Mining genetic epidemiology data with Bayesian networks application to APOE gene variation and plasma lipid levels.

There is a critical need for data-mining methods that can identify SNPs that predict among individual variation in a phenotype of interest and reverse-engineer the biological network of relationships between SNPs, phenotypes, and other factors. This problem is both challenging and important in light of the large number of SNPs in many genes of interest and across the human genome. A potentially fruitful form of exploratory data analysis is the Bayesian or Belief network. A Bayesian or Belief network provides an analytic approach for identifying robust predictors of among-individual variation in a disease endpoints or risk factor levels. We have applied Belief networks to SNP variation in the human APOE gene and plasma apolipoprotein E levels from two samples: 702 African-Americans from Jackson, MS, and 854 non-Hispanic whites from Rochester, MN. Twenty variable sites in the APOE gene were genotyped in both samples. In Jackson, MS, SNPs 4036 and 4075 were identified to influence plasma apoE levels. In Rochester, MN, SNPs 3937 and 4075 were identified to influence plasma apoE levels. All three SNPs had been previously implicated in affecting measures of lipid and lipoprotein metabolism. Like all data-mining methods, Belief networks are meant to complement traditional hypothesis-driven methods of data analysis. These results document the utility of a Belief network approach for mining large scale genotype-phenotype association data.     

Comparison of strategies to detect epistasis from eQTL data

Genome-wide association studies have been instrumental in identifying genetic variants associated with complex traits such as human disease or gene expression phenotypes. It has been proposed that extending existing analysis methods by considering interactions between pairs of loci may uncover additional genetic effects. However, the large number of possible two-marker tests presents significant computational and statistical challenges. Although several strategies to detect epistasis effects have been proposed and tested for specific phenotypes, so far there has been no systematic attempt to compare their performance using real data. We made use of thousands of gene expression traits from linkage and eQTL studies, to compare the performance of different strategies. We found that using information from marginal associations between markers and phenotypes to detect epistatic effects yielded a lower false discovery rate (FDR) than a strategy solely using biological annotation in yeast, whereas results from human data were inconclusive. For future studies whose aim is to discover epistatic effects, we recommend incorporating information about marginal associations between SNPs and phenotypes instead of relying solely on biological annotation. Improved methods to discover epistatic effects will result in a more complete understanding of complex genetic effects.     

Mining conifers’ mega-genome using rapid and efficient multiplexed high-throughput genotyping-by-sequencing (GBS) SNP discovery platform

Next-generation sequencing (NGS) technologies are revolutionizing both medical and biological research through generation of massive SNP data sets for identifying heritable genome variation underlying key traits, from rare human diseases to important agronomic phenotypes in crop species. We evaluated the performance of genotyping-by-sequencing (GBS), one of the emerging NGS-based platforms, for genotyping two economically important conifer species, lodgepole pine (Pinus contorta) and white spruce (Picea glauca). Both species have very large genomes (>20,000 Mbp), are highly heterozygous, and lack reference sequences. From a small set (six accessions each) of independent replicated DNA samples and a 48-plex read depth, we obtained ~60,000 SNPs per species. After stringent filtering, we obtained 17,765 and 17,845 high-coverage SNPs without missing data for lodgepole pine and white spruce, respectively. Our results demonstrated that GBS is a robust and suitable method for genotyping conifers. The application of GBS to forest tree breeding and genomic selection is discussed.     

Dispersal polymorphism and the speed of biological invasions

The speed at which biological range expansions occur has important consequences for the conservation management of species experiencing climate change and for invasion by exotic organisms. Rates of dispersal and population growth are known to affect the speed of invasion, but little is known about the effect of having a community of dispersal phenotypes on the rate of range expansion. We use reaction-diffusion equations to model the invasion of a species with two dispersal phenotypes into a previously unoccupied landscape. These phenotypes differ in both their dispersal rate and population growth rate. We find that the presence of both phenotypes can result in faster range expansions than if only a single phenotype were present in the landscape. For biologically realistic parameters, the invasion can occur up to twice as fast as a result of this polymorphism. This has implications for predicting the speed of biological invasions, suggesting that speeds cannot just be predicted from looking at a single phenotype and that the full community of phenotypes needs to be taken into consideration.     

