Multivariate analysis of quantitative trait loci influencing variation in anxiety-related behavior in laboratory mice

The number and mode of action of quantitative trait loci (QTL) that contribute to behavioral variation in rodents is still largely unknown. On theoretical grounds, multivariate techniques are expected to yield new insights into this problem, but there are only a few examples of its application in practice. Here we explore the power of multivariate approaches to uncover the genetic architecture of 23 anxiety-related phenotypes in 1636 F₂ laboratory mice. We detected QTL with a genome-wide significance threshold of P < 0.05 on 14 chromosomes, of which 10 correspond to those identified by univariate analysis. Novel QTL were found on Chromosomes 3, 9, 13, and 17. Thus, multivariate analyses increased the yield of QTL exceeding a genome-wide significance threshold by 40%. On the basis of these results and by the application of a QTL estimator, we show that the mean number of QTL influencing anxiety-related behavior in mice is 6, with a 95% upper limit of 14.     

基于单核苷酸多态性的基因互作分析方法学进展

基于单核苷酸多态性的关联分析已成为当前解析人类常见复杂疾病遗传机制的重要手段之一, 然而, 目前普遍使用的单位点分析策略仅能发现部分单独效应显著的易感SNP位点, 因此遗漏了重要的遗传力组分——基因上位效应或联合效应。识别全基因组多基因间复杂的互作关系已成为全面解析复杂疾病致病分子机制必不可少的一项任务。已有很多方法被应用于全基因组交互作用分析, 加深了人类对复杂疾病遗传机制的进一步认识。基于各类方法的理论基础及算法的异同, 文章对目前应用较为广泛的基于遗传互作模型的方法、不基于互作模型的方法和数据挖掘类算法3类方法进行了系统地评述, 着重介绍了这些方法的主要思想、实现过程及应用中的注意事项等, 并指出开展大规模全基因组范围互作检测面临的问题, 以期能为相关领域的研究者提供方法学参考。     

Invertebrate studies and their ongoing contributions to neuroscience

Invertebrates have been deployed very successfully in experimental studies of the nervous system and neuromuscular junctions. Many important discoveries on axonal conduction, synaptic transmission, integrative neurobiology and behaviour have been made by investigations of these remarkable animals. Their advantages as model organisms for investigations of nervous systems include (a) the large diameter of neurons, glia and muscle cells of some invertebrates, thereby facilitating microelectrode recordings; (b) simple nervous systems with few neurons, enhancing the tractability of neuronal circuitry; and (c) well-defined behaviours, which lend themselves to physiological and genetic dissection. Genetic model organisms such as Drosophila melanogaster and Caenorhabditis elegans have provided powerful genetic approaches to central questions concerning nervous system development, learning and memory and the cellular and molecular basis of behaviour. The process of attributing function to particular gene products has been greatly accelerated in recent years with access to entire genome sequences and the application of reverse genetic (e.g. RNA interference, RNAi) and other post-genome technologies (e.g. microarrays). Studies of many other invertebrates, notably the honeybee (Apis mellifera), a nudibranch mollusc (Aplysia californica), locusts, lobsters, crabs, annelids and jellyfish have all assisted in the development of major concepts in neuroscience. The future is equally bright with ease of access to genome-wide reverse genetic technologies, and the development of optical recordings using voltage and intracellular calcium sensors genetically targeted to selected individual and groups of neurons.     

A two-phase Bayesian methodology for the analysis of binary phenotypes in genome-wide association studies

Recent advances in sequencing and genotyping technologies are contributing to a data revolution in genome-wide association studies that is characterized by the challenging large p small n problem in statistics. That is, given these advances, many such studies now consider evaluating an extremely large number of genetic markers (p) genotyped on a small number of subjects (n). Given the dimension of the data, a joint analysis of the markers is often fraught with many challenges, while a marginal analysis is not sufficient. To overcome these obstacles, herein, we propose a Bayesian two-phase methodology that can be used to jointly relate genetic markers to binary traits while controlling for confounding. The first phase of our approach makes use of a marginal scan to identify a reduced set of candidate markers that are then evaluated jointly via a hierarchical model in the second phase. Final marker selection is accomplished through identifying a sparse estimator via a novel and computationally efficient maximum a posteriori estimation technique. We evaluate the performance of the proposed approach through extensive numerical studies, and consider a genome-wide application involving colorectal cancer.     

