extfieldsofstudy: Medicine Biology

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Comparing Different Statistical Models and Multiple Testing Corrections for Association Mapping in Soybean and Maize

Association mapping (AM) is a powerful tool for fine mapping complex trait variation down to nucleotide sequences by exploiting historical recombination events. A major problem in AM is controlling false positives that can arise from population structure and family relatedness. False positives are often controlled by incorporating covariates for structure and kinship in mixed linear models (MLM). These MLM-based methods are single locus models and can introduce false negatives due to over fitting of the model. In this study, eight different statistical models, ranging from single-locus to multilocus, were compared for AM for three traits differing in heritability in two crop species: soybean (Glycine max L.) and maize (Zea mays L.). Soybean and maize were chosen, in part, due to their highly differentiated rate of linkage disequilibrium (LD) decay, which can influence false positive and false negative rates. The fixed and random model circulating probability unification (FarmCPU) performed better than other models based on an analysis of Q-Q plots and on the identification of the known number of quantitative trait loci (QTLs) in a simulated data set. These results indicate that the FarmCPU controls both false positives and false negatives. Six qualitative traits in soybean with known published genomic positions were also used to compare these models, and results indicated that the FarmCPU consistently identified a single highly significant SNP closest to these known published genes. Multiple comparison adjustments (Bonferroni, false discovery rate, and positive false discovery rate) were compared for these models using a simulated trait having 60% heritability and 20 QTLs. Multiple comparison adjustments were overly conservative for MLM, CMLM, ECMLM, and MLMM and did not find any significant markers; in contrast, ANOVA, GLM, and SUPER models found an excessive number of markers, far more than 20 QTLs. The FarmCPU model, using less conservative methods (false discovery rate, and positive false discovery rate) identified 10 QTLs, which was closer to the simulated number of QTLs than the number found by other models.

INTRODUCTION
Connecting genotype to phenotype, known as genetic mapping, is important for modern crop breeding and improvement (Mackay, 2001). Two of the most commonly used approaches for genetic mapping are association mapping (AM) and biparental linkage mapping (LM). AM is an alternative approach to traditional mapping of biparental populations and is currently widely used in plant, animal (Goddard and Hayes, 2009), model species (Brachi et al., 2010), and human genetics (Risch and Merikangas, 1996;Nordborg and Tavaré, 2002). Most important traits in plants are complex and controlled by many genes and influenced by environment. With advancements in high throughput genotyping and sequencing technologies, single nucleotide polymorphisms (SNPs) provide relatively low cost and dense marker coverage across various genomes (Syvänen, 2005). Genotyping diverse lines provides thousands of SNPs across the genome that enables fine mapping complex trait variation down to nucleotide sequences by exploiting historical recombination events (Zhu et al., 2008). AM has lower overall statistical power to detect rare alleles and epistatic interactions than traditional LM, but it has several advantages, which include increased mapping resolution, broader allele coverage, reduced time and cost compared to developing biparental mapping populations, and potentially greater number of alleles evaluated (Yu et al., 2006).
Associating functional variants (alleles, loci) to phenotypes is a fundamental aim of both AM and LM (Botstein and Risch, 2003). The detection of quantitative trait loci (QTLs) through AM depends on the level of linkage disequilibrium (LD) between functional loci and markers. LD refers to a nonrandom association of alleles at different loci. LD is influenced by physical linkage and recombination, but it is a separate phenomenon-unlinked loci can be in a state of LD, and linked loci can be in a state of linkage equilibrium (Terwilliger and Weiss, 1998). The level of LD extent in a specific set of experimental genotypes can be measured statistically and has been widely leveraged in plants and animals to map and clone genes controlling complex genetic traits (Risch and Merikangas, 1996;Dunning et al., 2000;Pritchard and Przeworski, 2001;Nordborg and Tavaré, 2002). LD can be measured based upon the correlation between alleles at pairs of loci as physical distance between the loci increases. Outcrossing crop species, such as maize, have more genetic diversity (Remington et al., 2001;Yan et al., 2009) and also more rapid LD decay than self-pollinated species such as soybean, which has less overall genetic diversity (Gupta et al., 2006;Hyten et al., 2007;Schmutz et al., 2010;Song et al., 2015). Species with faster LD decay over physical distance, as compared to those with slow LD decay, require higher marker density over the genome to capture associations between marker and phenotype (Yu et al., 2006).
Several statistical models are available to identify associations between marker loci and numerous phenotypes that range from simple to increasingly complex. As genotypic data are becoming more readily available, accurately decoding the complexity of traits in a diverse population is only possible if accurate and more comprehensive statistical models can distinguish true biological associations from false positives arising from population structure and family relatedness without overcorrecting and resulting in false negatives. Using covariates for structure and kinship in the statistical model can control these confounding factors. STRUCTURE (Pritchard et al., 2000), principal component analysis (PCA) (Price et al., 2006), and a discriminant analysis of principal components (DAPC) (Jombart et al., 2010) are approaches that use genetic markers to determine population organization. Results from STRUCTURE and PCA are similar, but PCA is generally more commonly used due to lower required computational resources and time to generate covariates. False positives can also arise due to more recent common ancestry and family relatedness, which can be controlled by inclusion of a kinship matrix into the linear model. Identity-by-state is one of the most commonly used approaches to estimate familial relatedness among individuals in a diverse population (Loiselle et al., 1995).
The incorporation of population structure and a kinship matrix as covariates in mixed linear models (MLM) has become a popular approach to control false positives. Since the first MLM of AM was published by Yu et al. (2006), many MLMbased methods have been proposed (Zhang et al., 2010;Wang et al., 2014). All these models are single-locus models, which means that they comprise a one-dimensional genome scan by testing one marker at a time, iteratively for every marker in a dataset. This single-locus approach fails to match the true genetic model of complex traits that are controlled by numerous loci simultaneously. To cope with this problem, multilocus AM models have been recommended because these models consider the information of all loci simultaneously (Wang et al., 2016). MLM-based models can also induce false negatives due to over fitting of the model where some potentially important associations can be missed (Liu et al., 2016).
False negatives in AM can result when multiple comparison adjustments are used to determine statistical significance. Two commonly used multiple comparison methods in AM are Bonferroni correction (Holm, 1979) and false discovery rate (FDR) (Benjamini and Hochberg, 1995), which select the significant threshold. However, overly conservative thresholds can lead to high false negative error rates. Therefore, selection of an appropriate model and threshold are important steps in identifying markers that are truly associated with specific traits and which could be located within or very close to genes that control the trait variation, while controlling both false-positive and false-negative associations.
The objective of this study was to compare eight different AM statistical models, ranging from single to multilocus, for three previously reported traits and six simulated traits in soybean and maize. These crops were selected because of their difference in LD as indicated by the LD decay rate: maize, which is naturally outcrossing, displays much more rapid LD decay than soybean, a self-pollinating species. We also compared these eight statistical models for six qualitative traits in soybean, all of which have known causal genes with published genomic positions. Finally, we evaluated five multiple comparison methods when used in conjunction with these eight AM models.

Data Collection
This study included three datasets collected from previously published or online sources (referred to as "previously reported traits" subsequently). These previously reported datasets were the best linear unbiased predictions (BLUP) values across different environments for each trait. We also simulated six datasets from two crop species: soybean and maize. Previously reported data for soybean included canopy wilting (CW) with a broad sense heritability (H) of 80% (Kaler et al., 2017a), carbon isotope ratio (d 13 C, H = 60%, Kaler et al., 2017b), and oxygen isotope ratio (d 18 O, H = 20%, Kaler et al., 2017b). For maize, the previously reported data included days to tasseling (DT, H = 85%), ear height (EH, H = 80%), and ear diameter (ED, H = 85%) (Flint-Garcia et al., 2005). For both soybean and maize, six traits were simulated that varied in heritability and the number of QTLs (Q). These simulated traits were generated using the same genotypic markers that were used for AM of previously reported data. These six simulated datasets for each crop had varying heritabilities and genetic architectures. We simulated traits with H = 20% and Q = 20 (H20_Q20), H = 60% and Q = 20 (H60_Q20), H = 80% and Q = 20 (H80_Q20), H = 20% and Q = 40 (H20_Q40), H = 60% and Q = 40 (H60_Q40), and H = 80% and Q = 40 (H80_Q40). The R-script to generate the simulated data sets is provided in a supplement (Table S1). These data were simulated to have random QTLs effects. The simulated data for soybean and maize are provided in Supplementary Data Files 1 and 2, respectively. Previously reported data of soybean consisted of 346 accessions as described by Kaler et al. (2017b), and previously reported data of maize (Flint-Garcia et al., 2005) consisted of 279 accessions from the Panzea database website (www.panzea.org).