Prediction of phenotypes of missense mutations in human proteins from biological assemblies

Single nucleotide polymorphisms (SNPs) are the most frequent variation in the human genome. Nonsynonymous SNPs that lead to missense mutations can be neutral or deleterious, and several computational methods have been presented that predict the phenotype of human missense mutations. These methods use sequence‐based and structure‐based features in various combinations, relying on different statistical distributions of these features for deleterious and neutral mutations. One structure‐based feature that has not been studied significantly is the accessible surface area within biologically relevant oligomeric assemblies. These assemblies are different from the crystallographic asymmetric unit for more than half of X‐ray crystal structures. We find that mutations in the core of proteins or in the interfaces in biological assemblies are significantly more likely to be disease‐associated than those on the surface of the biological assemblies. For structures with more than one protein in the biological assembly (whether the same sequence or different), we find the accessible surface area from biological assemblies provides a statistically significant improvement in prediction over the accessible surface area of monomers from protein crystal structures (P = 6e‐5). When adding this information to sequence‐based features such as the difference between wildtype and mutant position‐specific profile scores, the improvement from biological assemblies is statistically significant but much smaller (P = 0.018). Combining this information with sequence‐based features in a support vector machine leads to 82% accuracy on a balanced dataset of 50% disease‐associated mutations from SwissVar and 50% neutral mutations from human/primate sequence differences in orthologous proteins. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Charged-particle activation analysis of biological material

Charged-particle activation analysis offers a number of interesting possibilities for the determination of trace elements in biological material. It allows the determination of those elements that are difficult or impossible to determine by neutron activation, such as Li, B, Al, Si, V, Cr, Ni, Cd, Sn, Tl, and Pb. Up to now, protons have been successfully applied to samples of both vegetale and human origin. A number of difficulties have to be overcome, one of which is excessive heating of the samples owing to the limited range of the charged particles, thus giving rise to a high energy deposition in a small volume. Moreover, the sample composition has to be known to allow the calculation of the range of the particles. an interesting alternative has been proposed using an internal standard together with a standard additions procedure. Proton activation analysis was tested on a wide variety of reference materials, giving evidence that accurate results can be obtained for many trace elements, even when applying a purely instrumental method. Thus, the method can also be applied in the certification of reference materials, since nuclear methods are independent of chemical properties of the sample.     

All SNPs Are Not Created Equal: Genome-Wide Association Studies Reveal a Consistent Pattern of Enrichment among Functionally Annotated SNPs

Recent results indicate that genome-wide association studies (GWAS) have the potential to explain much of the heritability of common complex phenotypes, but methods are lacking to reliably identify the remaining associated single nucleotide polymorphisms (SNPs). We applied stratified False Discovery Rate (sFDR) methods to leverage genic enrichment in GWAS summary statistics data to uncover new loci likely to replicate in independent samples. Specifically, we use linkage disequilibrium-weighted annotations for each SNP in combination with nominal p-values to estimate the True Discovery Rate (TDR = 1−FDR) for strata determined by different genic categories. We show a consistent pattern of enrichment of polygenic effects in specific annotation categories across diverse phenotypes, with the greatest enrichment for SNPs tagging regulatory and coding genic elements, little enrichment in introns, and negative enrichment for intergenic SNPs. Stratified enrichment directly leads to increased TDR for a given p-value, mirrored by increased replication rates in independent samples. We show this in independent Crohn''s disease GWAS, where we find a hundredfold variation in replication rate across genic categories. Applying a well-established sFDR methodology we demonstrate the utility of stratification for improving power of GWAS in complex phenotypes, with increased rejection rates from 20% in height to 300% in schizophrenia with traditional FDR and sFDR both fixed at 0.05. Our analyses demonstrate an inherent stratification among GWAS SNPs with important conceptual implications that can be leveraged by statistical methods to improve the discovery of loci.     

