Targeted genome disruptions and single-nucleotide conversions with the CRISPR/Cas system have greatly facilitated the development of gene therapy, basic biological research, and synthetic biology. With vast progress in this field, there are still aspects to be optimized, including the target range, the ability to multiplex, the mutation efficiency and specificity, as well as the requirement of adjusting protospacer adjacent motifs (PAMs). Here, we report the development of a highly efficient genome disruption and single-nucleotide conversion tool with a gRNA-tRNA array and SpCas9-NG (GTR 2.0). We performed gene disruptions in yeast cells covering all 16 possible NGN PAMs and all 12 possible single-nucleotide conversions (N to N) with near 100% efficiencies. Moreover, we applied GTR 2.0 for multiplexed single-nucleotide conversions, resulting in 66.67% mutation efficiency in simultaneous generation of 4 single-nucleotide conversions in one gene, as well as 100% mutation efficiency for simultaneously generating 2 single-nucleotide conversions in two different genes. GTR 2.0 will substantially expand the scope, efficiency, and capabilities of yeast genome editing, and will be a versatile and invaluable addition to the toolbox of synthetic biology and metabolic engineering.

The data comprise summary statistics for performance of a genotyping microarray for a test set of 87 samples for four pine species. The summary statistics comprise state (polymorphic, monomorphic), mean allele frequency and conversion rate, estimated for each locus as a mean across 87 sample genotypes. The array comprised 49,829 SNPs (single nucleotide polymorphisms) from several sources. The majority (N = 49,052) were obtained from transcriptome sequencing of four pine species: Pinus sylvestris, Pinus mugo, Pinus uncinata and Pinus uliginosa. The SNP set was filtered by the array manufacturer (Thermo Fisher) based on p-convert values signifying the SNP array quality, and a list of recommended and non-recommended SNP probes (avoiding SNPs with polymorphisms within 35 bp) was provided to the authors. These included SNPs that were common to all species and also SNPs fixed in one species and polymorphic within and among others. A further set of SNPs (N = 578) were included from candidate genes (N = 279), which had been resequenced in previous population genetic studies of the pine species. Variation in mitochondrial DNA (mtDNA) was targeted by inclusion of a set of mtDNA- specific SNPs (N = 14). Finally, a set of SNPs putatively associated with susceptibility to Dothistroma needle blight (discovered in Pinus radiata, European Nucleotide Archive accession numbers ERS1034542-53) were also included (N = 185). Full details about this dataset can be found at https://doi.org/10.5285/0ba33e96-67cb-4650-b2bd-6ee13fa7de97

Wheat breeders and academics alike use single nucleotide polymorphisms (SNPs) as molecular markers to characterize regions of interest within the hexaploid wheat genome. A number of SNP‐based genotyping platforms are available, and their utility depends upon factors such as the available technologies, number of data points required, budgets and the technical expertise required. Unfortunately, markers can rarely be exchanged between existing and newly developed platforms, meaning that previously generated data cannot be compared, or combined, with more recently generated data sets. We predict that genotyping by sequencing will become the predominant genotyping technology within the next 5–10 years. With this in mind, to ensure that data generated from current genotyping platforms continues to be of use, we have designed and utilized SNP‐based capture probes from several thousand existing and publicly available probes from Axiom® and KASP™ genotyping platforms. We have validated our capture probes in a targeted genotyping by sequencing protocol using 31 previously genotyped UK elite hexaploid wheat accessions. Data comparisons between targeted genotyping by sequencing, Axiom® array genotyping and KASP™ genotyping assays, identified a set of 3256 probes which reliably bring together targeted genotyping by sequencing data with the previously available marker data set. As such, these probes are likely to be of considerable value to the wheat community. The probe details, full probe sequences and a custom built analysis pipeline may be freely downloaded from the CerealsDB website (http://www.cerealsdb.uk.net/cerealgenomics/CerealsDB/sequence_capture.php).

BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2), enters human cells using the angiotensin-converting enzyme 2 (ACE-2) receptor. ACE2 single nucleotide polymorphisms (SNPs) can influence susceptibility by affecting viral binding or gene expression. This study investigated the association between ACE2 SNPs, rs2285666 and rs2106809, and the SARS-CoV-2 infection susceptibility in a Ghanaian population.MethodsGenomic DNA was extracted, using a magnetic bead-based method, from blood samples of a random-subset of 1,334 participants drawn from a two-stage cluster, population-based household cross-sectional SARS-CoV-2 IgG seroprevalence survey. Data collected included, socio-demographic characteristics, medical history, vaccination, and smoking status. Genotyping of the ACE2 SNPs was performed using Allele-Specific Oligonucleotide Polymerase Chain Reaction (ASO-PCR) combined with melting curve analysis. Logistic regression models were utilized to assess the association between the ACE2 SNPs and the susceptibility to SARS-CoV-2 infectionResultsThe median age of participants was 33 [Interquartile range (IQR) = 24–46] years. Females accounted for the majority of the sampled population, 64.3%. SARS-CoV-2-IgG seropositivity was (58.4%, 95%CI: 52.6%–64.2%) among the male population and (54.1%, 95%CI: 49.54%–58.61%) in the female population. There were no significant differences in overall allele or genotype frequencies of ACE2 SNPs between SARS-CoV-2 IgG seropositive and seronegative individuals for both females and males. Among females, those with the T allele of ACE2 rs2285666 had a 38% decreased susceptibility to SARS-CoV-2 infection under the dominant [adjusted odds ratio (aOR) = 0.62; 95%CI = 0.45–0.85, P = 0.003] and heterozygous advantage models (aOR = 0.62; 95%CI = 0.45–0.86, P = 0.004), after adjusting for confounders, but not thee recessive model (aOR = 0.41; 95%CI = 0.03–5.22, P = 0.490). No significant association was observed among males. Overall, the ACE2 rs2106809 was not associated with the susceptibility to SARS-CoV-2 infection in both males and females.ConclusionThis study found no association between ACE2 rs2106809 genetic variant and susceptibility to SARS-CoV-2 infection, whilst the rs2285666 T-allele was associated with a decreased frequency for SARS-CoV-2 infection among Ghanaian females. These findings enhance our understanding of genetic factors influencing SARS-CoV-2 susceptibility, which could help identify at-risk populations and inform more targeted public health interventions in future outbreaks.

A total of 2015 female great tits were genotyped using a custom made Affymetrix great tit 650K SNP chip (Kim et al. 2018) at Edinburgh Genomics (Edinburgh, United Kingdom). Axiom Analysis Suite 1.1 was used for SNP calling following the Affymetrix best practices workflow.

Kim, J. M., A. W. Santure, H. J. Barton, J. L. Quinn, E. F. Cole, Great Tit HapMap Consortium, M. E. Visser, et al. 2018. “A High-Density SNP Chip for Genotyping Great Tit ( Parus Major ) Populations and Its Application to Studying the Genetic Architecture of Exploration Behaviour.” Molecular Ecology Resources 18 (4): 877–91. https://doi.org/10.1111/1755-0998.12778.

Description This DataverseNL item contains the metadata of the Nanopore sequencing dataset and limited clinical data used in ‘Long-read sequencing identifies copy-specific markers of SMN gene conversion in spinal muscular atrophy’. Access to this data is restricted due to privacy regulations; conditions and instructions for access are listed below. Abstract Background: The complex 2 Mb survival motor neuron (SMN) locus on chromosome 5q13, including the spinal muscular atrophy (SMA)-causing gene SMN1 and modifier SMN2, remains incompletely resolved due to numerous segmental duplications. Variation in SMN2 copy number, presumably influenced by SMN1 to SMN2 gene conversion, affects disease severity, though SMN2 copy number alone has insufficient prognostic value due to limited genotype-phenotype correlations. With advancements in newborn screening and SMN-targeted therapies, identifying genetic markers to predict disease progression and treatment response is crucial. Progress has thus far been limited by methodological constraints. Methods: To address this, we developed HapSMA, a method to perform polyploid phasing of the SMN locus to enable copy-specific analysis of SMN and its surrounding genes. We used HapSMA on publicly available Oxford Nanopore Technologies (ONT) sequencing data of 29 healthy controls and performed long-read, targeted ONT sequencing of the SMN locus of 31 patients with SMA. Results: In healthy controls, we identified single nucleotide variants (SNVs) specific to SMN1 and SMN2 haplotypes that could serve as gene conversion markers. Broad phasing including the NAIP gene allowed for a more complete view of SMN locus variation. Genetic variation in SMN2 haplotypes was larger in SMA patients. 42% of SMN2 haplotypes of SMA patients showed varying SMN1 to SMN2 gene conversion breakpoints, serving as direct evidence of gene conversion as a common genetic characteristic in SMA and highlighting the importance of inclusion of SMA patients when investigating the SMN locus. Conclusions: Our findings illustrate that both methodological advances and the analysis of patient samples are required to advance our understanding of complex genetic loci and address critical clinical challenges. Github The code for HapSMA is available at: https://github.com/UMCUGenetics/HapSMA (v1.0.0 was used for analyses in this study, v1.1.0 contains extra support for different types of data input). The code for analyses subsequent to HapSMA and input files used in these analyses are available at: https://github.com/UMCUGenetics/ManuscriptSMNGeneConversion. IRB approval The study protocol (09307/NL29692.041.09) was approved by the Medical Ethical Committee of the University Medical Center Utrecht and registered at the Dutch registry for clinical studies and trials (https://www.ccmo.nl/). Written informed consent was obtained from all adult patients, and from patients and/or parents additionally in case of children younger than 18 years old. Contact information Requests for data can be made by contacting the principal investigators of this study, Ludo van der Pol (w.l.vanderPol@umcutrecht.nl), Gijs van Haaften (G.vanHaaften@umcutrecht.nl) or Ewout Groen (e.j.n.groen-3@umcutrecht.nl) at University Medical Center Utrecht UMC Utrecht Brain Center Heidelberglaan 100 3584 CX Utrecht The Netherlands Expected response time for processing a data sharing agreement is 4 to 6 weeks.

