Sequence-level Data Research Articles

An association weight matrix identified biological pathways associated with bull fertility traits in a multi-breed population.

Using seven indicator traits, we investigated the genetic basis of bull fertility and predicted gene interactions from SNP associations. We used percent normal sperm as the key phenotype for the association weight matrix-partial correlation information theory (AWM-PCIT) approach. Beyond a simple list of candidate genes, AWM-PCIT predicts significant gene interactions and associations for the selected traits. These interactions formed a network of 537 genes: 38 genes were transcription cofactors, and 41 genes were transcription factors. The network displayed two distinct clusters, one with 294 genes and another with 243 genes. The network is enriched in fertility-associated pathways: steroid biosynthesis, p53 signalling, and the pentose phosphate pathway. Enrichment analysis also highlighted gene ontology terms associated with 'regulation of neurotransmitter secretion' and 'chromatin formation'. Our network recapitulates some genes previously implicated in another network built with lower-density genotypes. Sequence-level data also highlights additional candidate genes relevant to bull fertility, such as FOXO4, FOXP3, GATA1, CYP27B1, and EBP. A trio of regulatory genes-KDM5C, LRRK2, and PME-was deemed core to the network because of their overarching connections. This trio probably influences bull fertility through their interaction with genes, both known and unknown as to their role in male fertility. Future studies may target the trio and their target genes to enrich our understanding of male fertility further.

MoCoLo: a testing framework for motif co-localization.

Sequence-level data offers insights into biological processes through the interaction of two or more genomic features from the same or different molecular data types. Within motifs, this interaction is often explored via the co-occurrence of feature genomic tracks using fixed-segments or analytical tests that respectively require window size determination and risk of false positives from over-simplified models. Moreover, methods for robustly examining the co-localization of genomic features, and thereby understanding their spatial interaction, have been elusive. We present a new analytical method for examining feature interaction by introducing the notion of reciprocal co-occurrence, define statistics to estimate it and hypotheses to test for it. Our approach leverages conditional motif co-occurrence events between features to infer their co-localization. Using reverse conditional probabilities and introducing a novel simulation approach that retains motif properties (e.g. length, guanine-content), our method further accounts for potential confounders in testing. As a proof-of-concept, motif co-localization (MoCoLo) confirmed the co-occurrence of histone markers in a breast cancer cell line. As a novel analysis, MoCoLo identified significant co-localization of oxidative DNA damage within non-B DNA-forming regions that significantly differed between non-B DNA structures. Altogether, these findings demonstrate the potential utility of MoCoLo for testing spatial interactions between genomic features via their co-localization.

Sequence level genome-wide associations for bull production and fertility traits in tropically adapted bulls

BackgroundThe genetics of male fertility is complex and not fully understood. Male subfertility can adversely affect the economics of livestock production. For example, inadvertently mating bulls with poor fertility can result in reduced annual liveweight production and suboptimal husbandry management. Fertility traits, such as scrotal circumference and semen quality are commonly used to select bulls before mating and can be targeted in genomic studies. In this study, we conducted genome-wide association analyses using sequence-level data targeting seven bull production and fertility traits measured in a multi-breed population of 6,422 tropically adapted bulls. The beef bull production and fertility traits included body weight (Weight), body condition score (CS), scrotal circumference (SC), sheath score (Sheath), percentage of normal spermatozoa (PNS), percentage of spermatozoa with mid-piece abnormalities (MP) and percentage of spermatozoa with proximal droplets (PD).ResultsAfter quality control, 13,398,171 polymorphisms were tested for their associations with each trait in a mixed-model approach, fitting a multi-breed genomic relationship matrix. A Bonferroni genome-wide significance threshold of 5 × 10− 8 was imposed. This effort led to identifying genetic variants and candidate genes underpinning bull fertility and production traits. Genetic variants in Bos taurus autosome (BTA) 5 were associated with SC, Sheath, PNS, PD and MP. Whereas chromosome X was significant for SC, PNS, and PD. The traits we studied are highly polygenic and had significant results across the genome (BTA 1, 2, 4, 6, 7, 8, 11, 12, 14, 16, 18, 19, 23, 28, and 29). We also highlighted potential high-impact variants and candidate genes associated with Scrotal Circumference (SC) and Sheath Score (Sheath), which warrants further investigation in future studies.ConclusionThe work presented here is a step closer to identifying molecular mechanisms that underpin bull fertility and production. Our work also emphasises the importance of including the X chromosome in genomic analyses. Future research aims to investigate potential causative variants and genes in downstream analyses.

