A Family‐Based Robust Multivariate Association Test Using Maximum Statistic

For characterizing the genetic mechanisms of complex diseases familial data with multiple correlated quantitative traits are usually collected in genetic studies. To analyze such data, various multivariate tests have been proposed to investigate the association between the underlying disease genes and the multiple traits. Although these multivariate association tests may have better power performance than the univariate association tests, they suffer from loss of testing power when the genetic models of the putative genes are misspecified. To address the problem, in this paper we aim to develop a family‐based robust multivariate association test. We will first establish the optimal multivariate score tests for the recessive, additive, and dominant genetic models. Based on these optimal tests, a maximum‐type robust multivariate association test is then obtained. Simulations are conducted to compare the power of our method with that of other existing multivariate methods. The results show that the robust multivariate test does manifest the robustness in power over all plausible genetic models. A practical data set is applied to demonstrate the applicability of our approach. The results suggest that the robust multivariate test is more powerful than the robust univariate test when dealing with multiple quantitative traits.     

Genome-wide shifts in climate-related variation underpin responses to selective breeding in a widespread conifer

Locally adapted temperate tree populations exhibit genetic trade-offs among climate-related traits that can be exacerbated by selective breeding and are challenging to manage under climate change. To inform climatically adaptive forest management, we investigated the genetic architecture and impacts of selective breeding on four climate-related traits in 105 natural and 20 selectively bred lodgepole pine populations from western Canada. Growth, cold injury, growth initiation, and growth cessation phenotypes were tested for associations with 18,600 single-nucleotide polymorphisms (SNPs) in natural populations to identify “positive effect alleles” (PEAs). The effects of artificial selection for faster growth on the frequency of PEAs associated with each trait were quantified in breeding populations from different climates. Substantial shifts in PEA proportions and frequencies were observed across many loci after two generations of selective breeding for height, and responses of phenology-associated PEAs differed strongly among climatic regions. Extensive genetic overlap was evident among traits. Alleles most strongly associated with greater height were often associated with greater cold injury and delayed phenology, although it is unclear whether potential trade-offs arose directly from pleiotropy or indirectly via genetic linkage. Modest variation in multilocus PEA frequencies among populations was associated with large phenotypic differences and strong climatic gradients, providing support for assisted gene flow polices. Relationships among genotypes, phenotypes, and climate in natural populations were maintained or strengthened by selective breeding. However, future adaptive phenotypes and assisted gene flow may be compromised if selective breeding further increases the PEA frequencies of SNPs involved in adaptive trade-offs among climate-related traits.

