extfieldsofstudy: Medicine Biology

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Using SNP Weights Derived From Gene Expression Modules to Improve GWAS Power for Feed Efficiency in Pigs

The “large p small n” problem has posed a significant challenge in the analysis and interpretation of genome-wide association studies (GWAS). The use of prior information to rank genomic regions and perform SNP selection could increase the power of GWAS. In this study, we propose the use of gene expression data from RNA-Seq of multiple tissues as prior information to assign weights to SNP, select SNP based on a weight threshold, and utilize weighted hypothesis testing to conduct a GWAS. RNA-Seq libraries from hypothalamus, duodenum, ileum, and jejunum tissue of 30 pigs with divergent feed efficiency phenotypes were sequenced, and a three-way gene x individual x tissue clustering analysis was performed, using constrained tensor decomposition, to obtain a total of 10 gene expression modules. Loading values from each gene module were used to assign weights to 49,691 commercial SNP markers, and SNP were selected using a weight threshold, resulting in 10 SNP sets ranging in size from 101 to 955 markers. Weighted GWAS for feed intake in 4,200 pigs was performed separately for each of the 10 SNP sets. A total of 36 unique significant SNP associations were identified across the ten gene modules (SNP sets). For comparison, a standard unweighted GWAS using all 49,691 SNP was performed, and only 2 SNP were significant. None of the SNP from the unweighted analysis resided in known QTL related to swine feed efficiency (feed intake, average daily gain, and feed conversion ratio) compared to 29 (80.6%) in the weighted analyses, with 9 SNP residing in feed intake QTL. These results suggest that the heritability of feed intake is driven by many SNP that individually do not attain genome-wide significance in GWAS. Hence, the proposed procedure for prioritizing SNP based on gene expression data across multiple tissues provides a promising approach for improving the power of GWAS.

INTRODUCTION
The "large p small n" problem has posed a significant challenge in the analysis and interpretation of genome-wide association studies (GWAS; Diao and Vidyashankar, 2013). The problem refers to the scenario in statistical inference where the dimension of independent variables, p, is larger than the sample size, n. Typically in GWAS, the number of observations, n, is in the hundreds or thousands and the number of markers, p, is in the hundreds of thousands. Statistical procedures such as shrinkage estimation and variable selection are often employed to ensure the existence solutions in large-p-small-n regressions in GWAS (Fernando et al., 2017).
The most commonly used approach to GWAS is single-SNP analysis, where linear or logistic regression is performed separately for each SNP followed by multiple-testing correction. This standard single-step adjustment disregards prior knowledge of potentially noteworthy regions, and, as a result, tests of significance for SNP in such regions may be overly down-weighted due to the other relatively inconsequential SNP. Hence, using prior information to rank genomic regions and perform SNP selection could increase the power of GWAS.
Recent advances in statistical methodology have made it possible to incorporate prior information through weighted hypothesis testing (Genovese et al., 2006). Roeder et al. (2006) introduced a method which uses linkage analysis information to up-or down-weight SNP according to their prior likelihood of association with a trait of interest, and the resulting weighted Pvalues are used in the false discovery rate (FDR) procedure. A similar approach using expression quantitative trait loci (eQTL) information to weight SNP was proposed by Li et al. (2013).
Transcriptome sequencing (RNA-Seq) is a widely used technology for genome-wide transcript quantification, used to analyze gene expression patterns, and provide insight into the mechanisms underlying complex traits in livestock species. Genome-wide gene expression data from thousands of studies have been accumulating and made available through public repositories such as the Gene Expression Omnibus (GEO; Edgar et al., 2002). Recently, GWAS results have been interpreted by interrogating significant SNP for associations with gene expression data in livestock (Ballester et al., 2017;Fang et al., 2017;Kommadath et al., 2017;Cai et al., 2018;Deng et al., 2019). These studies have integrated GWAS and gene expression data post-GWAS. In this study, we propose the use of gene expression data from RNA-Seq of multiple tissues (hypothalamus, duodenum, ileum, and jejunum) as prior information to assign weights to SNP, select SNP based on a weight threshold, and utilize weighted hypothesis testing to conduct a GWAS for swine feed efficiency.

MATERIAL AND METHODS
The U.S. Meat Animal Research Center (USMARC) Animal Care and Use committee reviewed and approved the use of animals in this study.

Population
Feed intake and body weight gain were measured on cohorts of growing pigs reared at USMARC. All pigs were sired by either Landrace or Yorkshire boars sourced from 5 different genetic suppliers and produced out of Landrace-Yorkshire cross sows. Two different genetic suppliers are represented in each group of pigs. Pigs entered the barn at approximately 95 days of age at the beginning of the feeding trial and had ad libitum access to a standard corn/soybean meal-based diet that met or exceeded NRC requirements (NRC, 2012). Pigs in each cohort (196 per cohort) were assigned to one of 14 same-sex pens (14 pigs per pen) containing a single Feed Intake Recording System (FIRE) feeder (Osborne Industries, Inc., Osborne KS). After a 1-week adjustment period, daily feed intakes for each pig were recorded via the FIRE feeders and pigs were weighed at the beginning (d0) and end (d 42) of the feeding trial. Twenty-two cohorts of pigs had individual feeding events recorded.
Different numbers of animals from the population were used in different stages of the study. Feed intake data was collected on a total of 4,200 animals across the 22 cohorts. Four of these 22 cohorts (n = 784 animals) were used to select 30 animals with extreme feed efficiency phenotypes for RNA-Seq. Lastly, GWAS was performed using data from the 2,813 animals that were both genotyped and phenotyped. Detailed descriptions of each stage of the study are provided in subsequent sections.

Sampling for RNA-Seq
Feed efficiency phenotypes were determined for each pig in four cohorts (n = 784 animals) by dividing average daily body weight gain (ADG) by average daily feed intake (ADFI) to determine the gain to feed ratio (Gain : Feed). From each cohort of pigs, a selection criterion was applied to select animals for further study that included ADG within ± 0.30 SD of the mean and the greatest and least ADFI (n = 7 or 8 per cohort). The descriptive statistics are presented in Table 1.

Tissue Collection, RNA Isolation, and Sequencing
Tissue collection and RNA extraction were performed using the same procedures in each contemporary group. Sample collection time frame was consistent across cohorts. Pigs identified as high and low feed efficiency were euthanized with barbiturates in accordance with the American Veterinary Medical Association guidelines (AVMA, 2013). The head was removed, and the hypothalamus was collected and stored at -80°C as previously described (Thorson et al., 2017). One 3-cm segment of midjejunum and one 3-cm segment of mid-ileum were collected from pigs as previously described (Oliver et al., 2002). In addition, a 3-cm segment of duodenum was collected approximately 5-cm caudal of the cranial duodenal flexure.
Total RNA was isolated from the tissue samples using the RNeasy Mini Plus kit and QiaShredder columns (Qiagen, Valenci, CA, USA). Briefly, 800 ul of RLT buffer with bmercaptoethanol were added to 50-100 mg of tissue samples and homogenized for 40 sec using an Omni Prep 6-station homogenizer (Omni International, Kennesaw, GA, USA). The homogenate was centrifuged in a QiaShredder column on full speed for 3 min. Genomic DNA was removed from the total RNA with the Qiagen RNeasy Plus mini-kit, according to the manufacturer's protocol, and the total RNA was eluted in 50 ul of RNase free water. Total RNA was quantified with a NanoDrop One spectrophotometer (Thermo Scientific, Wilmington, DE). The average 260/280 ratio was 2.05, with a range of 1.94-2.09. An Agilent Bioanalyzer RNA 6000 nano kit (Santa Clara, CA, USA) was used to determine the RNA integrity number (RIN). Only samples with a RIN of 8.0 and higher were used for the RNA sequencing. The average RIN was 9.1, with a range of 8.1-9.9.
Samples were prepared for RNA sequencing with the Illumina TruSeq Stranded mRNA High Throughput Sample kit and protocol (Illumina Inc., San Diego, CA, USA). The libraries were quantified with qRT-PCR using the NEBNext Library Quant Kit (New England Biolabs, Inc., Beverly, MA, USA) on a CFX384 thermal cycler (Bio-Rad, Hercules, CA, USA), and the quality of the library was determined with an Agilent Bioanalyzer DNA 1000 kit (Santa Clara, CA, USA). The libraries were diluted with Tris-HCL 10 mM, pH 8.5 with 0.1% Tween 20 to 4nM samples (Teknova, Hollister, CA. USA). All libraries were pairedend sequenced with 150 cycle high output sequencing kits for the Illumina NextSeq instrument. Bases of the paired-end reads for all sequenced libraries were identified with the Illumina BaseCaller, and FASTQ files were produced for downstream analysis of the sequence data. Sequence data is available for download from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) BioProjects PRJNA528599 (hypothalamus), PRJNA528884 (duodenum), PRJNA529214 (ileum), and PRJNA529662 (jejunum).