Development and validation of a horse reference panel for genotype imputation

BackgroundGenotype imputation is a cost-effective method to generate sequence-level genotypes for a large number of animals. Its application can improve the power of genomic studies, provided that the accuracy of imputation is sufficiently high. The purpose of this study was to develop an optimal strategy for genotype imputation from genotyping array data to sequence level in German warmblood horses, and to investigate the effect of different factors on the accuracy of imputation. Publicly available whole-genome sequence data from 317 horses of 46 breeds was used to conduct the analyses.ResultsDepending on the size and composition of the reference panel, the accuracy of imputation from medium marker density (60K) to sequence level using the software Beagle 5.1 ranged from 0.64 to 0.70 for horse chromosome 3. Generally, imputation accuracy increased as the size of the reference panel increased, but if genetically distant individuals were included in the panel, the accuracy dropped. Imputation was most precise when using a reference panel of multiple but related breeds and the software Beagle 5.1, which outperformed the other two tested computer programs, Impute 5 and Minimac 4. Genome-wide imputation for this scenario resulted in a mean accuracy of 0.66. Stepwise imputation from 60K to 670K markers and subsequently to sequence level did not improve the accuracy of imputation. However, imputation from higher density (670K) was considerably more accurate (about 0.90) than from medium density. Likewise, imputation in genomic regions with a low marker coverage resulted in a reduced accuracy of imputation.ConclusionsThe accuracy of imputation in horses was influenced by the size and composition of the reference panel, the marker density of the genotyping array, and the imputation software. Genotype imputation can be used to extend the limited amount of available sequence-level data from horses in order to boost the power of downstream analyses, such as genome-wide association studies, or the detection of embryonic lethal variants.

PD43-11 METHYLATION HETEROGENEITY IN CCRCC OFFERS INSIGHTS INTO TUMORIGENESIS AND BIOMARKER DEVELOPMENT

You have accessJournal of UrologyCME1 May 2022PD43-11 METHYLATION HETEROGENEITY IN CCRCC OFFERS INSIGHTS INTO TUMORIGENESIS AND BIOMARKER DEVELOPMENT Sabrina Rossi, Victoria Dombrowe, Sara Pita, Christopher Smith, Grant Stewart, Roland Schwarz, and Charlie Massie Sabrina RossiSabrina Rossi More articles by this author , Victoria DombroweVictoria Dombrowe More articles by this author , Sara PitaSara Pita More articles by this author , Christopher SmithChristopher Smith More articles by this author , Grant StewartGrant Stewart More articles by this author , Roland SchwarzRoland Schwarz More articles by this author , and Charlie MassieCharlie Massie More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002604.11AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Approximately 30% of patients with clear cell renal cell carcinoma (ccRCC) will develop a recurrence, however there are currently no validated prognostic biomarkers. This has been identified as a key research priority. Biomarker development has been hampered by significant genetic intra-tumoral heterogeneity (ITH), yet methylation ITH is largely uncharacterised. We therefore aim to evaluate methylation heterogeneity between patients, within patients and within individual tumour samples. METHODS: Multi-region samples were collected from fresh frozen nephrectomy specimens from individuals with ccRCC (Ethical approval: REC ID 03/018). Multiple tumour and normal samples were obtained from each patient (at least 5 samples per patient). Methylation analysis was performed using the Illumina EPIC-seq to obtain high-resolution sequence-level data. Matched samples were processed for copy-number alterations (whole exome sequencing) and gene expression (RNA-seq). An independent cohort of ccRCC tissue samples was sequenced for external validation. RESULTS: We sequenced 136 samples from 18 ccRCC patients, which consists of the largest multi-region methylation cohort to date. We observed extensive heterogeneity between patients, which dominated over within-patient heterogeneity. Although disordered methylation is believed to be a stochastic process, there were significant changes in methylation epipolymorphism between ccRCC and normal kidney at the promoter region of 47 known kidney cancer genes (including SLC16A3, MYC, WT1, KRT7 and KRT18). This finding was confirmed in an independent cohort of 71 ccRCC tumour and normal samples. Genes with significantly disordered methylation in tumour are more often associated with significant hypermethylation in ccRCC (87% genes hypermethylated) rather than hypomethylation, and are negatively correlated with gene expression, implying that methylation disorder may have a functional consequence. Several genes which have been implicated in ccRCC prognosis (including DKK2, SFRP1, CCND1, SERPINF1, SMAD3) demonstrated heterogeneous methylation patterns. CONCLUSIONS: Heterogeneous methylation between patients and within a sample may be a key determinant of the noted failure to validate prognostic genes in ccRCC. Disordered methylation and ITH may lead to inconsistent results amongst samples, which is exacerbated by different methylation platforms covering disparate CpGs within the promoter of genes. This key finding may help identify prognostic methylation markers in future. Source of Funding: Cancer Research UK © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e707 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Sabrina Rossi More articles by this author Victoria Dombrowe More articles by this author Sara Pita More articles by this author Christopher Smith More articles by this author Grant Stewart More articles by this author Roland Schwarz More articles by this author Charlie Massie More articles by this author Expand All Advertisement PDF DownloadLoading ...

