We present a statistical framework for estimation and application of sample allele frequency spectra from New-Generation Sequencing (NGS) data. In this method, we first estimate the allele frequency spectrum using maximum likelihood. In contrast to previous methods, the likelihood function is calculated using a dynamic programming algorithm and numerically optimized using analytical derivatives. We then use a bayesian method for estimating the sample allele frequency in a single site, and show how the method can be used for genotype calling and SNP calling. We also show how the method can be extended to various other cases including cases with deviations from Hardy-Weinberg equilibrium. We evaluate the statistical properties of the methods using simulations and by application to a real data set.

Although it is generally accepted that cellular differentiation requires changes to transcriptional networks, dynamic regulation of promoters and enhancers at specific sets of genes has not been previously studied en masse. Exploiting the fact that active promoters and enhancers are transcribed, we simultaneously measured their activity in 19 human and 14 mouse time courses covering a wide range of cell types and biological stimuli. Enhancer RNAs, then messenger RNAs encoding transcription factors, dominated the earliest responses. Binding sites for key lineage transcription factors were simultaneously overrepresented in enhancers and promoters active in each cellular system. Our data support a highly generalizable model in which enhancer transcription is the earliest event in successive waves of transcriptional change during cellular differentiation or activation.

Evolutionarily conserved RFX transcription factors (TFs) regulate their target genes through a DNA sequence motif called the X-box. Thereby they regulate cellular specialization and terminal differentiation. Here, we provide a comprehensive analysis of all the eight human RFX genes (RFX1-8), their spatial and temporal expression profiles, potential upstream regulators and target genes.

In this paper, we mine full mtDNA sequences from an exome capture data set of 2000 Danes, showing that it is possible to get high-quality full-genome sequences of the mitochondrion from this resource. The sample includes 1000 individuals with type 2 diabetes and 1000 controls. We characterise the variation found in the mtDNA sequence in Danes and relate the variation to diabetes risk as well as to several blood phenotypes of the controls but find no significant associations. We report 2025 polymorphisms, of which 393 have not been reported previously. These 393 mutations are both very rare and estimated to be caused by very recent mutations but individuals with type 2 diabetes do not possess more of these variants. Population genetics analysis using Bayesian skyline plot shows a recent history of rapid population growth in the Danish population in accordance with the fact that >40% of variable sites are observed as singletons.

The mouse and human brain express a large number of noncoding RNAs (ncRNAs). Some of these are known to participate in neural progenitor cell fate determination, cell differentiation, neuronal and synaptic plasticity and transposable elements derived ncRNAs contribute to somatic variation. Dysregulation of specific long ncRNAs (lncRNAs) has been shown in neuro-developmental and neuro-degenerative diseases thus highlighting the importance of lncRNAs in brain function. Even though it is known that lncRNAs are expressed in cells at low levels in a tissue-specific manner, bioinformatics analyses of brain-specific ncRNAs has not been performed. We analyzed previously published custom microarray ncRNA expression data generated from twelve human tissues to identify tissue-specific ncRNAs. We find that among the 12 tissues studied, brain has the largest number of ncRNAs. Our analyses show that genes in the vicinity of brain-specific ncRNAs are significantly up regulated in the brain. Investigations of repeat representation show that brain-specific ncRNAs are significantly more likely to originate in repeat regions especially DNA/TcMar-Tigger compared with non-tissue-specific ncRNAs. We find SINE/Alus depleted from brain-specific dataset when compared with non-tissue-specific ncRNAs. Our data provide a bioinformatics comparison between brain-specific and non tissue-specific ncRNAs. This article is part of a Directed Issue entitled: The Non-coding RNA Revolution.

