Quantifying single nucleotide variant detection sensitivity in exome sequencing

BACKGROUND: The targeted capture and sequencing of genomic regions has rapidly demonstrated its utility in genetic studies. Inherent in this technology is considerable heterogeneity of target coverage and this is expected to systematically impact our sensitivity to detect genuine polymorphisms. To fully interpret the polymorphisms identified in a genetic study it is often essential to both detect polymorphisms and to understand where and with what probability real polymorphisms may have been missed. RESULTS: Using down-sampling of 30 deeply sequenced exomes and a set of gold-standard single nucleotide variant (SNV) genotype calls for each sample, we developed an empirical model relating the read depth at a polymorphic site to the probability of calling the correct genotype at that site. We find that measured sensitivity in SNV detection is substantially worse than that predicted from the naive expectation of sampling from a binomial. This calibrated model allows us to produce single nucleotide resolution SNV sensitivity estimates which can be merged to give summary sensitivity measures for any arbitrary partition of the target sequences (nucleotide, exon, gene, pathway, exome). These metrics are directly comparable between platforms and can be combined between samples to give “power estimates” for an entire study. We estimate a local read depth of 13X is required to detect the alleles and genotype of a heterozygous SNV 95% of the time, but only 3X for a homozygous SNV. At a mean on-target read depth of 20X, commonly used for rare disease exome sequencing studies, we predict 5–15% of heterozygous and 1–4% of homozygous SNVs in the targeted regions will be missed. CONCLUSIONS: Non-reference alleles in the heterozygote state have a high chance of being missed when commonly applied read coverage thresholds are used despite the widely held assumption that there is good polymorphism detection at these coverage levels. Such alleles are likely to be of functional importance in population based studies of rare diseases, somatic mutations in cancer and explaining the “missing heritability” of quantitative traits

Neuroinflammation and Neuronal Loss Precede Aβ Plaque Deposition in the hAPP-J20 Mouse Model of Alzheimer's Disease

Recent human trials of treatments for Alzheimer's disease (AD) have been largely unsuccessful, raising the idea that treatment may need to be started earlier in the disease, well before cognitive symptoms appear. An early marker of AD pathology is therefore needed and it is debated as to whether amyloid-βAβ? plaque load may serve this purpose. We investigated this in the hAPP-J20 AD mouse model by studying disease pathology at 6, 12, 24 and 36 weeks. Using robust stereological methods, we found there is no neuron loss in the hippocampal CA3 region at any age. However loss of neurons from the hippocampal CA1 region begins as early as 12 weeks of age. The extent of neuron loss increases with age, correlating with the number of activated microglia. Gliosis was also present, but plateaued during aging. Increased hyperactivity and spatial memory deficits occurred at 16 and 24 weeks. Meanwhile, the appearance of plaques and oligomeric Aβ were essentially the last pathological changes, with significant changes only observed at 36 weeks of age. This is surprising given that the hAPP-J20 AD mouse model is engineered to over-expresses Aβ. Our data raises the possibility that plaque load may not be the best marker for early AD and suggests that activated microglia could be a valuable marker to track disease progression. © 2013 Wright et al

Dietary garlic and hip osteoarthritis: evidence of a protective effect and putative mechanism of action

Background Patterns of food intake and prevalent osteoarthritis of the hand, hip, and knee were studied using the twin design to limit the effect of confounding factors. Compounds found in associated food groups were further studied in vitro. Methods Cross-sectional study conducted in a large population-based volunteer cohort of twins. Food intake was evaluated using the Food Frequency Questionnaire; OA was determined using plain radiographs. Analyses were adjusted for age, BMI and physical activity. Subsequent in vitro studies examined the effects of allium-derived compounds on the expression of matrix-degrading proteases in SW1353 chondrosarcoma cells. Results Data were available, depending on phenotype, for 654-1082 of 1086 female twins (median age 58.9 years; range 46-77). Trends in dietary analysis revealed a specific pattern of dietary intake, that high in fruit and vegetables, showed an inverse association with hip OA (p = 0.022). Consumption of 'non-citrus fruit' (p = 0.015) and 'alliums' (p = 0.029) had the strongest protective effect. Alliums contain diallyl disulphide which was shown to abrogate cytokine-induced matrix metalloproteinase expression. Conclusions Studies of diet are notorious for their confounding by lifestyle effects. While taking account of BMI, the data show an independent effect of a diet high in fruit and vegetables, suggesting it to be protective against radiographic hip OA. Furthermore, diallyl disulphide, a compound found in garlic and other alliums, represses the expression of matrix-degrading proteases in chondrocyte-like cells, providing a potential mechanism of action