Sequence Data Processing
Read alignment of the RNA-Seq reads was carried out as follows. First, quality of the raw paired-end sequence reads in individual FASTQ files was assessed using FastQC (Version 0.11.5; www. bioinformatics.babraham.ac.uk/projects/fastqc), and reads were trimmed to remove adapter sequences and low-quality bases using the Trimmomatic software (Version 0.35; Bolger et al., 2014). The remaining reads were mapped to the Sscrofa 11.1 genome assembly using Hisat2 (Version 2.1.1; Kim et al., 2015) with its default parameters. The StringTie software (Pertea et al., 2015) was then used to calculate raw read counts for each of the 29,651 annotated genes in the NCBI Sscrofa 11.1 reference annotation (Release 106).
Filtering of lowly expressed genes and normalization of read counts was performed using a protocol that considers the multitissue structure of the data. First, raw read counts were normalized using the median of ratios normalization scheme from the DESeq2 software package (Love et al., 2014), where read counts are divided by sample-specific size factors determined by median ratio of gene counts relative to the geometric mean per gene. A normalized gene expression matrix was constructed for each tissue, and the arithmetic mean of expression values across samples within each tissue was computed. Genes with mean normalized expression < 100 in all 6 tissues were removed from further analysis.

Three-Way Clustering Via Constrained Tensor Decomposition to Detect Gene Expression Modules
Three-way clustering of multi-tissue, multi-individual gene expression data was performed using an adaption of the method described by Wang et al. (2017). Gene expression measurements for the four tissues were organized into a 3-way array, or order-3 tensor, with gene, individual, and tissue modes. That is, the input to the algorithm was an order-3 tensor given by, W = ⟦ wijk ⟧ ∈ R n G Ân I Ân T , where w ijk , denotes the normalized gene expression value for gene i in individual j in tissue k, n G the number of genes, n I the number of individuals, and n T the number of tissues. The tensor W was then decomposed into a sum R of rank-1 components, where l 1 ≥ l 1 ≥ … ≥ l R ≥0 are singular values in decreasing order, and G r , I r , and T r are norm-1 singular vectors that indicate the relative contribution of each gene, individual, and tissue to the r-th component, respectively, and e = [E ik ] is a noise tensor with each entry E ik i.i.d. N(0,s 2 ). Complete details of the algorithm used for tensor decomposition can be found in Wang et al. (2017). Briefly, the successive rank-1 approximation to Ω is determined by iteratively solving the following minimization problem: At each iteration, we imposed one of two conditions, either T r ≥ 0 or T r ≤ 0, by thresholding values in to 0. The appropriate sign of was selected to maximize T r . This constraint on eases the interpretation of the interaction at the tissue level. Non-zero tissue loading values indicate that the module is "active" in the tissue. Without constraining values in to a single sign, it is possible (in fact likely) that contains two expression modules, one for the tissues with positive loading values and one for the tissues with negative loading values. Consequently, gene and individual loadings become less informative since they cannot be explicitly assigned to either the positive or negative loading module. Note that the constraint on used in this work is slightly different that of Wang et al. (2017), where they imposed strict non-negativity on T r . Genes with large values in G r exhibit strong relationships with individuals and tissues in the r-th component, while these relationships are stronger in the individuals with larger I rvalues and tissues with larger (in absolute value) T r -values. The loading vectors G r , I r , and T r will be referred to as eigen-genes, eigen-individuals, and eigen-tissues, respectively, throughout the remainder of the manuscript.

Gene Ontology Enrichment Analysis
Enrichment analysis of gene ontology GO terms was performed using the PANTHER classification system (Version 14.1; Mi et al., 2016). PANTHER's implementation of the binomial test of overrepresentation with the default Ensembl Sus scrofa GO annotation as background was utilized. Data from PANTHER was considered statistically significant at FDR-corrected P ≤ 0.05.

Characterization of Gene Expression Modules
GO enrichment analysis was performed on the top genes within each expression module, where the top genes in module r were defined as genes having a loading value in G r greater than a specified cutoff value, c, which controls the significance level. A permutation-based approach was used to determine c with an arbitrarily selected significance level of a = 0.005. One hundred null tensors were generated by randomly and independently permuting gene expression values for every individual-tissue pair. That is, where G denotes the original set of gene expression values and PG denotes the permutated gene expression values. Each null tensor was decomposed, and their eigen-genes were used to represent the null distribution of gene expression values within each module. The cutoff value for module r, c r , was the 99.5percentile of the empirical distribution of G null r .

Proportion of Variance in Individual Loadings
Sources of variation in individual loadings were analyzed by fitting the following linear model: where ADFI denotes average daily feed intake, CG denotes contemporary group, I = (I 1 , …, I n I ) T , and e j~N (0, s 2 ) for all j = 1, 2,…, n I . After the model was fit, the proportion of variance explained by each covariate (ADFI, contemporary group, and gender) was calculated using ANOVA.
Tensor Projection for Identifying ADFI-Associated Genes Using the notation from above, let W ∈ R n G Ân I Ân T denote the expression tensor and fT r ∈ R n T g be the set of eigen-tissues from the tensor decomposition. Let W(·, ·, T r ) be the tensor projection of W through the eigen-tissue T r = (T r,1 , : : :, T r,n T ) T , i.e., Then, the following linear model was used for each gene tested, where e j~N (0, s 2 I). The ADFI-effect was assessed by testing

Phenotypic Data Collection for Genetic Association Analysis
Twenty-two cohorts of 196 pigs had individual feeding events recorded in a building fitted with Osbourne FIRE Feeders. The animals and facilities were previously described in Section 2.1. Records were removed for animals with incomplete data due to one of the following reasons: animal removed from the study due to health, failure of the electronic ID eartag, or failure of the FIRE Feeder for a majority of the test. As a result, 4,200 animals remained in the study. Aberrant feeding events were removed if they did not conform to a logical length of meal time (1 sec < meal time < 3,600 sec), amount of feed consumed (20 g < feed consumed < 3 kg), and consumption rate (rate < 2 kg/min). Once aberrant feeding events were removed, feeding parameters were computed for each pen and day of test to determine if a feeder was not operating properly. Statistics used to remove a pen x day included number of aberrant feeding events recorded, amount of feed distributed, and total number of events for each day. After all suspicious records were removed, the amount of feed consumed by each pig for each day of test was calculated, resulting in a total of 164,660 records of the 184,800 possible daily intake records. Data were analyzed with WOMBAT (Version 17-07-2017; Meyer, 2007) fitting a random regression mixed model. Fixed effects fitted were gender (barrow or gilt) and a combined grouppen effect. Day on test was fit as the independent variable using a cubic Legendre polynomial, and animal was fitted as a random effect. A cubic Legendre polynomial was selected as it dramatically improved the log likelihood of the model over a quadratic Legendre regression and only marginal improvements were seen when evaluating higher order polynomials. Random regression coefficients were projected to individual daily intake for each of the days on test, to fill the missing intake records and adjust for fixed effects. Daily projections were summed to obtain adjusted test intake for each individual.

Genotypic Data Collection for Genetic Association Analysis
Tail samples were collected on all pigs and stored at −20°C. Genomic DNA was extracted using the WIZARD genomic DNA purification kit according to the manufacturer's protocol (Promega Corp., Madison, WI, USA). Genotyping was conducted using three platforms: the NeoGen Porcine GGPHD chip (GeneSeek, Lincoln, USA), Illumina Porcine SNP60 v2 chip (Illumina, Inc., San Diego, USA), and NeoGen GGP-Porcine chip (GeneSeek, Lincoln, USA).