Analysis of Genome-Wide Association Studies with Multiple Outcomes Using Penalization

Genome-wide association studies have been extensively conducted, searching for markers for biologically meaningful outcomes and phenotypes. Penalization methods have been adopted in the analysis of the joint effects of a large number of SNPs (single nucleotide polymorphisms) and marker identification. This study is partly motivated by the analysis of heterogeneous stock mice dataset, in which multiple correlated phenotypes and a large number of SNPs are available. Existing penalization methods designed to analyze a single response variable cannot accommodate the correlation among multiple response variables. With multiple response variables sharing the same set of markers, joint modeling is first employed to accommodate the correlation. The group Lasso approach is adopted to select markers associated with all the outcome variables. An efficient computational algorithm is developed. Simulation study and analysis of the heterogeneous stock mice dataset show that the proposed method can outperform existing penalization methods.     

生物入侵的危害与防治对策

生物入侵是一个世界范围的生态学现象。入侵种通过竞争、捕食、改变生境和传播疾病等方式对本地种及其系统产生影响。生物入侵的危害表现为：造成巨大的经济损失,仅美国每年因外来种入侵造成的经济损失近1370亿美元;威胁到人类的健康和生存。成千上万的人被外来种传染疾病以致死亡;引起严重的社会恐慌和动荡,人们寝食不安;改变了生态系统的结构和功能,全球自然灾害频频爆发;导致生物多样性的急剧下降,威胁到子孙后代的生存和发展。防治生物入侵有3条途径：实行全面检疫,阻止外来种的偶然入侵;采取全面的生态评估和监测,防范引进品种的入侵灾难;对已入侵的外来种采用机械法、化学法和生物防治法进行根除和控制,应用生物防治法、筛选天敌和对其进行危害评估时要特别慎重。     

Kinetics of the cell biological changes occurring in the progression of DNA damage-induced senescence

Cellular senescence is characterized by cell-cycle arrest accompanied by various cell biological changes. Although these changes have been heavily relied on as senescence markers in numerous studies on senescence and its intervention, their underlying mechanisms and relationship to each other are poorly understood. Furthermore, the depth and the reversibility of those changes have not been addressed previously. Using flow cytometry coupled with confocal microscopy and Western blotting, we quantified various senescence-associated cellular changes and determined their time course profiles in MCF-7 cells undergoing DNA damage-induced senescence. The examined properties changed with several different kinetics patterns. Autofluorescence, side scattering, and the mitochondria content increased progressively and linearly. Cell volume, lysosome content, and reactive oxygen species (ROS) level increased abruptly at an early stage. Meanwhile, senescence associated β-galactosidase activity increased after a lag of a few days. In addition, during the senescence progression, lysosomes exhibited a loss of integrity, which may have been associated with the accumulation of ROS. The finding that various senescence phenotypes matured at different rates with different lag times suggests multiple independent mechanisms controlling the expression of senescence phenotypes. This type of kinetics study would promote the understanding of how cells become fully senescent and facilitate the screening of methods that intervene in cellular senescence.     

SNP discovery in associating genetic variation with human disease phenotypes

With the completion of the human genome project, attention is now rapidly shifting towards the study of individual genetic variation. The most abundant source of genetic variation in the human genome is represented by single nucleotide polymorphisms (SNPs), which can account for heritable inter-individual differences in complex phenotypes. Identification of SNPs that contribute to susceptibility to common diseases will provide highly accurate diagnostic information that will facilitate early diagnosis, prevention, and treatment of human diseases. Over the past several years, the advancement of increasingly high-throughput and cost-effective methods to discover and measure SNPs has begun to open the door towards this endeavor. Genetic association studies are considered to be an effective approach towards the detection of SNPs with moderate effects, as in most common diseases with complex phenotypes. This requires careful study design, analysis and interpretation. In this review, we discuss genetic association studies and address the prospect for candidate gene association studies, comparing the strengths and weaknesses of indirect and direct study designs. Our focus is on the continuous need for SNP discovery methods and the use of currently available prescreening methods for large-scale genetic epidemiological research until more advanced sequencing methods currently under development will become available.     