The African lion (Panthera leo), listed as a vulnerable species on the IUCN Red List of Threatened Species (Appendix II of CITES), is mainly impacted by indiscriminate killing and prey base depletion. Additionally, habitat loss by land degradation and conversion has led to the isolation of some subpopulations, potentially decreasing gene flow and increasing inbreeding depression risks. Genetic drift resulting from weakened connectivity between strongholds can affect the genetic health of the species. In the present study, we investigated the evolutionary history of the species at different spatiotemporal scales. Therefore, the mitochondrial cytochrome b gene (N = 128), 11 microsatellites (N = 103) and 9,103 SNPs (N = 66) were investigated in the present study, including a large sampling from Tanzania, which hosts the largest lion population among all African lion range countries. Our results add support that the species is structured into two lineages at the continental scale (West-Central vs East-Southern), underlining the importance of reviewing the taxonomic status of the African lion. Moreover, SNPs led to the identification of three lion clusters in Tanzania, whose geographical distributions are in the northern, southern and western regions. Furthermore, Tanzanian lion populations were shown to display good levels of genetic diversity with limited signs of inbreeding. However, their population sizes seem to have gradually decreased in recent decades. The highlighted Tanzanian African lion population genetic differentiation appears to have resulted from the combined effects of anthropogenic pressure and environmental/climatic factors, as further discussed.

Single nucleotide polymorphisms (SNPs) have become the marker of choice for genetic studies in organisms of conservation, commercial or biological interest. Most SNP discovery projects in nonmodel organisms apply a strategy for identifying putative SNPs based on filtering rules that account for random sequencing errors. Here, we analyse data used to develop 4723 novel SNPs for the commercially important deep-sea fish, orange roughy (Hoplostethus atlanticus), to assess the impact of not accounting for systematic sequencing errors when filtering identified polymorphisms when discovering SNPs. We used SAMtools to identify polymorphisms in a velvet assembly of genomic DNA sequence data from seven individuals. The resulting set of polymorphisms were filtered to minimize 'bycatch'—polymorphisms caused by sequencing or assembly error. An Illumina Infinium SNP chip was used to genotype a final set of 7714 polymorphisms across 1734 individuals. Five predictors were examined for their effect on the probability of obtaining an assayable SNP: depth of coverage, number of reads that support a variant, polymorphism type (e.g. A/C), strand-bias and Illumina SNP probe design score. Our results indicate that filtering out systematic sequencing errors could substantially improve the efficiency of SNP discovery. We show that BLASTX can be used as an efficient tool to identify single-copy genomic regions in the absence of a reference genome. The results have implications for research aiming to identify assayable SNPs and build SNP genotyping assays for nonmodel organisms.

