Statistical analysis of short template switch mutations in human genomes

Many complex rearrangements arise in human genomes through template switch mutations, which occur during DNA replication when there is a transient polymerase switch to an alternate template nearby in three-dimensional space. These variants are routinely captured at kilobase-to-megabase scales in studies of genetic variation by using methods for structural variant calling. However, the genomic and evolutionary consequences of replication-based rearrangements remain poorly characterised at smaller scales, where they are usually interpreted as complex clusters of independent substitutions, insertions and deletions. In this thesis, I describe statistical methods for the detection and interpretation of short template switch mutations within DNA sequence data. I then use my methods to explore small-scale template switch mutagenesis within human genome evolution, population variation, and cancer. I show that small-scale, replication- based rearrangements are a ubiquitous feature of the germline and somatic mutational landscape of human genomes.European Molecular Biology Laboratory National Institute for Health Researc

Short-range template switching in great ape genomes explored using pair hidden Markov models.

Many complex genomic rearrangements arise through template switch errors, which occur in DNA replication when there is a transient polymerase switch to an alternate template nearby in three-dimensional space. While typically investigated at kilobase-to-megabase scales, the genomic and evolutionary consequences of this mutational process are not well characterised at smaller scales, where they are often interpreted as clusters of independent substitutions, insertions and deletions. Here we present an improved statistical approach using pair hidden Markov models, and use it to detect and describe short-range template switches underlying clusters of mutations in the multi-way alignment of hominid genomes. Using robust statistics derived from evolutionary genomic simulations, we show that template switch events have been widespread in the evolution of the great apes' genomes and provide a parsimonious explanation for the presence of many complex mutation clusters in their phylogenetic context. Larger-scale mechanisms of genome rearrangement are typically associated with structural features around breakpoints, and accordingly we show that atypical patterns of secondary structure formation and DNA bending are present at the initial template switch loci. Our methods improve on previous non-probabilistic approaches for computational detection of template switch mutations, allowing the statistical significance of events to be assessed. By specifying realistic evolutionary parameters based on the genomes and taxa involved, our methods can be readily adapted to other intra- or inter-species comparisons

Distances to Populous Clusters in the LMC via the K-Band Luminosity of the Red Clump

We present results from a study of the distances and distribution of a sample of intermediate-age clusters in the Large Magellanic Cloud. Using deep near-infrared photometry obtained with ISPI on the CTIO 4m, we have measured the apparent K-band magnitude of the core helium burning red clump stars in 17 LMC clusters. We combine cluster ages and metallicities with the work of Grocholski & Sarajedini to predict each cluster's absolute K-band red clump magnitude, and thereby calculate absolute cluster distances. An analysis of these data shows that the cluster distribution is in good agreement with the thick, inclined disk geometry of the LMC, as defined by its field stars. We also find that the old globular clusters follow the same distribution, suggesting that the LMC's disk formed at about the same time as the globular clusters, ~ 13 Gyr ago. Finally, we have used our cluster distances in conjunction with the disk geometry to calculate the distance to the LMC center, for which we find (m-M)o = 18.40 +/- 0.04_{ran} +/- 0.08_{sys}, or Do = 47.9 +/- 0.9 +/- 1.8 kpc.Comment: 31 pages including 5 figures and 7 tables. Accepted for publication in the August 2007 issue of A

The distribution and ecology of Arenaria norvegica Gunn. in Ireland

Arenaria norvegica subsp. norvegica was re-discovered in Ireland in 2008 after an absence of 47 years. A detailed survey in 2009 revealed a small population restricted to skeletal soils on the edges of limestone pavement. A. norvegica subsp. anglica occurs in an almost identical habitat in Northern England. Irish plants also appear to flower at least a month earlier and produce more infloresences than in Britain. These differences warrant further investigation given the isolation of the Irish population. That it remained undetected for so long is remarkable and cautions against declaring a species as ‘extinct’ even in such a well botanized locality as the Burren

