Joint Analysis Method Research Articles

An Adaptive Fisher's Combination Method for Joint Analysis of Multiple Phenotypes in Association Studies.

Currently, the analyses of most genome-wide association studies (GWAS) have been performed on a single phenotype. There is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Therefore, using only one single phenotype may lose statistical power to identify the underlying genetic mechanism. There is an increasing need to develop and apply powerful statistical tests to detect association between multiple phenotypes and a genetic variant. In this paper, we develop an Adaptive Fisher’s Combination (AFC) method for joint analysis of multiple phenotypes in association studies. The AFC method combines p-values obtained in standard univariate GWAS by using the optimal number of p-values which is determined by the data. We perform extensive simulations to evaluate the performance of the AFC method and compare the power of our method with the powers of TATES, Tippett’s method, Fisher’s combination test, MANOVA, MultiPhen, and SUMSCORE. Our simulation studies show that the proposed method has correct type I error rates and is either the most powerful test or comparable with the most powerful test. Finally, we illustrate our proposed methodology by analyzing whole-genome genotyping data from a lung function study.

Analysis and synthesis of 3D shape families via deep‐learned generative models of surfaces

AbstractWe present a method for joint analysis and synthesis of geometrically diverse 3D shape families. Our method first learns part‐based templates such that an optimal set of fuzzy point and part correspondences is computed between the shapes of an input collection based on a probabilistic deformation model. In contrast to previous template‐based approaches, the geometry and deformation parameters of our part‐based templates are learned from scratch. Based on the estimated shape correspondence, our method also learns a probabilistic generative model that hierarchically captures statistical relationships of corresponding surface point positions and parts as well as their existence in the input shapes. A deep learning procedure is used to capture these hierarchical relationships. The resulting generative model is used to produce control point arrangements that drive shape synthesis by combining and deforming parts from the input collection. The generative model also yields compact shape descriptors that are used to perform fine‐grained classification. Finally, it can be also coupled with the probabilistic deformation model to further improve shape correspondence. We provide qualitative and quantitative evaluations of our method for shape correspondence, segmentation, fine‐grained classification and synthesis. Our experiments demonstrate superior correspondence and segmentation results than previous state‐of‐the‐art approaches.

DEM generation using bistatic interferometry: High-coherence pixel selection and residual reference phase compensation

The bistatic spaceborne/stationary InSAR configuration could be applied to generate digital elevation model (DEM). However, two factors limit the DEM precision improvement. On the one hand, due to the effects of the illuminator’s sidelobe and the high objects’ shadow, the pixels in the corresponding area might be polluted by the noise. On the other hand, owing to the presence of deviation (between the real value and the nominal value of the master/slave antenna positions), after utilizing the nominal value to compensate for the reference phase, the residual reference phase would remain. In order to solve the problems, we proposed a new bistatic DEM generation algorithm. Firstly, we applied the coherence-phase joint analysis method to select high-coherence points. Secondly, we deduced the transforming relationship from the deviation to the residual reference phase, and then compensated for the residual reference phase. In the experiment, the YaoGan-3 (an L-band spaceborne SAR) was selected as the transmitter and the two stationary receivers were mounted on the top of a tall building. The algorithm recommended in this paper was applied to process the measured bistatic data. The analysis of the results demonstrated that the elevation error of the generated DEM for some targets had been limited to the level of 1–2 m.

Genetic inheritance analysis of melon aphid (Aphis gossypii Glover) resistance in cucumber (Cucumis sativus L.)

AbstractThe melon aphid Aphis gossypii Glover is one of the most serious pests in cucumber production, often causing severe damage in commercial fields. Identifying and deploying resistant germplasm and understanding the inheritance of melon aphid resistance are required for cucumber geneticists to develop an effective breeding strategy. In this study, resistance of 30 cucumber selections to melon aphid was evaluated at the seedling stage. Six generations, P1, P2, F1, BC1, BC2 and F2 derived from the cross of JY30 (susceptible) × EP6392 (resistant), were used as genetic populations to study the inheritance of melon aphid resistance in cucumber with the mixed major gene plus polygene inheritance model with the joint analysis method of multiple generations. Eight of the 30 tested selections displayed resistance to the melon aphid. The resistance of cucumber to melon aphids was controlled by one additive and dominant major gene plus additive and dominant polygenes, and was affected by environment as well. The additive effect and the dominant effect of the major gene were greater than the additive effect and the dominant effect of the polygenes. The heritabilities of the major gene in BC1, BC2 and F2 were 63.62 %, 0 % and 70.39 %, respectively. The polygenic heritabilities were 22.62 %, 37.0 % and 9.32 %, and the ratios of the environmental variance to phenotype variance were 58.54 %, 63.16 % and 30.77 %. We conclude that selections of cucumber with high resistance to melon aphid could be screened in advanced generations.