The Japanese Archipelago consists of a series of isolated yet interconnected islands off the Eurasian continent. The linear topography of the archipelago presents a unique biogeographic context for the dispersal of organisms from the continent. In this study, mitochondrial DNA (mtDNA) and single-nucleotide polymorphism (SNP) variation were employed to elucidate the dispersal history of the Japanese clouded butterfly (Parnassius glacialis) across the Japanese Archipelago, including the northern island (Hokkaido), the main island (Honshu), and Shikoku Island. Network analysis of 1192 bp of mtDNA (cytochrome oxidase I and II) regions revealed 49 haplotypes and three distinct haplotype groups, which correspond geographically to Eastern Japan, Western Japan, and Chugoku–Shikoku. The Chugoku–Shikoku group is the most ancient lineage. Divergence time estimates using whole-genome sequencing of mtDNA suggest that the Japanese lineage diverged from the continental P. glacialis approximately 3.08 ..., SNPs were detected from the extracted DNA using the genotyping by random amplicon sequencing, direct (GRAS-Di) method designed by Enoki and Takeuchi (2018) and described by Hosoya et al. (2019); this method offers simple library construction and the capacity to detect many SNPs. Briefly, libraries were constructed through two sequential PCR steps, similar to multiplexed inter-simple sequence repeat genotyping by sequencing (Suyama and Matsuki 2015). The first PCR primers consisted of 10 bases of Illumina Nextera adaptor 3-end sequences plus 3-base random oligomers (13 bases). The final PCR product was purified using columns or magnetic beads without size selection and then employed for sequencing on an Illumina platform (Illumina, San Diego, CA, USA). Cyclized DNA was prepared using the produced library and the MGIEasy universal library conversion kit (App-A, MGI Tech) according to the manufacturer’s instructions. DNBs were prepared using the DNBSEQ DNB Rapid Make reagent kit (MGI Tech)..., , # Dual expansion routes likely underlie the present-day population structure in a Parnassius butterfly across the Japanese Archipelago

https://doi.org/10.5061/dryad.69p8cz9d3

Description of the data and file structure

Each sample’s raw fastq sequence data for SNP and haplotype of mtDNA

Files and variables

File: Supporting_Material_PG.xls

Description: Each sample’s raw fastq sequence data and haplotype

Variables

• Sheet 1: an overview of each sheet.
• Sheet 2: raw sequence data for each  SNP detected with the Grass-Di method.
• Sheet 3: haplotype number for each collected individual (collected year, individual code, collection site, Prefecture, Prefecture number, haplotype number)

Rapidly evolving taxa are excellent models for understanding the mechanisms that give rise to biodiversity. However, developing an accurate historical framework for comparative analysis of such lineages remains a challenge due to ubiquitous incomplete lineage sorting and introgression. Here, we use a whole-genome alignment, multiple locus-sampling strategies, and summary-tree and SNP-based species-tree methods to infer a species tree for eastern North American Neodiprion species, a clade of pine-feeding sawflies (Order: Hymenopteran; Family: Diprionidae). We recovered a well-supported species tree that—except for three uncertain relationships—was robust to different strategies for analyzing whole-genome data. Nevertheless, underlying gene-tree discordance was high. To understand this genealogical variation, we used multiple linear regression to model site concordance factors estimated in 50-kb windows as a function of several genomic predictor variables. We found that site concordance factors tended to be higher in regions of the genome with more parsimony-informative sites, fewer singletons, less missing data, lower GC content, more genes, lower recombination rates, and lower D-statistics (less introgression). Together, these results suggest that incomplete lineage sorting, introgression, and genotyping error all shape the genomic landscape of gene-tree discordance in Neodiprion. More generally, our findings demonstrate how combining phylogenomic analysis with knowledge of local genomic features can reveal mechanisms that produce topological heterogeneity across genomes. Methods DNA was extracted from field-caught larvae. Then, Illumina libraries were prepared and sequenced on an Illumina NextSeq 500 with PE150 reads, which produced 14-27 million reads per individual. To obtain a multi-genome alignment, we used a pseudo-reference-based approach, with an annotated, reference quality N. lecontei genome (iyNeoLeco1.1 RefSeq GCF_021901455.1) serving as the reference. Briefly, we first used bowtie2 v2.4.1 to map reads from each species to the N. lecontei reference genome. To allow for divergence between reads and the N. lecontei reference, we initially allowed a mismatch in the seed and “local” mapping options in bowtie2. New variants (excluding indels) were incorporated using samtools v1.10 and bcftools v1.10.2. In a second round of mapping, this process was repeated using the first iteration of the genome for each species as the new reference genome. The third round of mapping removed the seed mismatch. The fourth and fifth iterations required end-to-end mapping. After the fifth iteration, we replaced any nucleotide that had a read depth less than 4 or that had excessively high mapping depth (highest 1% of depths for each species) with an “N” using a custom script. All bioinformatics commands and scripts can be found on the LinnenLab GitHub page under the Herrig_etal_NeodiprionPhylogeny repository (https://github.com/LinnenLab/Herrig_etal_NeodiprionPhylogeny and Zenodo). This approach produced FASTA files for each species, with genome sequences in N. lecontei coordinates. All FASTA files are provided here. We next used the FASTA files to produce additional datasets for analysis. First, we used bedtools v2.30.0 to divide the seven Neodiprion chromosomes into non-overlapping windows of 50 kb. Second, to approximate a dataset of protein-coding genes analogous to an RNAseq or exon-capture phylogenomic dataset, we used gffread v0.11.7 with the –w flag to write fasta files with spliced exons for each transcript for each species using the NCBI Neodiprion lecontei Annotation Release (iyNeoLeco1.1 RefSeq GCF_021901455.1). Windowed and gene datasets are provided as nexus files that contain individual window/gene alignments. These can also be regenerated using scripts (available on GitHub: https://github.com/LinnenLab/Herrig_etal_NeodiprionPhylogeny and Zenodo) to cut up the genome into desired loci (windows or genes) and convert these to nexus format. Third, we called single nucleotide polymorphisms (SNPs) across the entire genome using SNP-sites v2.5.1. We then filtered the data to exclude SNPs that were absent in more than 10% of species and sites with more than two alleles. In addition to analyzing all SNPs (which likely contain tightly linked sites), we produced additional datasets with one SNP sampled every 1 kb, 5 kb, 10 kb, 50 kb, or 100 kb using SNP-sites, with more sparsely sampled SNPs on par with a dataset that might be generated via RADseq. We transformed each of the six datasets into nexus format. All six SNP nexus files are provided and can be used as input for SVDquartets. To investigate sources of phylogenetic discordance, we also generated estimates of site concordance factors in 50-kb windows and estimated 7 genomic predictor variables in for these same 50-kb windows, including: # parsimony informative sites, # singletons, proportion missing data, GC content, D-statistics, gene density, and recombination rate. This dataset is available as a csv file.