LAMP: Large Deep Nets with Automated Model Parallelism for Image Segmentation

Deep Learning (DL) models are becoming larger, because the increase in model size might offer significant accuracy gain. To enable the training of large deep networks, data parallelism and model parallelism are two well-known approaches for parallel training. However, data parallelism does not help reduce memory footprint per device. In this work, we introduce Large deep 3D ConvNets with Automated Model Parallelism (LAMP) and investigate the impact of both input's and deep 3D ConvNets' size on segmentation accuracy. Through automated model parallelism, it is feasible to train large deep 3D ConvNets with a large input patch, even the whole image. Extensive experiments demonstrate that, facilitated by the automated model parallelism, the segmentation accuracy can be improved through increasing model size and input context size, and large input yields significant inference speedup compared with sliding window of small patches in the inference. Code is available\footnote{https://monai.io/research/lamp-automated-model-parallelism}.Comment: MICCAI 2020 Early Accepted paper. Code is available\footnote{https://monai.io/research/lamp-automated-model-parallelism

Effects of gestational age at birth on cognitive performance : a function of cognitive workload demands

Objective: Cognitive deficits have been inconsistently described for late or moderately preterm children but are consistently found in very preterm children. This study investigates the association between cognitive workload demands of tasks and cognitive performance in relation to gestational age at birth. Methods: Data were collected as part of a prospective geographically defined whole-population study of neonatal at-risk children in Southern Bavaria. At 8;5 years, n = 1326 children (gestation range: 23–41 weeks) were assessed with the K-ABC and a Mathematics Test. Results: Cognitive scores of preterm children decreased as cognitive workload demands of tasks increased. The relationship between gestation and task workload was curvilinear and more pronounced the higher the cognitive workload: GA2 (quadratic term) on low cognitive workload: R2 = .02, p<0.001; moderate cognitive workload: R2 = .09, p<0.001; and high cognitive workload tasks: R2 = .14, p<0.001. Specifically, disproportionally lower scores were found for very (<32 weeks gestation) and moderately (32–33 weeks gestation) preterm children the higher the cognitive workload of the tasks. Early biological factors such as gestation and neonatal complications explained more of the variance in high (12.5%) compared with moderate (8.1%) and low cognitive workload tasks (1.7%). Conclusions: The cognitive workload model may help to explain variations of findings on the relationship of gestational age with cognitive performance in the literature. The findings have implications for routine cognitive follow-up, educational intervention, and basic research into neuro-plasticity and brain reorganization after preterm birth

Whole-exome re-sequencing in a family quartet identifies POP1 mutations as the cause of a novel skeletal dysplasia

Recent advances in DNA sequencing have enabled mapping of genes for monogenic traits in families with small pedigrees and even in unrelated cases. We report the identification of disease-causing mutations in a rare, severe, skeletal dysplasia, studying a family of two healthy unrelated parents and two affected children using whole-exome sequencing. The two affected daughters have clinical and radiographic features suggestive of anauxetic dysplasia (OMIM 607095), a rare form of dwarfism caused by mutations of RMRP. However, mutations of RMRP were excluded in this family by direct sequencing. Our studies identified two novel compound heterozygous loss-of-function mutations in POP1, which encodes a core component of the RNase mitochondrial RNA processing (RNase MRP) complex that directly interacts with the RMRP RNA domains that are affected in anauxetic dysplasia. We demonstrate that these mutations impair the integrity and activity of this complex and that they impair cell proliferation, providing likely molecular and cellular mechanisms by which POP1 mutations cause this severe skeletal dysplasia