Genotypic Data and LD
Genotypic data for both crops consisted of SNP markers. In soybean, SNP data were obtained from the Illumina Infinium SoySNP50K iSelect SNP BeadChip that provided 42,509 SNP markers for all 346 accessions (Song et al., 2013;Song et al., 2015). In maize, SNP data were obtained from the Illumina MaizeSNP50 BeadChip that provided 50,896 SNP markers for 273 accessions (Flint-Garcia et al., 2005). Quality control checks were performed, which included removing monomorphic markers, markers with minor allele frequency (MAF) ≤ 5%, and markers with a missing rate higher than 10%. An LD-kNNi method, which is based on a k-nearest-neighbor-genotype, was applied to impute the remaining missing marker datasets (Money et al., 2015).
After performing quality controls, 31,260 SNPs for soybean and 48,833 SNPs for maize with MAF > 5% were used for AM. For maize, SNPs were more or less equally distributed across the genome for both euchromatic and heterochromatic regions (Figures S1). For soybean, SNPs were not equally distributed across the genome; there was higher marker density in euchromatic than heterochromatic regions ( Figure S2). All chromosomes of maize had more SNPs than those of soybean ( Table S2). The decay rate of LD was estimated using the GAPIT R package (Lipka et al., 2012). The decay rate of LD was much greater in maize than soybean with an average LD across all chromosomes decaying to r 2 = 0.25 in less than 1 kb. In comparison, in soybean, an average LD across all chromosomes decayed to r 2 = 0.25 in approximately 2,000 kb ( Figure S3). In soybean, LD decay rates were different in euchromatic and heterochromatic regions (Hyten et al., 2007;Schmutz et al., 2010;Kaler et al., 2017a;Kaler et al., 2017b). Using both regions together affected the results of LD decay rate.
Broad sense heritability of traits was calculated using the formula: H = s 2 G = (s 2 G + ( s 2 ϵ r )), where s 2 G is the genotypic variance, s 2 ϵ is the residual variance, and r is the number of replications. Marker-based narrow sense heritability (h 2 ) was estimated to understand the variation and trend of predictive ability across traits (Kruijer et al., 2015) using the GAPIT R package. In the GAPIT package, the MLM model can be described as: Y=Xb+Zu+e, where Y is the vector of observed phenotypes; b is an unknown vector containing fixed effects, including the genetic marker, population structure (Q), and the intercept; u is an unknown vector of random additive genetic effects from multiple background QTL for individuals/ lines; X and Z are the known design matrices; and e is the unobserved vector of residuals. The u and e vectors are assumed to be normally distributed with a null mean and a variance of: Var u e = G 0 0 R , where G = s 2 a K with s 2 a as the additive genetic variance and K as the kinship matrix. Homogeneous variance is assumed for the residual effect; i.e., R = s 2 e I, where s 2 e is the residual variance. The proportion of the total variance explained by the genetic variance is defined as markerbased heritability.
The GLM with PCA model is expected to reduce the false positives that arise due to only population structure (Price et al., 2006). The MLM with PCA and K model includes the kinship matrix in the model and is expected to reduce the false positives that arise from family relatedness (Yu et al., 2006). Both GLM and MLM are reported to control false positives better than ANOVA (Price et al., 2006;Yu et al., 2006). The MLM model is reported to perform better than the GLM model alone by controlling false positives (Yu et al., 2006). Advantages of the MLM model to control false positives disappear for complex traits when they are associated with population structure having extensive genetic divergence. The MLM model controls the Pvalue inflation well, but it also leads to false negatives, thereby weakening identification of true associations (Zhang et al., 2010). To deal with this problem, the compressed MLM model (CMLM) was developed, which clusters the individuals into groups and fits genetic values of groups as random effects in the model (Zhang et al., 2010). The CMLM method improves statistical power compared to regular MLM methods (Zhang et al., 2010). Another suggested way to deal with P-value deflation due to MLM is to use a SUPER model in which only the associated genetic markers, instead of all the markers, are used as pseudo Quantitative Trait Nucleotides (QTNs) to derive kinship (Wang et al., 2014). Whenever a pseudo QTN is correlated with the testing marker, it is excluded from those used to derive kinship. The SUPER model applies a threshold on LD between the pseudo QTNs and the testing marker. This method improves the statistical power compared to using overall kinship from all markers.
FarmCPU is a multilocus model that was developed to control false positives without comprising false negatives (Liu et al., 2016). This model is not used extensively for AM of complex traits in crops because it has not been compared with other models for previously reported and simulated data. The FarmCPU model uses a modified MLM method, multiple loci linear mixed model (MLMM), and incorporates multiple markers simultaneously as covariates in a stepwise MLM to partially remove the confounding between testing markers and kinship. To completely eliminate the confounding, MLMM is divided into two parts: fixed effect model (FEM) and a random effect model (REM) and uses them iteratively. FEM contains testing markers, one at a time, and multiple associated markers as covariates to control false positives. To avoid model overfitting in FEM, the associated markers are estimated in REM by using them to define kinship. The P-values of testing markers and the associated markers are unified at each iteration. This model reportedly improves statistical power, increases computational efficiency, and the ability to control false positives and false negatives as compared to other models (Liu et al., 2016).

Interpretation of Q-Q Plots and Model Evaluation
Examining quantile-quantile (Q-Q) plots is one of the most common ways of determining if models control false positives and false negatives (Stich et al., 2008;Stich and Melchinger, 2009;Würschum et al., 2012;Riedelsheimer et al., 2012;Kristensen et al., 2018). The Q-Q plot shows the expected negative-log of association probability (X-axis) across all markers versus the observed negative-log of association probability values (Y-axis). If a Q-Q plot has a straight line close to the 1:1 line without any tail, then it follows a uniform distribution, which means the null hypothesis is true and that there is no significant association or causal polymorphism. Any deviation of this straight line would indicate that the null hypothesis was not true and there were significant associations present. If the Q-Q plot does not have a straight line and tail, it indicates that there are false positives when a line inflates upward and there are false negatives when line deflates downward. If a Q-Q plot has a straight line, close to the 1:1 line, with a sharp upward deviated tail, it indicates that both false positives and false negatives were controlled, and that there are true associations and causal polymorphisms. This happens because most of the P-values observed follow a

Model Description References
Analysis of variance (ANOVA) Single locus analysis, the null hypothesis of an ANOVA using a single SNP is that there is no difference between the trait means of any genotype group. Lewis, 2002 General Linear Model (GLM) Single locus analysis, the GLM uses principle components as covariates in the model to reduce the false positives that arise due to only population structure. Price et al., 2006 Mixed Linear Model (MLM) Single locus analysis, the MLM uses principle components and kinship matrix in the model to reduce the false positives that arise from the family relatedness and population structure. Yu et al., 2006 Compressed MLM (CMLM) Single locus analysis, the CMLM clusters the individuals into groups and fits genetic values of groups as random effects in the model that improves statistical power compared to regular MLM methods. Zhang et al., 2010 Enriched CMLM (ECMLM) Single locus analysis, the ECMLM calculates kinship using several different algorithms and then chooses the best combination between kinship algorithms and grouping algorithms. uniform distribution (i.e., they are not in LD with a causal polymorphism, so the null hypothesis is true) but the few that are in LD with a causal polymorphism will produce significant Pvalues [extremely low = extremely high -log (P-values)] and these are in the "tail". We evaluated these eight models for false positives and false negatives based on the Q-Q plots. A sharp deviation from the expected P-value distribution in the tail area would indicate that a model appropriately controlled both false positives and false negatives. Models were also compared using qualitative traits in soybean, which have known published genes for flower color (Takahashi et al., 2010), stem termination (Bernard, 1972), seedcoat luster (Gijzen et al., 2003), seed-coat color , hilum color (Carpentieri-Pipolo et al., 2015), and pubescence color (Toda et al., 2002;Zabala and Vodkin, 2003). Models were also compared using simulated data in which there were a known number of QTLs in the simulated data. The accuracy of a model was evaluated by identifying the number of QTLs in the simulated data.