Concatenated nexus file for BEAST (outgroup data set)Data file used for BEAST analysis, including D. carolinensis outgroup individuals.quads_out_concat.nexVCF file containing SNPs for DAPC (ingroup data set)Single nucleotide polymorphism data set with only in-group individuals, used in DAPC and for calculating f-statistics. (See "Population assignments" file for corresponding population data for individual IDs.)quads_no.recode.vcfPopulation assignmentsFile containing (1) ID codes, (2) species assignments, (3) population assignments, (4) latitude, and (5) longitude used in DAPC analyses and ecological niche modeling. Species assignment codes: Q = D. quadramaculatus, M = D. marmoratus, C = D. carolinensis. Population assignment codes: P = Pisgah, N = Nantahala, C = D. carolinensis.quads_out_pops.csvBPP loci data setReduced data set of 500 random loci used in Bayesian Phylogeography and Phylogenetics analyses.desmogs.lociInterleaved nexus file for SVDquartets (ingroup data set)In-group data set used for SVDquartets analysisquads_no_out.nexVCF file containing SNPs for DAPC (outgroup data set)Single nucleotide polymorphism data for all individuals. Used for DAPC analyses and calculating f-statistics. (See "Population assignments" file for corresponding population data for individual IDs.)quads_out.recode.vcfTreeMix/F4 data setData set used for TreeMix and F4 analyses. The data is in plink stratified allele frequency format, which can be converted to work in TreeMix and F4 using TreeMix's inbuilt conversion script. For more details see the TreeMix Bitbucket site: https://bitbucket.org/nygcresearch/treemix/wiki/Home F4 is available to download here: https://github.com/mmatschiner/F4plink.frq.strat.gzLink to raw dataThis is a text file that contains a link to the demultiplexed raw sequencing data for all individuals used in this study. The fastq files are named using each individual's ID; more information about individuals can be found in the "Population assignment" file. Note that the gzipped directory is 31Gb.desmog_raw_data_link.txt Closely related taxa with dissimilar morphologies are often considered to have diverged via natural selection favoring different phenotypes. However, some studies have found these scenarios to be paired with limited or no genetic differentiation. Desmognathus quadramaculatus and D. marmoratus are sympatric salamander species thought to represent a case of ecological speciation based on distinct morphologies, but the results of previous studies have not resolved corresponding patterns of lineage divergence. Here, we use genome-wide data to test this hypothesis of ecological speciation. Population structure analyses partitioned individuals geographically, but not morphologically, into two adjacent regions of western North Carolina: Pisgah and Nantahala. Phylogenetic analyses confirmed the nominal species are non-monophyletic and resolved deep divergence between the two geographic clusters. Model-testing overwhelmingly supported the hypothesis that lineage divergence followed geography. Finally, ecological niche modeling showed that Pisgah and Nantahala individuals occupy different climatic niches, and geographic boundaries for the two lineages correspond to a difference in precipitation regimes across southern Appalachia. Overall, we reject the previous hypothesis of ecological speciation based on microhabitat partitioning. Instead, our results suggest that there are two cryptic lineages, each containing the same pair of morphotypes.