Most methods for next-generation sequencing (NGS) data analyses incorporate information regarding allele frequencies using the assumption of Hardy-Weinberg equilibrium (HWE) as a prior. However, many organisms including those that are domesticated, partially selfing, or with asexual life cycles show strong deviations from HWE. For such species, and specially for low-coverage data, it is necessary to obtain estimates of inbreeding coefficients (F) for each individual before calling genotypes. Here, we present two methods for estimating inbreeding coefficients from NGS data based on an expectation-maximization (EM) algorithm. We assess the impact of taking inbreeding into account when calling genotypes or estimating the site frequency spectrum (SFS), and demonstrate a marked increase in accuracy on low-coverage highly inbred samples. We demonstrate the applicability and efficacy of these methods in both simulated and real data sets.

Genome-wide association studies and linkage studies have identified 20 validated genetic variants associated with obesity and/or related phenotypes. The variants are common, and they individually exhibit small-to-modest effect sizes.

Theophylline is a nonspecific inhibitor of phosphodiesterases that, despite exerting bronchodilator and anti-inflammatory effects, is a third-line therapy rarely used to treat chronic airflow limitation. We wished to evaluate the efficacy of oral theophylline as measured by improvements in trough (pre-dose) or peak (post-dose) FEV1 and FVC in patients with clinically stable COPD.

Association studies using genetic data from SNP-chip-based imputation or low-depth sequencing data provide a cost-efficient design for large-scale association studies. We explore methods for performing association studies applicable to such genetic data and investigate how using different priors when estimating genotype probabilities affects the association results. Our proposed method, ANGSD-asso's latent model, models the unobserved genotype as a latent variable in a generalized linear model framework. The software is implemented in C/C++ and can be run multi-threaded. ANGSD-asso is based on genotype probabilities, which can be estimated using either the sample allele frequency or the individual allele frequencies as a prior. We explore through simulations how genotype probability-based methods compare with using genetic dosages. Our simulations show that in a structured population using the individual allele frequency prior has better power than the sample allele frequency. In scenarios with sequencing depth and phenotype correlation ANGSD-asso's latent model has higher statistical power and less bias than using dosages. Adding additional covariates to the linear model of ANGSD-asso's latent model has higher statistical power and less bias than other methods that accommodate genotype uncertainty, while also being much faster. This is shown with imputed data from UK Biobank and simulations.

Capsicum is one of the major vegetable crops grown worldwide. Current subdivision in clades and species is based on morphological traits and coarse sets of genetic markers. Broad variability of fruits has been driven by breeding programs and has been mainly studied by linkage analysis. We discovered 746k variable sites by sequencing 1.8% of the genome in a collection of 373 accessions belonging to 11 Capsicum species from 51 countries. We describe genomic variation at population-level, confirm major subdivision in clades and species, and show that the known major subdivision of C. annuum separates large and bulky fruits from small ones. In C. annuum, we identify four novel loci associated with phenotypes determining the fruit shape, including a non-synonymous mutation in the gene Longifolia 1-like (CA03g16080). Our collection covers all the economically important species of Capsicum widely used in breeding programs and represent the widest and largest study so far in terms of the number of species and number of genetic variants analyzed. We identified a large set of markers that can be used for population genetic studies and genetic association analyses. Our results provide a comprehensive and precise perspective on genomic variability in Capsicum at population-level and suggest that future fine genetic association studies will yield useful results for breeding.

African wild pigs have a contentious evolutionary and biogeographic history. Until recently, desert warthog (Phacochoerus aethiopicus) and common warthog (P. africanus) were considered a single species. Molecular evidence surprisingly suggested they diverged at least 4.4 million years ago, and possibly outside of Africa. We sequenced the first whole-genomes of four desert warthogs and 35 common warthogs from throughout their range. We show that these two species diverged much later than previously estimated, 400,000-1,700,000 years ago depending on assumptions of gene flow. This brings it into agreement with the paleontological record. We found that the common warthog originated in western Africa and subsequently colonized eastern and southern Africa. During this range expansion, the common warthog interbred with the desert warthog, presumably in eastern Africa, underlining this region's importance in African biogeography. We found that immune system-related genes may have adaptively introgressed into common warthogs, indicating that resistance to novel diseases was one of the most potent drivers of evolution as common warthogs expanded their range. Hence, we solve some of the key controversies surrounding warthog evolution and reveal a complex evolutionary history involving range expansion, introgression, and adaptation to new diseases.