Genetic Association Analysis
Ancestors of the pigs having intake records were identified from USMARC pedigree records to create a 7,009 animal pedigree. Phenotyped pigs and their ancestors genotyped with a SNP assay, Illumina Porcine SNP60 v1 or v2 (Illumina, Inc., San Diego, USA), Illlumina Porcine SNP50 (Illumina, Inc., San Diego, USA), NeoGen GGP-Porcine chip (GeneSeek, Lincoln, USA), and NeoGen Porcine GGPHD chip (GeneSeek, Lincoln, USA) were identified. The SNP were ordered according to the Sscrofa11.1 genome assembly and available pedigree was used to impute genotypes to 49,695 SNP from at least one assay for the 4,632 genotyped animals (2,813 phenotyped, 1,819 ancestors) using findhap (VanRaden et al., 2013).
Following VanRaden (2008), weighted genomic relationship matrices (G), were constructed as where m is the number of SNP, p i the frequency of the B allele for the i th SNP, and M* a centered genotype matrix (M) weighted by a diagonal matrix of weighting factors (D) Genomic relationship matrices were constructed for equally weighted SNP (D = m x m identity matrix) as well as for genecentric weightings. Weights for SNP within gene boundaries were calculated as −log 10 (P), where P denotes the P-value obtained from testing the ADFI-effect in Equation (5) in the gene module of interest. If a SNP did not reside in a gene, it was assigned a weight of zero. For a given weight threshold, t, three G for each of the 10 sets of gene weightings were evaluated: (1) a weighted analysis with all SNP where all SNP had non-zero weightings (min = 0.00001), (2) an unweighted analysis using only SNP with weight > t, and (3) a weighted analysis using only SNP with weight > t. Arbitrary thresholds of t = 2 and t = 5 were evaluated.
The average information restricted maximum likelihood (AIREML) algorithm implemented in WOMBAT was used to estimate heritability (h 2 ) of test intake attributable to pedigree relationships and each weighted genomic relationship matrix.
Phenotypic variance should remain constant; all estimates of phenotypic variance from these data using different unweighted G were similar. Weighted G resulted in additive variance estimates much greater than phenotypic variance from unweighted G, and residual variances were similar to estimates using unweighted G. Assuming the residual variance estimate is appropriate for variation not explained by weighted G and phenotypic variance equal to that estimated with unweighted G, the amount of variation explained by weighted G should be the difference between phenotypic variance from unweighted G and residual variance from weighted G, and corrected heritability that difference divided by phenotypic variance. That is, where P u denotes the phenotypic variance from unweighted G, and E w denotes the residual variance from weighted G. After convergence, effects of individual SNP were estimated for each genomic relationship matrix. Following Wang et al. (2012) whereâ is a vector of SNP effect estimates andû g the vector of animal effects predicted for each genotyped animal. Z-scores were computed standardizingâ to a mean of zero and variance of one: where a and SD(â ) denote the mean and standard deviation of a, respectively.

Sequencing, Read Mapping, and Gene Expression
RNA-Seq libraries from hypothalamus, duodenum, ileum, and jejunum tissue of 30 pigs with divergent feed efficiency phenotypes were sequenced, generating over 7.4 billion 75-bp paired-end reads, with an average of 61.8 million reads per library ( Table 2). After adapter removal and read trimming, the resulting high-quality reads were mapped to the Sscrofa 11.1 genome assembly (NCBI accession AEMK00000000.2) with an average 98.6% read mapping rate per library. Sequencing statistics for individual libraries are given in Table S1. Normalized gene expression values were computed for the 29,651 annotated genes in the porcine genome, and lowly expressed genes across the six tissues were removed, resulting in a set of 19,365 genes to be used in downstream analyses. Table 3 shows the number of genes expressed in each of the tissues, where a gene is considered expressed if normalized expression ≥ 100 in at least fifteen (half) of the libraries in the tissue. An average of 13,351 genes were expressed per tissue.

Expression Modules Across Individuals and Tissues
A three-way gene x individual x tissue clustering analysis was performed, using constrained tensor decomposition, to obtain a total of 10 gene expression modules.

Module I -Shared, Global Expression
In the first gene expression module, the eigen-tissue and eigenindividual loading distributions are essentially flat ( Figure 1I). Hence this module captures baseline, global gene expression common to all samples in all tissues. Enrichment analysis showed that many GO terms related to basic eukaryotic cell activities were enriched in the set of 1,307 top genes, including ion binding, protein binding, nucleotide binding, and transport (Table S2).

Module II -Hypothalamus
The second gene expression module clearly separated the hypothalamus from the intestinal tissues ( Figure 1II). In the eigen-individual, more of the proportional variance in loading values was explained by ADFI than contemporary group or gender (6.8% compared to 1.15% and 0.96%, respectively; Table  4). The top 130 genes were enriched for functions related to nucleotide binding, protein binding, ion binding, hydrolase activity, and glutamate transporter activity.

Module III -Proximal Small Intestine
The third component captures expression specific to tissues in the proximal small intestine, the duodenum and jejunum. The eigentissue is primarily driven by the duodenum (Figure 1III). A moderate amount of variation among individuals was explained by both gender (8.13%) and ADFI (5.58%), while the variance explained by contemporary group was negligible (~0%). A total of 88 genes passed the thresholding to be considered a top gene in the module. These genes were primarily enriched for binding GO terms, including G protein-coupled receptor binding, sulfur compound binding, carbohydrate derivative binding, bile acid binding, cytoskeletal protein binding, ubiquitin protein ligase binding, nucleotide binding, and metal ion binding. Nearly 83% (73/88) of the top genes were also identified as top genes in the hypothalamus expression module (Module II).

Module IV -Distal Small Intestine (positive loadings)
The fourth gene expression module was comprised of the distal small intestinal tissues, the jejunum and ileum, with the ileum being the main driver (larger loading value; Figure 1IV). Although contemporary group explained the largest amount of proportional variance (10.53%), a moderate amount of variation, 6.05%, was explained by ADFI. Top genes in the module were enriched for functions related to peptide transport, lipid transport, chemokine receptor binding, hydrolase activity, bile acid binding, peptidase inhibition, and ion binding. Only one of the top genes, COX1, overlapped with the top genes from Module II, while 15 genes from Module III's top set were overlapped.

Module V -Jejunum
Expression in the jejunum tissue was captured in the fifth component ( Figure 1V). Contemporary group was the only covariate to account for more than 1% of the variation among individuals. GO analysis of the 121 top genes identified that translation regulation, RNA binding, fatty acid binding, and rRNA binding were significantly enriched.

Module VI -Jejunum and Hypothalamus (negative loadings)
The sixth module included the hypothalamus and the jejunum in the eigen-gene, with the jejunum tissue having a much stronger effect ( Figure 1VI). Again, contemporary group was the main covariate explaining individual loading value variation, as it explained approximately 6% of the variation and ADFI and gender each explained less than 1%. No GO terms were significantly enriched in the set of top genes.

Module VII -Small Intestine
Expression in all three parts of the small intestine, the duodenum, jejunum, and ileum, was captured in the seventh module. The duodenum was the most significant driver, while the jejunum and ileum had very similar loading values ( Figure  1VII). Once again, variation in loading values in the eigenindividual was predominantly explained by contemporary group.  The GO term CMP-N-acetylneuraminate monooxygenase activity was significantly enriched in the top genes.

Module VIIIileum
Ileum gene expression was highlighted in the eighth component ( Figure 1VIII). Variation between individual loading values was not well-explained by any of the covariates in the model, ADFI (1.97%), contemporary group (1.47%), and gender (2.11%). No GO terms were significantly enriched in the set of top genes. Additionally, no top genes were overlapped with those from Module IV, which was also driven by gene expression in the ileum.

Module IXdistal small intestine (negative loadings)
The fourth gene expression module was comprised of the distal small intestinal tissues, the jejunum and ileum ( Figure 1IX). It should be noted that this module corresponds to negative loading values for the tissues, while the results in Module IV corresponded to positive loading values. Similar to Module IV, ileum was the main driver of expression in the module, and contemporary group explained the largest amount of variation between individual loadings. However, none of the top genes were found to be top genes in Module IV, and no GO terms were significantly enriched.

Module Xjejunum and hypothalamus (positive loadings)
The sixth module included the hypothalamus and the jejunum in the eigen-gene, with the jejunum tissue having a much stronger effect ( Figure 1X). This module corresponds to positive loading values in the eigen-tissue, while Module VI gave the results for negative loading values. A larger amount of variation among individuals was explained by covariates in the model than that from Module IV, contemporary group explained 21.22% and ADFI explained 5.05%. The GO term CMP-Nacetylneuraminate monooxygenase activity was significantly enriched in the top genes. There was no overlap between the set of top genes and the top genes from Module IV.

Genetic Association Analysis
For each of the gene modules, three genetic association analyses were conducted: (1) a weighted analysis with all SNP, (2) an unweighted analysis using only SNP with weight > 5, and (3) a weighted analysis using only SNP with weight > 5. Removal of low weight SNP resulted in SNP sets ranging in size from 101 to 944 markers (Table 5). Results from these analyses are shown in Tables 5 and 6. Utilization of all 49,691 SNP with pedigree and genomic relationships resulted in heritabilities of 0.366 and 0.269, respectively. In general, applying SNP weights derived from each of the gene models resulted in heritabilities that remained close to those derived from the unweighted pedigree and genomic models ( Table 7). The removal of SNP with weight < 5 and leaving SNP unweighted in the model decreased performance in all 10 modules (Table 5), i.e., heritabilities were below those of the pedigree and unweighted models. Removal of SNP with weight < 5 and utilizing the SNP weights in the model increased performance from the unweighted case in all ten modules, but overall heritability was still lower than that obtained from using all SNP (Table 6). Output from the association analyses for feed intake is shown in Table S3. A total of 36 unique significant SNP associations were identified across the ten gene modules, while 2 only SNP were significant in the standard analysis using all 49,691 SNP with no SNP weights. Neither of the 2 SNP identified in the unweighted analysis were identified in the weighted analyses. The number of significant SNP identified in each module's analysis ranged from 0 to 22, with Modules I, II, VI, VII, and X having only no significant SNP and Module III having 22 significant SNP. For the weighted analyses, significant SNP were identified on chromosomes SSC 2,4,5,7,8,9,13,14,15,18, and X, with SSC 9 and SSC 8 having the largest numbers of significant SNP, 12 and 6, respectively.

