsewhere. Our results provide novel insights into the relationship between isolation-driven genetic structure and local heterozygosity patterns. These may help improve translocation efforts, by identifying populations in need of assistance, and by providing an individualised metric for identifying source animals for translocation.

Comprehensive Catalog of Variants Potentially Associated with Hidradenitis Suppurativa, Including Newly Identified Variants from a Cohort of 100 Patients

Hidradenitis suppurativa (HS) is a chronic skin disease characterized by painful, recurrent abscesses, nodules, and scarring, primarily in skin folds. The exact causes of HS are multifactorial, involving genetic, hormonal, and environmental factors. It is associated with systemic diseases such as metabolic syndrome and inflammatory bowel disease. Genetic studies have identified mutations in the γ-secretase complex that affect Notch signaling pathways critical for skin cell regulation. Despite its high heritability, most reported HS cases do not follow a simple genetic pattern. In this article, we performed whole-exome sequencing (WES) on a cohort of 100 individuals with HS, and we provide a comprehensive review of the variants known to be described or associated with HS. 91 variants were associated with the γ-secretase complex, and 78 variants were associated with other genes involved in the Notch pathway, keratinization, or immune response. Through this new genetic analysis, we have added ten new variants to the existing catalogs. All variants are available in a .vcf file and are provided as a resource for future studies.

CDK4 is co-amplified with either TP53 promoter gene fusions or MDM2 through distinct mechanisms in osteosarcoma

Amplification of the MDM2 and CDK4 genes on chromosome 12 is commonly associated with low-grade osteosarcomas. In this study, we conducted high-resolution genomic and transcriptomic analyses on 33 samples from 25 osteosarcomas, encompassing both high- and low-grade cases with MDM2 and/or CDK4 amplification. We discerned four major subgroups, ranging from nearly intact genomes to heavily rearranged ones, each harbouring CDK4 and MDM2 amplification or CDK4 amplification with TP53 structural alterations. While amplicons involving MDM2 exhibited signs of an initial chromothripsis event, no evidence of chromothripsis was found in TP53-rearranged cases. Instead, the initial disruption of the TP53 locus led to co-amplification of the CDK4 locus. Additionally, we observed recurring promoter swapping events involving the regulatory regions of the FRS2, PLEKHA5, and TP53 genes. These events resulted in ectopic expression of partner genes, with the ELF1 gene being upregulated by the FRS2 and TP53 promoter regions in two distinct cases.

Exome sequencing of 20,979 individuals with epilepsy reveals shared and distinct ultra-rare genetic risk across disorder subtypes

Identifying genetic risk factors for highly heterogeneous disorders like epilepsy remains challenging. Here, we present the largest whole-exome sequencing study of epilepsy to date, with >54,000 human exomes, comprising 20,979 deeply phenotyped patients from multiple genetic ancestry groups with diverse epilepsy subtypes and 33,444 controls, to investigate rare variants that confer disease risk. These analyses implicate seven individual genes, three gene sets, and four copy number variants at exome-wide significance. Genes encoding ion channels show strong association with multiple epilepsy subtypes, including epileptic encephalopathies, generalized and focal epilepsies, while most other gene discoveries are subtype-specific, highlighting distinct genetic contributions to different epilepsies. Combining results from rare single nucleotide/short indel-, copy number-, and common variants, we offer an expanded view of the genetic architecture of epilepsy, with growing evidence of convergence among different genetic risk loci on the same genes. Top candidate genes are enriched for roles in synaptic transmission and neuronal excitability, particularly postnatally and in the neocortex. We also identify shared rare variant risk between epilepsy and other neurodevelopmental disorders. Our data can be accessed via an interactive browser, hopefully facilitating diagnostic efforts and accelerating the development of follow-up studies.

Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries

Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared with existing single-cell methodologies, raising questions regarding the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.

