MLIP: using multiple processors to compute the posterior probability of linkage

<p>Abstract</p> <p>Background</p> <p>Localization of complex traits by genetic linkage analysis may involve exploration of a vast multidimensional parameter space. The posterior probability of linkage (PPL), a class of statistics for complex trait genetic mapping in humans, is designed to model the trait model complexity represented by the multidimensional parameter space in a mathematically rigorous fashion. However, the method requires the evaluation of integrals with no functional form, making it difficult to compute, and thus further test, develop and apply. This paper describes MLIP, a multiprocessor two-point genetic linkage analysis system that supports statistical calculations, such as the PPL, based on the full parameter space implicit in the linkage likelihood.</p> <p>Results</p> <p>The fundamental question we address here is whether the use of additional processors effectively reduces total computation time for a PPL calculation. We use a variety of data – both simulated and real – to explore the question "how close can we get?" to linear speedup. Empirical results of our study show that MLIP does significantly speed up two-point log-likelihood ratio calculations over a grid space of model parameters.</p> <p>Conclusion</p> <p>Observed performance of the program is dependent on characteristics of the data including granularity of the parameter grid space being explored and pedigree size and structure. While work continues to further optimize performance, the current version of the program can already be used to efficiently compute the PPL. Thanks to MLIP, full multidimensional genome scans are now routinely being completed at our centers with runtimes on the order of days, not months or years.</p

Novel caries loci in children and adults implicated by genome-wide analysis of families

Background: Dental caries is a common chronic disease among children and adults alike, posing a substantial health burden. Caries is affected by multiple genetic and environmental factors, and prior studies have found that a substantial proportion of caries susceptibility is genetically inherited. Methods: To identify such genetic factors, we conducted a genome-wide linkage scan in 464 extended families with 2616 individuals from Iowa, Pennsylvania and West Virginia for three dental caries phenotypes: (1) PRIM: dichotomized as zero versus one or more affected primary teeth, (2) QTOT1: age-adjusted quantitative caries measure for both primary and permanent dentitions including pre-cavitated lesions, and (3) QTOT2: age-adjusted quantitative caries excluding pre-cavitated lesions. Genotyping was conducted for approximately 600,000 SNPs on an Illumina platform, pruned to 127,511 uncorrelated SNPs for the analyses reported here. Results: Multipoint non-parametric linkage analyses generated peak LOD scores exceeding 2.0 for eight genomic regions, but no LOD scores above 3.0 were observed. The maximum LOD score for each of the three traits was 2.90 at 1q25.3 for PRIM, 2.38 at 6q25.3 for QTOT1, and 2.76 at 5q23.3 for QTOT2. Some overlap in linkage regions was observed among the phenotypes. Genes with a potential role in dental caries in the eight chromosomal regions include CACNA1E, LAMC2, ALMS1, STAMBP, GXYLT2, SLC12A2, MEGF10, TMEM181, ARID1B, and, as well as genes in several immune gene families. Our results are also concordant with previous findings from association analyses on chromosomes 11 and 19. Conclusions: These multipoint linkage results provide evidence in favor of novel chromosomal regions, while also supporting earlier association findings for these data. Understanding the genetic etiology of dental caries will allow designing personalized treatment plans based on an individual’s genetic risk of disease

Evaluating the Performance of PRISMA Shortwave Infrared Imaging Sensor for Mapping Hydrothermally Altered and Weathered Minerals Using the Machine Learning Paradigm

Satellite images provide consistent and frequent information that can be used to estimate mineral resources over a large spatial extent. Advances in spaceborne hyperspectral remote sensing (HRS) and machine learning can help to support various remote-sensing-based applications, including mineral exploration. Leveraging these advances, the present study evaluates recently launched PRISMA spaceborne satellite images to map hydrothermally altered and weathered minerals using various machine-learning-based classification algorithms. The study was performed for the town of Jahazpur in Rajasthan, India (75°06′23.17″E, 25°25′23.37″N). The distribution map for minerals such as kaolinite, talc, and montmorillonite was generated using the spectral angle mapper technique. The resultant mineral distribution map was verified through an intensive field validation survey on surface exposures of the minerals. Furthermore, the obtained pixels of the end-members were used to develop the machine-learning-based classification models. Measures such as accuracy, kappa coefficient, F1 score, precision, recall, and ROC curve were employed to evaluate the performance of developed models. The results show that the stochastic gradient descent and artificial-neural-network-based multilayer perceptron classifiers were more accurate than other algorithms. Results confirm that the PRISMA dataset has enormous potential for mineral mapping in mountainous regions utilizing a machine-learning-based classification framework

Identifying genetic risk loci for diabetic complications and showing evidence for heterogeneity of type 1 diabetes based on complications risk

<div><p>There is a growing body of evidence suggesting that type 1 diabetes (T1D) is a genetically heterogeneous disease. However, the extent of this heterogeneity, and what observations may distinguish different forms, is unclear. One indicator may be T1D-related microvascular complications (MVCs), which are familial, but occur in some families, and not others. We tested the hypothesis that T1D plus MVC is genetically distinct from T1D without MCV. We studied 415 families (2,462 individuals, 896 with T1D) using genome-wide linkage analysis, comparing families with and without MVC. We also tested for interaction between identified loci and alleles at the <i>HLA-DRB1</i> locus. We found significant linkage scores at 1p36.12, 1q32.1, 8q21.3, 12p11.21 and 22q11.21. In all regions except 1p36.12, linkage scores differed between MVC-based phenotype groups, suggesting that families with MVCs express different genetic influences than those without. Our linkage results also suggested gene-gene interaction between the above putative loci and the HLA region; HLA-based strata produced significantly increased linkage scores in some strata, but not others within a phenotype group. We conclude that families with type 1 diabetes plus MVCs are genetically different from those with diabetes alone.</p></div

Sample sizes for phenotype groups.

<p>Sample sizes for phenotype groups.</p

Hypothetical linkage score profiles expected under (A) absence of genetic heterogeneity, (B) presence of heterogeneity due to additional loci conferring MVC risk only, and (C) presence of heterogeneity due to different T1D risk conferring loci MVC vs. non-MVC pedigrees.

<p>Hypothetical linkage score profiles expected under (A) absence of genetic heterogeneity, (B) presence of heterogeneity due to additional loci conferring MVC risk only, and (C) presence of heterogeneity due to different T1D risk conferring loci MVC vs. non-MVC pedigrees.</p

MMLS of T1D, Complications and No complications phenotype groups.

<p>Note: In each plot, solid lines represent T1D, dashed line Complications, and dotted lines No Complications; x-axis represents genetic position on the chromosome in cM; y-axis represents MMLS score. Each plot shows a region spanning approximately 30 cM around the peak position.</p

Regions with significant linkage MMLS.

<p>Regions with significant linkage MMLS.</p

Counts of (A) subjects and (B) pedigrees by specific MVCs, retinopathy, nephropathy, and neuropathy showing overlap across the three categories.

<p>Counts of (A) subjects and (B) pedigrees by specific MVCs, retinopathy, nephropathy, and neuropathy showing overlap across the three categories.</p