Evaluation of Multiple Comparisons Methods for AM
Three common multiple comparison methods were compared for determining statistical significance with a cutoff of P = 0.05. These methods included Bonferroni, false discovery rate, and positive false discovery rate. These comparisons were made using the PROC MULTTEST procedure of SAS version 9.4 (SAS Institute, 2013). The models were also compared to no multiple comparison adjustment at a P-value of 0.0003.

Phenotype Descriptions
There were broad phenotypic ranges for all the traits evaluated in both soybean and maize ( Table 2), which is required for dissecting complex traits through association analysis (McCarthy et al., 2008). Among the three traits in maize, broad and marker-based narrow sense heritability ranged between 80% to 85% and 70% to 80%, respectively. Among the three traits in soybean, broad and marker-based narrow sense heritability ranged between 20% to 80% and 3% to 71%, respectively ( Table 2).

Model Comparison With Soybean Data
Eight different AM models that ranged from simple to complex were compared using three previously reported traits and six simulated traits for soybean and maize (Figures 1 and 2). These eight AM models identified different numbers of significant markers associated with the previously reported and simulated traits for soybean when we consider the same significance threshold ( Table 3). For example, if we consider the significance threshold as −Log10 (P) > 3.5 to declare a significant association for a simulated trait with 20 QTLs, we identified 2465 SNPs from ANOVA, 520 from GLM, 24 from MLM, 24 from CMLM, 16 from ECMLM, 229 from SUPER, 26 from MLMM, and 19 from FarmCPU ( Table 3). All models, except the FarmCPU and MLMM, identified multiple significant SNP marker associations in close physical distance on the chromosome. These large peaks were generated because one SNP from these peaks had the highest significant association with traits, but the other markers at a given peak were in high LD with this most significant marker.
For the CW trait (H = 80%), the ANOVA, GLM, and SUPER models had a large number of false positives as indicated by a substantial inflation of P-values ( Figure 1A). Q-Q plots of complex models including MLM, CMLM, and ECMLM had a straight line with a slightly deviated tail, which indicated that these models reduced the false positives. However, most markers were close to the straight line of 1:1, indicating that they may have been reported as false negatives ( Figure 1A). In contrast, the FarmCPU model followed a straight line close to 1:1, with a sharp upward deviated tail, indicating that this model controlled both false positives and false negatives ( Figure 1A). For d 13 C (moderate H = 60%), results of all models were similar to the CW trait, indicating that the FarmCPU model controlled both false positives and false negatives more effectively than other models ( Figure 1B). For a low heritability trait, d 18 O, the Q-Q plot for all models, except FarmCPU, deflated downward, indicating that these models increased false negatives. In contrast, Q-Q plots of the FarmCPU model for d 18 O had a straight line close to the 1:1 with a slightly deviated tail, indicating that FarmCPU controlled both false positives and false negatives ( Figure 1C).
Results from Q-Q plots of the six simulated traits in soybean were consistent with results from the previously reported data ( Figures 1D-I). That is, ANOVA, GLM, and SUPER models had an inflation of P-values indicating there were a large number of false positives whereas MLM, CMLM, ECMLM, and MLMM controlled false positives but not false negatives. The Q-Q plots for FarmCPU indicated control of both false negatives and false positives. For all simulated traits, the ANOVA model had a large number of false positives because it inflated the P-value in the Q-Q plots (Figures 1D-I). When a simulated trait had a low heritability (H = 20) and a large QTL number (40), all complex models that incorporated the PCs and kinship matrix increased the number of false negatives, except the FarmCPU model ( Figure 1G). When a simulated trait had a high heritability with 20 or 40 QTLs, complex models that included both PCs and kinship matrix reduced the false positives ( Figures  1F, I), but still the FarmCPU model had a straight line that followed the 1:1 line with a sharp deviated tail compared to other models.

Maize
In maize, we observed large effects of population structure and family relatedness. Similar to soybean (Table 3), the models that included the PCs and kinship matrix for maize identified a smaller number of markers than models that did not (data not shown). Likewise, the models that had no adjustment (ANOVA) or included only PCs (GLM) increased the number of significant markers for both previously reported and simulated traits when a specific threshold level was used compared with other complex models ( Table 3). All single-locus models gave a peak of multiple, significant SNPs, which may result in missing the identification of other important genomic regions that may not have that high level of significance (P-value) as the markers in the peak region that are in high LD with the most significant marker. However, the multilocus model, FarmCPU and MLMM, did not show any clusters of significant markers in maize; instead they provided the highest significant marker at a specific genomic location, which led to identification of more markers at different locations (data not shown). Based on the Q-Q plots for all previously reported and simulated traits, the FarmCPU model performed much better than other models as indicated by the Q-Q plots with a straight line close to the 1:1 line with most sharply deviated tail (Figure 2).

Qualitative Traits of Soybean
Flower color in soybean is a qualitative trait that is conferred by the W1 gene. A small (65 bp) insertion of tandem repeats in exon 3 that truncates the translation product prematurely, resulting in a white flower instead of the wild-type purple flower (Zabala and Vodkin, 2005;Zabala and Vodkin, 2007). The W1 locus is located on Gm13 at 4552540-4557331 base pairs in the Wm82.a1.v1.1 genomic assembly (Schmutz et al., 2010). As both alleles are widespread in soybean germplasm, this trait is ideal to determine which model would best identify markers closely linked to the known causative allele. The FarmCPU, GLM, and ANOVA models identified the most significant SNP associated with flower color on Gm13 (Figure 3). Other models, except the MLMM, identified most significant markers at different positions on Gm13 that were further away from the published gene on the same chromosome. For example, MLM identified the highest significant SNP at 3,822,639 base pairs. W1gene, in the Wm82.a1.v1.1 genomic assembly (Schmutz et al., 2010) (Figure 3). Similar results were observed when models were compared using five other qualitative traits in soybean including hilum color, pubescence color, seed-coat color, seed-coat luster, and stem termination (determinacy) (data not shown). Figure 4 shows the comparison of FarmCPU models with MLM for different qualitative traits. We chose a comparison of FarmCPU with MLM because it is a commonly used model for AM. The FarmCPU model identified a single significant SNP close to the genes associated with qualitative traits, instead of identifying a large peak of SNPs with MLM ( Figure 4). For example, the FarmCPU model identified the single most significant SNP associated with stem termination on Gm19 at the position 45,000,827 base pairs closest to the position of the Dt1 gene (45,183,185,175 base pairs), instead of a large peak of SNPs with MLM. For three qualitative traits including, hilum color, pubescence color, and stem termination, the identified significant SNPs with the largest -Log 10 P-value from the peak were similar to the position of the peak identified by the FarmCPU model ( Figure 4). The most significant marker for hilum color and pubescence color was on Gm06 at the position 18,766,611 base pairs which was 28,586 3 | Comparison of the number of significant markers (P ≤ 0.05) identified by multiple comparison methods including Bonferroni (Bon), false discovery rate (FDR), and positive false discovery rate (PFDR) using a simulated trait for soybean that had a heritability 60% and 20 QTLs (H60_Q20) in eight different association models eight including analysis of variance (ANOVA), general linear model ( For comparative purposes, a P-value threshold (-Log 10 P ≥ 3.5) without any adjustment is included. base pairs distant from the T locus. Pubescence coloration and hilum coloration are in part determined by loss of function mutation affecting the Glyma06g21920 gene which results in grey pubescence at plant maturity and, in the right genetic background, can result in buff or imperfect hila (Zabala and Vodkin, 2003). For seed-coat color and luster, the MLM identified the most significant SNPs on different chromosomes compared to the FarmCPU model (Figure 4). For seed-coat color, the FarmCPU model identified the most significant SNP on Gm8 at 21,385,804 base pairs close to the Glyma08g27050 gene (21392963-21395430 bp), which is involved in flavonol biosynthesis (Palmer et al., 2004;Zabala and Vodkin, 2007;Yang et al., 2010). This marker association is most likely reporting the natural gene silencing cluster which results in yellow seed-coats Tuteja et al., 2004) However, MLM identified the most significant SNP on Gm13 at the position 22,126,286 base pairs, where there was no gene present associated with seed coat color.
For seed-coat luster, the FarmCPU model identified the most significant SNP on Gm15 at 12,986,703 base pairs within a gene Glyma15g16670 (12,982,823-12,987,622), which is involved in a function of epidermis development. In contrast, MLM identified the SNP on Gm16 at 3,791,771 base pairs, which was not located close to any known gene for seed coat luster. Seed coat color and luster are controlled by more than one gene, hence, the FarmCPU model identified additional significant SNPs on other chromosomes, and all those regions are located close to previously reported genes for these traits (Palmer et al., 2004;Yang et al., 2010).

