The k-nearest neighbor technique with local linear regression

In a standard k-nearest neighbor (kNN) technique, imputations of unit-level values in the variables of interest (Y) are based on the k-nearest neighbors in a set of reference units. Nearest is defined with respect to a distance metric in the space of auxiliary variables (X). This study evaluates kNN imputations of Y with a selection, by the same distance metric, of k-nearest locally weighted regression models. Imputations are obtained as predictions using the X values of the k-nearest neighbors in the population. In simulated random sampling from three artificial multivariate populations and two actual univariate populations and sampling units composed of a single population element or a cluster of four elements, the new kNN technique: (1) improved the correlation between an imputation and its actual value; (2) lowered the root mean square error (RMSE) of imputations; (3) increased the slope in regressions of actual y values regressed against their imputed values; (4) performed relatively best with k values of 4 and sample sizes of 200 or greater; (5) compared favorably with a recently proposed kNN calibration procedure; and (6) had a higher (15–28%) RMSE than with a simple local linear regression. Distribution matching had a consistent negative effect (+10%) on RMSE.     

Assessing single-nucleotide polymorphism selection methods for the development of a low-density panel optimized for imputation in South African Drakensberger beef cattle

A major obstacle in applying genomic selection (GS) to uniquely adapted local breeds in less-developed countries has been the cost of genotyping at high densities of single-nucleotide polymorphisms (SNP). Cost reduction can be achieved by imputing genotypes from lower to higher densities. Locally adapted breeds tend to be admixed and exhibit a high degree of genomic heterogeneity thus necessitating the optimization of SNP selection for downstream imputation. The aim of this study was to quantify the achievable imputation accuracy for a sample of 1,135 South African (SA) Drakensberger cattle using several custom-derived lower-density panels varying in both SNP density and how the SNP were selected. From a pool of 120,608 genotyped SNP, subsets of SNP were chosen (1) at random, (2) with even genomic dispersion, (3) by maximizing the mean minor allele frequency (MAF), (4) using a combined score of MAF and linkage disequilibrium (LD), (5) using a partitioning-around-medoids (PAM) algorithm, and finally (6) using a hierarchical LD-based clustering algorithm. Imputation accuracy to higher density improved as SNP density increased; animal-wise imputation accuracy defined as the within-animal correlation between the imputed and actual alleles ranged from 0.625 to 0.990 when 2,500 randomly selected SNP were chosen vs. a range of 0.918 to 0.999 when 50,000 randomly selected SNP were used. At a panel density of 10,000 SNP, the mean (standard deviation) animal-wise allele concordance rate was 0.976 (0.018) vs. 0.982 (0.014) when the worst (i.e., random) as opposed to the best (i.e., combination of MAF and LD) SNP selection strategy was employed. A difference of 0.071 units was observed between the mean correlation-based accuracy of imputed SNP categorized as low (0.01 < MAF ≤ 0.1) vs. high MAF (0.4 < MAF ≤ 0.5). Greater mean imputation accuracy was achieved for SNP located on autosomal extremes when these regions were populated with more SNP. The presented results suggested that genotype imputation can be a practical cost-saving strategy for indigenous breeds such as the SA Drakensberger. Based on the results, a genotyping panel consisting of ~10,000 SNP selected based on a combination of MAF and LD would suffice in achieving a <3% imputation error rate for a breed characterized by genomic admixture on the condition that these SNP are selected based on breed-specific selection criteria.     

先锋精神：从新诗的民族神话、传说写作 看中国新诗用典出路

研究了多态不确定性环境下的城市垃圾处理问题.在一定假设条件下, 建立了一类固废管理问题的区间模糊优化模型. 提出了区间大小关系可能度算子的公理化定义,并基于这种可能度算子的方法, 对给定的权重系数和置信水平, 推导了原模型的确定型等价类, 从而把区间模糊优化问题转化为普通的线性规划问题求解.将所建立的模型和求解方法用于解决一个实际固废管理问题, 结果证实了该模型及其求解方法的有效性.     