Use of CRISPR-Cas tools to engineer Trichoderma species

Given their lignocellulose degradability and biocontrol activities, fungi of the ubiquitously distributed genus Trichoderma have multiple industrial and agricultural applications. Genetic manipulation plays a valuable role in tailoring novel engineered strains with enhanced target traits. Nevertheless, as applied to fungi, the classic tools of genetic manipulation tend to be time-consuming and tedious. However, the recent development of the CRISPR-Cas system for gene editing has enabled researchers to achieve genome-wide gene disruptions, gene replacements, and precise editing, and this technology has emerged as a primary focus for novel developments in engineered strains of Trichoderma. Here, we provide a brief overview of the traditional approaches to genetic manipulation, the different strategies employed in establishing CRSIPR-Cas systems, the utilization of these systems to develop engineered strains of Trichoderma for desired applications, and the future trends in biotechnology.     

Application of genome-wide single nucleotide polymorphism typing: simple association and beyond

The International HapMap Project and the arrival of technologies that type more than 100,000 SNPs in a single experiment have made genome-wide single nucleotide polymorphism (GW-SNP) assay a realistic endeavor. This has sparked considerable debate regarding the promise of GW-SNP typing to identify genetic association in disease. As has already been shown, this approach has the potential to localize common genetic variation underlying disease risk. The data provided from this technology also lends itself to several other lines of investigation; autozygosity mapping in consanguineous families and outbred populations, direct detection of structural variation, admixture analysis, and other population genetic approaches. In this review we will discuss the potential uses and practical application of GW-SNP typing including those above and beyond simple association testing.     

MAX-rank: a simple and robust genome-wide scan for case-control association studies

In genome-wide association studies (GWAS), single-marker analysis is usually employed to identify the most significant single nucleotide polymorphisms (SNPs). The trend test has been proposed for analysis of case-control association. Three trend tests, optimal for the recessive, additive and dominant models respectively, are available. When the underlying genetic model is unknown, the maximum of the three trend test results (MAX) has been shown to be robust against genetic model misspecification. Since the asymptotic distribution of MAX depends on the allele frequency of the SNP, using the P-value of MAX for ranking may be different from using the MAX statistic. Calculating the P-value of MAX for 300,000 (300 K) or more SNPs is computationally intensive and the software and program to obtain the P-value of MAX are not widely available. On the other hand, the MAX statistic is very easy to calculate without complex computer programs. Thus, we study whether or not one could use the MAX statistic instead of its P-value to rank SNPs in GWAS. The approaches using the MAX and its P-value to rank SNPs are referred to as MAX-rank and P-rank. By applying MAX-rank and P-rank to simulated and four real datasets from GWAS, we found the ranks of SNPs with true association are very similar using both approaches. Thus, we recommend to use MAX-rank for genome-wide scans. After the top-ranked SNPs are identified, their P-values based on MAX can be calculated and compared with the significance level. The work of Q. Li was partially supported by the Knowledge Innovation Program of the Chinese Academy of Sciences, No. 30465W0 and 30475V0. The research of Z Li was partially sponsored by NIH grant EY014478.     

Risk estimation and risk prediction using machine-learning methods

After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.     

Toward forward genetic screens in malaria-causing parasites using the piggyBac transposon

The ability to analyze gene function in malaria-causing Plasmodium parasites has received a boost with a recent paper in BMC Genomics that describes a genome-wide mutagenesis system in the rodent malaria species Plasmodium berghei using the transposon piggyBac. This advance holds promise for identifying and validating new targets for intervention against malaria. But further improvements are still needed for the full power of genome-wide molecular genetic screens to be utilized in this organism.     

