Biomedical subjects

Nicola J Armstrong

Publications and source records attributed to Nicola J Armstrong.

6 recordsLinked to original sources

PubMed2021

1q21.1 distal copy number variants are associated with cerebral and cognitive alterations in humans.

Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers-the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function.

Brain↗

PubMed2006

A continuum approach to modelling cell-cell adhesion.

Cells adhere to each other through the binding of cell adhesion molecules at the cell surface. This process, known as cell-cell adhesion, is fundamental in many areas of biology, including early embryo development, tissue homeostasis and tumour growth. In this paper we develop a new continuous mathematical model of this phenomenon by considering the movement of cells in response to the adhesive forces generated through binding. We demonstrate that our model predicts the aggregation behaviour of a disassociated adhesive cell population. Further, when the model is extended to represent the interactions between multiple populations, we demonstrate that it is capable of replicating the different types of cell sorting behaviour observed experimentally. The resulting pattern formation is a direct consequence of the relative strengths of self-population and cross-population adhesive bonds in the model. While cell sorting behaviour has been captured previously with discrete approaches, it has not, until now, been observed with a fully continuous model.

Animals↗

PubMed2006

Mind the gap: analysis of marker-assisted breeding strategies for inbred mouse strains.

The development of congenic mouse strains is the principal approach for confirming and fine mapping quantitative trait loci, as well as for comparing the phenotypic effect of a transgene or gene-targeted disruption between different inbred mouse strains. The traditional breeding scheme calls for at least nine consecutive backcrosses before establishing a congenic mouse strain. Recent availability of genome sequence and high-throughput genotyping now permit the use of polymorphic DNA markers to reduce this number of backcrosses, and empirical data suggest that marker-assisted breeding may require as few as four backcrosses. We used simulation studies to investigate the efficiency of different marker-assisted breeding schemes by examining the trade-off between the number of backcrosses, the number of mice produced per generation, and the number of genotypes per mouse required to achieve a quality congenic mouse strain. An established model of crossover interference was also incorporated into these simulations. The quality of the strain produced was assessed by the probability of an undetected region of heterozygosity (i.e., "gaps") in the recipient genetic background, while maintaining the desired donor-derived interval. Somewhat surprisingly, we found that there is a relatively high probability for undetected gaps in potential breeders for establishing a congenic mouse strain. Marker-assisted breeding may decrease the number of backcross generations required to generate a congenic strain, but only additional backcrossing will guarantee a reduction in the number and length of undetected gaps harboring contaminating donor alleles.

Animals↗

PubMed2005

Incorporating interference into linkage analysis for experimental crosses.

The phenomenon of interference in genetic recombination is well-known and studied in a wide variety of organisms. Multilocus linkage analysis, which makes use of recombination patterns among all genetic markers simultaneously, is routinely used with data on humans and experimental organisms to build genetic maps. It is also used to try to determine the genes involved in traits of interest, such as common diseases. Most linkage analyses performed today ignore the occurrence of genetical interference. We present an extension to the Lander-Green algorithm for experimental crosses (backcross and intercross) to incorporate crossover interference according to the chi2 model. Simulation results show the impact of using this model on the accuracy of estimated genetic maps.

Algorithms↗

PubMed2004

Power calculations for selective genotyping in QTL mapping in backcross mice.

Selective genotyping concerns the genotyping of a portion of individuals chosen on the basis of their phenotypic values. Often individuals are selected for genotyping from the high and low extremes of the phenotypic distribution. This procedure yields savings in cost and time by decreasing the total number of individuals genotyped. Previous work by Darvasi et al. (1993) has shown that the power to detect a QTL by genotyping 40-50 % of a population is roughly equivalent to genotyping the entire sample. However, these power studies have not accounted for different strategies of analysing the data when phenotypes of individuals in the middle are excluded, nor have they investigated the genome-wide type I error rate under these different strategies or different selection percentages. Further, these simulation studies have not considered markers over the entire genome. In this paper, we present simulation studies of power for the maximum likelihood approach to QTL mapping by Lander & Botstein (1989) in the context of selective genotyping. We calculate the power of selectively genotyping the individuals from the middle of the phenotypic distribution when performing QTL mapping over the whole mouse genome.

Animals↗

PubMed2004

Microarray data analysis: from hypotheses to conclusions using gene expression data.

We review several commonly used methods for the design and analysis of microarray data. To begin with, some experimental design issues are addressed. Several approaches for pre-processing the data (filtering and normalization) before the statistical analysis stage are then discussed. A common first step in this type of analysis is gene selection based on statistical testing. Two approaches, permutation and model-based methods are explained and we emphasize the need to correct for multiple testing. Moreover, powerful approaches based on gene sets are mentioned. Clustering of either genes or samples is frequently performed when analyzing microarray data. We summarize the basics of both supervised and unsupervised clustering (classification). The latter may be of use for creating diagnostic arrays, for example. Construction of biological networks, such as pathways, is a statistically challenging but complex task that is a relatively new development and hence mentioned only briefly. We finish with some remarks on literature and software. The emphasis in this paper is on the philosophy behind several statistical issues and on a critical interpretation of microarray related analysis methods.

Cluster Analysis↗