Multiple Comparisons Methods for AM
Different multiple comparison methods were compared for determining statistical significance with a cutoff of P = 0.05. These comparison methods included: Bonferroni, false discovery rate, and positive false discovery rate (Holm, 1979;Hommel, 1988;Hochberg, 1988;Benjamini and Hochberg, 1995). The significance level of -Log10 (P > 1.3, which is an equivalent to the P < 0.05) was used as a threshold before and after performing multiple comparison adjustments. We compared these methods for all traits in maize and soybean, but for brevity, we only show results of the simulated trait, H60_Q20 in soybean ( Table 3). Results of all other traits were consistent with this trait (data not shown). These results indicated that these multiple comparison methods for AM were very conservative and only depended on the P-value of the association test. These results were evaluated based on the number of markers identified after adjustments. If the number of markers identified was more than 20, it indicates that there were false positives. If the number of markers identified was less than 20, it indicates that there were false negatives. In this study, ANOVA, GLM, and SUPER models had very large -Log10 P-values for significant associations; when multiple comparisons adjustments were performed, all 20 QTLs were above the significance level of -Log10 P > 1.3 ( Table 3). Checking the Q-Q plots for this trait ( Figure 1E), indicated that the ANOVA, GLM, and SUPER models did not control false positives well in a diverse population. The FDR and PFDR methods gave more false positives than the Bonferroni method in the ANOVA, GLM, and SUPER models ( Table 3). Complex models (MLM, CMLM, ECMLM, and MLMM), which were expected to control false positives arising from population structure and family relatedness, did not identify any significant associations after performing multiple comparisons adjustments. These complex models reduced the P-value inflations (Table 3), which led to an increase in the false negative error rates. For the FarmCPU model, the Bonferroni adjustment identified 4 out of 20 highly significant associations, which means that these methods gave 16 false negative associations; the false discovery rate and positive false discovery rate adjustments with the FarmCPU model, identified 10 out of 20 highly significant associations, resulting in 10 false negatives above the selected cutoff value of -Log10 P > 1.3 (which is an equivalent to the P < 0.05) after adjustments ( Table 3). Without any multiple comparison adjustments, FarmCPU identified 19 out 20 associations at a cutoff of 3.5 (-Log10 (P) ≥ 3.5; P ≤ 0.0003).

DISCUSSION
AM is based on the LD of marker with a QTL and is a popular approach for fine mapping traits of interest. LD in an AM population can also result from population structure, family relatedness, selection, and genetic drift (Flint-Garcia et al., 2003;Yu et al., 2006), which are the major reasons of false positive associations. The success of AM to identify true associations depends on the ability to separate LD of the marker with a QTL from LD due to other causes. There is a need for an appropriate model that can correctly identify LD caused by population structure and family relatedness.
In this study, eight different statistical models, ranging from single to multilocus, were compared for AM of three empirical phenotype traits differing in heritability in two crop species, soybean and maize, that vary in LD decay rates. The power of SNP identification is determined by several factors including the size of the population, the population structure, the extent of LD in the population, the heritability and underlying genetic architecture of the trait (Yu et al., 2006). For all previously reported traits, several SNPs were identified in this study, which indicated that all these traits were complex, quantitative traits, controlled by a large number of genes with small effects. The power of detecting SNP and mapping resolution for complex traits depend on the LD exploited in the population by the statistical model (Yu et al., 2006). As expected, faster LD decay over physical distance was observed in maize compared to soybean because maize is a cross-pollinated with a higher recombination rate and soybean is a self-pollinated with a lower recombination rate.
Based on the Q-Q plots, we observed a nonuniform distribution of P values in the ANOVA, GLM, and SUPER models of all empirical traits (Figures 1 and 2). These results are similar to previous studies (Yu et al., 2006;Stich et al., 2008;Zhao et al., 2011) indicating that these models are inappropriate for AM of complex traits in plants because they generate spurious marker-trait associations. Complex models including MLM, CMLM, and ECMLM were proposed to correct population structure and family relatedness (Yu et al., 2006). We observed a straight line close to the 1:1 line with slightly deviated tail in the Q-Q plots of MLM, CMLM, and ECMLM, indicating that these models reduced the false positives, but increased false negatives because most significant markers were present close to the 1:1 line. These false negatives were generated due to the overfitting of these complex models. Similar results were observed in other studies (Wen et al., 2015;Tamba et al., 2017;Li et al., 2018;Wen et al., 2018) where these complex models generated more false negatives. In contrast, the Q-Q plot of the FarmCPU model, a multilocus model, controlled both false positives and false negatives as indicated by a straight line (close to the 1:1 line) with a sharp deviated tail for all empirical traits in both crops.
Some studies, where multilocus models, including mrMLM (Wang et al., 2016), FASTmrEMMA (Wen et al., 2018), and LASSO (ISIS EM-BLASSO) (Tamba et al., 2017), were used, performed better than MLM-based models. Liu et al. (2016) reported that the FarmCPU model avoids overfitting by using two types of adjustments for testing markers. The first type of adjustment was fitting covariates of population structure, family relatedness, and pseudo-quantitative trait nucleotides; the second type of adjustment either refines how family relatedness is derived from all the markers, or selectively includes or excludes pseudo-quantitative trait nucleotides based on their relationship with the testing markers.
These eight AM models were also compared based on simulated traits in which a known number of QTLs were simulated. Among these models, the FarmCPU model identified the number of QTLs close to the number of simulated QTLs for all traits in both crops. Comparison of Q-Q plots of different models for all simulated traits indicated that the FarmCPU controlled better the false positives and false negatives. Additionally, FarmCPU identified markers of qualitative traits closer to the published location of genes controlling these traits compared to the other models. Instead of providing a large peak as in other models, the FarmCPU model provided a single most significant marker, which was always present closest to the published genes.
For determining statistical significance in AM, different multiple comparison methods are used with a cutoff of P = 0.05, and several of these methods were compared when used in combination with the eight AM models. Complex models (MLM, CMLM, ECMLM, and MLMM) were particularly conservative and did not find any markers after adjustment; these complex models and multiple comparison methods are apparently increasing the number of false negatives. In contrast, ANOVA, GLM, and SUPER models identified more than 20 QTLs after multiple comparison adjustments, indicating that these models increased the false positives. In contrast, the FarmCPU model performed better than other models for these multicomparison adjustments by identifying 10 QTLs with less conservative methods, FDR and PFDR. Based on the Q-Q plots and the number of known simulated QTLs, the FarmCPU was an appropriate model for controlling false positives and false negatives compared to other models. Other multiple comparison methods were overly conservative for selection of significant threshold for AM. Determination of the correct significant threshold for AM can be determined by an empirical relationship based upon marker-based heritability (Kaler and Purcell, 2019).

CONCLUSIONS
This study compared eight statistical models for AM of three empirical phenotypic traits differing in heritability and six simulated traits in two crop species, soybean, and maize, varying in LD decays rates. Based on the Q-Q plots and the number of known simulated QTLs, the FarmCPU was an appropriate model for controlling false positives and false negatives compared to other models. These finding were also supported by the AM of six qualitative traits, which identified a single most significant SNP closest to the known published genes. The FarmCPU model performed better for multiple comparison adjustments compared to other models because adjustments were overly conservative for MLM, CMLM, ECMLM, and MLMM and did not find any QTL. In contrast, for ANOVA, GLM, and SUPER models, these adjustments found more than 20 QTLs. From this study, we conclude that FarmCPU provides a robust model for AM of complex traits in plants, which effectively controls both false positives and false negatives.