A Gene-Set Enrichment and Protein-Protein Interaction Network-Based GWAS with Regulatory SNPs Identifies Candidate Genes and Pathways Associated with Carcass Traits in Hanwoo Cattle.

Non-synonymous SNPs and protein coding SNPs within the promoter region of genes (regulatory SNPs) might have a significant effect on carcass traits. Imputed sequence level data of 10,215 Hanwoo bulls, annotated and filtered to include only regulatory SNPs (450,062 SNPs), were used in a genome-wide association study (GWAS) to identify loci associated with backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). A total of 15, 176, and 1 SNPs were found to be significantly associated (p < 1.11 × 10−7) with BFT, CWT, and EMA, respectively. The significant loci were BTA4 (CWT), BTA6 (CWT), BTA14 (CWT and EMA), and BTA19 (BFT). BayesR estimated that 1.1%~1.9% of the SNPs contributed to more than 0.01% of the phenotypic variance. So, the GWAS was complemented by a gene-set enrichment (GSEA) and protein–protein interaction network (PPIN) analysis in identifying the pathways affecting carcass traits. At p < 0.005 (~2,261 SNPs), 25 GO and 18 KEGG categories, including calcium signaling, cell proliferation, and folate biosynthesis, were found to be enriched through GSEA. The PPIN analysis showed enrichment for 81 candidate genes involved in various pathways, including the PI3K-AKT, calcium, and FoxO signaling pathways. Our finding provides insight into the effects of regulatory SNPs on carcass traits.

Reassessing the role of morphology in bryophyte phylogenetics: Combined data improves phylogenetic inference despite character conflict

Morphological data has gained renewed attention and has been shown to be crucial in clarifying the phylogenetic relationship in a wide range of taxa. In the last decades, phylogenetic analyses of sequence-level data have radically modified the systematic schemes within bryophytes (early non-vascular land plants) and have revealed a widespread pattern of conflict with morphology-based classifications. Yet, a comprehensive evaluation of character conflict has not yet been performed in the context of combined matrices. In this study, we evaluate the impact of morphology on bryophyte phylogeny following a total-evidence approach across 10 published matrices. The analysed matrices span a wide range of bryophytes, taxonomic levels, gene sampling and number of morphological characters and taxa. Data conflict was addressed by measuring: (i) the topological congruence between individual partitions, (ii) changes in support values of the combined data relative to the molecular partition and (iii) clade stability. The association between these measures and the number of morphological characters per taxon (Nc/T ratio) and the proportion of non-fixed characters (i.e., inapplicable, polymorphic and missing data) was explored. In the individual partition analyses, the Nc/T ratio correlated positively with the topological congruence in six to seven datasets depending on the weighting scheme. The proportion of non-fixed cells had a minor influence on congruence between data partitions. The number of characters and proportion of non-fixed data varied significantly between morphological datasets that improved congruence between data types. This variation suggests that morphological datasets affect the results of combined analyses in different ways, depending on the taxa studied. Combined analyses revealed that, despite the low congruence values between partitions, integrating data types improves support values and stability. However, while non-fixed data had no negative effect on support values, stability was reduced as the proportion of non-fixed cells increased. Nc/T ratio was negatively associated with support values and it showed ambiguous responses in stability evaluations. Overall, the results indicate that adding morphology may contribute to the inference of phylogenetic relationships of bryophytes despite character conflict. Our findings suggest that merely comparing (a) morphology-based classifications with molecular phylogenies or (b) the outcome from individual data partitions can misestimate data conflict. These findings imply that analyses of combined data may provide conservative assessments of data conflict and, eventually, lead to an improved sampling of morphological characters in large-scale analyses of bryophytes.