Road freight energy efficiency and CO2 emissions in the Nordic countries

Nordic countries have committed to improve the energy efficiency and decrease the CO2 emissions of freight transport. The aim of this paper is to compare the energy efficiency and CO2 emissions in the road industry for the Nordic countries in 2010, in order to identify the key factors and their impact on energy efficiency and CO2 emissions. A joint analysis method was developed to compare data. Quantitative data was used to conduct a decomposition analysis for several sectors, taking several indicators into account. Statistics from Denmark, Finland, Norway and Sweden include continuous road haulier surveys, national account data and fuel consumption data. The CO2 emissions of road freight transport in the Nordic countries vary from 1.14Mt in Denmark to 2.27Mt in Sweden. While the size of the economy, measured in gross value added (GVA), is a major determinant for the emissions, the differences in transport intensity and energy efficiency also have a significant effect on the total emissions. This study is the first of its kind for the Nordic countries. Our research can be used as a first step in a continuous evaluation of the determinants of road freight CO2 emissions in the European countries.

Quantitative chemical composition determination and thermal analysis for typical biomass ashes in China

ABSTRACTFor better use of biomass resources, a complete understanding of the chemical compositions for the biomass ashes is important. By the joint adoption of several analysis tools, the ashes of four typical biomass resources in rural areas of north China, i.e. corn stalk, wheat stalk, cotton branch, and poplar, were studied. It was found that after heating for 24 h at 450 and 850 °C, the biomass ashes exhibited quite different chemical and mineral compositions. At 450 °C, potassium salts (KCl, K2SO4, or K2CO3), carbonates, phosphates, and oxides (SiO2 and MgO) were the primary mineral phases; however, at 850 °C, all of the KCl and CaCO3 broke down, and the primary mineral phases became silicates, phosphates, and undecomposed potassium salts. The detailed mineral compositions of the biomass ashes were determined by carefully balancing the X‐ray fluorescence spectrometry and X‐ray diffraction results, which were then well confirmed by the thermal analysis via FactSage modeling and thermogravimetric analyses. The complete understanding of the chemical compositions and thermal properties of the biomass ashes will aid in their utilization or, in some cases, enable the avoidance of their negative effects. In addition, the joint analysis method here can be referred in the determination of mineral contents of any other biomass ash. © 2014 Curtin University of Technology and John Wiley & Sons, Ltd.

Potential Involvement of Maternal Cytoplasm in the Regulation of Flowering Time via Interaction with Nuclear Genes in Maize

ABSTRACTFlowering time is a complex trait that controls adaptation of plants to their local environment in the outcrossing species maize (Zea mays L.). Previous studies have identified numerous quantitative trait loci (QTL) and genetic sequences associated with flowering time. However, beyond the nuclear genome, the maternal cytoplasm may also be involved in the regulation of flowering time. The objectives of this study were to locate the associated QTL based on a special mating design and to verify the hypothesis that the maternal cytoplasm may also contribute to the variation in flowering time. In the present study, a reciprocal mating design was conducted to generate F2 mapping populations comprising 120 F2 plants from the direct cross (JB × Y53) and 120 F2 plants from the reciprocal cross (Y53 × JB). F2:3 mapping populations were further generated with 91 direct F2:3 families and 120 reciprocal F2:3 families (10 plants per family). The days to tassel (DTT) and the days to pollen shed (DPS) of the above F2 and F2:3 mapping populations were evaluated in the same field and at the same experimental station in 2007 and 2008. A genetic linkage map with 154 microsatellite markers was constructed that covered 1735 cM of the maize genome with an average marker spacing of 11.3 cM. A joint analysis method incorporating the cytonuclear interaction mapping approach was proposed and performed to detect the QTL affecting DTT and DPS. As a result, eight and 11 QTL were detected for DTT and DPS, respectively. Interestingly, although the cytoplasmic effects were not significant between the direct and reciprocal populations, the interactions among the cytoplasm and the QTL were detected for both DTT and DPS. In the present study, four and eight cytonuclear epistatic QTL significantly contributed to the variation in DTT and DPS, respectively. Importantly, most of these cytonuclear epistatic QTL cannot be detected using the interval mapping method. These results suggest that the maternal cytoplasm might also be involved in the regulation of flowering time by interacting with nuclear genes in maize.