The current status of biological control of weeds in southern China and future options

China has become one of the countries most seriously affected by invasive alien weeds in the world. Weeds impact agriculture, the environment and human health, and conventional control methods such as herbicides are expensive, damaging to human health and unsustainable. As the impacts and costs of weed control in China increase, there is an urgent need to manage some of the more important weeds through more sustainable methods. Classical biological control of invasive alien weeds is environmentally-friendly and sustainable. Biological control in China began in the 1930s with the introduction of two agents into Hong Kong for the control of Lantana camara. Since then, a further seven biological control agents have been introduced into China to control four weed species. In addition, 11 biological control agents targeting seven weed species have naturally spread into China. Together, these biological control agents are helping to control some of China's worst weeds. However, these efforts are only a small portion of the weeds that could be targeted for weed biological control. This paper reviews the current status of weed biological control efforts against introduced weeds in ten provinces and regions in southern China and provides a platform to identify the most effective and appropriate weed biological control opportunities and programmes to pursue in the future. Introducing additional safe and effective biological control agents into China to help manage some of the worst weeds in the region should reduce the use of herbicides and impacts on human health and the environment, while increasing productivity and food security.     

Detection, imputation, and association analysis of small deletions and null alleles on oligonucleotide arrays

Copy-number variation (CNV) is a major contributor to human genetic variation. Recently, CNV associations with human disease have been reported. Many genome-wide association (GWA) studies in complex diseases have been performed with sets of biallelic single-nucleotide polymorphisms (SNPs), but the available CNV methods are still limited. We present a new method (TriTyper) that can infer genotypes in case-control data sets for deletion CNVs, or SNPs with an extra, untyped allele at a high-resolution single SNP level. By accounting for linkage disequilibrium (LD), as well as intensity data, calling accuracy is improved. Analysis of 3102 unrelated individuals with European descent, genotyped with Illumina Infinium BeadChips, resulted in the identification of 1880 SNPs with a common untyped allele, and these SNPs are in strong LD with neighboring biallelic SNPs. Simulations indicate our method has superior power to detect associations compared to biallelic SNPs that are in LD with these SNPs, yet without increasing type I errors, as shown in a GWA analysis in celiac disease. Genotypes for 1204 triallelic SNPs could be fully imputed, with only biallelic-genotype calls, permitting association analysis of these SNPs in many published data sets. We estimate that 682 of the 1655 unique loci reflect deletions; this is on average 99 deletions per individual, four times greater than those detected by other methods. Whereas the identified loci are strongly enriched for known deletions, 61% have not been reported before. Genes overlapping with these loci more often have paralogs (p = 0.006) and biologically interact with fewer genes than expected (p = 0.004).     

Multitrait GWAS to connect disease variants and biological mechanisms

Genome-wide association studies (GWASs) have uncovered a wealth of associations between common variants and human phenotypes. Here, we present an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link with biological mechanisms. Our framework incorporates multitrait association mapping along with an investigation of the breakdown of genetic associations into clusters of variants harboring similar multitrait association profiles. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how genetic variants within clusters can be mapped to biological pathways and disease mechanisms. Finally, for the metabolism set, we investigate the link between gene cluster assignment and the success of drug targets in randomized controlled trials.     

Applications of single nucleotide polymorphisms in crop genetics

The discovery of single nucleotide polymorphisms (SNPs) and insertions/deletions, which are the basis of most differences between alleles, has been simplified by recent developments in sequencing technology. SNP discovery in many crop species, such as corn and soybean, is relatively straightforward because of their high level of intraspecific nucleotide diversity, and the availability of many gene and expressed sequence tag (EST) sequences. For these species, direct readout of SNP haplotypes is possible. Haplotype-based analysis is more informative than analysis based on individual SNPs, and has more power in analyzing association with phenotypes. The elite germplasm of some crops may have been subjected to bottlenecks relatively recently, increasing the amount of linkage disequilibrium (LD) present and facilitating the association of SNP haplotypes at candidate gene loci with phenotypes. Whole-genome scans may help identify genome regions that are associated with interesting phenotypes if sufficient LD is present. Technological improvements make the use of SNP and indel markers attractive for high-throughput use in marker-assisted breeding, EST mapping and the integration of genetic and physical maps.     