Chromosome-level genome assembly of the glass catfish ( Kryptopterus vitreolus) reveals molecular clues to its transparent phenotype

Glass catfish ( Kryptopterus vitreolus) are notable in the aquarium trade for their highly transparent body pattern. This transparency is due to the loss of most reflective iridophores and light-absorbing melanophores in the main body, although certain black and silver pigments remain in the face and head. To date, however, the molecular mechanisms underlying this transparent phenotype remain largely unknown. To explore the genetic basis of this transparency, we constructed a chromosome-level haplotypic genome assembly for the glass catfish, encompassing 32 chromosomes and 23 344 protein-coding genes, using PacBio and Hi-C sequencing technologies and standard assembly and annotation pipelines. Analysis revealed a premature stop codon in the putative albinism-related tyrp1b gene, encoding tyrosinase-related protein 1, rendering it a nonfunctional pseudogene. Notably, a synteny comparison with over 30 other fish species identified the loss of the endothelin-3 ( edn3b) gene in the glass catfish genome. To investigate the role of edn3b, we generated edn3b -/- mutant zebrafish, which exhibited a remarkable reduction in black pigments in body surface stripes compared to wild-type zebrafish. These findings indicate that edn3b loss contributes to the transparent phenotype of the glass catfish. Our high-quality chromosome-scale genome assembly and identification of key genes provide important molecular insights into the transparent phenotype of glass catfish. These findings not only enhance our understanding of the molecular mechanisms underlying transparency in glass catfish, but also offer a valuable genetic resource for further research on pigmentation in various animal species.

Exome wide association study for blood lipids in 1,158,017 individuals from diverse populations

Rare coding alleles play crucial roles in the molecular diagnosis of genetic diseases. However, the systemic identification of these alleles has been challenging due to their scarcity in the general population. Here, we discovered and characterized rare coding alleles contributing to genetic dyslipidemia, a principal risk for coronary artery disease, among over a million individuals combining three large contemporary genetic datasets (the Million Veteran Program, n = 634,535, UK Biobank, n = 431,178, and the All of Us Research Program, n = 92,304) totaling 1,158,017 multi-ancestral individuals. Unlike previous rare variant studies in lipids, this study included 238,243 individuals (20.6%) from non-European-like populations. Testing 2,997,401 rare coding variants from diverse backgrounds, we identified 800 exome-wide significant associations across 209 genes including 176 predicted loss of function and 624 missense variants. Among these exome-wide associations, 130 associations were driven by non-European-like populations. Associated alleles are highly enriched in functional variant classes, showed significant additive and recessive associations, exhibited similar effects across populations, and resolved pathogenicity for variants enriched in African or South-Asian populations. Furthermore, we identified 5 lipid-related genes associated with coronary artery disease (RORC, CFAP65, GTF2E2, PLCB3, and ZNF117). Among them, RORC is a potentially novel therapeutic target through the down regulation of LDLC by its silencing. This study provides resources and insights for understanding causal mechanisms, quantifying the expressivity of rare coding alleles, and identifying novel drug targets across diverse populations.

A comprehensive atlas of nuclear sequences of mitochondrial origin (NUMT) inserted into the pig genome

BACKGROUND

The integration of nuclear mitochondrial DNA (mtDNA) into the mammalian genomes is an ongoing, yet rare evolutionary process that produces nuclear sequences of mitochondrial origin (NUMT). In this study, we identified and analysed NUMT inserted into the pig (Sus scrofa) genome and in the genomes of a few other Suinae species. First, we constructed a comparative distribution map of NUMT in the Sscrofa11.1 reference genome and in 22 other assembled S. scrofa genomes (from Asian and European pig breeds and populations), as well as the assembled genomes of the Visayan warty pig (Sus cebifrons) and warthog (Phacochoerus africanus). We then analysed a total of 485 whole genome sequencing datasets, from different breeds, populations, or Sus species, to discover polymorphic NUMT (inserted/deleted in the pig genome). The insertion age was inferred based on the presence or absence of orthologous NUMT in the genomes of different species, taking into account their evolutionary divergence. Additionally, the age of the NUMT was calculated based on sequence degradation compared to the authentic mtDNA sequence. We also validated a selected set of representative NUMT via PCR amplification.

RESULTS

We have constructed an atlas of 418 NUMT regions, 70 of which were not present in any assembled genomes. We identified ancient NUMT regions (older than 55 million years ago, Mya) and NUMT that appeared at different time points along the Suinae evolutionary lineage. We identified very recent polymorphic NUMT (private to S. scrofa, with < 1 Mya), and more ancient polymorphic NUMT (3.5-10 Mya) present in various Sus species. These latest polymorphic NUMT regions, which segregate in European and Asian pig breeds and populations, are likely the results of interspecies admixture within the Sus genus.