SeqOthello: querying RNA-seq experiments at scale

We present SeqOthello, an ultra-fast and memory-efficient indexing structure to support arbitrary sequence query against large collections of RNA-seq experiments. It takes SeqOthello only 5 min and 19.1 GB memory to conduct a global survey of 11,658 fusion events against 10,113 TCGA Pan-Cancer RNA-seq datasets. The query recovers 92.7% of tier-1 fusions curated by TCGA Fusion Gene Database and reveals 270 novel occurrences, all of which are present as tumor-specific. By providing a reference-free, alignment-free, and parameter-free sequence search system, SeqOthello will enable large-scale integrative studies using sequence-level data, an undertaking not previously practicable for many individual labs.

Abstract 393: Predicting DNA accessibility in the pan-cancer tumor genome using RNA-Seq, WGS, and deep learning

Abstract DNA accessibility, chromatin regulation, and genome methylation are key drivers of cancer transcription. However, there is much left to be understood about the functional implications of sequence-level data to the regulation of gene expression, especially when it comes to the noncoding genome. Recently [Kelley, D., Snoek, J., and Rinn, J., Genome Res. 2016] trained neural networks to effectively predict DNA accessibility in multiple cell types. These models make it possible to explore the impact of mutations on the predicted accessibility and thus directly link one aspect of the gene regulation puzzle all the way down to the sequence level. We present a model with improved performance on the original dataset of 164 ENCODE and Roadmap Epigenomics Consortium sample types, and then extend the method to provide predictions on any sample with RNA-Seq data without need of DNase-seq for the sample. We first demonstrate that with several model and algorithmic changes we improve performance across 164 cell types from a mean AUC of 0.895 to a mean AUC of 0.910. Unfortunately current accessibility models require DNase-seq for each new cell type. Models for detecting transcription factor binding sites, which rely on ChIP-seq for training data, also share this issue. In order to generalize sequence-based predictive models to apply to unseen cell types without requiring re-training we investigate using RNA-Seq as a proxy signature of cell type. The model aims to capture the interdependence of gene expression levels that characterize a cell with the regulatory logic in which sequence-level signatures are combined to determine accessibility without restriction to cell type. We explore the model’s performance when applied to held-out cell types in the ENCODE and Roadmap Epigenomics Consortium data as well as data from the TCGA Pan-Cancer initiative. We look for the impact of non-coding changes in whole-genome sequencing data in TCGA samples, and report on predicted differences in DNA accessibility across cancer subtypes. Citation Format: Kamil Wnuk, Jeremi Sudol, Shahrooz Rabizadeh, Patrick Soon-Shiong, Christopher Szeto, Charles Vaske. Predicting DNA accessibility in the pan-cancer tumor genome using RNA-Seq, WGS, and deep learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 393. doi:10.1158/1538-7445.AM2017-393

Early mutation bursts in colorectal tumors.

Tumor growth is an evolutionary process involving accumulation of mutations, copy number alterations, and cancer stem cell (CSC) division and differentiation. As direct observation of this process is impossible, inference regarding when mutations occur and how stem cells divide is difficult. However, this ancestral information is encoded within the tumor itself, in the form of intratumoral heterogeneity of the tumor cell genomes. Here we present a framework that allows simulation of these processes and estimation of mutation rates at the various stages of tumor development and CSC division patterns for single-gland sequencing data from colorectal tumors. We parameterize the mutation rate and the CSC division pattern, and successfully retrieve their posterior distributions based on DNA sequence level data. Our approach exploits Approximate Bayesian Computation (ABC), a method that is becoming widely-used for problems of ancestral inference.