适应白洋淀湿地健康评价的IBI方法

IBI指数法是美国湿地生态系统健康评价的常用方法之一,在国内应用较少,目前为止只有少数学者尝试了底栖动物完整性指数(B-IBI)和鱼类完整性指数进行河流生态系统健康评价。试图建立适合白洋淀地区的生物完整性指数湿地健康状况评价的方法。在白洋淀的23个淀区进行土壤、水体、植物的实地调查与采样分析。根据已有研究所选择的植物属性,选择了30个植物属性作为备选参数。通过考察备选参数与人类干扰的相关性,最后确定了9个对人类干扰敏感的IBI参数。通过分析,建立了适合白洋淀湿地健康评价的标准:IBI,35-45,好;27-34,一般;19-26,差;＜18,极差。评价结果表明,白洋淀23个淀区中,6个健康状况好,5个一般,7个差,5个很差。     

Underestimated effect sizes in GWAS: fundamental limitations of single SNP analysis for dichotomous phenotypes

Complex diseases are often highly heritable. However, for many complex traits only a small proportion of the heritability can be explained by observed genetic variants in traditional genome-wide association (GWA) studies. Moreover, for some of those traits few significant SNPs have been identified. Single SNP association methods test for association at a single SNP, ignoring the effect of other SNPs. We show using a simple multi-locus odds model of complex disease that moderate to large effect sizes of causal variants may be estimated as relatively small effect sizes in single SNP association testing. This underestimation effect is most severe for diseases influenced by numerous risk variants. We relate the underestimation effect to the concept of non-collapsibility found in the statistics literature. As described, continuous phenotypes generated with linear genetic models are not affected by this underestimation effect. Since many GWA studies apply single SNP analysis to dichotomous phenotypes, previously reported results potentially underestimate true effect sizes, thereby impeding identification of true effect SNPs. Therefore, when a multi-locus model of disease risk is assumed, a multi SNP analysis may be more appropriate.     

Selection on a behaviour‐related gene during the first stages of the biological invasion pathway

Human‐induced biological invasions are common worldwide and often have negative impacts on wildlife and human societies. Several studies have shown evidence for selection on invaders after introduction to the new range. However, selective processes already acting prior to introduction have been largely neglected. Here, we tested whether such early selection acts on known behaviour‐related gene variants in the yellow‐crowned bishop (Euplectes afer), a pet‐traded African songbird. We tested for nonrandom allele frequency changes after trapping, acclimation and survival in captivity. We also compared the native source population with two independent invasive populations. Allele frequencies of two SNPs in the dopamine receptor D4 (DRD4) gene—known to be linked to behavioural activity in response to novelty in this species—significantly changed over all early invasion stages. They also differed between the African native population and the two invading European populations. The two‐locus genotype associated with reduced activity declined consistently, but strongest at the trapping stage. Overall genetic diversity did not substantially decrease, and there is little evidence for new alleles in the introduced populations, indicating that selection at the DRD4 gene predominantly worked on the standing genetic variation already present in the native population. Our study demonstrates selection on a behaviour‐related gene during the first stages of a biological invasion. Thus, pre‐establishment stages of a biological invasion do not only determine the number of propagules that are introduced (their quantity), but also their phenotypic and genetic characteristics (their quality).     

Risk analysis and management decisions for weed biological control agents: Ecological theory and modeling results

Biologically based control methods offer many advantages for the control of invasive plant species; however, these methods are not without risks to native species. Thus, there is a need for more effective and efficient methods of risk analysis for biological control agents. We show how the process of ecological risk assessment established by the United States’ Environmental Protection Agency may be adapted to improve assessment of the risks of proposed biological control agents. We discuss the risks posed by weed biological control agents, and present a simple individual-based model of herbivorous insect movement and oviposition on two species of host plant, a target invasive plant species and a non-target native species, in simulated landscapes. The model shows that risks of non-target impacts may be influenced by the details of the movement behavior of biological control agents in heterogeneous landscapes. The specific details of insect movement that appear to be relevant are readily measured in field trials and the general modeling approach is readily adapted to real landscapes. Current biological control risk assessments typically emphasize effects analysis at the expense of exposure analysis; the modeling approach presented here provides a simple and feasible way to incorporate exposure analyses. We conclude that models such as ours should be given serious consideration as part of a comprehensive strategy of risk assessment for proposed weed biological control agents.     

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome‐wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single‐nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype‐to‐phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme‐coding genes. To this end, we also constructed a genome‐scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth‐determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.