CONCLUSIONS

This study provided a first comprehensive analysis of NUMT present in the Sus scrofa genome, comparing them to NUMT found in other species within the order Cetartiodactyla. The NUMT-based evolutionary window that we reconstructed from NUMT integration ages could be useful to better understand the micro-evolutionary events that shaped the modern pig genome and enriched the genetic diversity of this species.

Genetic Implication of Specific Glutamatergic Neurons of the Prefrontal Cortex in the Pathophysiology of Schizophrenia

Background

The prefrontal cortex (PFC) has been strongly implicated in the pathophysiology of schizophrenia. Here, we combined high-resolution single-nuclei RNA sequencing data from the human PFC with large-scale genomic data for schizophrenia to identify constituent cell populations likely to mediate genetic liability to the disorder.

Methods

Gene expression specificity values were calculated from a single-nuclei RNA sequencing dataset comprising 84 cell populations from the human PFC, spanning gestation to adulthood. Enrichment of schizophrenia common variant liability and burden of rare protein-truncating coding variants were tested in genes with high expression specificity for each cell type. We also explored schizophrenia common variant associations in relation to gene expression across the developmental trajectory of implicated neurons.

Results

Common risk variation for schizophrenia was prominently enriched in genes with high expression specificity for a population of mature layer 4 glutamatergic neurons emerging in infancy. Common variant liability to schizophrenia increased along the developmental trajectory of this neuronal population. Fine-mapped genes at schizophrenia genome-wide association study risk loci had significantly higher expression specificity than other genes in these neurons and in a population of layer 5/6 glutamatergic neurons. People with schizophrenia had a higher rate of rare protein-truncating coding variants in genes expressed by cells of the PFC than control individuals, but no cell population was significantly enriched above this background rate.

Conclusions

We identified a population of layer 4 glutamatergic PFC neurons likely to be particularly affected by common variant genetic risk for schizophrenia, which may contribute to disturbances in thalamocortical connectivity in the condition.

Fine-mapping across diverse ancestries drives the discovery of putative causal variants underlying human complex traits and diseases

Genome-wide association studies (GWAS) of human complex traits or diseases often implicate genetic loci that span hundreds or thousands of genetic variants, many of which have similar statistical significance. While statistical fine-mapping in individuals of European ancestry has made important discoveries, cross-population fine-mapping has the potential to improve power and resolution by capitalizing on the genomic diversity across ancestries. Here we present SuSiEx, an accurate and computationally efficient method for cross-population fine-mapping. SuSiEx integrates data from an arbitrary number of ancestries, explicitly models population-specific allele frequencies and linkage disequilibrium patterns, accounts for multiple causal variants in a genomic region and can be applied to GWAS summary statistics. We comprehensively assessed the performance of SuSiEx using simulations. We further showed that SuSiEx improves the fine-mapping of a range of quantitative traits available in both the UK Biobank and Taiwan Biobank, and improves the fine-mapping of schizophrenia-associated loci by integrating GWAS across East Asian and European ancestries.

Association mapping and candidate gene identification for yield traits in European hazelnut (Corylus avellana L.)

European hazelnut (Corylus avellana L.) is an important nut crop due to its nutritional benefits, culinary uses, and economic value. Türkiye is the leading producer of hazelnut, followed by Italy and the United States. Quantitative trait locus studies offer promising opportunities for breeders and geneticists to identify genomic regions controlling desirable traits in hazelnut. A genome-wide association analysis was conducted with 5,567 single nucleotide polymorphisms on a Turkish core set of 86 hazelnut accessions, revealing 189 quantitative trait nucleotides (QTNs) associated with 22 of 31 traits (p < 2.9E-07). These QTNs were associated with plant and leaf, phenological, reproductive, nut, and kernel traits. Based on the close physical distance of QTNs associated with the same trait, we identified 23 quantitative trait loci. Furthermore, we identified 23 loci of multiple QTs comprising chromosome locations associated with more than one trait at the same position or in close proximity. A total of 159 candidate genes were identified for 189 QTNs, with 122 of them containing significant conserved protein domains. Some candidate matches to known proteins/domains were highly significant, suggesting that they have similar functions as their matches. This comprehensive study provides valuable insights for the development of breeding strategies and the improvement of hazelnut and enhances the understanding of the genetic architecture of complex traits by proposing candidate genes and potential functions.

