Estimation of the Rate of SNP Genotyping Errors From DNA Extracted From Different Tissues

High density single nucleotide polymorphism (SNP) genotyping panels provide an alternative to microsatellite markers for genome scans. However, genotype errors have a major impact on power to detect linkage or association and are difficult to detect for SNPs. We estimated error rates with the Affymetrix GeneChip® SNP platform in samples from a family with a mixed set of monozygotic (MZ) and dizygotic (DZ) triplets using lymphocyte, buccal DNA and samples from whole genome amplification using the multiple displacement amplification (MDA) technique. The average call rate from 58,960 SNPs for five genomic samples was 99.48%. Comparison of results for the MZ twins showed only three discordant genotypes (concordance rate 99.995%). The mean concordance rate for comparisons of samples from lymphocyte and buccal DNA was 99.97%. Mendelian inconsistencies were identified in 46 SNPs with errors in one or more family members, a rate of 0.022%. Observed genotype concordance rates between parents, between parents and children, and among siblings were consistent with previously reported allele frequencies and Hardy-Weinberg equilibrium. Using the MDA technique, results for two samples had equivalent high accuracy to results with genomic samples. However, the SNP call rate for the remaining seven samples varied from 72.5% to 99.5%, with an average of 86.11%. Quality of the DNA sample following the MDA reaction appears to be the critical factor in SNP call rate for MDA samples. Our results demonstrate highly accurate and reproducible genotyping for the Affymetrix GeneChip® Human Mapping Set in lymphocyte and buccal DNA samples.</p

Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization

Ancient Lowland Maya neighborhoods: Average Nearest Neighbor analysis and kernel density models, environments, and urban scale

Many humans live in large, complex political centers, composed of multi-scalar communities including neighborhoods and districts. Both today and in the past, neighborhoods form a fundamental part of cities and are defined by their spatial, architectural, and material elements. Neighborhoods existed in ancient centers of various scales, and multiple methods have been employed to identify ancient neighborhoods in archaeological contexts. However, the use of different methods for neighborhood identification within the same spatiotemporal setting results in challenges for comparisons within and between ancient societies. Here, we focus on using a single method—combining Average Nearest Neighbor (ANN) and Kernel Density (KD) analyses of household groups—to identify potential neighborhoods based on clusters of households at 23 ancient centers across the Maya Lowlands. While a one-size-fits all model does not work for neighborhood identification everywhere, the ANN/KD method provides quantifiable data on the clustering of ancient households, which can be linked to environmental zones and urban scale. We found that centers in river valleys exhibited greater household clustering compared to centers in upland and escarpment environments. Settlement patterns on flat plains were more dispersed, with little discrete spatial clustering of households. Furthermore, we categorized the ancient Maya centers into discrete urban scales, finding that larger centers had greater variation in household spacing compared to medium-sized and smaller centers. Many larger political centers possess heterogeneity in household clustering between their civic-ceremonial cores, immediate hinterlands, and far peripheries. Smaller centers exhibit greater household clustering compared to larger ones. This paper quantitatively assesses household clustering among nearly two dozen centers across the Maya Lowlands, linking environment and urban scale to settlement patterns. The findings are applicable to ancient societies and modern cities alike; understanding how humans form multi-scalar social groupings, such as neighborhoods, is fundamental to human experience and social organization

Evaluation of Urine CCA Assays for Detection of Schistosoma mansoni Infection in Western Kenya

Although accurate assessment of the prevalence of Schistosoma mansoni is important for the design and evaluation of control programs, the most widely used tools for diagnosis are limited by suboptimal sensitivity, slow turn-around-time, or inability to distinguish current from former infections. Recently, two tests that detect circulating cathodic antigen (CCA) in urine of patients with schistosomiasis became commercially available. As part of a larger study on schistosomiasis prevalence in young children, we evaluated the performance and diagnostic accuracy of these tests—the carbon test strip designed for use in the laboratory and the cassette format test intended for field use. In comparison to 6 Kato-Katz exams, the carbon and cassette CCA tests had sensitivities of 88.4% and 94.2% and specificities of 70.9% and 59.4%, respectively. However, because of the known limitations of the Kato-Katz assay, we also utilized latent class analysis (LCA) incorporating the CCA, Kato-Katz, and schistosome-specific antibody results to determine their sensitivities and specificities. The laboratory-based CCA test had a sensitivity of 91.7% and a specificity of 89.4% by LCA while the cassette test had a sensitivity of 96.3% and a specificity of 74.7%. The intensity of the reaction in both urine CCA tests reflected stool egg burden and their performance was not affected by the presence of soil transmitted helminth infections. Our results suggest that urine-based assays for CCA may be valuable in screening for S. mansoni infections

Insights into hominid evolution from the gorilla genome sequence.

Gorillas are humans' closest living relatives after chimpanzees, and are of comparable importance for the study of human origins and evolution. Here we present the assembly and analysis of a genome sequence for the western lowland gorilla, and compare the whole genomes of all extant great ape genera. We propose a synthesis of genetic and fossil evidence consistent with placing the human-chimpanzee and human-chimpanzee-gorilla speciation events at approximately 6 and 10 million years ago. In 30% of the genome, gorilla is closer to human or chimpanzee than the latter are to each other; this is rarer around coding genes, indicating pervasive selection throughout great ape evolution, and has functional consequences in gene expression. A comparison of protein coding genes reveals approximately 500 genes showing accelerated evolution on each of the gorilla, human and chimpanzee lineages, and evidence for parallel acceleration, particularly of genes involved in hearing. We also compare the western and eastern gorilla species, estimating an average sequence divergence time 1.75 million years ago, but with evidence for more recent genetic exchange and a population bottleneck in the eastern species. The use of the genome sequence in these and future analyses will promote a deeper understanding of great ape biology and evolution

Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data.

Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a "phenotype first" approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands

Defining public open spaces: an investigation framework to inform planning and design decision-making processes

Typomorphological analysis has been used to study building types, but it is seldom applied to open spaces. This paper argues that the same systematic and rigorous approach can be applied to define public open spaces. It explores the potential of the application of a systematic analysis of types and forms to define urban landscapes. Drawing on existing literature, this paper identifies specific attributes related to urban landscape elements including formal and spatial aspects. This paper highlights the potential of open space networks to respond to the contemporary challenges facing urban designers working to create better places to live in. This paper contributes to the field of design research through the development of a method of survey and analysis to inform design decision-making processes. Its significance lies in proposing a comprehensive framework to contribute to a more detailed definition of urban landscape character and inform the development of sustainable urban strategies

Prevalence and architecture of de novo mutations in developmental disorders.

The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximately half of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year