Molecular diversity and association mapping of fiber quality traits in exotic G. hirsutum L. germplasm

The narrow genetic base of cultivated cotton germplasm is hindering the cotton productivity worldwide. Although potential genetic diversity exists in Gossypium genus, it is largely ‘underutilized’ due to photoperiodism and the lack of innovative tools to overcome such challenges. The application of linkage disequilibrium (LD)-based association mapping is an alternative powerful molecular tool to dissect and exploit the natural genetic diversity conserved within cotton germplasm collections, greatly accelerating still ‘lagging’ cotton marker-assisted selection (MAS) programs. However, the extent of genome-wide linkage disequilibrium (LD) has not been determined in cotton. We report the extent of genome-wide LD and association mapping of fiber quality traits by using a 95 core set of microsatellite markers in a total of 285 exotic Gossypium hirsutum accessions, comprising of 208 landrace stocks and 77 photoperiodic variety accessions. We demonstrated the existence of useful genetic diversity within exotic cotton germplasm. In this germplasm set, 11–12% of SSR loci pairs revealed a significant LD. At the significance threshold (r² ≥ 0.1), a genome-wide average of LD declines within the genetic distance at < 10 cM in the landrace stocks germplasm and > 30 cM in variety germplasm. Genome wide LD at r² ≥ 0.2 was reduced on average to  1–2 cM in the landrace stock germplasm and 6–8 cM in variety germplasm, providing evidence of the potential for association mapping of agronomically important traits in cotton. We observed significant population structure and relatedness in assayed germplasm. Consequently, the application of the mixed liner model (MLM), considering both kinship (K) and population structure (Q) detected between 6% and 13% of SSR markers associated with the main fiber quality traits in cotton. Our results highlight for the first time the feasibility and potential of association mapping, with consideration of the population structure and stratification existing in cotton germplasm resources. The number of SSR markers associated with fiber quality traits in diverse cotton germplasm, which broadly covered many historical meiotic events, should be useful to effectively exploit potentially new genetic variation by using MAS programs.     

Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe

Key message

Genome-wide association mapping as well as marker- and haplotype-based genome-wide selection unraveled a complex genetic architecture of grain yield with absence of large effect QTL and presence of local epistatic effects.

Abstract

The genetic architecture of grain yield determines to a large extent the optimum design of genomic-assisted wheat breeding programs. The main goal of our study was to examine the potential and limitations to dissect the genetic architecture of grain yield in wheat using a large experimental data set. Our study was based on phenotypic information and genomic data of 13,901 SNPs of a diverse set of 3816 elite wheat lines adapted to Central Europe. We applied genome-wide association mapping based on experimental and simulated data sets and performed marker- and haplotype-based genomic prediction. Computer simulations revealed for our mapping population a high power to detect QTL, even if they individually explained only 2.5% of the genetic variation. Despite this, we found no stable marker–trait associations when validating in independent subsets. A two-dimensional scan for marker–marker interactions indicated presence of local epistasis which was further supported by improved prediction abilities when shifting from marker- to haplotype-based genome-wide prediction approaches. We observed that marker effects estimated using genome-wide prediction approaches strongly varied across years albeit resulting in high prediction abilities. Thus, our results suggested that the prediction accuracy of genomic selection in wheat is mainly driven by relatedness rather than by exploiting knowledge of the genetic architecture.

     

Genetic susceptibility to coronary artery disease: from promise to progress

Family history is an important independent risk factor for coronary artery disease (CAD), and identification of susceptibility genes for this common, complex disease is a vital goal. Although there has been considerable success in identifying genetic variants that influence well-known risk factors, such as cholesterol levels, progress in unearthing novel CAD genes has been slow. However, advances are now being made through the application of large-scale, systematic, genome-wide approaches. Recent findings particularly highlight the link between CAD and inflammation and immunity, and highlight the biological insights to be gained from a genetic understanding of the world's biggest killer.     