Long-term stability of cortisol production and metabolism throughout adolescence: longitudinal twin study

Life-course experiences have been postulated to program hypothalamus-pituitary-adrenal (HPA) axis activity, suggesting that HPA axis activity is, at least partially, stable over time. Yet, there is paucity of data on the long-term stability of cortisol production and metabolism. We performed a prospective follow-up study in twins recruited from a nationwide register to estimate the stability of cortisol production and metabolism over time, and the contribution of genetic and environmental factors to this stability. In total, 218 healthy mono- and dizygotic twins were included. At the ages of 9, 12 and 17 years, morning urine samples were collected for assessment (by gas chromatography-tandem mass spectrometry) of cortisol metabolites, enabling the calculation of cortisol metabolite excretion rate and cortisol metabolism activity. Our results showed a low stability for both cortisol metabolite excretion rate (with correlations <.20) and cortisol metabolism activity indices (with correlations of .25 to .46 between 9 and 12 years, -.02 to .15 between 12 and 17 years and .09 to .28 between 9 and 17 years). Because of the low stability over time, genetic and environmental contributions to this stability were difficult to assess, although it seemed to be mostly determined by genetic factors. The low stability in both cortisol production and metabolism between ages 9 and 17 years reflects the dynamic nature of the HPA axis

Exploring the Temporal Relation between Body Mass Index and Corticosteroid Metabolite Excretion in Childhood

Childhood obesity is associated with alterations in hypothalamus–pituitary–adrenal (HPA) axis activity. However, it is unknown whether these alterations are a cause or a consequence of obesity. This study aimed to explore the temporal relationship between cortisol production and metabolism, and body mass index (BMI). This prospective follow-up study included 218 children (of whom 50% were male), born between 1995 and 1996, who were assessed at the ages of 9, 12 and 17 years. Morning urine samples were collected for assessment of cortisol metabolites by gas chromatography-tandem mass spectrometry, enabling the calculation of cortisol metabolite excretion rate and cortisol metabolic pathways. A cross-lagged regression model was used to determine whether BMI at various ages during childhood predicted later cortisol production and metabolism parameters, or vice versa. The cross-lagged regression coefficients showed that BMI positively predicted cortisol metabolite excretion (p = 0.03), and not vice versa (p = 0.33). In addition, BMI predicted the later balance of 11β-hydroxysteroid dehydrogenase (HSD) activities (p = 0.07), and not vice versa (p = 0.55). Finally, cytochrome P450 3A4 activity positively predicted later BMI (p = 0.01). Our study suggests that changes in BMI across the normal range predict alterations in HPA axis activity. Therefore, the alterations in HPA axis activity as observed in earlier studies among children with obesity may be a consequence rather than a cause of increased BMI

Feature Extraction and Classification from Planetary Science Datasets enabled by Machine Learning

In this paper we present two examples of recent investigations that we have undertaken, applying Machine Learning (ML) neural networks (NN) to image datasets from outer planet missions to achieve feature recognition. Our first investigation was to recognize ice blocks (also known as rafts, plates, polygons) in the chaos regions of fractured ice on Europa. We used a transfer learning approach, adding and training new layers to an industry-standard Mask R-CNN (Region-based Convolutional Neural Network) to recognize labeled blocks in a training dataset. Subsequently, the updated model was tested against a new dataset, achieving 68% precision. In a different application, we applied the Mask R-CNN to recognize clouds on Titan, again through updated training followed by testing against new data, with a precision of 95% over 369 images. We evaluate the relative successes of our techniques and suggest how training and recognition could be further improved. The new approaches we have used for planetary datasets can further be applied to similar recognition tasks on other planets, including Earth. For imagery of outer planets in particular, the technique holds the possibility of greatly reducing the volume of returned data, via onboard identification of the most interesting image subsets, or by returning only differential data (images where changes have occurred) greatly enhancing the information content of the final data stream