Objectives: Systemic sclerosis (SSc) is a disease with cardiovascular impairment and polymorphisms of the gene coding of angiotensin-converting-enzyme 2 (ACE2) may account for its development. Three single nucleotide polymorphisms of ACE2 (C>G rs879922, G>A rs2285666 and A>G rs1978124) were found to increase the risk for development of arterial hypertension (AH) and cardiovascular (CVS) diseases in different ethnicities. We investigated associations of polymorphisms rs879922, rs2285666 and rs1978124 with the development of SSc.

Methods: Genomic DNA was isolated from whole blood. Restriction-fragment-length polymorphism was used for genotyping of rs1978124, while detection of rs879922 and rs2285666 was based on TaqMan SNP Genotyping Assay. Serum level of ACE2 was assayed with commercially available ELISA test.

Results: 81 SSc patients (60 women, 21 men) were enrolled. Allele C of rs879922 polymorphism was associated with significantly greater risk for development of AH (OR=2.5, p=0.018), but less frequent joint involvement. A strong tendency to earlier onset of Raynaud's phenomenon and SSc was seen in carriers of allele A of rs2285666 polymorphism. They had lower risk for development of any CVS disease (RR=0.4, p=0.051) and tendency to less frequent gastrointestinal involvement. Women with genotype AG of rs1978124 polymorphism had significantly more frequent digital tip ulcers and lower serum level of ACE2.

Conclusions: Polymorphisms of ACE2 may account for the development of AH and CVS disorders in SSc patients. Strong tendencies to more frequent occurrence of disease specific characteristics distinct to macrovascular involvement will require further studies evaluating significance of ACE2 polymorphisms in SSc.

The implications of transitioning to single nucleotide polymorphism (SNPs) from microsatellite markers (MSs) have been investigated in a number of population genetics studies, but the effect of genomic location on the amount of information each type of marker reveals has not been explored in detail. We developed novel SNP markers flanking 1 kb regions of 13 genic (within gene or <1 kb away from gene) and 13 nongenic (>10 kb from annotated gene) MSs in the threespine stickleback genome to obtain comparable data for both types of markers. We analysed patterns of genetic diversity and divergence on various geographic scales after converting the SNP loci within each genomic region into haplotypes. Marker type (SNP haplotype or MS) and location (genic or nongenic) significantly affected most estimates of population diversity and divergence. Between-lineage divergence was significantly higher in SNP haplotypes (genic and nongenic), however, within-lineage divergence was similar between marker types. Most divergence and diversity measures were uncorrelated between markers, except for population differentiation which was correlated between MSs and SNP haplotypes (both genic and nongenic). Broad-scale population structure and assignment were similarly resolved by both marker types, however, only the MSs were able to delimit fine-scale population structuring, particularly when genic and nongenic markers were combined. These results demonstrate that estimates of genetic variability and differentiation among populations can be strongly influenced by marker type, their genomic location in relation to genes and by the interaction of these two factors. This highlights the importance of having an awareness of the inherent strengths and limitations associated with different molecular tools to select the most appropriate methods for accurately addressing various ecological and evolutionary questions.

Protein-Protein, Genetic, and Chemical Interactions for O'Leary VB (2005):Analysis of methionine synthase reductase polymorphisms for neural tube defects risk association. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Methionine synthase reductase (MTRR) regenerates methylated cobalamin levels from the oxidised cob(II)alamin form and in so doing plays a crucial role in maintaining the active state of methionine synthase (MTR). MTR is an essential enzyme catalyzing the conversion of homocysteine to methionine. Single nucleotide polymorphisms (SNPs) within the MTRR gene may potentially compromise MTR activity leading to elevated homocysteine levels, a known risk factor for neural tube defects (NTDs). We studied the MTRR polymorphisms I22M (66A-->G), S175L (524C-->T), and K350R (1049A-->G) as potential NTD risk factors in a large homogeneous Irish NTD population. Degree of risk was assessed via case/control comparison, log-linear analysis, and transmission disequilibrium testing. No association was found between NTDs and I22M in mothers (p = 0.16, OR1.14 [0.95-1.38], n = 447) or cases (p = 0.13, OR1.15 [0.96-1.38], n = 470) compared to controls (n = 476). A dominant I22M paternal effect was found through case/control comparison and log-linear modelling (p = 0.019) (goodness-of-fit p=0.91, OR 1.46 [1.10-1.93], n = 423). No significant NTD association was found with S175L or K350R in cases or their parents and no interactions were observed between these polymorphisms and the D919G variant of MTR or the A222V variant of 5,10-methylenetetrahydrofolate reductase (MTHFR). We also compared the frequencies of I22M, S175L, and K350R in African-Americans versus American-Caucasians. The frequencies of I22M and K350R differed significantly between the two groups (p = 0.0005 and p = 0.0001, respectively). Our findings do not support an important role for these MTRR variants in NTDs.

