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Cartwright, R.

Publications and source records attributed to Cartwright, R..

2 recordsLinked to original sources

accuMUlate: A mutation caller designed for mutation accumulation experiments

MotivationMutation accumulation (MA) is the most widely used method for directly studying the effects of mutation. Modern sequencing technologies have led to an increased interest in MA experiments. By sequencing whole genomes from MA lines, researchers can directly study the rate and molecular spectra of spontaneous mutations and use these results to understand how mutation contributes to biological processes. At present there is no software designed specifically for identifying mutations from MA lines. Studies that combine MA with whole genome sequencing use custom bioinformatic pipelines that implement heuristic rules to identify putative mutations.\n\nResultsHere we describe O_SCPLOWACCUC_SCPLOWMUO_SCPLOWLATEC_SCPLOW, a program that is designed to detect mutations from MA experiments. O_SCPLOWACCUC_SCPLOWMUO_SCPLOWLATEC_SCPLOW implements a probabilistic model that reflects the design of a typical MA experiments while being flexible enough to accommodate properties unique to any particular experiment. For each putative mutation identified from this model O_SCPLOWACCUC_SCPLOWMUO_SCPLOWLATEC_SCPLOW calculates a set of summary statistics that can be used to filter sites that may be false positives. A companion tool, O_SCPLOWDENOMINATEC_SCPLOW, can be used to apply filtering rules based on these statistics to simulated mutations and thus identify the number of callable sites per sample.\n\nAvailabilitySource code and releases available from https://github.com/dwinter/accuMUlate.

bioinformatics

Genetic architecture of early childhood growth phenotypes gives insights into their link with later obesity

Early childhood growth patterns are associated with adult metabolic health, but the underlying mechanisms are unclear. We performed genome-wide meta-analyses and follow-up in up to 22,769 European children for six early growth phenotypes derived from longitudinal data: peak height and weight velocities, age and body mass index (BMI) at adiposity peak (AP ~9 months) and rebound (AR ~5-6 years). We identified four associated loci (P< 5x10-8): LEPR/LEPROT with BMI at AP, FTO and TFAP2B with Age at AR and GNPDA2 with BMI at AR. The observed AR-associated SNPs at FTO, TFAP2B and GNPDA2 represent known adult BMI-associated variants. The common BMI at AP associated variant at LEPR/LEPROT was not associated with adult BMI but was associated with LEPROT gene expression levels, especially in subcutaneous fat (P<2x10-51). We identify strong positive genetic correlations between early growth and later adiposity traits, and analysis of the full discovery stage results for Age at AR revealed enrichment for insulin-like growth factor 1 (IGF-1) signaling and apolipoprotein pathways. This genome-wide association study suggests mechanistic links between early childhood growth and adiposity in later childhood and adulthood, highlighting these early growth phenotypes as potential targets for the prevention of obesity.

genomics