Phylogeny of Neckeropsis and Himantocladium (Neckeraceae, Bryophytina)

Two closely related tropical genera from the pleurocarpous moss family Neckeraceae are revised: the second largest genus in the family, Neckeropsis, currently with 29 species, and Himantocladium, comprising six species. Twenty-one species of Neckeropsis and five of Himantocladium were included in this study, which is based on phylogenetic analyses using sequence level data from the plastid (rps4)-trnT-trnL-trnF cluster and rpl16 as well as nuclear ITS1 &amp; 2. Neckeropsis appeared as polyphyletic. Neckeropsis s. str. comprises 12 species and a further four species, not included in the analysis, are tentatively retained in the genus based on morphology. Four new genera are segregated from Neckeropsis: Pengchengwua (with one sp.), Planicladium (two spp.), Pseudoparaphysanthus (five spp.), and Neckeromnion (five spp.). Neckeropsis gracilis is synonymized with Neckeromnion urocladum and reported for the first time from Laos. Four molecular analyzed species are resolved in the Himantocladium s. str. clade, with a fifth species being added solely on morphological basis. Himantocladium formosicum is transferred to Neckeropsis. Leaf-like paraphyses (ramenta), unique in Neckeropsis s. str., have evolved two times. In addition, the rare Noguchiodendron sphaerocarpum from the Himalayan region, the single species in its genus, was included in a phylogenetic analysis for the first time and it was found to be closely related to the Asian genus Taiwanobryum.

Critical Path to Tuberculosis Drug Regimens: Global collaboration to accelerate development of novel drug regimens and rapid drug susceptibility tests for tuberculosis

The Critical Path to Tuberculosis Drug Regimens (CPTR) initiative aims to support the rational deployment of new tuberculosis (TB) therapies by speeding the development and impact of new and markedly improved drug regimens as well as rapid drug susceptibility tests. Co-founded by the Bill &amp; Melinda Gates Foundation, the Critical Path Institute, and the TB Alliance in 2010, CPTR is a coalition comprising the world’s leading pharmaceutical companies, product development sponsors, diagnostic companies, regulatory agencies, and civil society organizations which support and catalyze advances in regulatory science, the development of infrastructure, and other progress needed to accelerate the pace of development and introduction of novel regimens and rapid drug susceptibility tests. This manuscript summarizes the work of two subgroups within CPTR, the Regulatory Sciences and Rapid Drug Susceptibility Test consortia, and their efforts to drive innovation. These consortia are supported by a robust TB clinical data platform, which continues to evolve through contributions of contemporary TB clinical trial data sets as well as whole genome sequence level data from isolates across the globe. Examples of innovation are described and include a recently-qualified drug development tool and emerging programs to support the development of clinical trial simulation tools.

Critical Path to Tuberculosis Drug Regimens: Global collaboration to accelerate development of novel drug regimens and rapid drug susceptibility tests for tuberculosis

The Critical Path to Tuberculosis Drug Regimens (CPTR) initiative aims to support the rational deployment of new tuberculosis (TB) therapies by speeding the development and impact of new and markedly improved drug regimens as well as rapid drug susceptibility tests. Co-founded by the Bill &amp; Melinda Gates Foundation, the Critical Path Institute, and the TB Alliance in 2010, CPTR is a coalition comprising the world’s leading pharmaceutical companies, product development sponsors, diagnostic companies, regulatory agencies, and civil society organizations which support and catalyze advances in regulatory science, the development of infrastructure, and other progress needed to accelerate the pace of development and introduction of novel regimens and rapid drug susceptibility tests. This manuscript summarizes the work of two subgroups within CPTR, the Regulatory Sciences and Rapid Drug Susceptibility Test consortia, and their efforts to drive innovation. These consortia are supported by a robust TB clinical data platform, which continues to evolve through contributions of contemporary TB clinical trial data sets as well as whole genome sequence level data from isolates across the globe. Examples of innovation are described and include a recently-qualified drug development tool and emerging programs to support the development of clinical trial simulation tools.

Genome-wide association analysis of milk yield traits in Nordic Red Cattle using imputed whole genome sequence variants.