DATA AVAILABILITY STATEMENT
All datasets generated for this study are included in the article/ Supplementary Material.

AUTHOR CONTRIBUTIONS
AK and LP conceived of the idea and wrote the manuscript. AK performed simulations and data analysis. JG and TB provided theoretical insights and valuable edits. All authors read and approved the final version.


extfieldsofstudy: Medicine Chemistry

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

text:
Induction of mutation in Monascus purpureus isolated from Thai fermented food to develop low citrinin-producing strain for application in the red koji industry.

Red koji is produced from cultivating rice with Monascus strains that contain various types of fungal secondary metabolites, such as red pigments and monacolin K. Monascus strain also produces citrinin-a mycotoxin. In this study, Monascus purpureus KUPM5 isolated from the Thai fermented food, sufu, was mutagenized to reduce its citrinin production using UV irradiation, NTG treatment, and a combination of UV and NTG. Screening of the mutants using plate bioassay based on the inhibitory effect against Bacillus subtilis enables the selection of 10 mutants. The mutant strains KS301U and KS302U showed an 80% reduction in citrinin production in red koji compared with the wild type (wt), and maintained the ability to produce red pigments similar to the wild type. Activities of enzymes, α-amylase, protease, and lipase, from red koji extract produced by the mutant strain KS302U, were higher than those of the wt, whereas those of the mutant strain KS301U were similar to those of the wt. Consequently, strains KS301U and KS302U were successfully selected as strains suitable for producing red koji and fermented food.

Introduction
Red koji (red mold rice) is used as a food additive and colorant in the food industry. It is produced from Monascus purpureus and has been traditionally used to produce several fermented products, such as red soy cake, red sufu, sour pork, fish sauce, and rice fermented wine in east Asia (Chow, 2017). It has several beneficial effects such as cancer prevention and blood sugar level reduction as well as antiinflammatory and antitumor effects (Yasukawa et al., 1996).
However, certain Monascus sp. produce various amounts of citrinin-a secondary metabolite mycotoxin. Citrinin has negative effects in humans and animals, including nephrotoxic, hepatotoxic, and cytotoxic effects (Blanc et al., 1995;Liu et al., 2005;Liu et al., 2012). It also exhibits antibacterial activity (Wong and Bau, 1977). In recent years, there has been a greater awareness of the consequences of citrinin to human health. The European Food Safety Authority has established a maximum level of citrinin in food supplements, based on red koji, as 2,000 µg/ kg because of concerns that citrinin directly affects kidney toxicity (European Food Safety Authority, 2012). Therefore, the selection of Monascus strains is the first important step in the production of red koji with low amounts of citrinin. Citrinin biosynthesis occurs in Monascus, Penicillium, and Aspergillus species (Flajs and Peraica, 2009). The pathway involves the synthesis of an unreduced trimethylated pentaketide by a non-reducing polyketide synthase (nrPKS), known as CitS. cit cluster centered on the 7.9 kbp gene, which encodes an iterative type I non-reducing polyketide synthase (CitS), which was discovered in M. purpureus. (Sakai et al., 2008;Shimizu et al., 2007). The disruption of citS resulted in the complete elimination of citrinin production in M. purpresus (He et al., 2013). Thus, the construction of citrininnonproducing Monascus strains using gene recombinant technology is a powerful tool (Huang et al., 2017;Liang et al., 2018), although using classical mutagenesis might be more favorable for use in the fermented food industry. Isolation and screening of low citrinin-producing mutants of M. purpureus has been reported by Wang et al. (2004) and Putri et al. (2005). In addition to low citrinin-productivity, they intended to obtain the mutants showing a high productivity of monacolin K and γ-aminobutyric (Wang et al., 2004), and red pigments (Putri et al., 2005). In the present study, we induced mutations in M. purpureus KUPM5 isolated from Thai fermented food, sufu (Kanlayakrit et al., 2011) as a parental wild type (wt) strain, which was different from Monascus strains reported previously, to produce mutant strains with low citrinin production and maintaining the enzyme activities for sufu making. The selected mutant strains were characterized with respect to the citrinin production, red pigments, and enzyme activity to evaluate mutant strains suitable for production red koji in the food industry.

Materials and Methods
Microorganisms and culture media. M. purpureus strain KUPM5 isolated from Thai fermented food, sufu (Kanlayakrit et al., 2011), was obtained from the Faculty of Agro-industry, Kasetsart University, Thailand. Bacillus subtilis NBRC13719 was obtained from the National Bioresource Center, Japan. KUPM5 was maintained on potato dextrose agar (PDA) medium (Becton Dickinson company) at 30°C for 7 days. B. subtilis strain was maintained in Luria-Bertani (LB) broth.
Red koji production. Red koji was prepared by Monascus strains in triplicate according to the procedure described by Kanlayakrit et al. (2011). Fifty grams of long grain rice (Oryza sativa) were soaked in tap water for 8 h. Excess water was drained, and soaked rice was transferred into a 500-ml Erlenmeyer flask, after which 15 ml of distilled water was added and then autoclaved at 121°C for 30 min. After the steamed rice was cooled to room temperature, 2 ml of fungal spore suspension (10 6 spores/ml) was inoculated and incubated at 30°C for 7 d. The resultant red koji was stored at -80°C until use.

Mutagenesis. Ultraviolet (UV) irradiation:
Monascus spore suspension was prepared from Monascus grown on PDA plates with 0.01% Tween80 solution, and the number of spores was adjusted to 10 6 spores/ml. Four milliliters of spore suspension was poured upto a dish (d.i. 90 mm), which was then placed under a UV lamp at a distance of 55 cm for 20, 30, and 40 min (Wang et al., 2004). The spores were collected by centrifugation and suspended in fresh potato dextrose broth (PDB) medium. The spores were incubated at 30°C in the dark for 6 h and then spread on PDA plates at 30°C for 72 h. Colonies developed, which were then screened by plate bioassay. NTG (N-methyl-N′-nitro-N-nitrosoguanidine) treatment: The number of spores was adjusted to 10 6 spores/ml using 0.1M citrate buffer (pH 5.0). Two milliliters of an NTG solution (50, 100, and 150 µg/ml) was added to 2 ml of spore suspension. After incubation for 30 min, the spores were immediately collected by centrifugation and washed three times with sterilized distilled water. The spores were grown in fresh PDB medium at 30°C for 6 h and spread on PDA plates and then incubated at 30°C for 72 h. Colonies developed, which were then screened by plate bioassay. Combination of UV irradiation and NTG treatment: Two milliliters of an NTG (100 µg/ml) solution was added to 2 ml of spore suspension and placed under a UV lamp for 10, 20, and 30 min. The spore suspension was centrifuged and washed three times with sterilized distilled water. The spores were incubated in PDB medium at 30°C for 6 h and spread on PDA plates and then incubated at 30°C for 72 h. Colonies developed, which were then screened by plate bioassay.

Screening of Monascus mutants showing low citrinin production.
Screening of Monascus mutants by a plate bioassay was performed according to the method described by Wang et al. (2004). Mutagenized Monascus cells were spread on PDA plates containing 0.01% TritonX-100 and incubated at 30°C for 72 h. B. subtilis strain NBRC 13719 was grown in LB medium at 30°C for 24 h. One hundred microliters of Bacillus culture was added to 8 ml of soft LB agar medium (0.7% agar) and then poured onto the Monascus-grown plate. After 72 h, mutant strains with low citrinin production were selected by observing bacterial inhibition zones around the fungal colonies. The inhibition ratio was calculated using the following formula (Wang et al., 2004): where C is the average zone of inhibition of the wt and E is the average zone of inhibition of the mutant.

Determination of amounts of citrinin in the koji using HPLC.
Red koji was prepared using the selected mutant strains. Citrinin produced by the Monascus strains in the koji were extracted by the method described by Putri et al. (2005). One gram of dried red koji was ground and extracted with 40 ml of 70% ethanol by shaking at 80 rpm at 30°C. After 3 h, the extracts were filtrated through a membrane (Dismic-13CP, 0.45 µm pore size, Advantec Inc., Japan), and the amounts of citrinin in the red koji was determined by HPLC. HPLC was performed using a C18 column (TSKgel ODS 4.6 mm × 250 mm, Tosho, Japan) and 80% acetonitrile:0.1% phosphoric acid (60:40) as the mobile phase at a flow rate of 1.0 ml/min, with a fluorescence detector (excitation at 330 nm and emission at 500 nm).