Reverse regression: A method for joint analysis of multiple endpoints in randomized clinical trials

In clinical trials, treatment comparisons are often cast in a regression framework that evaluates the dependence of the relevant clinical outcomes on treatment assignment and possibly other baseline characteristics. This article introduces a reverse regression approach to randomized clinical trials, with focus on the dependence of treatment assignment on the clinical outcomes of interest. A reverse regression model is essentially a semiparametric density ratio model for the outcome distributions in the two treatment groups. The resulting inferences can be expected to be more robust than those based on fully parametric models for the outcome distributions and more efficient than nonparametric inferences. In the presence of multiple endpoints, the reverse regression approach leads to a novel procedure for multiplicity adjustment that is readily available in standard logistic regression routines. The proposed approach is evaluated in simulation experiments and illustrated with an example.

Genetic analysis of the major gene plus polygene model in soybean resistance to Leguminivora glycinivorella.

In order to investigate the genetic characteristics of soybean Leguminivora glycinivorella resistance and to improve soybean resistance insectivorous breeding efficiency by applying the multi-generation joint analysis method of the major gene plus polygene model, 5 pedigrees and generations (P1, F1, P2, F2, and F2:3) were used as the materials to perform the soybean L. glycinivorella resistance multi-generation joint analysis. The results showed that soybean resistance to L. glycinivorella was controlled and inherited by an additive major gene plus additive, dominant polygene. The major gene had a negative additive effect (d = -0.1633). The combination of the anti-L. glycinivorella genes showed negative heterosis. Because the polygene additive effects were positive, the polygene effects would increase the insect herbivory rate in the F1 generation. This hybrid combination showed an insect herbivory rate polygenic heritability of 21.9556 and 54.3490% in the F2 and F2:3 pedigrees, which presented a high heritability. Therefore, it was appropriate to perform the selective breeding of the insect herbivory rate in the late generation.

Systems biology approaches to epidemiological studies of complex diseases.

Systems biology approaches to epidemiological studies of complex diseases include collection of genetic, genomic, epigenomic, and metagenomic data in large-scale epidemiological studies of complex phenotypes. Designs and analyses of such studies raise many statistical challenges. This article reviews some issues related to integrative analysis of such high dimensional and inter-related datasets and outline some possible solutions. I focus my review on integrative approaches for genome-wide genetic variants and gene expression data, methods for joint analysis of genetic and epigenetic variants, and methods for analysis of microbiome data. Statistical methods such as mediation analysis, high-dimensional instrumental variable regression, sparse signal recovery, and compositional data regression provide potential frameworks for integrative analysis of these high-dimensional genomic data.

Robust Joint Analysis with Data Fusion in Two-Stage Quantitative Trait Genome-Wide Association Studies

Genome-wide association studies (GWASs) in identifying the disease-associated genetic variants have been proved to be a great pioneering work. Two-stage design and analysis are often adopted in GWASs. Considering the genetic model uncertainty, many robust procedures have been proposed and applied in GWASs. However, the existing approaches mostly focused on binary traits, and few work has been done on continuous (quantitative) traits, since the statistical significance of these robust tests is difficult to calculate. In this paper, we develop a powerful F-statistic-based robust joint analysis method for quantitative traits using the combined raw data from both stages in the framework of two-staged GWASs. Explicit expressions are obtained to calculate the statistical significance and power. We show using simulations that the proposed method is substantially more robust than the F-test based on the additive model when the underlying genetic model is unknown. An example for rheumatic arthritis (RA) is used for illustration.

Jointly modeling the relationship between longitudinal and survival data subject to left truncation with applications to cystic fibrosis.

Numerous methods for joint analysis of longitudinal measures of a continuous outcome y and a time to event outcome T have recently been developed either to focus on the longitudinal data y while correcting for nonignorable dropout, to predict the survival outcome T using the longitudinal data y, or to examine the relationship between y and T. The motivating problem for our work is in joint modeling of the serial measurements of pulmonary function (FEV1% predicted) and survival in cystic fibrosis (CF) patients using registry data. Within the CF registry data, an additional complexity is that not all patients have been followed from birth; therefore, some patients have delayed entry into the study while others may have been missed completely, giving rise to a left truncated distribution. This paper shows in joint modeling situations where y and T are not independent, that it is necessary to account for this left truncation to obtain valid parameter estimates related to both survival and the longitudinal marker. We assume a linear random effects model for FEV1% predicted, where the random intercept and slope of FEV1% predicted, along with a specified transformation of the age at death follow a trivariate normal distribution. We develop an expectation-maximization algorithm for maximum likelihood estimation of parameters, which takes left truncation and right censoring of survival times into account. The methods are illustrated using simulation studies and using data from CF patients in a registry followed at Rainbow Babies and Children's Hospital, Cleveland, OH.

