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Motivation: Bulked segregant analysis combined with next generation sequencing has proven to be a simple and efficient approach for fast mapping of quantitative trait loci (QTLs). However, how to estimate the proportion of phenotypic variance explained by a QTL (or termed QTL heritability) in such pooled QTL mapping is an unsolved problem. Results: In this paper, we propose a method called PQHE to estimate QTL heritability using pooled sequencing data obtained under different experimental designs. Simulation studies indicated that our method is correct and feasible. Four practical examples from rice and yeast are demonstrated, each representing a different situation. [ABSTRACT FROM AUTHOR]

Mapping quantitative trait loci (QTL) using genetic marker information is a time-consuming analysis that has interested the mapping community in recent decades. The increasing amount of genetic marker data allows one to consider ever more precise QTL analyses while increasing the demand for computation. Part of the difficulty of detecting QTLs resides in finding appropriate critical values or threshold values, above which a QTL effect is considered significant. Different approaches exist to determine these thresholds, using either empirical methods or algebraic approximations. In this article, we present a new implementation of existing software, QTLMap, which takes advantage of the data parallel nature of the problem by offsetting heavy computations to a graphics processing unit (GPU). Developments on the GPU were implemented using Cuda technology. This new implementation performs up to 75 times faster than the previous multicore implementation, while maintaining the same results and level of precision (Double Precision) and computing both QTL values and thresholds. This speedup allows one to perform more complex analyses, such as linkage disequilibrium linkage analyses (LDLA) and multiQTL analyses, in a reasonable time frame. [ABSTRACT FROM AUTHOR]

This work revisits the classic problem of coverage in genomic shotgun assembly (the 'Lander-Waterman statistics'). A novel formulation, based on the analysis of an autonomous Markov automaton, is presented, and two main conclusions are derived. The first is an evaluation of the minimum multiplicity ('coverage') required to achieve uninterrupted covering (one single contig) with a prescribed confidence level. The second is a detailed analysis of the effect of replacing the hypothesis of fixed-length genomic fragments with that of an arbitrary distribution of lengths over a finite interval. [ABSTRACT FROM AUTHOR]

Current statistical methods to test association between rare variants and phenotypes are essentially the group-wise methods that collapse or aggregate all variants in a predefined group into a single variant. Comparing with the variant-by-variant methods, the group-wise methods have their advantages. However, two factors may affect the power of these methods. One is that some of the causal variants may be protective. When both risk and protective variants are presented, it will lose power by collapsing or aggregating all variants because the effects of risk and protective variants will counteract each other. The other is that not all variants in the group are causal; rather, a large proportion is believed to be neutral. When a large proportion of variants are neutral, collapsing or aggregating all variants may not be an optimal solution. We propose two alternative methods, adaptive clustering (AC) method and adaptive weighting (AW) method, aiming to test rare variant association in the presence of neutral and/or protective variants. Both of AC and AW are applicable to quantitative traits as well as qualitative traits. Results of extensive simulation studies show that AC and AW have similar power and both of them have clear advantages from power to computational efficiency comparing with existing group-wise methods and existing data-driven methods that allow neutral and protective variants. We recommend AW method because AW method is computationally more efficient than AC method. [ABSTRACT FROM AUTHOR]

Most important agronomic and quality traits of crops are quantitative in nature. The genetic variations in such traits are usually controlled by sets of genes called quantitative trait loci (QTLs), and the interactions between QTLs and the environment. It is crucial to understand the genetic architecture of complex traits to design efficient strategies for plant breeding. In the present study, a new experimental design and the corresponding statistical method are presented for QTL mapping. The proposed mapping population is composed of double backcross populations derived from backcrossing both homozygous parents to DH (double haploid) or RI (recombinant inbreeding) lines separately. Such an immortal mapping population allows for across-environment replications, and can be used to estimate dominance effects, epistatic effects, and QTL-environment interactions, remedying the drawbacks of a single backcross population. In this method, the mixed linear model approach is used to estimate the positions of QTLs and their various effects including the QTL additive, dominance, and epistatic effects, and QTL-environment interaction effects (QE). Monte Carlo simulations were conducted to investigate the performance of the proposed method and to assess the accuracy and efficiency of its estimations. The results showed that the proposed method could estimate the positions and the genetic effects of QTLs with high efficiency. [ABSTRACT FROM AUTHOR]

Estimation of the position and effect of sterility genes is an important problem to be solved to understand the sterility mechanism in remote hybridization in plants. In this study, a maximum likelihood (ML) method was used for estimation of the position and effect of sterility genes that exhibit epistasis in an F population using the distorted segregation of markers. The ML solutions for recombination fraction and viability were obtained via an expectation-maximization algorithm. The results of Monte Carlo simulations showed that the estimates of recombination fraction and viability were consistent with their true values. The bias and standard deviation of parameters indicated that a larger sample size, closer linkage and lower viability of sterile genes led to better estimates of the parameters involved. A subset of marker data of the F population derived from a single cross between the rice japonica cultivar Nipponbare and the indica cultivar Kasalath was analyzed using this method. Eight sterility genes were identified on chromosomes 1, 3, 6, 8 and 10, and significant epistasis was detected among four pairs of sterility genes. [ABSTRACT FROM AUTHOR]