Genomic diversity and demographic history of the endangered Sichuan hill-partridge (Arborophila rufipectus)

Species conservation can be improved by knowledge of genetic diversity and demographic history. The Sichuan hill-partridge (Arborophila rufipectus, SP) is an endangered species endemic to the mountains in southwestern China. However, little is known about this species' genomic variation and demographic history. Here, we present a comprehensive whole-genome analysis of six SP individuals from the Laojunshan National Nature Reserve in Sichuan Province, China. We observe a relatively high genetic diversity and low level of recent inbreeding in the studied SP individuals. This suggests that the current population carries genetic variability that may benefit the long-term survival of this species, and that the present population may be larger than currently recognized. Analyses of demographic history showed that fluctuations in the effective population size of SP are inconsistent with changes of the historical climate. Strikingly, evidence from demographic modeling suggests SPs population decreased dramatically 15,100 years ago after the Last Glacial Maximum, possibly due to refugial isolation and later human interference. These results provide the first detailed and comprehensive genomic insights into genetic diversity, genomic inbreeding levels, and demographic history of the Sichuan hill-partridge, which are crucial for the conservation and management of this endangered species.

Limitations in next-generation sequencing-based genotyping of breast cancer polygenic risk score loci

Considering polygenic risk scores (PRSs) in individual risk prediction is increasingly implemented in genetic testing for hereditary breast cancer (BC) based on next-generation sequencing (NGS). To calculate individual BC risks, the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) with the inclusion of the BCAC 313 or the BRIDGES 306 BC PRS is commonly used. The PRS calculation depends on accurately reproducing the variant allele frequencies (AFs) and, consequently, the distribution of PRS values anticipated by the algorithm. Here, the 324 loci of the BCAC 313 and the BRIDGES 306 BC PRS were examined in population-specific database gnomAD and in real-world data sets of five centers of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC), to determine whether these expected AFs can be reproduced by NGS-based genotyping. Four PRS loci were non-existent in gnomAD v3.1.2 non-Finnish Europeans, further 24 loci showed noticeably deviating AFs. In real-world data, between 11 and 23 loci were reported with noticeably deviating AFs, and were shown to have effects on final risk prediction. Deviations depended on the sequencing approach, variant caller and calling mode (forced versus unforced) employed. Therefore, this study demonstrates the necessity to apply quality assurance not only in terms of sequencing coverage but also observed AFs in a sufficiently large cohort, when implementing PRSs in a routine diagnostic setting. Furthermore, future PRS design should be guided by the technical reproducibility of expected AFs across commonly used genotyping methods, especially NGS, in addition to the observed effect sizes.

Variant calling and genotyping accuracy of ddRAD-seq: Comparison with 20X WGS in layers

Whole Genome Sequencing (WGS) remains a costly or unsuitable method for routine genotyping of laying hens. Until now, breeding companies have been using or developing SNP chips. Nevertheless, alternatives methods based on sequencing have been developed. Among these, reduced representation sequencing approaches can offer sequencing quality and cost-effectiveness by reducing the genomic regions covered by sequencing. The aim of this study was to evaluate the ability of double digested Restriction site Associated DNA sequencing (ddRAD-seq) to identify and genotype SNPs in laying hens, by comparison with a presumed reliable WGS approach. Firstly, the sensitivity and precision of variant calling and the genotyping reliability of ddRADseq were determined. Next, the SNP Call Rate (CRSNP) and mean depth of sequencing per SNP (DPSNP) were compared between both methods. Finally, the effect of multiple combinations of thresholds for these parameters on genotyping reliability and amount of remaining SNPs in ddRAD-seq was studied. In raw form, the ddRAD-seq identified 349,497 SNPs evenly distributed on the genome with a CRSNP of 0.55, a DPSNP of 11X and a mean genotyping reliability rate per SNP of 80%. Considering genomic regions covered by expected enzymatic fragments (EFs), the sensitivity of the ddRAD-seq was estimated at 32.4% and its precision at 96.4%. The low CRSNP and DPSNP values were explained by the detection of SNPs outside the EFs theoretically generated by the ddRAD-seq protocol. Indeed, SNPs outside the EFs had significantly lower CRSNP (0.25) and DPSNP (1X) values than SNPs within the EFs (0.7 and 17X, resp.). The study demonstrated the relationship between CRSNP, DPSNP, genotyping reliability and the number of SNPs retained, to provide a decision-support tool for defining filtration thresholds. Severe quality control over ddRAD-seq data allowed to retain a minimum of 40% of the SNPs with a CcR of 98%. Then, ddRAD-seq was defined as a suitable method for variant calling and genotyping in layers.