DISCUSSION
The most widely used approach to GWAS has been to assign equal prior probability of association to all sequence variants tested. Recent findings suggest that incorporating prior information can increase the power for identifying associations. Such prior information can be obtained from several different sources, including but not limited to linkage analysis , gene expression (Li et al., 2013;Gamazon et al., 2015;Gusev et al., 2016;Xu et al., 2017), and functional annotation of variants (Sveinbjornsson et al., 2016). In this work, we present a methodology that exploits multi-tissue transcriptional data from a small set of individuals with extreme phenotypes to assign SNP weights for a GWAS on an expanded set of phenotyped individuals. It has been shown that any set of nonnegative weights can guarantee substantial power gain if the weights are informative and little power loss if the weights are uninformative (Genovese et al., 2006). Hence, the weighting procedure is robust to the informativeness of the weights.
We applied our method to identify genetic markers associated with feed intake in swine. The gut-brain axis is comprised of bidirectional communication between the central and enteric nervous systems, linking cognitive centers of the brain with peripheral intestinal functions. The gut-brain axis modulates short-term satiety and hunger responses to regulate the delivery of nutrients and transit of nutrients through the gastrointestinal tract (Hussain and Bloom, 2012). RNA-Seq was performed on tissues involved in the gut-brain axis, including hypothalamus, duodenum, ileum, and jejunum, originating from pigs with extreme feed intake phenotypes. A tensor decomposition method, which performs three-way clustering across genes, Heritability estimates were corrected using the difference between the phenotypic variance estimated with the unweighted G and residual variance estimated with each weighted G. Heritability estimates were corrected using the difference between the phenotypic variance estimated with the unweighted G and residual variance estimated with each weighted G.
tissues, and individuals, was used to identify gene expression modules that were either common to all tissues and individuals or exclusive to particular tissue/individual combinations. The top ten gene modules from the tensor decomposition were considered. Note that since the clustering algorithm generates expression modules via successive rank-1 approximations, if more expression modules were desired the algorithm could simply be applied to the residual tensor. Module I captured baseline, global gene expression common to all samples in all tissues, indicated by the flat distributions of the eigen-tissue and eigen-individual loading values. Other gene modules captured expression specific portions of the gut-brain axis, including the hypothalamus, the proximal and distal small intestine, the entire small intestine, and the individual components of the small intestine. A tensor projection model was used to identify ADFIassociated genes within each of the ten modules. The P-values obtained from testing the ADFI-effect were used to weight SNP in order to conduct a weighted GWAS. P-values were chosen over regression coefficients for weighting in order to rank SNP according to the significance of their respective genomic regions rather than simply an effect size. Results from both the weighted and unweighted analyses are shown in Tables 7-9. Preliminary analyses using weighted SNP revealed what appeared to be inflated estimates of heritability. There was substantially less change in residual variance estimates, indicating that inflated heritability was not a result of explaining substantially more phenotypic variation with the weighted G, but an artifact of weighted G resulting in inflated additive and phenotypic variance estimates. Phenotypic variance should remain constant, so heritability estimates were corrected using the difference between the phenotypic variance estimated with the unweighted G and residual variance estimated with each weighted G.
There was a common pattern to the change in heritability estimates as the SNP prioritization changed. When using all 50K unweighted SNP, the heritability increased from 0.269 using genomic relationships to 0.366 using pedigree. In all ten modules, the use of weighted SNP restricted to those with weight > 2 resulted in a heritability slightly lower, but comparable to that from the usual unweighted, genomic model. Randomization of SNP weights (Table S4) resulted in nearly the same overall and average per SNP heritabilities, suggesting that the weighting threshold may be suboptimal.
To investigate if a more stringent SNP weight threshold could increase model performance, SNP with weight < 5 were removed from the analysis. This resulted in an average 21-fold drop in the number of SNP included in each analysis ( Table 5). Although overall heritability estimates were lower than those obtained using SNP with weight > 2, the heritability per SNP increased. Additionally, in most modules, both overall and per SNP heritabilities were higher than those obtained when the SNP weights were randomized. The numbers of SNP (101 < p < 944) in these analyses were smaller than the number of animals (n =  Heritability estimates were corrected using the difference between the phenotypic variance estimated with the unweighted G and residual variance estimated with each weighted G. 4,200), eliminating the 'p greater than n' problem. Hence, applying a more stringent threshold results in a more informative set of SNP. Note the weight threshold values of 2 and 5 were chosen arbitrarily. Additional investigation will be needed to determine the optimal weight threshold for SNP inclusion, but this was outside the scope of this study. Across the ten gene modules (weight > 5), 36 unique SNP were identified as having significant effects, while only 2 SNP were significant in the unweighted analysis utilizing all 50K SNP. Neither SNP from the unweighted analysis resided in known QTL related to swine feed efficiency (feed intake, average daily gain, and feed conversion ratio) compared to 29 (80.6%) in the weighted analyses, with 9 SNP being located in feed intake QTL (Table S4). Additionally, many of the genes harboring significant SNP have been identified in previous studies as candidate genes related to feed efficiency in several species (Table S5). In particular, the genes ROBO2 (2 SNP), PLA2G4A (4 SNP), and MEGF10 (1 SNP) were previously identified as candidate genes for residual feed intake and feed conversion ratio in swine (Ding et al., 2018;Horodyska et al., 2019). Hence, the results from this study suggest that a considerable proportion of heritability of feed intake is driven by many SNP that individually do not attain genome-wide significance in GWAS and therefore support a highly polygenic architecture for feed intake.
Our integrated methodology, at present, is obviously partial to genotyped SNP within genes. Because most available biological resources are biased toward genes, SNP pertaining to known genes likely have more relevant prior information. Consequently, the resulting weights may be more effective for associated SNP residing in or close to known genes. Therefore, results derived from our method can still be informative regardless of their intrinsic bias. Future work will focus on extending the scope of the tensor decomposition step to leverage data from other genomic sources, including but not limited to expression of non-coding RNA, miRNA expression, transcription factors, methylation targets, and miRNA binding. Additionally the method will be extended to prioritize variants from whole genome sequencing for assay development based on functional effects.

DATA AVAILABILITY STATEMENT
The datasets generated for this study can be found in the NCBI SRA Accession Numbers PRJNA528599, PRJNA528884, PRJNA529214, and PRJNA529662.


extfieldsofstudy: Physics Chemistry

provenance: AgriculturalAndFoodSciences,2019-2020-Agricultural And Food Sciences-2019.jsonl.gz.gz

text:
LED (Light Emitting Diode) Light Provides Positive Effects on Growth and Productivity of Pakcoy Mustard (Brassica Rapa L.)

Plant photosynthesis is strongly influenced by light, carbon dioxide, the availability of water and nutrients found in the soil. In this study, LED light combined with gambelan music Gong Kebyar was applied to pakcoy plants to see its effect on growth and productivity. The LED light that was applied is white, red-blue, and red-white-blue LED light, which is given for 20 hours from 06.00 to 02.00. Gamelan music Gong Kebyar is applied for 3 hours, from 06.00 to 12.00. Variables observed included plant height, canopy area, leaf greenery, root length, and plant biomass. The results showed that white LED light had a negative effect, while the red-blue LED, and red-white-blue light had a positive effect on the growth and productivity of Pakcoy mustard. The use of red-blue LED light combined with gamelan music Gong Kebyar gave the highest value on plant height (27,176 cm), canopy area (1457.59 cm2), leaf greenery (160.03), root length (23.6 cm), and plant biomass (5.05 g). It can be concluded that the use of red-blue LED light combined with gamelan music Gong Kebyar increases the growth and productivity of pakcoy mustard.