Maximizing the Power of Genome-Wide Association Studies: A Novel Class of Powerful Family-Based Association Tests

For genome-wide association studies in family-based designs, a new, universally applicable approach is proposed. Using a modified Liptak’s method, we combine the p-value of the family-based association test (FBAT) statistic with the p-value for the Van Steen-statistic. The Van Steen-statistic is independent of the FBAT-statistic and utilizes information that is ignored by traditional FBAT-approaches. The new test statistic takes advantages of all available information about the genetic association, while, by virtue of its design, it achieves complete robustness against confounding due to population stratification. The approach is suitable for the analysis of almost any trait type for which FBATs are available, e.g. binary, continuous, time-to-onset, multivariate, etc. The efficiency and the validity of the new approach depend on the specification of a nuisance/tuning parameter and the weight parameters in the modified Liptak’s method. For different trait types and ascertainment conditions, we discuss general guidelines for the optimal specification of the tuning parameter and the weight parameters. Our simulation experiments and an application to an Alzheimer study show the validity and the efficiency of the new method, which achieves power levels that are comparable to those of population-based approaches.     

Rosaceae conserved orthologous sequences marker polymorphism in sweet cherry germplasm and construction of a SNP-based map

The Rosaceae Conserved Orthologous Set (RosCOS) provides a gene-based genome-wide set of markers that have been used in comparative analyses of peach (Prunus persica), apple (Malus × domestica), and strawberry (Fragaria spp.). In order to extend the use of these RosCOS to sweet cherry (Prunus avium L.), we identified markers that are polymorphic in breeding germplasm. Ninety-five percent (595/627) of previously designed RosCOS primer pairs amplified a product in six sweet cherry cultivars predicted to represent the range of genetic diversity in breeding germplasm. A total of 45% (282/627) RosCOS were polymorphic among the six cultivars, and allele number ranged from 2 to 6, with a genome-wide mean of 2.35. A subset of 92 genome-wide single nucleotide polymorphisms (SNPs) corresponding to 76 RosCOS was analyzed in 36 founder accessions and progeny. The expected and observed heterozygosity suggested that 83% of the RosCOS were in Hardy–Weinberg equilibrium, implying that most RosCOS behave as neutral markers. Principal coordinate analysis (PCO) identified one wild accession and two Spanish landraces that clustered differently from the other accessions. The relatively high number of unique alleles found in the three differentially clustered selections suggested that their use as parents has potential to increase the genetic diversity in future US-bred cultivars. Of the 92 RosCOS SNPs, 81 SNPs that represented 68 genome-wide RosCOS segregated in four mapping populations. These RosCOS were mapped in four F₁ populations, thereby greatly improving the genetic linkage map of sweet cherry.     

Selection and localised genetic structure in the threatened Manauense Harlequin Frog (Bufonidae: Atelopus manauensis)

Knowledge of genome-wide variation and the processes influencing gene flow are critical to managing threatened species. Here, we characterise genetic diversity and the environmental features associated with connectivity for a narrowly distributed and threatened Amazonian frog, Atelopus manauensis. We sampled 94 individuals throughout the upper, middle and lower courses from each of the five major rivers covering the species’ known geographic range and genotyped each individual at 3859 single nucleotide polymorphisms (SNPs). Genetic variation was significantly subdivided into six groups, each mostly containing individuals sampled within the same major river system. The genetic distances among these groups increased with geographic distance, and open forests and the extremities of the altitude gradient were associated with less genetic connectivity. Using F_ST outlier approaches and environmental association analyses, we identified SNPs indicative of localised adaptation, with 28 SNPs significantly associated with forest biomass and altitude. Evidence of divergent selection among the six genetic clusters suggests the presence of six Evolutionary Significant Units (ESUs). Overall, the ESUs were characterized by low contemporary effective population sizes (N_E?<?100) suggesting that genetic variation will be lost by random genetic drift. We demonstrate surprisingly high levels of divergence across the limited distribution of A. manauensis and suggest that each of the six adaptively divergent lineages be considered in conservation planning.