BackgroundThe Nordic Red Cattle consisting of three different populations from Finland, Sweden and Denmark are under a joint breeding value estimation system. The long history of recording of production and health traits offers a great opportunity to study production traits and identify causal variants behind them. In this study, we used whole genome sequence level data from 4280 progeny tested Nordic Red Cattle bulls to scan the genome for loci affecting milk, fat and protein yields.ResultsUsing a genome-wise significance threshold, regions on Bos taurus chromosomes 5, 14, 23, 25 and 26 were associated with fat yield. Regions on chromosomes 5, 14, 16, 19, 20 and 25 were associated with milk yield and chromosomes 5, 14 and 25 had regions associated with protein yield. Significantly associated variations were found in 227 genes for fat yield, 72 genes for milk yield and 30 genes for protein yield. Ingenuity Pathway Analysis was used to identify networks connecting these genes displaying significant hits. When compared to previously mapped genomic regions associated with fertility, significantly associated variations were found in 5 genes common for fat yield and fertility, thus linking these two traits via biological networks.ConclusionThis is the first time when whole genome sequence data is utilized to study genomic regions affecting milk production in the Nordic Red Cattle population. Sequence level data offers the possibility to study quantitative traits in detail but still cannot unambiguously reveal which of the associated variations is causative. Linkage disequilibrium creates difficulties to pinpoint the causative genes and variations. One solution to overcome these difficulties is the identification of the functional gene networks and pathways to reveal important interacting genes as candidates for the observed effects. This information on target genomic regions may be exploited to improve genomic prediction.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-016-0363-8) contains supplementary material, which is available to authorized users.

Effect of population stratification analysis on false-positive rates for common and rare variants

Principal components analysis (PCA) has been successfully used to correct for population stratification in genome-wide association studies of common variants. However, rare variants also have a role in common disease etiology. Whether PCA successfully controls population stratification for rare variants has not been addressed. Thus we evaluate the effect of population stratification analysis on false-positive rates for common and rare variants at the single-nucleotide polymorphism (SNP) and gene level. We use the simulation data from Genetic Analysis Workshop 17 and compare false-positive rates with and without PCA at the SNP and gene level. We found that SNPs’ minor allele frequency (MAF) influenced the ability of PCA to effectively control false discovery. Specifically, PCA reduced false-positive rates more effectively in common SNPs (MAF > 0.05) than in rare SNPs (MAF < 0.01). Furthermore, at the gene level, although false-positive rates were reduced, power to detect true associations was also reduced using PCA. Taken together, these results suggest that sequence-level data should be interpreted with caution, because extremely rare SNPs may exhibit sporadic association that is not controlled using PCA.

Genome-Wide Association Studies and Beyond

Genome-wide association studies (GWAS) provide an important avenue for undertaking an agnostic evaluation of the association between common genetic variants and risk of disease. Recent advances in our understanding of human genetic variation and the technology to measure such variation have made GWAS feasible. Over the past few years a multitude of GWAS have identified and replicated many associated variants. These findings are enriching our knowledge about the genetic basis of disease and leading some to advocate using GWA study results for genetic testing. For many of the GWA study results, however, the underlying mechanisms remain unclear and the findings explain only a limited amount of heritability. These issues may be clarified by more detailed investigations, including analyses of less common variants, sequence-level data, and environmental exposures. Such studies should help clarify the potential value of genetic testing to the public's health.

Fregene: Simulation of realistic sequence-level data in populations and ascertained samples

BackgroundFREGENE simulates sequence-level data over large genomic regions in large populations. Because, unlike coalescent simulators, it works forwards through time, it allows complex scenarios of selection, demography, and recombination to be modelled simultaneously. Detailed tracking of sites under selection is implemented in FREGENE and provides the opportunity to test theoretical predictions and gain new insights into mechanisms of selection. We describe here main functionalities of both FREGENE and SAMPLE, a companion program that can replicate association study datasets.ResultsWe report detailed analyses of six large simulated datasets that we have made publicly available. Three demographic scenarios are modelled: one panmictic, one substructured with migration, and one complex scenario that mimics the principle features of genetic variation in major worldwide human populations. For each scenario there is one neutral simulation, and one with a complex pattern of selection.ConclusionFREGENE and the simulated datasets will be valuable for assessing the validity of models for selection, demography and population genetic parameters, as well as the efficacy of association studies. Its principle advantages are modelling flexibility and computational efficiency. It is open source and object-oriented. As such, it can be customised and the range of models extended.

Sequence-level population simulations over large genomic regions.