Base editing technologies enable programmable single-nucleotide changes in target DNA without double-stranded DNA breaks. Adenine base editors (ABEs) allow precise conversion of adenine (A) to guanine (G). However, limited availability of optimized deaminases as well as their variable efficiencies across different target sequences can limit the ability of ABEs to achieve effective adenine editing. Here, we explored the use of a TurboCas9 nickase in an ABE to improve its genome editing activity. The resulting TurboABE exhibits amplified editing efficiency on a variety of adenine target sites without increasing off-target editing in DNA and RNA. An interesting feature of TurboABE is its ability to significantly improve the editing frequency at bases with normally inefficient editing rates in the editing window of each target DNA. Development of improved ABEs provides new possibilities for precise genetic modification of genes in living cells.

PREMISE OF THE STUDY: There is a misinterpretation in the literature regarding the variable orientation of the small single copy region of plastid genomes (plastomes). The common phenomenon of small and large single copy inversion, hypothesized to occur through intramolecular recombination between inverted repeats (IR) in a circular, single unit-genome, in fact more likely occurs through recombination-dependent replication (RDR) of linear plastome templates. If RDR can be primed through both intra- and intermolecular recombination, then this mechanism could not only create inversion isomers of so-called single copy regions, but also an array of alternative sequence arrangements. METHODS: We used Illumina paired-end and PacBio single-molecule real-time (SMRT) sequences to characterize repeat structure in the plastome of Monsonia emarginata L'Hér. (Geraniaceae). We used OrgConv and inspected nucleotide alignments to infer ancestral nucleotides and identify gene conversion among repeats and mapped long (>1 kb) SMRT reads against the unit-genome assembly to identify alternative sequence arrangements. RESULTS: Although M. emarginata lacks the canonical IR, we found that large repeats (>1 kilobase; kb) represent ~22% of the plastome nucleotide content. Among the largest repeats (>2 kb) we identified GC-biased gene conversion and mapping filtered, long SMRT reads to the M. emarginata unit-genome assembly revealed alternative, substoichiometric sequence arrangements. CONCLUSION: We offer a model based on RDR and gene conversion between long repeated sequences in the M. emarginata plastome, and provide support that both intra-and intermolecular recombination between large repeats, particularly in repeat-rich plastomes, varies unit-genome structure while homogenizing the nucleotide sequence of repeats.