     

Performance of Polygenic Scores for Predicting Phobic Anxiety

Context

Anxiety disorders are common, with a lifetime prevalence of 20% in the U.S., and are responsible for substantial burdens of disability, missed work days and health care utilization. To date, no causal genetic variants have been identified for anxiety, anxiety disorders, or related traits.

Objective

To investigate whether a phobic anxiety symptom score was associated with 3 alternative polygenic risk scores, derived from external genome-wide association studies of anxiety, an internally estimated agnostic polygenic score, or previously identified candidate genes.

Design

Longitudinal follow-up study. Using linear and logistic regression we investigated whether phobic anxiety was associated with polygenic risk scores derived from internal, leave-one out genome-wide association studies, from 31 candidate genes, and from out-of-sample genome-wide association weights previously shown to predict depression and anxiety in another cohort.

Setting and Participants

Study participants (n = 11,127) were individuals from the Nurses'' Health Study and Health Professionals Follow-up Study.

Main Outcome Measure

Anxiety symptoms were assessed via the 8-item phobic anxiety scale of the Crown Crisp Index at two time points, from which a continuous phenotype score was derived.

Results

We found no genome-wide significant associations with phobic anxiety. Phobic anxiety was also not associated with a polygenic risk score derived from the genome-wide association study beta weights using liberal p-value thresholds; with a previously published genome-wide polygenic score; or with a candidate gene risk score based on 31 genes previously hypothesized to predict anxiety.

Conclusion

There is a substantial gap between twin-study heritability estimates of anxiety disorders ranging between 20–40% and heritability explained by genome-wide association results. New approaches such as improved genome imputations, application of gene expression and biological pathways information, and incorporating social or environmental modifiers of genetic risks may be necessary to identify significant genetic predictors of anxiety.     

Using evolutionary genomics,transcriptomics, and systems biology to reveal gene networks underlying fungal development

Fungal model species have contributed to many aspects of modern biology, from biochemistry and cell biology to molecular genetics. Nevertheless, only a few genes associated with morphological development in fungi have been functionally characterized in terms of their genetic or molecular interactions. Evolutionary developmental biology in fungi faces challenges from a lack of fossil records and unresolved species phylogeny, to homoplasy associated with simple morphology. Traditionally, reductive approaches use genetic screens to reveal phenotypes from a large number of mutants; the efficiency of these approaches relies on profound prior knowledge of the genetics and biology of the designated development trait—knowledge which is often not available for even well-studied fungal model species. Reductive approaches become less efficient for the study of developmental traits that are regulated quantitatively by more than one gene via networks. Recent advances in genome-wide analysis performed in representative multicellular fungal models and non-models have greatly improved upon the traditional reductive approaches in fungal evo-devo research by providing clues for focused knockout strategies. In particular, genome-wide gene expression data across developmental processes of interest in multiple species can expedite the advancement of integrative synthetic and systems biology strategies to reveal regulatory networks underlying fungal development.     

The meta-analysis of genome-wide association studies

The pressure to publish novel genetic associations has meant that meta-analysis has been applied to genome-wide association studies without the time for a careful consideration of the methods that are used. This review distinguishes between the use of meta-analysis to validate previously reported genetic associations and its use for gene discovery, and advocates viewing gene discovery as an exploratory screen that requires independent replication instead of treating it as the application of hundreds of thousands of statistical tests. The review considers the use of fixed and random effects meta-analyses, the investigation of between-study heterogeneity, adjustment for confounding, assessing the combined evidence and genomic control, and comments on alternative approaches that have been used in the literature.