Local adaptation of climate-related traits in widespread temperate conifers has been demonstrated for centuries using extensive long-term common garden experiments (1, 2). As early as the 17th century, foresters were instructed to recognize variation in desirable traits and select seed from trees with favorable phenotypes (3). Modern tree improvement programs systematically select upon genetic variation, primarily to achieve growth gains and meet economic objectives. Estimates of genetic variation and gains from selection made using quantitative genetic models assume many anonymous loci of small effect underlie both variation in continuously distributed phenotypes and their responses to selective breeding. However, the type, quantity, effect size, distribution, and dynamics of genes underlying locally adaptive phenotypic variation and responses to selective breeding in forest trees are still poorly understood (4).Directional selection over hundreds or thousands of generations has led to genomic features of domestication in agricultural crops including simplified genetic architectures underlying many traits, reduced genome-wide diversity, and numerous selective sweeps (5–7). Beyond high-gain, short-rotation clonal forestry [e.g., Eucalyptus spp. (8)], we know little about the effects of artificial selection on adaptive genetic variation in forest trees, yet many tree species undergo some degree of selective breeding. Two or three generations of conifer breeding is not expected to have the same magnitude of genetic effects seen in domesticated crops, but if artificial selection for increased productivity is detectable in conifer genomes, it may expose genetic relationships and potential sources of trade-offs between growth and climatically adaptive phenotypes.Climate-related adaptive traits are often intercorrelated due to pleiotropy, natural selection, or linkage disequilibrium (LD), so that strong directional selection on one trait can cause correlated responses in others. Pleiotropic allelic variants associated with phenotypes do not function in isolation. Antagonistic pleiotropic effects among traits can generate adaptive trade-offs among traits within populations, and limit gains from selection on a focal trait (9). Trait–trait correlations can also arise through strong selection acting in parallel on unlinked loci or from LD mediated by physical linkage of loci on chromosomes. Average genome-wide LD estimates in conifers appear to be low (10, 11) but may be greater (r² of 0.2–0.4) within genes under strong selection (12).Conifer studies have identified putatively adaptive phenotype-associated alleles on a locus-by-locus basis using quantitative trait loci (QTL) mapping or genotype–phenotype associations (GPAs) (also known as genome-wide association studies [GWAS]) (13). Genotype–environment association analyses in conifers have identified putatively adaptive environmentally associated loci (e.g., 14, 15), but loci are usually anonymous relative to adaptive phenotypes. All of these approaches are biased toward detecting loci with large phenotypic effects, but expectations that genome scans will discover individual adaptive loci with large effects or frequency differences among populations may be biologically and statistically unrealistic (16, 17). Conifer GPA studies typically detect relatively few statistically significant loci, and locus-by-locus analyses are insufficient to characterize adaptive genome-wide variation associated with adaptive traits and signatures of selective breeding. Multilocus tests for adaptive polygenic signatures of selection have been developed (e.g., refs. 18 and 19), but significant limitations remain (reviewed in refs. 20 and 21).Uncertainty about the effects of selective breeding on adaptive genetic variation is layered upon expectations that forest trees will become maladapted as climates shift (22). Efforts are being made to estimate maladaptation using genome-wide variation associated with adaptive traits and climate (23, 24), because conserving, managing, and efficiently redeploying genetic variation associated with adaptive phenotypes will be a necessary element of strategies to mitigate the effects of shifting climates on forest resources (25). Assisted gene flow strategies in temperate and subboreal forests generally aim to move trees to cooler climates in anticipation of future warming, but trees must then contend with the increased short- to medium-term risk of maladaptation to damaging frost. This means cold tolerance is, perhaps surprisingly, an important trait when planning for warming climates.Genetic approaches have the potential to efficiently and accurately characterize local adaptation to climate. Understanding whether this potential can be realized in a technically robust and operationally feasible way, and whether selection for faster growth compromises genetic variation associated with phenotypic adaptation to climate, has far-reaching implications for developing effective assisted gene flow strategies that mitigate negative climate change impacts on forest health and timber production (26, 27). In this context, our research objectives are to 1) identify the genetic architecture of climate-related adaptive traits in interior lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.); 2) identify genome-wide effects of artificial selection for increased productivity on climate-related traits; and 3) assess the implications of genetic responses to selection for assisted gene flow strategies.Our study combines climatic data, genotype data from ∼50,000 lodgepole pine single-nucleotide polymorphisms (SNPs), and seedling phenotypic data for height, cold injury, growth initiation, and growth cessation traits. These data were collected from a seedling common garden that sampled reforestation seed lots from 105 natural populations and 20 breeding populations from across the species’ range in Alberta (AB) and British Columbia (BC), Canada (Fig. 1 and SI Appendix, Table S1). For each of the four traits, we identify range-wide GPAs using 929 seedlings from all 105 natural populations. Then for the 1% most strongly phenotype-associated SNPs, we examine how artificial selection within breeding populations has changed allele frequencies at individual SNP loci, within individual seedlings, populations (breeding zones), and three climatic regions (Fig. 1). Using elements from the approach of Turchin et al. (18), we study changes in frequency of the alleles that have a positive effect on adaptive traits (positive effect alleles [PEAs]). At each SNP locus, a PEA is the allele associated with increasing numeric values of the respective phenotype, determined in this case through GPA analyses in the natural seedling populations. PEAs reported here are associated with greater seedling height, greater cold injury, delayed growth initiation, and delayed growth cessation. To parse physical genetic linkage from allelic associations due to other causes, we compare LD estimated from our natural seedlings with estimates of recombination among haploid megagametophytes from a single maternal parent, where physical linkage is the only cause of LD. Integrating genetic, climatic, and phenotypic data gives us a robust basis to detect the effects of artificial selection on climate-related genotypes that are relevant to breeding and assisted gene flow strategies.Open in a separate windowFig. 1.Geographic origins of the natural and selected seedling populations sampled from across the range of lodgepole pine in Alberta (AB) and British Columbia (BC). Natural populations are represented by filled circles; selected seedling breeding zones are represented by filled polygons. The three climatic regions we used were AB, BC-Central, and BC-South. AB breeding zones are formally identified as A, B1, B2, C, J, and K1. BC-Central breeding zone abbreviations are as follows: BV, Bulkley Valley; CP, Central Plateau; and PG, Prince George. BC-South breeding zone abbreviations are as follows: EK, East Kootenay; NE, Nelson; and TO, Thompson–Okanagan. Reprinted from ref. 35, with permission from Elsevier.     

Associations of eating alone with type D personality,depression and rejection sensitivity among South Korean university students

Purpose: To identify the associations of eating alone with type D personality, depression, and rejection sensitivity among South Korean university students. Design and Methods: Data were collected from 370 students recruited from five universities using a self-reported questionnaire. Findings: Eating alone had significant correlations with type D personality and depression. Significant factors affecting eating alone were age, place of residence, type D personality, and depression. These factors accounted for 17% in eating alone. Implications for Nursing Practice: There is a need for interventional programs that can reduce the negative effects of eating alone and prevent depression among students with type D personality.     