Introduction
The indoor cultivation system is a cultivation technique carried out in a room with controlled environmental conditions that can be adapted to the micro-climate of the plant. According to [12] Indoor cultivation systems produce room temperatures that are not as hot as open spaces and can minimize pest disturbances, so that plants can grow optimally. In indoor cultivation systems plants are not optimal in getting sunlight for photosynthesis. To overcome this, artificial light is used to manipulate sunlight. Artificial light used in cultivation must have the right quality so that plants can carry out photosynthesis optimally. In the process of photosynthesis chlorophyll can absorb blue waves (400 -500 nm) to red (600 -700 nm) so that artificial light designed for plant growth must emit these waves [18]. LED light (light emitting diode) has a wavelength of 400 nm -700 nm [1] and does not produce high temperatures so that it can be used to increase plant growth [14].
Many research on the utilisation of LED light in plants has been carried out [28] found that red LED lights had a positive impact on the growth of chrysanthemum plants. While, [27] found that combination of red-blue LED lights gave the best growth of chrysanthemum plants. According to [9] LED lights can speed up the harvest of lettuce, where blue LED lights can stimulate vegetative growth  [11] stated that the combination of red, white and blue LED lights for 16 hours produced many positive effects on the growth of lettuce. The use of white light is thought produce a balanced amount of light spectrum so the photosynthesis processes is running better.
Besides light, other factors that can affect plant photosynthesis are carbon dioxide, water and nutrients. The optimal photosynthesis process will produce maximum plant growth. One of the technologies that have been developed to produce maximum growth in plants is by utilizing sound waves which is commonly called sonic blooms. Sonic bloom utilizes high frequency sound waves to stimulate the opening of stomata [15] so it accelerate the rate of absorption of carbon dioxide, water and minerals in the soil. Naturally, plants get sound sources from natural environments such as animal sounds and sounds that come from daily activities [4] examined the exposure of Balinese gamelan music on pakcoy mustard with different musical nuances, and he was found that Balinese gamelan music with joyful nuances had a positive effect on the growth and productivity of pakcoy mustard. Sonic Bloom also utilized by [20] to increase the productivity of pakcoy mustard. They found that combination of sonic bloom and monochromatic light increased pakcoy production by 40.08%.
Based on those studies, this research was conducted to determine the effect of gamelan music Gong Kebyar combined with LED light on the growth and productivity of pakcoy mustard, and to determine the combination of gamelan music Gong Kebyar and LED light that gave the best growth and productivity to pakcoy mustard. Pakcoy mustard is selected as an object of this research because pakcoy mustard responsive to environmental changes and high demand for consumption. The use of Balinese gamelan music Gong Kebyar is based on the study of [4] and each plant can respond and like different music [24].

Tools and materials
The tools used in the study include growth chamber, analytic scales, oven, thermo-hygrometer, lux meter and sound meter level. The growth chamber is equipped with speakers with a frequency response of 280 Hz -18 kHz, HPL (High Power LED) with 12-18 volt LED drivers 300mA ± 5%. Every 1 Watt HPL has a red HPL specification of 2.2 -2.4 volts 350 mA, blue HPL 3 -3.2 volts 350 mA, and white HPL 3.3 -3.5 volts 350mA. In addition, the growth chamber is also equipped with exhaust and exhaust. The materials used in the study were P1 pakcoy hybrid seedlings, fertile soil, compost, NPK fertilizer, and polybags diameter 25 cm.

Experimental design
This study used a factorial complete randomized design with 3 treatments and 1 control, so that 4 combinations were obtained. Each treatment was repeated 5 times. The experimental treatment were as follows: P1: Combination of gamelan music Gong Kebyar and white LED light P2: Combination of gamelan music Gong Kebyar and blue-red LED light P3: Combination of gamelan music Gong Kebyar and blue-red-white LED light K : Control (without treatment and cultivated at greenhouse conditions)

Implementation of Research
Experiment begins with the preparation of planting media, seed seeding, planting seeds, maintaining plants, giving treatment and observing and measuring parameters. The planting medium used was a mixture of fertile soil and compost with a weight ratio of 50: 50, which was added to the polybag. Seed seeding is done in 10 days using fertile soil planting media. After sowing 10 days, the seeds are transferred to the planting media. The treatment of combination of gamelan music Gong Kebyar and LED light in the growth chamber was carried out 3 days after planting. The treatment is ilustrated in figure 1. The music given to each treatment has same frequency and amplitude

Combination of gamelan music Gong
Kebyar and white LED light The combination of gamelan music Gong Kebyar and blue-red LED light The combination of gamelan music Gong Kebyar and blue-red-white LED light Figure 1. Picture of Pakcoy mustard inside the growth chamber (side view).

Research Parameters
The parameters observed in this research included: 1. Plant height, measured from the base of the stem to the highest leaf and repeated on three different leaves. Height measurements are carried out every week until harvest. 2. The area of the canopy, measured using the image estimation method using Matlab 2017a software.
The image is taken perpendicular to the top using a camera [4]. Measuring the area of the canopy is done every week until the harvest. 3. The greenness of the leaves, measured by green intensity values on the green channel of the leaf image taken [4]. Measuring the greenness of leaves is done at harvest time. 4. The length of the root, measured using a ruler from the base of the root to the tip of the longest root and repeated at three different root tips and then averaged. Measurement of root length is carried out after harvest. 5. Wet weight, measured after harvest using analytical scales. 6. Biomass, measured after harvest by drying the plant oven at 105 o C for 24 hours. The drying results are then weighed using analytical scales [8].

Effect of Combination of Gamelan Music Gong Kebyar and LED Light on the Height of Pakcoy Mustard
The results of analysis of variance showed that the combination treatment of gamelan music Gong Kebyar and LED light did not significantly affect the height of Pakcoy mustard at the age of 0 and 1 week after planting. However, at the age of 3, 4, and 5 weeks after planting, the combination treatment of gamelan music Gong Kebyar and LED light showed a significant effect on the height of pakcoy mustard. The change of height of the Pakcoy mustard in each treatment are ilustrated in figure 2, and the Duncan test results on the height of the pakcoy mustard are tabulated in table 1.    24.8% compared to the control. This is presumably because the intensity of the white light produced is not optimal for carrying out photosynthesis. According to [5] the light absorbed by plants for photosynthesis depends on the wavelength, intensity, and time of irradiation. The light intensity of the LEDs might be smaller than the controls, so that the growth of plants given white LED light becomes obstructed. Thus it can be stated that the treatment of the combination of gamelan music Gong Kebyar and white LED light has a negative effect on the height of the pakcoy mustard.
It can be also seen from table 1 that the combination treatment of the gamelan music Gong Kebyar and the blue-red LED light produced the highest value of pakcoy height. This is in accordance with the  [4] that Balinese gamelan music gong kebyar has a positive effect on the high growth of pakcoy mustard. Beside of music, light availability is an important influence in the cultivation process. According to [25] the red light that applied to plants can affect the elongation of plant leaves which causes the leaves to become long and slender. The red light needs to be combined with blue light which is thought to have a role to inhibit the lengthening of plant organs.
The combination treatment of gamelan music gong kebyar and blue-red LED light increase the height by 1.9% compared to controls. This is presumably due to the combination of gong kebyar music and light blue-red LEDs can accelerate the process of plant photosynthesis. Gamelan music Gong kebyar can stimulate the opening of leaf stomata so that chlorophyll is more optimal in absorbing light, carbon dioxide and nutrients used for photosynthesis. This is in accordance with the statement of [19] which states that music can widen the opening of leaf stomata thus accelerating the absorption of water and minerals and CO2 for photosynthesis. According to [17] in the photosynthesis process of chlorophyll pigments a and b absorb more red and blue light. Thus it can be stated that the treatment to the plant by gamelan music gong kebyar and blue-red LED light has a positive effect on the height of the pakcoy mustard.

Effect of Combination of Gamelan Music Gong Kebyar and LED Light on Canopy Area of
Pakcoy Mustard Based on the results of analysis of variance, it was found that the combination treatment of gamelan music Gong Kebyar and LED light had a significant effect on the canopy area of the pakcoy mustard, so Duncan's further test was performed to determine the difference in value between treatments. The Duncan test results on the canopy area of the pakcoy mustard are tabulated in table 2, while the difference in plant canopy area in each treatment can be seen visually in figure 3. It can be seen dfrom table 2 that at 5 weeks after planting, the canopy area resulted from the combination of gamelan music gong kebyar and blue-red LED light was not significantly different from the combination of gamelan music gong kebyar and blue-red-white LED light, but significantly different with the combination of gamelan music gong kebyar and white LED light and control. It can be also seen that the combination of Balinese gamelan music gong kebyar and blue-red LED light produced the highest area of plant canopy compared to other treatments. This is in accordance with [4] which stated that the administration of Gong Kebyar gamelan music had a positive effect on the canopy area of pakcoy mustard. According to [26] red and blue lights are good for plant growth in terms of number and width of leaves. But it is not in accordance with the statement of [11] that the application of blue-red-white LED light on lettuce plants produced a higher leaf area than the application of blue-red light. This is presumably because the ratio of the combination of light given is  The combination of gamelan music Gong Kebyar and blue-red LED light increased the growth of the canopy area of pakcoy mustard by 24.2% compared to controls. This is allegedly due to the combination of gamelan music Gong Kebyar and the light blue-red LED accelerate the absorption of light, carbon dioxide and nutrients so that the photosynthesis process takes place more optimally. According to [13] that red and blue light produces more effective waves of light for photosynthesis. This was confirmed by the statement of [10] that red and blue light is the most effective light for photosynthesis because leaves absorb more than 90% of that light. Thus it can be stated that the treatment of the gamelan music gong kebyar and blue-red LED light has a positive effect on the canopy area of the pakcoy mustard.
The combination of gong kebyar music and white LED light showed a significantly different value to other treatments, with the smallest value of plant canopies. The combination of gong kebyar and white LED light shows the inhibition of growth of the canopy area of pakcoy mustard up to 53.4% compared to controls. Pakcoy mustard that treated by a combination of gamelan music Gong Kebyar and white LED light produce a smaller area and number of leaves compared to other treatments, as seen in figure 3. This is presumably because the white light is not optimal for photosynthesis compared to light blue-red and blue-red-white. It can be assumed that photosynthesis will be optimal if plants get the right light so that the process of absorption of light by chlorophyll will be optimal. If combined with the music, it can stimulate the opening of leaf stomata. So, it can be said that the treatment of the combination of gamelan Gong Kebyar and white LED light has a negative effect on the canopy area of the pakcoy mustard.