Age-related changes in DNA methylation occurring in blood leukocytes during early childhood may reflect epigenetic maturation. We hypothesized that some of these changes involve gene networks of critical relevance in leukocyte biology and conducted a prospective study to elucidate the dynamics of DNA methylation. Serial blood samples were collected at 3, 6, 12, 24, 36, 48 and 60 months after birth in ten healthy girls born in Finland and participating in the Type 1 Diabetes Prediction and Prevention Study. DNA methylation was measured using the HumanMethylation450 BeadChip.

PROM1 is the gene encoding prominin-1 or CD133, an important cell surface marker for the isolation of both normal and cancer stem cells. PROM1 transcripts initiate at a range of transcription start sites (TSS) associated with distinct tissue and cancer expression profiles. Using high resolution Cap Analysis of Gene Expression (CAGE) sequencing we characterize TSS utilization across a broad range of normal and developmental tissues. We identify a novel proximal promoter (P6) within CD133(+) melanoma cell lines and stem cells. Additional exon array sampling finds P6 to be active in populations enriched for mesenchyme, neural stem cells and within CD133(+) enriched Ewing sarcomas. The P6 promoter is enriched with respect to previously characterized PROM1 promoters for a HMGI/Y (HMGA1) family transcription factor binding site motif and exhibits different epigenetic modifications relative to the canonical promoter region of PROM1.

Genome-wide association studies have mainly relied on common HapMap sequence variations. Recently, sequencing approaches have allowed analysis of low frequency and rare variants in conjunction with common variants, thereby improving the search for functional variants and thus the understanding of the underlying biology of human traits and diseases. Here, we used a large Icelandic whole genome sequence dataset combined with Danish exome sequence data to gain insight into the genetic architecture of serum levels of vitamin B(12) (B12) and folate. Up to 22.9 million sequence variants were analyzed in combined samples of 45,576 and 37,341 individuals with serum B(12) and folate measurements, respectively. We found six novel loci associating with serum B(12) (CD320, TCN2, ABCD4, MMAA, MMACHC) or folate levels (FOLR3) and confirmed seven loci for these traits (TCN1, FUT6, FUT2, CUBN, CLYBL, MUT, MTHFR). Conditional analyses established that four loci contain additional independent signals. Interestingly, 13 of the 18 identified variants were coding and 11 of the 13 target genes have known functions related to B(12) and folate pathways. Contrary to epidemiological studies we did not find consistent association of the variants with cardiovascular diseases, cancers or Alzheimer's disease although some variants demonstrated pleiotropic effects. Although to some degree impeded by low statistical power for some of these conditions, these data suggest that sequence variants that contribute to the population diversity in serum B(12) or folate levels do not modify the risk of developing these conditions. Yet, the study demonstrates the value of combining whole genome and exome sequencing approaches to ascertain the genetic and molecular architectures underlying quantitative trait associations.

We evaluated the impact on diabetes-related intermediary traits of common novel type 2 diabetes-associated variants in the JAZF1 (rs864745), CDC123/CAMK1D (rs12779790), TSPAN8 (rs7961581), THADA (rs7578597), ADAMTS9 (rs4607103), and NOTCH2 (rs10923931) loci, which were recently identified by meta-analysis of genome-wide association data.

Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non-model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low-depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low-depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species.

Long-term treatment is recommended in major depressive disorder (MDD) to prevent relapse and to restore functioning. The aim of this study (Orion; NCT01360866) was to assess the long-term safety, tolerability, and efficacy of open-label treatment with adjunctive brexpiprazole in adult patients with MDD.