Aim: Gene flow from central to edge populations is thought to limit population growth at range edges by constraining local adaptation. In this study, we explore the thesis that range edges can differ in their dynamics and be either “hard” (e.g. a river) or “soft” (e.g. ecological gradients). We hypothesize that soft edge populations will have smaller effective population sizes than central populations and that gene flow will be greater from the center to the edge than vice versa. Conversely, we hypothesize that hard edge populations should have similar effective population sizes to central populations and that gene flow will be equal between the two. Location: Kentucky, West Virginia, and Virginia, USA. Taxon: Plethodon kentucki (Caudata: Plethodontidae). Methods: We evaluated landscape suitability using an ecological niche model, then we compared gene flow and effective population sizes between edge and central populations and quantified gene flow between populations. Finally, we characterized landscape genetic variation, testing for isolation by distance and isolation by environment. Results: We found continuously decreasing habitat quality along soft edges, with hard edges more variable. Additionally, we found that soft edges had lower effective population sizes than central populations and that gene flow was greater from the center of the range to the soft edges than the reverse. In hard edges, by contrast, we found effective population sizes in edge populations were similar to central populations, with relatively equal gene flow in both directions. Main conclusions: Understanding why species have range limits is central to investigations of the structure of biodiversity, yet the evolutionary dynamics of range edges remain poorly understood. We show that within a single species with a small range, the evolutionary dynamics operating at range boundaries may depend on the nature of the boundary. Methods We gathered blood samples and tail tips from 39 individuals in 31 localities of Plethodon kentucki. Plethodon glutinosus, a morphologically cryptic relative, occurs sympatrically, so a known P. glutinosus sample was included to identify species. Genomic DNA was extracted using Qiagen DNeasy Blood and Tissue Kits (Qiagen Corp., Valencia CA) following the manufacturer’s protocol. We quantified genetic variation using single nucleotide polymorphisms (SNPs). To obtain SNPs, we used double-digest restriction-site associated DNA sequencing (ddRAD) (Peterson et al., 2012), including a protocol that has been optimized for use in salamanders (Jones and Weisrock, 2018). Briefly, we double-digested extracted DNA using equal amounts of the restriction enzymes EcoRI and SphI (New England BioLabs). DNA fragments were indexed for each individual and pooled for size selection of 420 bp +/- 10% on a Pippin Prep (Sage Science, Beverly MA). The resulting libraries were amplified by PCR for 12 cycles with Phusion high-fidelity DNA polymerase (New England Biolabs, Ipswich MA) and cleaned with Dynabeads (ThermoFisher, Waltham MA) and AMPure XP beads (Beckman Coulter, Inc., Brea CA). They were sequenced with 150 bp paired-end reads using a 1% PhiX DNA spike-in on an Illumina (Illumina, San Diego CA) HiSeq 2500 at Novogene. To filter the raw sequencing data, we first checked the quality of reads with fastQC v. 0.11.9 (Andrews, 2010). Then, we demultiplexed raw sequences in Stacks v. 2.61 (Catchen et al., 2011; Catchen et al. 2013) following Rochette and Catchen (2017). We built a custom pipeline based on Hime et al. (2019) (Appendix A1-10). We demultiplexed the raw, stitched reads by individual with the process_radtags function, allowing for one mismatch between observed and expected barcodes. We retained reads with both restriction enzyme cut sites and had a mean Phred quality score greater than 20 over 45 bp sliding-window intervals (Hime et al., 2019). We excluded two individuals with fewer than 900,000 reads (DH_64627 and SRK_2997). We optimized parameters by testing M 1-12 using the R80 method following Rochette and Catchen (2017) and found M12 to retain the greatest number of reads. We then assembled the sequences de novo. We used ustacks to build loci within individuals, cstacks to assemble a catalog of loci across individuals, sstacks to match samples to catalog, tsv2bam to convert files, gstacks to genotype individuals, and populations to compute summary statistics and export files. We then further excluded two individuals with > 95% missing data (EFW_0006 and SRK_3200), resulting in a final sample of 35 individuals from 31 localities. When allowing 10% missing data per SNP, we obtained 30,155 SNPs, and when we did not allow any missing data, we retained 6,803 SNPs. For other methods, see the Materials and Methods section of Watts et al. 2024.

The importance of next generation sequencing (NGS) rises in cancer research as accessing this key technology becomes easier for researchers. The sequence data created by NGS technologies must be processed by various bioinformatics algorithms within a pipeline in order to convert raw data to meaningful information. Mapping and variant calling are the two main steps of these analysis pipelines, and many algorithms are available for these steps. Therefore, detailed benchmarking of these algorithms in different scenarios is crucial for the efficient utilization of sequencing technologies. In this study, we compared the performance of twelve pipelines (three mapping and four variant discovery algorithms) with recommended settings to capture single nucleotide variants. We observed significant discrepancy in variant calls among tested pipelines for different heterogeneity levels in real and simulated samples with overall high specificity and low sensitivity. Additional to the individual evaluation of pipelines, we also constructed and tested the performance of pipeline combinations. In these analyses, we observed that certain pipelines complement each other much better than others and display superior performance than individual pipelines. This suggests that adhering to a single pipeline is not optimal for cancer sequencing analysis and sample heterogeneity should be considered in algorithm optimization.

The regions surrounding transcription start sites (TSSs) of genes play a critical role in the regulation of gene expression. At the same time, current evidence indicates that these regions are particularly stressed by transcription-related mutagenic phenomena. In this work we performed a genome-wide analysis of the distribution of single nucleotide polymorphisms (SNPs) inside the 10 kb region flanking human TSSs by dividing SNPs into four classes according to their frequency (rare, two intermediate classes, and common). We found that, in this 10 kb region, the distribution of variants depends on their frequency and on their localization relative to the TSS. We found that the distribution of variants is generally different for TSSs located inside or outside of CpG islands. We found a significant relationship between the distribution of rare variants and nucleosome occupancy scores. Furthermore, our analysis suggests that evolutionary (purifying selection) and nonevolutionary (biased gene conversion) forces both play a role in determining the relative SNP frequency around TSSs. Finally, we analyzed the potential pathogenicity of each class of variant using the Combined Annotation Dependent Depletion score. In conclusion, this study provides a novel and detailed view of the distribution of genomic variants around TSSs, providing insight into the forces that instigate and maintain variability in such critical regions.