Effect of Combination of Gamelan Music Gong Kebyar and LED Light on the Greenness of Pakcoy Mustard Leaves
The results of analysis of variance showed that the combination treatment of gong kebyar music and LED light had a significant effect on the greeness of the packcoy leaves. The greeness value of each treatment is presented in figure 4 and the Duncan test results are tabulated in table 3.  The results of Duncan test indicated that the intensity of the green color in the image of the pakcoy mustard shows a significantly different value in each treatment. The combination of gamelan music gong kebyar and blue-red LED light resulted the highest green color intensity which is laso indicated by dark green color of the pakcoy leaves. This is in accordance with the opinion of [21] that the exposure of gamelan music provides the highest leaf green index compared to other treatments. According to [4] gamelan music gong kebyar produces the highest leaf greenness value of packcoy. According to [22] the red and blue LED lights give positive results to the anthocyanin and chlorophyll content of plants. The combination of gamelan music Gong Kebyar and blue-red LED light can increase the greenness of the leaves of the pakcoy mustard plant by 7.8% compared to the control.  Combination of Balinese gamelan music gong kebyar and white LED light produced the lowest greenish leaf value, which indicates a lighter green pakcoy mustard leaf compared to other treatments.  This is presumably because the intensity of the white light used for photosynthesis is not optimal [7] state that several factors influence the synthesis of chlorophyll, namely light, water, temperature, genes and nutrients in the soil. According to [6] the higher the green color of a leaf, the higher the chlorophyll content. So that it can be said that giving a combination of gamelan music gong kebyar and blue-red LED light has a positive result on the greenness of the leaves of the pakcoy mustard. However, the combination of gamelan music gong kebyar and white LED light showed a negative influence on the greenish level of pakcoy mustard leaves.

Effect of Combination of Gamelan Music Gong Kebyar and LED Light on the Roots Length of
Pakcoy Mustard Analysis of variance indicated that the combination treatment of gamelan music gong kebyar and LED light significantly affected the root length of the pakcoy mustard. The difference in root length in each treatment can be visually seen in figure 5, while the Duncan test results on plant root length can be seen in table 3. It can be seen from table 3 that the length of the root of the pakcoy mustard resulted from the combination treatment of the gamelan music Gong Kebyar and the blue-red LED light was not significantly different then the combination of gamelan music Gong Kebyar and blue-red-white LED light. The combination of Balinese gamelan music gong kebyar and blue-red LED light gives the highest average value of root length compared to other treatments. The combination of Gong Kebyar gamelan music and blue-red LED light increase the root length of pakcoy mustard by 26% compared to controls. This is because the canopy growth of pakcoy mustard plants goes well. According to [3] good growth of plant canopy causes photosynthesis to take place optimally so that food reserves can be translocated throughout parts of the body including roots, resulting in good root growth as well. In addition, gamelan gong kebyar music in the pakcoy plant can widen the opening of the stomata thereby accelerating the absorption of water and nutrients by the roots for photosynthesis. Photosynthesis takes place optimally because it is given red and blue light that can be absorbed by more than 90% by plants [10].
The combination of gong kebyar music and white LED light gives a significantly different value to the other treatments with the smallest root length value. The combination of Gong Kebyar gamelan music and white LED light inhibits plant root growth by 5.7% compared to control. This is because the intensity and wavelength of white LED light is smaller than the control, so that the photosynthesis process does not take place optimally, which causes water and nutrients absorbed by the roots are not maximally utilized for photosynthesis. So it can be stated that the the combination of gamelan music Gong Kebyar and white LED light has a negative efect on the root length of the pakcoy mustard. Whereas the combination of gamelan music gong kebyar and blue-red LED light showed a positive effect on root length of the pakcoy mustard.

The Effect of Combination of Gamelan Gong Kebyar and LED Light on Biomass of Pakcoy
Mustard Figure 6 shows the biomass of pakcoy mustard in each treatments, and the results of analysis of variance are tabulated in table 3. The analysis of variance indicated that the combination treatment of gamelan music gong kebyar and LED light significantly affects the biomass of pakcoy mustard.
Note: K = without a combination of gong kebyar and LED light (control) gamelan music, P1 = Combination of gamelan music Gong Kebyar and white LED light, P2 = Combination of gamelan music Gong Kebyar and blue-red LED light, P3 = Combination of gamelan music Gong Kebyar and blue-red-white LED light It can be seen from table 3 that the biomass of pakcoy mustard resulted from combination of gamelan music Gong Kebyar and blue-red LED light significantly different with other treatments. This is consistent with the statement of [21] that the treatment of gamelan music can increase the dry weight of pakcoy mustard plants. The same thing was also conveyed by [4], that Balinese gamelan music gong kebyar can increase the productivity of pakcoy mustard [22] states that red and blue LED light has a positive influence on plant biomass.
The combination of gamelan music gong kebyar and blue-red LED light increase the biomass and pakcoy mustard by 36.1% compared to control. This is presumably because exposure the music to plants can widen the opening of leaf stomata, thus accelerating the entry of carbon dioxide and the rate of absorption of nutrients for photosynthesis which can provide increased photosynthate accumulation [23]. Biomass is the result of net CO2 assimilation during the vegetative growth period of plants [17]. Thus it can be stated that the treatment of combination of gamelan music Gong Kebyar and blue-red LED light can increase the biomass of pakcoy mustard. Thus it can be said that the treatment of the combination of gamelan music gong kebyar and blue-red LED light has positive results on biomass of pakcoy mustard.

Conclusion
Based on the results of the study it can be concluded that 1) combination of gamelan music Gong Kebyar and white LED light has a negative effect on the growth and productivity of pakcoy mustard, while the combination of gamelan music Gong Kebyar and blue-red, and blue-red-white red LEDs light have a positive effect on growth and productivity pakcoy mustard, 2) combination of gamelan music Gong Kebyar and blue-red LED light give the best growth and productivity.


extfieldsofstudy:
Medicine
Environmental Science

text:
Collembola laterally move biochar particles

Biochar is being discussed as a soil amendment to improve soil fertility and mitigate climate change. While biochar interactions with soil microbial biota have been frequently studied, interactions with soil mesofauna are understudied. We here present an experiment in which we tested if the collembolan Folsomia candida I) can transport biochar particles, II) if yes, how far the particles are distributed within 10 days, and III) if it shows a preference among biochars made from different feedstocks, i.e. pine wood, pine bark and spelt husks. In general, biochar particles based on pine bark and pine wood were consistently distributed significantly more than those made of spelt husks, but all types were transported more than 4cm within 10 days. Additionally, we provide evidence that biochar particles can become readily attached to the cuticle of collembolans and hence be transported, potentially even over large distances. Our study shows that the soil mesofauna can indeed act as a vector for the transport of biochar particles and show clear preferences depending on the respective feedstock, which would need to be studied in more detail in the future.

Introduction
Biochar has received much attention as a potential means to mitigate climate change via the sequestration of carbon, but it may also be useful for improve soil fertility (e.g. [1]; [2]; [3]; [4]; [5] for review). The latter effect has been shown to be related to an increase in soil pH ( [6]), and may also improve nutrient retention ( [7]). There have been several studies showing that biochar has the potential to change soil biological communities in regard to their composition and abundance ( [8]; [9]; [10]; [11]; [12]; [13]; [14]; [15]). Also, there is evidence that biochar can reduce the infection rate of nematodes causing root-lesions in carrots ( [16]), increases soil microbial biomass ( [11]; [14]Ji; [12]) and reduce or not affect colonization of roots by arbuscular mycorrhizal fungi ( [17]; see [18] for extensive review). Although there has been intense research in terms of the effect of biochar amendments on soil microorganisms and earthworms and their respective interactions ( [18]), the two most abundant groups of mesofauna, i.e. Collembola (springtails) and Acari (mites), have received less attention ( [19]). As these groups are partly part of the fungal energy channel in the soil food web ( [20]), one should expect a close interaction with microbial populations ( [18]). In addition, there is little evidence of how the mesofauna contributes to the distribution of biochar in the soil, which might be a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 especially important in terms of the long-term persistence of biochar amendments in the soil. Presumably, the distribution happens via i) attachment of particles on setae or cuticle; ii) feeding and defecation elsewhere; iii) animal movement over particles and hence pushing ( [21]). However, Gormsen et al. ([22]) showed that Collembola are indeed able to act as vectors for fungal spores, and other studies support this activity for charcoal ([23]; [24]), hydrochar ( [19]; [25]; [26]) and microplastics ( [21]). As biochars of different feedstocks show different characteristics, we wanted to determine if the collembolan Folsomia candida I) can transport biochar particles, II) if yes, how far the particles are distributed within 10 days, and III) if it shows a preference among biochars made from different feedstocks. To test these questions, we conducted an arena experiment with three biochars originating from different feedstocks: pine wood, pine bark and spelt husks.

