25. Multiple Imputation for Nonresponse in Surveys. By D. B. Rubin. ISBN 0 471 08705 X. Wiley, Chichester, 1987. 258 pp. £30.25.

Multiple Imputation for Nonresponse in Surveys

This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

Machine learning with Python

High-throughput technologies have revolutionized medical research. The advent of genotyping arrays enabled large-scale genome-wide association studies and methods for examining global transcript levels, which gave rise to the field of "integrative genetics". Other omics technologies, such as proteomics and metabolomics, are now often incorporated into the everyday methodology of biological researchers. In this review, we provide an overview of such omics technologies and focus on methods for their integration across multiple omics layers. As compared to studies of a single omics type, multi-omics offers the opportunity to understand the flow of information that underlies disease.

/pdf/multi-omics-approaches-to-disease-3sfcj2uagy.pdf

Multi-omics approaches to disease.

Integrative Functional Genomic Analyses Implicate Specific Molecular Pathways and Circuits in Autism

Activation of the immune system during pregnancy can have varied effects on fetal development, and converging evidence highlights maternal immune activation as a risk factor for multiple neurological conditions. In this Review, Knuesel and colleagues discuss the involvement of maternal immune activation in schizophrenia, austim spectrum disorders, epilepsy and other disorders. The authors then discuss how preclinical data indicate a possible link between prenatal exposure to infection and susceptibility to neurodegenerative disease, and they go on to identify fertile ground for further translational research.

Maternal immune activation and abnormal brain development across CNS disorders.

We demonstrate that a deep neural network can be trained to virtually refocus a two-dimensional fluorescence image onto user-defined three-dimensional (3D) surfaces within the sample. Using this method, termed Deep-Z, we imaged the neuronal activity of a Caenorhabditis elegans worm in 3D using a time sequence of fluorescence images acquired at a single focal plane, digitally increasing the depth-of-field by 20-fold without any axial scanning, additional hardware or a trade-off of imaging resolution and speed. Furthermore, we demonstrate that this approach can correct for sample drift, tilt and other aberrations, all digitally performed after the acquisition of a single fluorescence image. This framework also cross-connects different imaging modalities to each other, enabling 3D refocusing of a single wide-field fluorescence image to match confocal microscopy images acquired at different sample planes. Deep-Z has the potential to improve volumetric imaging speed while reducing challenges relating to sample drift, aberration and defocusing that are associated with standard 3D fluorescence microscopy.

Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning

Combined analysis of exome sequencing points toward a major role for transcription regulation during brain development in autism

Autism spectrum disorders (ASD) are neurodevelopmental disorders with phenotypic and genetic heterogeneity. Recent studies have reported rare and de novo mutations in ASD, but the allelic architecture of ASD remains unclear. To assess the role of common and rare variations in ASD, we constructed a gene co-expression network based on a widespread survey of gene expression in the human brain. We identified modules associated with specific cell types and processes. By integrating known rare mutations and the results of an ASD genome-wide association study (GWAS), we identified two neuronal modules that are perturbed by both rare and common variations. These modules contain highly connected genes that are involved in synaptic and neuronal plasticity and that are expressed in areas associated with learning and memory and sensory perception. The enrichment of common risk variants was replicated in two additional samples which include both simplex and multiplex families. An analysis of the combined contribution of common variants in the neuronal modules revealed a polygenic component to the risk of ASD. The results of this study point toward contribution of minor and major perturbations in the two sub-networks of neuronal genes to ASD risk.

/pdf/networks-of-neuronal-genes-affected-by-common-and-rare-4jh3w164dy.pdf

Networks of Neuronal Genes Affected by Common and Rare Variants in Autism Spectrum Disorders

Recombination events are not uniformly distributed and often cluster in narrow regions known as recombination hotspots. Several studies using different approaches have dramatically advanced our understanding of recombination hotspot regulation. Population genetic data have been used to map and quantify hotspots in the human genome. Genetic variation in recombination rates and hotspots usage have been explored in human pedigrees, mouse intercrosses, and by sperm typing. These studies pointed to the central role of the PRDM9 gene in hotspot modulation. In this study, we used single nucleotide polymorphisms (SNPs) from whole-genome resequencing and genotyping studies of mouse inbred strains to estimate recombination rates across the mouse genome and identified 47,068 historical hotspots—an average of over 2477 per chromosome. We show by simulation that inbred mouse strains can be used to identify positions of historical hotspots. Recombination hotspots were found to be enriched for the predicted binding sequences for different alleles of the PRDM9 protein. Recombination rates were on average lower near transcription start sites (TSS). Comparing the inferred historical recombination hotspots with the recent genome-wide mapping of double-strand breaks (DSBs) in mouse sperm revealed a significant overlap, especially toward the telomeres. Our results suggest that inbred strains can be used to characterize and study the dynamics of historical recombination hotspots. They also strengthen previous findings on mouse recombination hotspots, and specifically the impact of sequence variants in Prdm9.

https://www.genetics.org/content/genetics/191/3/757.full.pdf

Fine-Scale Maps of Recombination Rates and Hotspots in the Mouse Genome

Selfish genetic elements spread in natural populations and have an important role in genome evolution. We discovered a selfish element causing embryonic lethality in crosses between wild strains of the nematode Caenorhabditis elegans . The element is made up of sup-35 , a maternal-effect toxin that kills developing embryos, and pha-1 , its zygotically expressed antidote. pha-1 has long been considered essential for pharynx development on the basis of its mutant phenotype, but this phenotype arises from a loss of suppression of sup-35 toxicity. Inactive copies of the sup-35/pha-1 element show high sequence divergence from active copies, and phylogenetic reconstruction suggests that they represent ancestral stages in the evolution of the element. Our results suggest that other essential genes identified by genetic screens may turn out to be components of selfish elements.

/pdf/a-maternal-effect-selfish-genetic-element-in-caenorhabditis-1skngzgoui.pdf

Eyal Ben-David

Papers

Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning

Combined analysis of exome sequencing points toward a major role for transcription regulation during brain development in autism

Networks of Neuronal Genes Affected by Common and Rare Variants in Autism Spectrum Disorders

Fine-Scale Maps of Recombination Rates and Hotspots in the Mouse Genome

A maternal-effect selfish genetic element in Caenorhabditis elegans