Objective: Heat shock protein 47 (HSP47) is a collagen-specific molecular chaperone that facilitates collagen maturation. Its role in cancer remains largely unknown. In this study, we investigated the roles of HSP47 in colorectal cancer (CRC) and therapy resistance. Methods: Expression of HSP47 in CRC tissues was examined (1) in paired human CRC/adjacent normal tissues, using real time quantitative reverse transcription polymerase chain reaction (qRT-PCR), The Cancer Genome Atlas (TCGA) database, and 22 independent microarray databases (curated CRC). In vitro studies on several CRC cell lines (HCT116, RKO and CCL228) with modulated HSP47 expression were conducted to assess cell viability and apoptosis (TUNEL assay and caspase-3/-7) during exposure to chemotherapy. AKT signaling and co-immunoprecipitation studies were performed to examine HSP47 and PHLPP1 interaction. In vivo studies using tumor xenografts were conducted to assess the effects of HSP47 modulation on tumor growth and therapy response. Results: HSP47 was upregulated in CRC and was associated with poor prognosis in individuals with CRC. In vitro, HSP47 overexpression supported the survival of CRC cells, whereas its knockdown sensitized cells to 5-fluorouracil (5-FU). HSP47 promoted survival by inhibiting apoptosis, enhancing AKT phosphorylation, and decreasing expression of the AKT-specific phosphatase PHLPP1 when cells were exposed to chemotherapy. These effects were partly results of the interaction between HSP47 and PHLPP1, which decreased PHLPP1 stability and led to more persistent AKT activity. In vivo, HSP47 supported tumor growth despite 5-FU treatment. Conclusions: HSP47 supports the growth of CRC tumors and suppresses the efficacy of chemotherapy via modulation of AKT signaling.

The Inuit ancestors of the Greenlandic people arrived in Greenland close to 1,000 years ago.1 Since then, Europeans from many different countries have been present in Greenland. Consequently, the present-day Greenlandic population has ∼25% of its genetic ancestry from Europe.2 In this study, we investigated to what extent different European countries have contributed to this genetic ancestry. We combined dense SNP chip data from 3,972 Greenlanders and 8,275 Europeans from 14 countries and inferred the ancestry contribution from each of these 14 countries using haplotype-based methods. Due to the rapid increase in population size in Greenland over the past ∼100 years, we hypothesized that earlier European interactions, such as pre-colonial Dutch whalers and early German and Danish-Norwegian missionaries, as well as the later Danish colonists and post-colonial immigrants, all contributed European genetic ancestry. However, we found that the European ancestry is almost entirely Danish and that a substantial fraction is from admixture that took place within the last few generations.

Principal component analysis (PCA) is widely used in statistics, machine learning, and genomics for dimensionality reduction and uncovering low-dimensional latent structure. To address the challenges posed by ever-growing data size, fast and memory-efficient PCA methods have gained prominence. In this paper, we propose a novel randomized singular value decomposition (RSVD) algorithm implemented in PCAone, featuring a window-based optimization scheme that enables accelerated convergence while improving the accuracy. Additionally, PCAone incorporates out-of-core and multithreaded implementations for the existing Implicitly Restarted Arnoldi Method (IRAM) and RSVD. Through comprehensive evaluations using multiple large-scale real-world data sets in different fields, we show the advantage of PCAone over existing methods. The new algorithm achieves significantly faster computation time while maintaining accuracy comparable to the slower IRAM method. Notably, our analyses of UK Biobank, comprising around 0.5 million individuals and 6.1 million common single nucleotide polymorphisms, show that PCAone accurately computes the top 40 principal components within 9 h. This analysis effectively captures population structure, signals of selection, structural variants, and low recombination regions, utilizing <20 GB of memory and 20 CPU threads. Furthermore, when applied to single-cell RNA sequencing data featuring 1.3 million cells, PCAone, accurately capturing the top 40 principal components in 49 min. This performance represents a 10-fold improvement over state-of-the-art tools.