Material & methods
We used three biochars originating from different feedstocks: pine bark and pine wood of Pinus sylvestris (PB and PW, respectively) and spelt husks (SH). These biochars were produced for the experiment of George et al. ( [16]) and stored in glass bottles until we used it for our experiment. All feedstocks were air-dried at room temperature prior to carbonization. The initial feedstocks were covered with sand and then wrapped in aluminum foil to create an atmosphere reduced in oxygen necessary for carbonization, which lasted for five hours at 500˚C (highest treatment temperature) in a muffle oven. The carbonized material was then sieved to separate it from the sand. Each biochar was crushed with a hammer and then sieved to a particle fraction of 100-200 μm, which we used for the experiment. We decided to use this particle fraction because it has proven to be the optimal size for F. candida--mediated potentially transport in previous experiments ( [21]) and is still countable on photos for later quantitative analysis. Details about each biochar's properties such as water-holding capacity and nutrient concentrations have been reported elsewhere ( [16]). Experimental units were 10-cm-diameter specimen cups filled with a 1-cm layer of plaster of Paris which was wetted to saturation. Treatments consisted of 2 mg of the respective biochar type distributed in a 0.5-cm-diameter circle ('feeding station') in the middle of the cups. We did not offer any additional food source. To avoid airflow that could potentially distribute the biochar particles, we carefully placed lids on the specimen cups. They were stored at room temperature (20˚C ± 2˚C) during the experiment.
The target organism was Folsomia candida (Collembola) with a body size of up to 3 mm ( [27]) from our laboratory cultures, originating from Northern Germany. The individuals were kept on a Plaster of Paris-activated charcoal mix and fed with Baker's yeast before starvation which started 14 days prior to the experiment. We set up 8 replicates of each treatment with 15 F. candida per cup. Controls duplicated the F. candida treatments but did not contain any Collembola, resulting in a total of 48 samples.
For ten days, each sample was photographed once a day from a distance of 30 cm. For the analysis of the images, four concentric circles of 1, 2, 3 and 4 cm diameter (corresponding to ring 1, ring 2, ring 3 and ring 4, see Fig 1) were digitally placed around the feeding station and the particles in each ring were counted ( [21]).

Data analysis
For the analysis of the data we used R, version 3.3.1 ( [28]). We used generalized least square models of the 'nlme' package ( [29]) and used the function 'varIdent' to account for heterogeneity in our data ( [30]). We checked the model residuals for normal distribution and homogeneity of variances. Pairwise comparisons of least square means of factors were performed by the package 'lsmeans' ( [31]). For generating the figures, we used 'ggplot2' ( [32]).
In general, biochar particles from the feedstocks pine bark and pine wood (PB and PW, respectively), were consistently distributed significantly more than those made of spelt husks (SH, Fig 1).  Additionally, we found Folsomia candida individuals which, after jumping into the pine bark particles, were covered with a large number of particles that remained on them even when the animals left the pile of biochar (Fig 2). In addition, we observed in nearly every collembolan black dots in the gut, indicating that they ingested microparticles by grazing the surface of larger biochar particles and hence could defecate them somewhere else ( [23]; [24]; [19]; [15]).

Discussion
The transport of objects has been much-studied in soil macrofauna such as earthworms. However, there is evidence that the highly abundant soil mesofauna is involved in the transport of small particles such as fungal spores, microplastics and hydrochar (e.g. [22]; [21]; [19]; respectively). We tested the ability of a springtail species to distribute biochar particles of various feedstocks and whether transport occurred differentially among the three biochars. Indeed, all three biochar types were transported more than 4 cm within the experimental time span; however, there were clear differences in how frequently and far the biochar types were transported: biochar based on pine bark (PB) was transported most, followed by pine wood and spelt husk biochar.
Biochar particles can be attached to the setae and hence transported even over relatively large distances; in soil, however, efficient transport might be limited to particles of smaller size. Additionally, collembolans seem to be actively involved in the production of microparticles from bigger chunks of biochar by grazing activities (e.g. [19]) that result in ingestion of microparticles, as we observed in terms of biochar particles in the gut of many individuals. This feeding presumably would result in defecation in another place. Presumably, ingestion/defecation might only be important for particles smaller than 100μm, but the abundance of mesofauna in soils suggests that transport might be substantial and should be considered in future studies.
Folsomia candida transported the three biochar types at different frequencies (with preference for biochar based on pine bark). It is primarily a fungal hypha grazer with strong preferences for particular species ( [27]). Most likely our observations are the result of the different abiotic characteristics ( [16]) of the respective biochar types resulting in differing microbial communities on the surface (e.g. [18]; [15]; [5]). The microbial diversity might be increased in presence of biochar ( [17]; [15]), however, bacteria and fungi react differently to changes e.g. in pH ( [33]; [34]; [35]). Other reasons may relate to particle shape or surface characteristics (i.e., how readily particles accumulate on the springtail body), however, biochar can also be used as a food source by Collembola as Ding et al. ([36]) report that organic components can be of nutritional advantage for the respective symbiotic gut bacteria. Additionally, the passage through the gut could presumably enhance the decomposition of the biochar by an inoculation with bacteria which might be able to survive outside the gut on the feces ( [37]) and hence potentially modify the soil's microbial community to some extent ( [38]) Our study shows that microarthropods can be involved in the horizontal transport of biochar particles. This transport could be quite important for spreading biochar particles from the locations to which they were applied. Horizontal, and perhaps vertical ( [39]), transport can thus contribute to explaining the exposure of soil biota to biochar particles on a local scale. Additionally, the distribution of biochar particles to deeper soil layers presumably has consequences for the interaction of these particles with soil minerals and hence soil fertility ( [40]). Future studies should involve testing the transport of biochar particles of different sizes in soil to get a more detailed understanding of the interaction with soil organisms and potential ecotoxicology ( [41]; [26]) over time ( [15]).


text:
Heavy metal pollution comprehensive evaluation of contaminated soil in lead-zinc mining area based on the fuzzy mathematics

A new comprehensive evaluation method of heavy metal pollution based on the fuzzy mathematics was constructed. The results showed that mean concentrations of the heavy metals in the contaminated soils decreased in the order of Zn > Pb > Cd>Cu > As>Ni>Cr. Leaching experiment results showed that concentration of heavy metal in leaching agent of soil is not proportional to the content of heavy metal in soil. Through fuzzy comprehensive evaluation, the solid soil environmental quality grade was heavy pollution and leaching liquid water quality standards was grade V.

Introduction
Heavy metal, as a kind of persistent potentially toxic pollutants, once be released into the environment can not be biodegradable and exist in environment for a long time, which is one of the most serious environmental problems (Zhao et al. 2014). More attention are paid to metal pollution, because of heavy metals' persistence and hazardous effects on the environment and human health (Bhargava et al. 2012). Therefore, the objective and practical comprehensive evaluation of the pollution is of great significance.
The fuzzy evaluation method based on fuzzy mathematics, quantifies some obscure boundary and not easy to quantitative factors. It can reflect objective existence fuzziness and uncertainty factors, which is widely used in the assessment of water quality (Soenmez et al. 2013) and water classification ). The fuzzy comprehensive evaluation was used in many other respects. A fuzzy synthetic evaluation model with four air pollutants was designed to evaluate air quality (Gorai et al. 2014); Jiang introduced fuzzy mathematics into urban ecological security evaluation and established an evaluation model (Jiang et al. 2011). Zhao applied the membership function of fuzzy synthetic evaluation to assess the' multi-metals comprehensive accumulation ability of the trees (Zhao et al.2014). Through these researches it can be discover that fuzzy evaluation research mainly aimed at making the evaluation of the single way of pollution, however, the fuzzy evaluation of composite contamination is rare relatively. In this research, a new pollution comprehensive evaluation method of heavy metal based on the fuzzy mathematics was constructed, and the mixture of slag and contaminated soil as pollution source was evaluated more objectively and practically which can provide more reasonable basis for pollution protection and management.