Metacognitive Interpersonal Therapy in group (MIT-G) for young adults with personality disorders: A pilot randomized controlled trial

Young adults with personality disorders (PD) other than borderline are in urgent need of validated treatments to help them in managing important life transitions. Therapeutic interventions focused upon social and interpersonal difficulties may facilitate these individuals in maximizing opportunities for employment, forming stable romantic relationships, and belong to social groups. It is also important that they are offered evidence-based, first-line time-limited treatments in order to maximize effectiveness and reduce costs. We developed a 16-session programme of group-based Metacognitive Interpersonal Therapy (MIT-G) including psychoeducation on the main interpersonal motives, an experiential component enabling practice of awareness of mental states; and use of mentalistic knowledge for purposeful problem-solving. We report a feasibility, acceptability, and clinical significance randomized clinical trial. Participants meeting inclusion criteria were randomized to receive MIT-G (n = 10) or waiting list+TAU (n = 10). Dropout rate was low and session attendance high (92.19%). Participants in the MIT-G arm had symptomatic and functional improvements consistent with large effect sizes. In the MIT-G arm similarly large effects were noted for increased capacity to understand mental states and regulate social interactions using mentalistic knowledge. Results were sustained at follow-up. Our findings suggest potential for applying MIT-G in larger samples to further test its effectiveness in reducing PD-related symptoms and problematic social functioning.     

独生子女士兵和非独生子女士兵主动性人格与心理应激关系的比较

目的比较独生子女士兵和非独生子女士兵主动性人格与心理应激的关系,为部队有针对性地开展心理教育和心理服务提供依据。方法选取独生子女士兵247人和非独生子女277人,采用主动性人格和心理应激等问卷进行调查。结果独生子女士兵和非独生子女士兵之间在主动性人格的3个维度(积极、坚韧和变革)和心理应激的得分差异无显著性。士兵的主动性人格对其心理应激具有显著的预测作用,其中积极性人格具有显著的负向预测作用,即积极性人格得分越高的士兵,其体验到的心理应激可能越少,而变革性人格具有显著的正向预测作用,即变革性人格得分越高的士兵,其体验到的心理应激可能越高;是否独生子女对主动性人格和心理应激的关系起调节作用,独生子女积极性人格和心理应激具有显著的负向预测作用,非独生子女积极性人格对心理应激的负向预测作用更加明显。结论主动性人格对士兵心理应激具有显著预测作用;士兵是否独生子女可影响主动性人格和心理应激的关系。     

大学生的阈下自闭特质及与共情能力的关系

目的:了解大学生的阈下自闭特质及与共情能力的关系,为高阈下自闭特质个体的筛查和干预提供依据。方法:方便抽选宁夏某高校大学生515名作为研究对象,选用自闭商数问卷(AQ)和共情商数问卷(EQ)探查大学生的阈下自闭特质及与共情能力的关系。结果:男性大学生在AQ分维度行为模式上的得分显著高于女性大学生(t=9.927,P0.01)。理科生在AQ总分(t=13.115,P0.001)及分维度心智阅读(t=24.974,P0.001)上的得分显著高于文科生。本科生在AQ分维度行为模式(t=-4.613,P0.05)、细节注意(t=-5.566,P0.05)上的得分显著低于研究生,在分维度社交性(t=13.498,P0.001)上的得分显著高于研究生。共情能力和AQ总分(r=-0.518,P0.01)及分维度社交性(r=-0.354,P0.01)、心智阅读(r=-0.644,P0.01)、注意转换(r=-0.352,P0.01)、细节注意(r=0.271,P0.01)显著相关,且共情能力对阈下自闭特质及这些分维度均有显著的预测作用。结论:阈下自闭特质在大学生中呈连续性分布,且共情能力影响阈下自闭特质水平。     

大学生就业压力、人格特质与择业取向的关系

目的:探讨本科毕业生人格特质在就业压力与择业取向间的中介作用。方法:采用大学生择业取向量表,大学生就业压力量表和大五人格量表对314名本科毕业生进行集体施测。结果:男生在择业目的、职业定位和薪酬期望上得分高于女生,而在择业标准上低于女生(P0.05);在择业标准、职业定位和薪酬期望得分上理文艺术,而在地域选择上,理科生得分要低于文科生和艺术生(P0.05)。就业压力与择业目的呈显著正相关(r=0.248,P0.01),与择业标准和薪酬期望呈显著负相关(r=-0.175,-0.143;P0.01)。中介效应分析表明,外向性在就业压力与薪酬期望之间起部分中介作用,中介效应占总效应的16.5%;情绪稳定性和尽责性在就业压力与择业目的之间起部分中介作用,中介效应分别占总效应的26.9%和15.8%。结论:大学生的就业压力直接或间接地通过人格特质影响其择业取向。