Joint Analysis for Integrating Two Related Studies of Different Data Types and Different Study Designs Using Hierarchical Modeling Approaches

Background: A chronic disease such as asthma is the result of a complex sequence of biological interactions involving multiple genes and pathways in response to a multitude of environmental exposures. However, methods to model jointly all factors are still evolving. Some of the current challenges include how to integrate knowledge from different data types and different disciplines, as well as how to utilize relevant external information such as gene annotation to identify novel disease genes and gene-environment inter-actions. Methods: Using a Bayesian hierarchical modeling framework, we developed two alternative methods for joint analysis of an epidemiologic study of a disease endpoint and an experimental study of intermediate phenotypes, while incorporating external information. Results: Our simulation studies demonstrated superior performance of the proposed hierarchical models compared to separate analysis with the standard single-level regression modeling approach. The combined analyses of the Southern California Children's Health Study and challenge study data suggest that these joint analytical methods detected more significant genetic main and gene-environment interaction effects than the conventional analysis. Conclusion: The proposed prior framework is very flexible and can be generalized for an integrative analysis of diverse sources of relevant biological data.

GENETIC ANALYSIS OF RESISTANCE TO RACE 2F. OF PODOSPHAERA XANTHII IN WILD MELON MATERIAL 'YUNTIAN-930'

ISHS XXVIII International Horticultural Congress on Science and Horticulture for People (IHC2010): III International Symposium on Plant Genetic Resources GENETIC ANALYSIS OF RESISTANCE TO RACE 2F. OF PODOSPHAERA XANTHII IN WILD MELON MATERIAL 'YUNTIAN-930'

Genetic analysis of resistance of wild melon to Podosphaera xanthii race 2F

Yuntian-930 is a wild melon that is highly resistant to powdery mildew (PM). To determine the inheritance of resistance to PM, Yuntian-930 was crossed with another cultivated melon, Hualaishi, which is susceptible to PM. Inheritance of resistance to Podosphaera xanthii race 2F. in six plant generations (P1, P2, F1, B1, B2, and F2) was studied using the mixed major-gene plus polygene inheritance mode with joint analysis method. Inheritance of resistance in wild melons fitted the model of two pairs of additive-dominance-epistasis major genes plus additive-dominant-epistasis polygene. The additive, dominant and epistatic effects between the two major genes played an important role in inheritance. The estimated values of heritability of major genes in B1, B2 and F2 were 62.98, 58.58 and 90.89% and those of polygene heritability were 28.93, 31.47 and 3.22%, respectively. The ratios of the environmental variance to phenotype variance were 5.89 to 9.94%. Thus, resistance of Yuntian-930 to PM was controlled not just by two major genes, but was also affected by polygene and the environment. The selection efficiency for a major gene of F2 was highest in the resistance breeding program. Key words: Wild melon, powdery mildew, resistance, P. xanthii race 2F, inheritance.

Spatial patterns of brain amyloid- burden and atrophy rate associations in mild cognitive impairment

Amyloid-β accumulation in the brain is thought to be one of the earliest events in Alzheimer's disease, possibly leading to synaptic dysfunction, neurodegeneration and cognitive/functional decline. The earliest detectable changes seen with neuroimaging appear to be amyloid-β accumulation detected by (11)C-labelled Pittsburgh compound B positron emission tomography imaging. However, some individuals tolerate high brain amyloid-β loads without developing symptoms, while others progressively decline, suggesting that events in the brain downstream from amyloid-β deposition, such as regional brain atrophy rates, play an important role. The main purpose of this study was to understand the relationship between the regional distributions of increased amyloid-β and the regional distribution of increased brain atrophy rates in patients with mild cognitive impairment. To simultaneously capture the spatial distributions of amyloid-β and brain atrophy rates, we employed the statistical concept of parallel independent component analysis, an effective method for joint analysis of multimodal imaging data. Parallel independent component analysis identified significant relationships between two patterns of amyloid-β deposition and atrophy rates: (i) increased amyloid-β burden in the left precuneus/cuneus and medial-temporal regions was associated with increased brain atrophy rates in the left medial-temporal and parietal regions; and (ii) in contrast, increased amyloid-β burden in bilateral precuneus/cuneus and parietal regions was associated with increased brain atrophy rates in the right medial temporal regions. The spatial distribution of increased amyloid-β and the associated spatial distribution of increased brain atrophy rates embrace a characteristic pattern of brain structures known for a high vulnerability to Alzheimer's disease pathology, encouraging for the use of (11)C-labelled Pittsburgh compound B positron emission tomography measures as early indicators of Alzheimer's disease. These results may begin to shed light on the mechanisms by which amyloid-β deposition leads to neurodegeneration and cognitive decline and the development of a more specific Alzheimer's disease-specific imaging signature for diagnosis and use of this knowledge in the development of new anti-therapies for Alzheimer's disease.