A comprehensive review of deep learning-based variant calling methods

Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.

Targeted panel sequencing of pharmacogenes and oncodrivers in colorectal cancer patients reveals genes with prognostic significance

BACKGROUND

Colorectal cancer is still the second leading cause of cancer-related deaths and thus biomarkers allowing prediction of the resistance of patients to therapy and estimating their prognosis are needed. We designed a panel of 558 genes with pharmacogenomics records related to 5-fluorouracil resistance, genes important for sensitivity to other frequently used drugs, major oncodrivers, and actionable genes. We performed a target enrichment sequencing of DNA from tumors and matched blood samples of patients, and compared the results with patient prognosis stratified by systemic adjuvant chemotherapy.

RESULTS

The median number of detected variants per tumor sample was 18.5 with 4 classified as having a high predicted functional effect and 14.5 moderate effect. APC, TP53, and KRAS were the most frequent mutated genes (64%, 59%, and 42% of mutated samples, respectively) followed by FAT4 (23%), FBXW7, and PIK3CA (16% for both). Patients with advanced stage III had more frequently APC, TP53, or KRAS mutations than those in stages I or II. KRAS mutation counts followed an increasing trend with grade (G1 < G2 < G3). The response to adjuvant therapy was worse in carriers of frameshift mutations in APC or 12D variant in KRAS, but none of these oncodrivers had prognostic value. Carriage of somatic mutations in any of the genes ABCA13, ANK2, COL7A1, NAV3, or UNC80 had prognostic relevance for worse overall survival (OS) of all patients. In contrast, mutations in FLG, GLI3, or UNC80 were prognostic in the same direction for patients untreated, and mutations in COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH, or TENM4 for patients treated with adjuvant therapy. The first association was externally validated. From all germline variants with high or moderate predicted functional effects (median 326 per patient), > 5% frequency and positive Manhattan plot based on 3-year RFS, rs72753407 in NFACS, rs34621071 in ERBB4, and rs2444274 in RIF1 were significantly associated with RFS, OS or both.

CONCLUSIONS

The present study identified several putative somatic and germline genetic events with prognostic potential for colorectal cancer that should undergo functional characterization.

Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank

The phenotypic impact of compound heterozygous (CH) variation has not been investigated at the population scale. We phased rare variants (MAF ∼0.001%) in the UK Biobank (UKBB) exome-sequencing data to characterize recessive effects in 175,587 individuals across 311 common diseases. A total of 6.5% of individuals carry putatively damaging CH variants, 90% of which are only identifiable upon phasing rare variants (MAF < 0.38%). We identify six recessive gene-trait associations (p < 1.68 × 10-7) after accounting for relatedness, polygenicity, nearby common variants, and rare variant burden. Of these, just one is discovered when considering homozygosity alone. Using longitudinal health records, we additionally identify and replicate a novel association between bi-allelic variation in ATP2C2 and an earlier age at onset of chronic obstructive pulmonary disease (COPD) (p < 3.58 × 10-8). Genetic phase contributes to disease risk for gene-trait pairs: ATP2C2-COPD (p = 0.000238), FLG-asthma (p = 0.00205), and USH2A-visual impairment (p = 0.0084). We demonstrate the power of phasing large-scale genetic cohorts to discover phenome-wide consequences of compound heterozygosity.

Variant calling in polyploids for population and quantitative genetics

Advancements in genome assembly and sequencing technology have made whole genome sequence (WGS) data and reference genomes accessible to study polyploid species. Compared to popular reduced-representation sequencing approaches, the genome-wide coverage and greater marker density provided by WGS data can greatly improve our understanding of polyploid species and polyploid biology. However, biological features that make polyploid species interesting also pose challenges in read mapping, variant identification, and genotype estimation. Accounting for characteristics in variant calling like allelic dosage uncertainty, homology between subgenomes, and variance in chromosome inheritance mode can reduce errors. Here, I discuss the challenges of variant calling in polyploid WGS data and discuss where potential solutions can be integrated into a standard variant calling pipeline.