Determination of the amount of red pigments.
Red pigments in the koji were extracted according to the method described above in citrinin extraction. The amount of red pigments in the koji was estimated by measuring the absorbance at 500 nm. Results were expressed as absorbance unit per gram koji (Putri et al., 2005).
Enzyme assays. Water (150 ml) was added to the red koji (7 d growth), and enzymes were extracted at 4°C for 4 h.
The resultant crude enzyme solution was filtrated through a membrane (Whatman No. 1, 125 mm) and stored at -20°C (Kanlayakrit et al., 2011). α-Amylase assay: α-Amylase activity was determined using the method described by Onishi and Sonoda (1979). A substrate solution (3.9 ml of 1% gelatinized starch in 0.1 M Mcllvaine buffer, pH 4.0) was preincubated at 50°C for 10 min and then added to 0.1 ml of the crude enzyme solution. After 10 min, 0.2 ml of the reaction mixture was added to 5 ml of 0.167 mM iodine solution. The α-amylase activity of the red koji was determined by measuring the absorbance at 770 nm. One unit (U) of α-amylase was defined as the change in absorbance per min. Protease assay: Protease activity was estimated according to Chancharoonpong et al. (2012) using casein as a substrate. A substrate solution (1 ml of 1.5% casein in 0.1 M phosphate buffer pH 7.0) was preincubated at 37°C for 10 min. One milliliter of the crude enzyme solution was added, well shaken, and incubated at 37°C for 10 min. Two milliliters of 0.44 M trichloroacetic acid (TCA) solution was added to terminate the reaction and then incubated for 30 min. Two milliliters of the mixture was filtrated and then 2.5 ml of 0.4 M Na 2 CO 3 solution was added and well shaken, and 0.5 ml of Folin reagent was added. Protease activity was determined by measuring the absorbance at 660 nm. One unit (U) of protease activity was defined as the amount of 1 µg tyrosine released per minute. Lipase assay: Lipase activity was assayed using Size of inhibition zone and inhibition ratio of Monascus mutants. Stuckmann's method (Stuckmann and Winkler, 1979) with p-nitrophenyl palmitate (p-NPP) as a substrate. The substrate solution was prepared by mixing solution A (10 ml of 30 mg p-NPP in isopropanol) with solution B (90 ml of 0.05M phosphate buffer, pH 7.0, containing 100 mg arabic gum (acacia), 207 mg sodium deoxycholate, and 2 ml TritonX-100). The substrate solution (2.4 ml) was preincubated at 37°C for 10 min, and 0.1 ml of the crude enzyme solution was then added and incubated at 37°C for 15 min. Lipase activity in the red koji was determined by measuring the absorbance at 410 nm. One unit (U) of lipase activity was defined as 1 µmol of p-nitrophenol released per minute.

Selection of the low citrinin-producing mutants
M. purpureus KUPM5 (wt strain) was isolated from Thai fermented food, sufu (Kanlayakrit et al., 2011). To make this strain safe for producing red koji, we attempted to improve it by reducing the citrinin production. UV irradiation, NTG treatment, and the combination of both UV irradiation and NTG treatment were used to induce the mutation in wt KUPM5. The irradiation period of UV and the concentration of NTG treatment were determined by the survival rate of the wt as an index. After UV irradiation for 30 and 40 min, the survival rate of the wt strain was 10%, and 7%, respectively. Meanwhile, the spores treated with 50, 100, and 150 µg/ml NTG showed a 50%, 10%, and 0% survival, respectively. The survival rate of spores after UV irradiation for 10 min in the presence of 100 µg/ml of NTG was <10%, whereas spores after UV treatment for 20 and 30 min showed a 0% survival. We determined the mutagenic conditions to the wt strain KUPM5 with UV irradiation to be 30 min and with 100 µg/ml NTG treatment to be 30 min.
In total, 2,420 colonies, including 983 colonies by UV irradiation (termed U), 850 colonies by NTG treatment (termed N), and 587 colonies by the combination of both UV and NTG (termed C), were screened by plate bioassay using B. subtilis NBRC13719. Ten colonies (U: 2, N: 4, and C: 4) were selected as showing >80% of diameter of colony formed by the wt strain, and the size of bacterial inhibition zone was >30% smaller than that of the wt (Fig.  1, Table1). These 10 mutants were serially subcultured 5 times on the PDA medium to stabilize the mutation. The mutant strains KS301U (d.i. 20.2 mm), KS1001C (d.i. 18.7 mm), KS1003C (d.i. 17.3 mm), and KS1004C (d.i. 18.4 mm) formed smaller sizes of colonies than the wt (d.i. 21.4 mm) did, indicating that the growth of the selected mutants was slightly repressed on the PDA medium (Fig. 1).

Citrinin production in the koji produced by mutants
Red koji was individually produced by the selected 10 Monascus mutant strains. Citrinin was extracted from the red koji and analyzed by HPLC to confirm the citrinin production levels in the mutants (Fig. 2). Among these 10 mutants, all mutants KS1001C, KS1002C, KS1003C, and KS1004C obtained after the combination of UV and NTG treatments completely eliminated citrinin production in the red koji (Table 2). Strains KS301U and KS302U showed citrinin productivity of 32.0 ± 0.3 and 33.2 ± 0.2 ng/g koji, respectively, which was 80% lower than that of the wt (174.3 ± 1.5 ng/g koji). However, strains KS101N, KS102N, KS103N, and KS104N, selected after NTG treatment, produced higher amounts of citrinin in red koji than the wt (Table 2).

Pigment production
M. purpureus produces the red pigment, azaphilone, as a secondary metabolite (Chen et al., 2017). This property of Monascus allows it to be used as a food colorant. The result of red pigment production is shown in Table 3. Strains KS103N and KS104N produced red pigments at levels of 10.03 ± 0.18 and 14.27 ± 0.86 U/g koji, respectively, higher than the wt (9.17 ± 0.41 U/g koji). Strain KS302U maintained red pigment production with 9.05 ± 0.80 U/g koji, which was similar to the wt. KS301U produced 24% less red pigment than the wt at 6.95 ± 0.30 U/ g koji, whereas all mutants KS1001C, KS1002C, KS1003C, and KS1004C obtained by the combination of UV and NTG showed more than 70% reduction in red pig-Different superscript letters (a, b, c, d, e, f, g) in the same column represent significantly different values (p ≤ 0.05) and preparation of koji were carried out in triplicate. Table 3.
Enzyme activity and red pigment production of Monascus mutant strains. ment production. Thus, the combination mutants almost lost the ability to produce red pigments and became albino mutants (Fig. 3).

Enzyme activities in red koji produced by Monascus mutant strains
The quality of Thai fermented food sufu is closely related with activities of α-amylase, protease, and lipase from red koji (Han et al., 2001). Thus, it is important to obtain mutants showing these enzymatic activities. To characterize the mutant strains, enzyme activities in the red koji extract of selected mutants were determined (Table 3). The red koji extract produced by strains KS301U and KS302U showed 4.51 ± 0.65 U/ml and 4.26 ± 0.22 U/ ml of α-amylase, respectively, which were similar to the α-amylase activity of the wt (4.89 ± 0.65 U/ml). As compared with the enzyme activities of the wt (2.63 ± 0.67 U/ ml of protease, 5.39 ± 0.27 U/ml of lipase), strains KS301U and KS302U exhibited enhanced activities with 3.38 ± 0.30 U/ml of protease, 9.21 ± 0.73 U/ml of lipase, 6.43 ± 0.72 U/ml of protease, and 12.72 ± 0.27 U/ml of lipase, respectively. The red koji made by the mutant strain KS104N showed 4.39 ± 0.22 U/ml of α-amylase and indicated high activities of lipase (13.60 ± 1.00 U/ml) and the highest activities of protease (20.09 ± 1.06 U/ml). The mutant strains KS102N, KS1002C, and KS1003C produced lower α-amylase activities and higher lipase activities than the wt.