SurvJamda: an R package to predict patients' survival and risk assessment using joint analysis of microarray gene expression data

SurvJamda (Survival prediction by joint analysis of microarray data) is an R package that utilizes joint analysis of microarray gene expression data to predict patients' survival and risk assessment. Joint analysis can be performed by merging datasets or meta-analysis to increase the sample size and to improve survival prognosis. The prognosis performance derived from the combined datasets can be assessed to determine which feature selection approach, joint analysis method and bias estimation provide the most robust prognosis for a given set of datasets. The survJamda package is available at the Comprehensive R Archive Network, http://cran.r-project.org. hyasrebi@yahoo.com.

A variable selection method for genome-wide association studies

Genome-wide association studies (GWAS) involving half a million or more single nucleotide polymorphisms (SNPs) allow genetic dissection of complex diseases in a holistic manner. The common practice of analyzing one SNP at a time does not fully realize the potential of GWAS to identify multiple causal variants and to predict risk of disease. Existing methods for joint analysis of GWAS data tend to miss causal SNPs that are marginally uncorrelated with disease and have high false discovery rates (FDRs). We introduce GWASelect, a statistically powerful and computationally efficient variable selection method designed to tackle the unique challenges of GWAS data. This method searches iteratively over the potential SNPs conditional on previously selected SNPs and is thus capable of capturing causal SNPs that are marginally correlated with disease as well as those that are marginally uncorrelated with disease. A special resampling mechanism is built into the method to reduce false positive findings. Simulation studies demonstrate that the GWASelect performs well under a wide spectrum of linkage disequilibrium patterns and can be substantially more powerful than existing methods in capturing causal variants while having a lower FDR. In addition, the regression models based on the GWASelect tend to yield more accurate prediction of disease risk than existing methods. The advantages of the GWASelect are illustrated with the Wellcome Trust Case-Control Consortium (WTCCC) data. The software implementing GWASelect is available at http://www.bios.unc.edu/~lin. Access to WTCCC data: http://www.wtccc.org.uk/.

GENETIC STUDY ON SUGAR AND SOUR TRAITS OF MELON (CUCUMIS MELO L.)

Four generations of P 1 , P 2 , F 1 and F 2 were used to study the inheritance of melon sugar content, sour content and sugar acid radio traits in mutation inbred line with sour taste (76-2 by 60 Co- γ irradiation) and 'Huangpicui' by joint analysis method of multiple generations. The results showed that the sugar content was controlled by two pairs of equal additive major genes plus additive-dominant polygene (E-4 model). The major gene heritability is 88.8%; the polygene heritability is 6.94%. The inheritance of sour content fitted one pair of additive-dominance major gene plus additive-dominance-epitasis polygene (D-0 model). The major gene heritability is 26.68%; the polygene heritability is 72.77%. Sugar acid radio trait fitted two pairs of additive-dominance-epitasis major genes plus additive-dominant polygene (E-1 model). The major gene heritability is 82.86%; the polygene heritability is 16.02%.

Distribution Characterization of Leaf and Hull Pubescences and Genetic Analysis of Their Numbers in japonica Rice ( Oryza sativa)

Distributions of pubescences on leaf blade and hull in japonica rice were observed under an optical microscope. Numbers of leaf and hull pubescences in P 1, P 2, F 1, B 1, B 2 and F 2 generations were investigated in three combinations of japonica rice (Sidao 10A/Wuyujing 3R, Wuyujing 3A/Sidao 10R and Liuyan 189A/HR-122), and genetic analysis for these two traits were conducted by using the joint analysis method of P 1, P 2, F 1, B 1, B 2 and F 2 generations with the mixed major gene plus polygene inheritance models. Leaf pubescences characterized by swollen base and fine tip distributed regularly on the boundary between dark green stripe and light green stripe of leaf blade. Hull pubescences with various lengths distributed irregularly on the whole hull. Numbers of leaf pubescences in the reciprocal combinations of Sidao 10A/Wuyujing 3R and Wuyujing 3A/Sidao 10R and numbers of hull pubescences in all the three combinations were controlled by one pair of additive major genes plus additive-dominant polygenes. In the combination of Liuyan 189A/HR-122, number of leaf pubescences was controlled by one pair of additive-dominant major genes plus additive-dominant polygenes. Both numbers of leaf and hull pubescences were mainly governed by major genes.