Current hypotheses for the evolution of sex and recombination

The evolution of sex is one of the most important and controversial problems in evolutionary biology. Although sex is almost universal in higher animals and plants, its inherent costs have made its maintenance difficult to explain. The most famous of these is the twofold cost of males, which can greatly reduce the fecundity of a sexual population, compared to a population of asexual females. Over the past century, multiple hypotheses, along with experimental evidence to support these, have been put forward to explain widespread costly sex. In this review, we outline some of the most prominent theories, along with the experimental and observational evidence supporting these. Historically, there have been 4 classes of theories: the ability of sex to fix multiple novel advantageous mutants (Fisher-Muller hypothesis); sex as a mechanism to stop the build-up of deleterious mutations in finite populations (Muller's ratchet); recombination creating novel genotypes that can resist infection by parasites (Red Queen hypothesis); and the ability of sex to purge bad genomes if deleterious mutations act synergistically (mutational deterministic hypothesis). Current theoretical and experimental evidence seems to favor the hypothesis that sex breaks down selection interference between new mutants, or it acts as a mechanism to shuffle genotypes in order to repel parasitic invasion. However, there is still a need to collect more data from natural populations and experimental studies, which can be used to test different hypotheses.     

不同筛选方法的低密度SNP集合填充准确性比较

【目的】尝试通过在华西牛参考群高密度标记芯片位点中,使用两种标记筛选方法挑选具有代表性的且密度梯度不同的SNP位点集合,后利用基因组填充策略在相同填充参数下将低密度芯片数据填充至高密度继而进行后续基因组研究,从而达到降低华西牛基因型分型成本的目的。研究分别比较了不同标记集合填充准确性和填充一致性的差异,阐述了标记筛选方法、标记密度、最小等位基因频率和参考群体数量等4个因素对填充结果的影响,为华西牛低密度SNP填充芯片设计提供参考。【方法】将质控后剩余的1 233头华西牛群体随机分为参考群(986头)和验证群(247头)。使用等间距法(equidistance,EQ)和高MAF法(high MAF,HM)两种标记筛选方法分别从华西牛参考群体的Illumina Bovine HD芯片位点集合中筛选出16种不同密度的SNP集合,共生成32种不同SNP梯度密度集合。随后在验证群体中利用Beagle(v5.1)软件将各低密度集合填充至770 k密度水平,计算填充准确性和填充一致性并对填充性能影响因素进行分析。【结果】32种低密度SNP集合的标记数量在100—16 000之间,窗口最大为24 17...     

Multi-mycotoxin exposure and risk assessments for Chinese consumption of nuts and dried fruits

In this study, 15 mycotoxins were detected in 233 nut and dried fruit samples from China. The 15 mycotoxins included aflatoxins(AFs: AFB_1, AFB_2, AFG_1 and AFG_2), trichothecene toxins(TCs: T-2, ZEA, ENA, ENA_1, ENB, ENB_1 and BEA), Alternaria toxins(ATs: TEN, AOH and AME) and ochratoxin A(OTA). The mycotoxins were detected in 47.6% of the samples and all 15 of the mycotoxins were found. Two samples were positive for AFB_1 and exceeded the maximum tolerable levels allowed in China. The contamination levels of the mycotoxins found in nuts, dried jujubes, raisins, dried figs and dried longans were in the ranges of 0.1–462.7, 0.2–247.3, 0.8–10.1, 0.2–384.1 and 0.1–89.2 μg kg–1, respectively. Dried figs(80.0%) had the highest incidence of mycotoxins, followed by dried longans(60.0%), dried jujubes(57.1%), nuts(43.6%) and raisins(26.7%). The estimated daily intake(EDI) values of each individual mycotoxin and all of the mycotoxins collectively were calculated by both the deterministic approach(DA) and the probability approach(PA). For risk characterization, dietary exposure to TCs, ATs and OTA through consumption of nuts and dried fruits according to both approaches, showed no health risk to Chinese adults by exposure to either individual mycotoxins or in combination. To the best of our knowledge, this is the first work in which risk assessment of multimycotoxins is performed, specifically including the emerging ENNs and BEA, in nuts and dried fruits of China.     