Discussion
Red koji (red mold rice) is the product of M. purpureus, which has been widely used in the food industry. Citrinin, a secondary metabolite derived from the polyketide pathway of M. purpureus, is a known mycotoxin. Currently, there are concerns relating to citrinin contamination in red koji. Therefore, methods to reduce citrinin production by Monascus have been studied. The most powerful technology to produce a citrinin non-producing mutant is to rely on genetic engineering (Huang et al., 2017;Liang et al., 2018). In fact, the loss of function of citS involved in the first step of the reaction of citrinin production leads to the complete elimination of citrinin production (He et al., 2013). However, in the field of food industry, classical mutagenesis using mutagens has been an effective technique to improve microorganisms. Wang et al. (2004) and Putri et al. (2005) have used UV irradiation as a physical mutagen, and NTG as a chemical mutagen, to induce the mutation of the Monascus strain to obtain a citrinin nonproducing strain showing a high productivity of monacolin K and γ-aminobutyric acid (Wang et al., 2004) and red pigments (Putri et al., 2005). In this study, we used UV irradiation and NTG treatment as mutagens to develop Monascus mutant strains with a low citrinin production, and screened them using plate bioassay. Citrinin production in the red koji produced by strains KS301U, KS302U, KS1001C, KS1002C, KS1003C, and KS1004C were 80-100% less than that in the parental strain. However, strains KS101N, KS102N, KS103N, and KS104N produced higher amounts of citrinin than the parental strain, which conflicted with the results of our plate bioassay. This might be due to the difference in culture conditions between plate bioassay and red koji production (Pattanagul et al., 2008;Zhou et al., 2015).
In this study, we used the M. purpureus strain KUPM5 isolated from the Thai fermented food sufu. Although Monascus strains that have been previously mutagenized are different from our Monascus strain, the present results are in agreement with those of previous researchs that UV irradiation efficiently induced mutation within genes related to citrinin production, but not within other genes, such as those related to red pigment production, enzyme activity, and fungi hyphal development (Kalaivani and Rajasekaran, 2014;Pattanagul et al., 2008; Wang et al., 2004). Strains KS1001C, KS1002C, KS1003C, and KS1004C, selected after combined mutagenesis of UV and NTG treatments, eliminated citrinin production and simultaneously reduced red pigment production, leading to an albino koji (Fig. 3). A fungal regulator protein, LaeA, is an upstream key regulator for secondary metabolism and sporulation. Alteration of the laeA expression leads to simultaneous change in secondary metabolite production in Monascus (Lee et al., 2013;Liu et al., 2016). It is possible that these mutants have a mutation within the regulatory gene that functions at the upper level of secondary metabolism and commonly regulates each secondary metabolism. In contrast, strains KS301U and KS302U obtained by UV irradiation showed reduced citrinin production in red koji but maintained the ability to form red pigment similar to the parental strain (Table 3). It is likely that the mutation occurred only in genes involved in citrinin production. We are in the process of conducting a comparative genome analysis between the wt and mutant strains, which will allow us to identify the mutation point in mutant strains.
Reduced citrinin production while maintaining red pigment production in strains KS301U and KS302U make them favorable mutants to produce red koji. With regards to the Thai sufu production, α-amylase, protease, and lipase activities were closely associated with the quality of sufu, such as flavor, taste and texture (Han et al., 2001). Strains KS301U and KS302U have the possibility to produce another type of sufu because of their higher protease and lipase activities than the wt. Further studies examining sufu production using Monascus mutants should be undertaken to confirm the effect of each mutant and to determine the quality of the sufu.


text:
Estimation of leaf antioxidant activity using image processing

Physiologically, the influence of inundated conditions on many plants varies. The eco-physiological response of most plantations to flooding is wilt and stomata closure, root damage, reduction of root biomass2. This results in a decrease in the production of bioactive compounds, which have an effect on decreasing antioxidant activity. Various methods of measuring antioxidant activity still use chemicals through extraction processes that require a long time. So, it is necessary to examine alternative methods that are cheap, easy and fast. This method is digital processing. The leaf samples were then photographed for image analysis in the Telkom School Banjarbaru Multimedia Laboratory. Antioxidant activity was analyzed through the hydroxyl scavenging method at the laboratory of Biochemistry and Biomolecular, Faculty of Medicine, Lambung Mangkurat University, Banjarbaru. The results of image analysis showed that the antioxidant activity model of RGB values was AA= -54.9592 +1.0295 R + 0.3757 G + 2,006 B -0,0004 R*G - 0.0012 R*B + 0.004 G*B (AA = antioxidant activity; R = red; G = green; B = blue) with p = 0.001 (p <0.005) and r = 0.76. In this model, the R value has a significant effect on antioxidant activity. Image analysis using RGB can be used to measure leaf antioxidant activity. However, further research is needed.

Introduction
Indonesia's peat swamp forests have been degraded due to more drainage and conversion of forests to agricultural land, and irreversible environments, and large-scale fires [1]. The condition of postcombustion peat swamp forests is not beneficial for plants. Site conditions that have become open areas, physically and chemically damaged peat, burning peat forms a lower surface which is flooded in the rainy season. This condition is a barrier to the success of restoration.Physiologically, the influence of inundated conditions on many plants varies. The eco-physiological response of most plantations to flooding is wilt and stomata closure, root damage, reduction of root biomass [2].
Research using the image method continues to increase in development. In classifying plant diseases, the last 25 years continue to improve the ability to detect and classify approval, accuracy and accuracy of image analysis [3]. Some reports show increasing use of image processing in plant research. Study on plant recognition based on image processing has been rising, and more and more 2 popular because this method is rapid and objective, is not dependent on the person's subjective judgment, effectively and improves work efficiency [4].
Digital images processing techniques to analyze and detect, classify, quantify plant leaf diseases [5] and help to support in the identification of diseases at an early or initial stage and provide useful information for its control [6]. Various methods of measuring antioxidant activity still use chemicals through extraction processes that require a long time. So, it is necessary to examine alternative methods that are cheap, easy and fast. This method is digital processing. Therefore, the digital image processing should be useful as a simplified method for estimating antioxidant activity of plantations.

Materials and method 2.1. Study design
This research is an experimental in-vitro study for one week in May 2019.

Measurement of RGB
The leaves samples were then photographed for image analysis in the Telkom School Banjarbaru Multimedia Laboratory, Banjarbaru, South Kalimantan. Fresh leaves are then cleaned. To get a picture, the leaves are scanned with Canon LiDE 110. Nikai RGB is determined with imageJ the free software

Measurement of antioxidant activity
Gemor leaf obtained in maceration for 3 days with methanol as a solvent. The extract obtained was determined antioxidant activity using the method by Purwanto et al [8]. Antioxidant activity was analyzed through the hydroxyl scavenging method at the laboratory of Biochemistry and Biomolecular, Faculty of Medicine, Lambung Mangkurat University, Banjarbaru, South Kalimantan.

Results and discussion
Wet and dry leaves obtained are scanned. The scanned image is seen in Figure 1.  Figures 1 and 2 show that in wet leaves the value of Red (R) and Green (G) is higher than that of dry leaves. Changes in the value of RGB indicate changes in chemical structure such as covalent bonds or groups of molecules that are bound. This structural change causes the molecule to release H + ions to be donated to other molecules.This molecule is called an antioxidant.
Antioxidants in plants have long been used by people for herbal medicine. Antioxidants are believed to inhibit aging, prevent coronary heart disease, hypercholesterolemia, enhance immunity and others.Antioxidants work by hydroxyl radical scavenging, peroxide scavenging, and metal chlelating. A high green color is assumed to be a leaf green or chlorophyll or anthocianin compound which gives green pigment (figure 3).


extfieldsofstudy: Biology Medicine

text:
Effects of graded levels of Azadirachta indica seed oil on growth performance and biochemical profiles of broiler chickens