Simulation is an invaluable tool for investigating the effects of various population genetics modeling assumptions on resulting patterns of genetic diversity, and for assessing the performance of statistical techniques, for example those designed to detect and measure the genomic effects of selection. It is also used to investigate the effectiveness of various design options for genetic association studies. Backward-in-time simulation methods are computationally efficient and have become widely used since their introduction in the 1980s. The forward-in-time approach has substantial advantages in terms of accuracy and modeling flexibility, but at greater computational cost. We have developed flexible and efficient simulation software and a rescaling technique to aid computational efficiency that together allow the simulation of sequence-level data over large genomic regions in entire diploid populations under various scenarios for demography, mutation, selection, and recombination, the latter including hotspots and gene conversion. Our forward evolution of genomic regions (FREGENE) software is freely available from www.ebi.ac.uk/projects/BARGEN together with an ancillary program to generate phenotype labels, either binary or quantitative. In this article we discuss limitations of coalescent-based simulation, introduce the rescaling technique that makes large-scale forward-in-time simulation feasible, and demonstrate the utility of various features of FREGENE, many not previously available.

Phylogeny of the Brachytheciaceae (Bryophyta) based on morphology and sequence level data.

Brachytheciaceae is often considered a taxonomically difficult group of mosses. For example, morphological variation has led to difficulty in generic delimitation. We used DNA sequence data (chloroplast psbT-H and trnL-F and nuclear ITS2) together with morphology (63 characters) to examine the relationships within this family. The combined unaligned length of the DNA sequences used in the phylogenetic analyses varied between 1277 and 1343 bp. For phylogeny reconstruction we performed direct optimization, as implemented in POY. Analyses were performed with three different gap costs and the morphological data partition was weighted both: (1) equal to gap cost, and (2) with a weight of one. The utility of sensitivity analysis has recently been cast into doubt; hence in this study it was performed only to explore the effects of weighting on homology statements and topologies and to enable more detailed comparisons between earlier studies utilizing the direct optimization method. The wide sequence length variation of non-coding ITS2 sequences resulted in character optimizations (i.e., "alignments") of very different lengths when various gap costs were applied. Despite this variation, the topologies of equally parsimonious trees remained fairly stable. The inclusion of several outgroups, instead of only one, was observed to increase the congruence between data sets and to slightly increase the resolution. An inversion event in the 9 bp loop region in the chloroplast psbT-N spacer in mosses has been postulated to include only uninformative variation, thus possibly negatively impacting the phylogeny reconstruction. Despite this inversion, its variation within Brachytheciaceae was clearly congruent with information from other sources, but inclusion of these 9 bp in the analysis had only a minor effect on the phylogenetic results. In the most parsimonious topology, which was obtained with equal weighting of all data, Meteoriaceae and Brachytheciaceae were resolved as monophyletic sister groups, which had recently been suggested based on a few shared morphological characters. Our study revealed some new generic relationships within the Brachytheciaceae, which are discussed in light of the morphological characters traditionally used for generic delimitation.

Micrococcal nuclease as a DNA structural probe: Its recognition sequences, their genomic distribution and correlation with DNA structure determinants

We have analyzed micrococcal nuclease (MNase) DNA cleavage patterns at the sequence level by examining 2.3 × 10 3 base-pairs of data derived from the Drosophila melanogaster 44D larval cuticle locus. Within this region, MNase preferentially cleaved 140 sites. Clusters of these sites appear to generate the preferential MNase eukaryotic DNA cleavage sites seen on agarose gels at roughly 100 to 300 base-pair intervals. These clusters of preferential cleavage sites rarely occur within gene coding regions. The analysis revealed that duplex DNA sequences preferentially cleaved by MNase are generally determined by a single strand sequence: d(A-T) n , where n ⩾ 1, flanked by a 5′ dC or dG. Cleavage of the other strand is generally staggered 5′ by several nucleotides and occurs even if such sequences are absent on that strand. An empirical predictive DNA cleavage model derived from a statistical analysis of the sequence level data was applied to seven eukaryotic gene loci of known sequence. The predicted patterns were in good general agreement with the previously observed eukaryotic gene/spacer cleavage pattern. Statistical analysis also revealed that sites of predicted preferential DNA cleavage occur less frequently in protein coding regions than for randomized sequences of the same length and nucleotide content. Comparison of the MNase cleavage patterns to the sequence-dependent pattern of binding energies between duplex DNA strands indicates that MNase preferentially cleaves sequences with low helix stability.