Research materials
The study was conducted in Maxipu in Yuanling County, Huaihua City in Hunan province (Fig. 1). There is a Pb/Zn mine, named jingshi mining. Initially, the land around the mine was relatively flat and was cultivated with rice every year. However, some lands were left uncultivated because of the low productivity caused by pollution. Sampling was conducted randomly in an area around the mine, with 8 composite soil samples marked as S1-S8. Four sub-samples were collected in each sample site.and the sampling depth was between 0-20 cm.

Fig. 1 Location of investigation area and sampling sites
Soil samples collected were stored in polyethylene bags for transport and storage, and were airdried, then sieved through a 2.0-mm mesh sieve to remove stones, coarse materials and other debris. After that, the samples were placed in sealed polyethylene container, labeled and wait to use.

methods
Accurately weighed sample powder 0.2 g, take samples and placed in Ptfe digestion pot, add 2-3 drops of deionized water to wetting, add 6 ml nitric acid, 6 ml hydrofluoric acid and 2 ml hydrochloric acid. After microwave digestion, cooling add to 50 ml with 1% nitric acid, save at 4°C.
The mixed sample was quartered. Then each part was divided into two parts, one for microwave digestion and one for leaching experiments. Leaching experiments of the sample was conducted referring to HJ557-2009《Solid waste-Extraction procedure for leaching toxicity-Horizontal vibration method》. Add mixed acid (m(H2SO4):m(HNO3)=2:1) to deionized water (1 L water about 2 drops of mixture), ensure ph=3.20±0.05. Accurately weighed soil powder 0.2 g and placed in extraction flask, add 100 mL extracting agent, overturn and vibration for 18± 2h (at 23±2 °C), rotate speed is 30±2 r/min, after which collect the supernatant, then membranes and collect the leach liquor, saved at 4 °C and wait to use. Set up three groups of parallel experiments and two groups of blank tests.

XRD analysis
The voltage was 40 KV and the current was 40 mA. The 2θ step of 0.02°/min with scanning range of 10.00°80.00° and a longer dwell time than normal were used to reduce interferences from background noise and to increase the counting statistics. Also peak/background ratios can be increased by using longer dwell times.  3 which is determined by the value of the pollution degree. Where, Σai=1,ai ≥0, m is the number of selected environment factors, n is the number of environment quality standard grades, and ai is weight which can be calculated by formula (1).

Construction
where, c i is measured concentration of factor i, s i is average of grading standards of contamination factor i.

2.4.2.
Determining fuzzy relationship matrix. The membership grades are calculated by membership function based on the measured concentration values, which constitutes the corresponding relationship matrix shown as the formulation (2).If there are m evaluation factors attaching to n membership grades, it can constitutes matrix R (order of the matrix is m×n).  B is normalized according to the principle of maximum membership degree of fuzzy math, and select the greatest level as rating of environmental quality. When the operation result is in the emergence of two identical (or nearly equal) maximum value, level is determined in accordance with the principles of the second largest level.

Concentration and correlation of heavy metals in the soil
The maximum value, minimum value, average value and standard values of deviation are listed in Table 1. As the results shown, the average concentrations of Cr, Ni, Cu, Zn, As, Cd and Pb were 9.25, 37.96, 191.29, 3606.98, 120.64, 342.44, 1260.38 mg· kg -1 . The highest concentration of heavy metals in the soil is Zn, and the lowest is Cr. Mean concentrations of the heavy metals in the contaminated soils decreased in the order of Zn > Pb > Cd>Cu > As>Ni>Cr. The results from correlative analysis showed that combined heavy metal pollution exists in the soil. The highest correlation coefficient between different heavy metals was Cd and Pb (The correlation coefficient = 0.952). Significant positive correlations existed between the seven elements (Table.2), which indicated that the heavy metal pollution was leaded to by the same pollution source. Cu and Cd were main associated components of Pb/Zn mine, furthermore, the different kinds of heavy metals had greater significant positive correlation, therefore, heavy metal contamination of the soil around the lead-zinc mining area mainly originated in Pb/Zn smelting production. Correlation is significant at the ** 0.01 or * 0.05 level (two-tailed). (n = 8)

Results of leaching experiment
Heavy metals are determined after microwave digesting of residue which is in the lead-zinc slag pile area and the test results are shown in Table 3.  Leaching experiments were operated referring to standard HJ557-2009 "level oscillation for leaching toxicity of solid waste". The supernatant was collected, extract was obtained after filtration membrane, contents of heavy metals were determined by Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) and Atomic Fluorescence Spectrometry (AFS). The results are shown in Table 4. The leaching concentration of different heavy metals in the leaching agent and the heavy metal concentration of contaminated soil were compared, shown in Fig.3. The heavy metal with highest content in contaminated soil is zinc, up to 3072.25 mg/kg. Its leaching concentration in leach agent is 1.96E-01 mg/L, and the concentration is not the corresponding maximum value in concentrations of heavy metals. The heavy metal with highest content in leaching agent is cadmium, and its concentration is 2.77E-01 mg/L. The concentration of cadmium in contaminated soil is 404.03 mg/kg, and its total amount in soil is not the highest, indicating that concentration of heavy metal in leaching agent of soil is not proportional to the content of heavy metal in soil. The dissolubility of all metals was correlated to many factors (Milinovic.

XRD analysis
The analysis showed that main minerals of the sample included α-quartz, β-feldspar potassian, χkaolinite, δ-montmorillonite, etc. Fig.4 shows the X-ray diffraction of the soil around lead-zinc mine. The most abundant inorganic mineral is quartz. It is the most stable silicate which is namely silicon dioxide (SiO2). Its crystal is oxide mineral with trigonal system. Its adsorption capacity on metal ion is weak. Feldspar group mineral is silicate with frame structure which has obvious mineral characteristics of pore structure. Kaolinite is clay mineral, which is mainly the product of natural alteration of feldspar and other silicate minerals. Montmorillonite crystal is silicate mineral with hydrous layered structure which is belong to monoclinic.  Fig. 3 The X-ray diffraction patterns of soils

Results of solid contamination level evaluation
The soil contamination level was assessed using fuzzy evaluation method. Table 5"Level standard of soil heavy metal pollution" was used as a single factor score sheet when evaluating.The soil contamination level was assessed using fuzzy evaluation method. Table 5"Level standard of soil heavy metal pollution" was used as a single factor score sheet when evaluating.

Standard allowable values and weights.
The weight of single factor index in water quality assessment sub-index is not directly related to grading standard of a certain factor, so intermediate standard or average of five level standards can be defined as the demarcation point between "clean" and "pollution". The average of standard levels was taken as the allowable value of standard si in this study, and the calculation results are shown in Table 6.

Fuzzy relationship matrix.
According to the weight from the table above, membership of each environmental factor to each level of environmental standard can be obtained combining with the formula 3 given in 2.5.2, and fuzzy relation matrix R can also be obtained. membership of still clean is 1.57E-03; membership of light pollution is 0.00; the highest membership of comprehensive evaluation vector is 1.00 which is heavy pollution. Therefore, its environment quality rating is heavy pollution.

Results of leaching agent quality standards evaluation
Impact of leaching agent of residue in Lead-zinc slag pile area on water quality was assessed using fuzzy evaluation method. "Quality standard of surface water environment" was used as a single factor score sheet when evaluating. Quality standard of surface water environment of associated heavy metals in leaching agent are shown in Table 7.

Comprehensive evaluation computing.
According to the formula of fuzzy comprehensive evaluation vector B given in 2.5.4, fuzzy comprehensive evaluation vector B is got by composite operating combining with the fuzzy weight vector A calculated in 2.5.1 and fuzzy relationship matrix R calculated in 2.5.2, and , after normalization, Membership of level Ⅰ in B is 9.28E-03; membership of level Ⅱ is 5.78E-04; the highest membership of comprehensive evaluation vector is 1.00 which is belong to Ⅴ water body. Therefore, its water quality standard is level 5.

Conclusion
(1) The results showed that mean concentrations of the heavy metals in the contaminated soils decreased in the order of Zn > Pb > Cd> Cu > As>Ni>Cr. The significant positive correlations existed between the seven elements, which indicated that the heavy metal pollution was leaded to by the same pollution source.
(2) The mixture of slag and heavy metal contaminated soil in the lead-zinc mining area was analyzed by xrd and the results showed that component is complicated including quartz, feldspar, kaolinite and montmorillonite, etc, the maximum inorganic mineral composition is SiO2.
(3) After the fuzzy comprehensive evaluation of the heavy metal contaminated soil, soil pollution levels and leaching liquid environment quality standards were evaluated synthetically. The results shows that the fuzzy comprehensive evaluation vectors of soil pollution levels and leaching liquid environment quality standards are , respectively. Through analyzing, the soil environmental quality grade is heavy pollution and leaching liquid water quality standards is grade 5, belonging to Ⅴ class water.