Most of the important agronomic traits in crops, such as yield and quality, are complex traits affected by multiple genes with gene × gene interaction as well as gene × environment interaction. Understanding the genetic architecture of complex traits is a long-term task for quantitative geneticists and plant breeders who wish to design efficient breeding programs. Conventionally, the genetic properties of traits can be revealed by partitioning the total variation into variation components caused by specific genetic effects. With recent advances in molecular genotyping and high-throughput technology, the unraveling of the genetic architecture of complex traits by analyzing quantitative trait locus (QTL) has become possible. The improvement of complex traits has also been achieved by pyramiding individual QTL. In this review, we describe some statistical methods for QTL mapping that can be used to analyze QTL × QTL interaction and QTL × environment interaction, and discuss their applications in crop breeding for complex traits. [ABSTRACT FROM AUTHOR]

An advanced intercross line (AIL) is an easier and more cost-effective approach compared to recombinant inbred lines for fine mapping of quantitative trait loci (QTL) identified by F2 designs. In an AIL, a complex binary trait can be mapped through analysis of either continuously distributed proxy traits for the liability of the binary trait or the liability itself, the latter presenting the greater statistical challenge. In another work, we successfully applied both approaches in an AIL to fine map previously identified QTL underlying anatomical parameters of the cardiac inter-atrial septum including patent foramen ovale. Here, we describe the statistical methods that we used to analyse complex binary traits in our AIL design. This is achieved using a likelihood-based method, with the expectation-maximisation algorithm allowing use of standard logistic regression methods for model fitting. [ABSTRACT FROM AUTHOR]

As metagenomic studies continue to increase in their number, sequence volume and complexity, the scalability of biological analysis frameworks has become a rate-limiting factor to meaningful data interpretation. To address this issue, we have developed JCVI Metagenomics Reports (METAREP) as an open source tool to query, browse, and compare extremely large volumes of metagenomic annotations. Here we present improvements to this software including the implementation of a dynamic weighting of taxonomic and functional annotation, support for distributed searches, advanced clustering routines, and integration of additional annotation input formats. The utility of these improvements to data interpretation are demonstrated through the application of multiple comparative analysis strategies to shotgun metagenomic data produced by the National Institutes of Health Roadmap for Biomedical Research Human Microbiome Project (HMP) (http:// nihroadmap.nih.gov). Specifically, the scalability of the dynamic weighting feature is evaluated and established by its application to the analysis of over 400 million weighted gene annotations derived from 14 billion short reads as predicted by the HMP Unified Metabolic Analysis Network (HUMAnN) pipeline. Further, the capacity of METAREP to facilitate the identification and simultaneous comparison of taxonomic and functional annotations including biological pathway and individual enzyme abundances from hundreds of community samples is demonstrated by providing scenarios that describe how these data can be mined to answer biological questions related to the human microbiome. These strategies provide users with a reference of how to conduct similar large-scale metagenomic analyses using METAREP with their own sequence data, while in this study they reveal insights into the nature and extent of variation in taxonomic and functional profiles across body habitats and individuals. Over one thousand HMP WGS datasets and the latest open source code are available at http://www.jcvi.org/hmp-metarep. [ABSTRACT FROM AUTHOR]

The article presents the findings of a research conducted on examining the statistical properties of interval mapping methods on quantitative trait loci (QTL). It identifies the sources of the bias, in particular in the case of analysis of data issued from outbred populations. It discusses the methodology of the research. It concludes that one should be attentive when one QTL is mapped at the location of one marker, especially under low power conditions.

Dental tissues play a very Important role for forensic purposes, because it is the hardest of all human tissues and can be preserved for a prolonged duration even subsequent to death, The aim of the this study was to determine the ABO blood grouping from the pulpal tissue of extracted teeth and to correlate the same with blood group details obtained from the study subjects. This descriptive cross sectional study was conducted on 50 subjects. Blood group was determined on blood which was obtained from blood already taken for Hepatitis screening. To determine association Goodman Kruskal Gamma test was applied between blood sample from dental pulp and control in SPSS 20.0. Out of total participants, 29(58%) were females and 21(42%) were males. The mean age was 25.56±5.17 years, ranging from 18 to 38 years. All the correlation (association) was very high and statistically significant. Overall correlation was very (r=0.99, P=0.00). The correlation was ranging from 0.87(blood group B) to 1.00 (blood AB and O). Similarly, the correlation among Rh+ and Rhwas also very high i.e. 0.98 and 0.88 respectively and was statistically significantly. The finidngs of this investigation showed that there is high correlation of the pulpal blood group with venous blood. So the blood group of this population can determined from dental pulp. [ABSTRACT FROM AUTHOR]

The article discusses a technique reported after a study for functional annotations of fine mapping, which is an integrative statistical method known as the Probabilistic Annotation Integrator (PAINTOR).