缺省值问题中一种新的比较补充方法的方法

讨论了当缺省值问题中有多种补充方法时,从补充完整样本的损失的角度,并引进了统计决策理论来比较这些补充方法.当补充风险r(X ,X)=∫∫f(X (x)-x)dPXmisθxmis)dPθ(xobs)在决策空间是最小时,相应的补充方法就是容许决策法则.     

Estimation of variance and genomic prediction using genotypes imputed from low‐density marker subsets for carcass traits in Japanese black cattle

The influence of genotype imputation using low‐density single nucleotide polymorphism (SNP) marker subsets on the genomic relationship matrix (G matrix), genetic variance explained, and genomic prediction (GP) was investigated for carcass weight and marbling score in Japanese Black fattened steers, using genotype data of approximately 40,000 SNPs. Genotypes were imputed using equally spaced SNP subsets of different densities. Two different linear models were used. The first (model 1) incorporated one G matrix, while the second (model 2) used two different G matrices constructed using the selected and remaining SNPs. When using model 1, the estimated additive genetic variance was always larger when using all SNPs obtained via genotype imputation than when using only equally spaced SNP subsets. The correlations between the genomic estimated breeding values obtained using genotype imputation with at least 3,000 SNPs and those using all available SNPs without imputation were higher than 0.99 for both traits. While additive genetic variance was likely to be partitioned with model 2, it did not enhance the accuracy of GP compared with model 1. These results indicate that genotype imputation using an equally spaced low‐density panel of an appropriate size can be used to produce a cost‐effective, valid GP.     

Improving accuracy of genomic prediction in Brangus cattle by adding animals with imputed low‐density SNP genotypes

Reliable genomic prediction of breeding values for quantitative traits requires the availability of sufficient number of animals with genotypes and phenotypes in the training set. As of 31 October 2016, there were 3,797 Brangus animals with genotypes and phenotypes. These Brangus animals were genotyped using different commercial SNP chips. Of them, the largest group consisted of 1,535 animals genotyped by the GGP‐LDV4 SNP chip. The remaining 2,262 genotypes were imputed to the SNP content of the GGP‐LDV4 chip, so that the number of animals available for training the genomic prediction models was more than doubled. The present study showed that the pooling of animals with both original or imputed 40K SNP genotypes substantially increased genomic prediction accuracies on the ten traits. By supplementing imputed genotypes, the relative gains in genomic prediction accuracies on estimated breeding values (EBV) were from 12.60% to 31.27%, and the relative gain in genomic prediction accuracies on de‐regressed EBV was slightly small (i.e. 0.87%–18.75%). The present study also compared the performance of five genomic prediction models and two cross‐validation methods. The five genomic models predicted EBV and de‐regressed EBV of the ten traits similarly well. Of the two cross‐validation methods, leave‐one‐out cross‐validation maximized the number of animals at the stage of training for genomic prediction. Genomic prediction accuracy (GPA) on the ten quantitative traits was validated in 1,106 newly genotyped Brangus animals based on the SNP effects estimated in the previous set of 3,797 Brangus animals, and they were slightly lower than GPA in the original data. The present study was the first to leverage currently available genotype and phenotype resources in order to harness genomic prediction in Brangus beef cattle.     

A comprehensive evaluation of factors affecting the accuracy of pig genotype imputation using a single or multi-breed reference population

Genotype imputation has become an indispensable part of genomic data analysis. In recent years, imputation based on a multi-breed reference population has received more attention, but the relevant studies are scarce in pigs. In this study, we used the Illumina Porcine SNP50 Bead Chip to investigate the variations of imputation accuracy with various influencing factors and compared the imputation performance of four commonly used imputation software programs. The results indicated that imputation...