Abstract Because of speculated risk in generating antibiotic resistance in pathogenic microbiota, natural products from plant origin due to their diverse biological activities, have recently gained a great attention in animal nutrition. This study was designed to evaluate graded levels of neem seed oil on growth performance of broiler chickens. A total of 400‐day‐old chicks were randomly allocated to five experimental treatment groups. Experimental rations consisted of supplementing basal diet (R0−) with 1 g antibiotic (R0 + ), 15, 20 and 25 g neem seed oil/kg of feed. Data were recorded on feed intake, weight gain, feed conversion ratio, and serum biochemical parameters. Result revealed that feeding broiler chicks with 25 g of neem oil/kg of feed resulted in a marked (P < 0.05) decreased in feed intake as compared to the other treatments in the starter phase. The average live body weight and the weight gain decreased with increasing level of neem oil in the ration. Supplementation of poultry feed with graded levels of neem oil has no marked (P > 0.05) effect on carcass yield and relative weight of organs except for liver weight which significantly (P < 0.05) increased with the highest dose of neem oil. Serum content in total proteins, total cholesterol, HDL‐cholesterol, aspartate aminotransferase (AST), alanine aminotransferase (ALT) and creatinine were not significantly (P > 0.05) affected by the graded levels of neem oil. LDL‐cholesterol significantly (P < 0.05) decreased with diets supplemented 20 g of neem oil/kg while triglycerides significantly increased with the highest doses of oil (20 and 25 g/kg) as compared to the negative and positive control rations. In conclusion, feeding broilers with Azadirachta indica seed oil has no beneficial effect on growth performance but may lead to the production of low‐cholesterol chicken meat as demand by health‐conscious consumers.

Introduction
Sub therapeutic use of antibiotics in the poultry industry for decades helped to maintain the equilibrium of the gut ecosystem and improve growth performance of chickens through good nutrition (Huyghebaert et al. 2011). However, this practice although very effective, has been questioned, given the accumulation of antibiotic residues in livestock products and the resistance developed by pathogenous microbes in poultry farms (Kabir 2009). As consequence, farmers are more and more fighting against fatal diseases in poultry through the use of active compounds of plant origin. Plant products such as essential oils, spices, plant extracts and oil such as neem seed oil were investigated as a possible solution that would address public health concerns without compromising the efficiency of poultry production. However, there is no single product that has been successful in replicating the consistent effects of antibiotic growth promoters.
Neem (Azadirachta indica), is a tropical plant widely distributed in Africa and available all year round (Koona & Budida 2011;Ogbuewu et al. 2011). This plant is well adapted to the climatic and edaphic conditions of the rainforest and Sahelian tropical forest zones. Its leaves have a very bitter taste and have a garlic smell. Recent studies on the effects of neem leaves on broiler and table egg production have been documented (Bonsu et al. 2012;Jahanzeb et al. 2012). Those studies have shown that neem leaves at 2.5 g/kg feed have very beneficial effects on growth performance and hemato-biochemical parameters in broilers (Jahanzeb et al. 2012). Ogbuewu et al. (2010Ogbuewu et al. ( , 2011 investigated the effect of dietary A. indica oil on body weight gain, linear body measurements and blood chemistry of pre-puberal rabbits. The result revealed that rabbits could tolerate up to 15% dietary inclusion of neem oil without deleterious effects on body weight gain, linear body measurements, and reproductive tract morphometry and biochemical parameters. A. indica seed oil contains triterpenoid compounds such as azadirachtin, gedunine, nimbine and nimbidine which have antibacterial and antifungal properties (Makeri et al. 2007;Valarmathy et al. 2010) that can make its use as an alternative to antibiotic. These compounds in poultry feed can produce additive or synergistic effects on production performance (Valarmathy et al. 2010). The objective of the present study was to determine the effect of Azadirachta indica seeds oil as antibiotic substitute on the growth performance, carcass characteristics and biochemical profile of broiler chickens.

Study area
This study was carried out at the Poultry Unit of the Teaching and Research Farm of the Faculty of Agronomy and Agricultural Sciences of the University of Dschang, Cameroon. Dschang, is located in the West region of Cameroon at 05°26 0 N and 10°26 0 E. The area experiences a wet season from March to November and a hot dry season for the rest of the year. Maximum ambient temperature is around 21°C, while an average annual rainfall of 2000 mm is prevalent.

Experimental birds
A total of 400-day-old Cobb 500 strain chicks with an average body weight of 38 g obtained from a local hatchery were randomly divided into 25 floor pens (each 10 9 15 feet) of 16 chicks each and five replicates per treatment group. Birds were provided with ad libitum access to feed and water during the experiment period which lasted for 49 days. Birds were given vaccines in drinking water against Newcastel disease and Infectious Bronchitis on the 8th day with a booster dose on the 23th day of age, and against Gumboro disease on the 10th day of age. Coccidiosis prevention was done using Vetacox â for 3 consecutive days per week from the 2nd to the 6th week of age. Birds were administered commercial antistress (Amintotal â , LAPROVET, Tours Cedex 2, France) in drinking water during the first 3 days upon arrival, after each vaccination, weighing session and transfer from brooding to finishing building.

Experimental diet
Fresh neem fruits were collected at Garoua in the Northern Region of Cameroon between September and October 2017. They were thoroughly washed with running tap water and separated from the sheet manually. The seeds were oven dried at 50°C until constant moisture content was achieved. The dried seeds were ground in a mill for size reduction. The extraction of oil was carried out by kneading the paste. The phytochemical analysis of the extracted oil sample was carried out by Chromatographic method as described by Talukdar et al. (2010). The analysis revealed that alkaloids, flavonoids, terpenoids, phenols and steroids were present (Table 1).
Experimental diet calculated based on NRC (1994) tables of feed composition consisted of control ration without any supplement (R 0 À ), positive control supplemented with 1 g of antibiotic/kg of feed (R 0 + ) and three others rations supplemented with 15 g (R 15 ), 20 g (R 20 ) and 25 g (R 25 ) of neem oil/kg of feed. The antibiotic used in positive control ration was Doxycycline â . The proximate composition of the basal diet is summarized in Table 2.
per treatment were randomly selected, fasted for 24 h weighed, slaughtered and eviscerated to evaluate and record carcass yield as preceded by Kana et al. (2017). Dressing percentage was calculated by dividing the carcass weight by live body weight of the bird and the result was expressed as a percentage. The length of the intestine was measured with the cut done from the start of the duodenal loop to the end of the cloacal. The density of the intestine was calculated by dividing the intestine weight by its length (Kana et al. 2017). Blood samples of slaughtered birds were collected in non-heparinised tube and serum was collected for the determination of AST, ALT, creatinine, urea, total proteins, total cholesterol, HDL-cholesterol and LDL-cholesterol. The AST and ALT content were estimated using the nicotinamide adenine dinucleotide dehydrogenase (NADH) oxidation reaction method. The serum (0.1 mL) was added to 1.0 mL of auto-reagent and incubated at 37°C for 3 min. The absorbance was measured at 340 nm, and the values were expressed as unit L À1 . Serum creatinine and urea levels were evaluated by colorimetric method as described by Bergmeyer & Wanlefeld (1980) and the results were expressed as mg/dL. Total serum protein content was determined by the Biuret method and serum cholesterol by the enzymatic colorimetric method.

Statistical analysis
Results recorded on growth and serological parameters were subjected to one-way analysis of variance procedure using the Statistical Package for Social Sciences (SPSS 20.0) computer software. A P-value of <0.05 was considered a significant difference among groups and the comparison of means was made using Duncan's Multiple Range Test (Steel & Torrie 1984).

Growth performance
The effects of graded levels of neem oil on feed intake, live body weight, weight gain and feed conversion ratio are summarized in Table 3. Feed intake decreased with increasing level of oil during the starter phase (days 1-21), meanwhile during the finisher phase (days 22-49) and throughout the entire period of the study (days 1-49), there was no significant (P > 0.05) effect of neem seed oil on feed intake. Feeding broilers with graded levels of neem oil had no marked effect on weight gain and feed conversion ratio during the starter phase. During the finisher phase and the entire period of the study, when compared to the negative control ration, feeding broilers with graded levels of neem oil has no significant (P > 0.05) effect on live body weight, weight gain and feed conversion ratio (FCR). However, when compared to the positive control ration supplemented with antibiotic, body weight gain markedly (P < 0.05) reduced with neem oil whatever the dose in the ration.

Carcass characteristics
Carcass yield and relative weight of broilers organs as affected by the graded levels of neem oil are presented in Table 4. It can be observed that neem oil had no significant (P > 0.05) effect on the carcass yield. Except, for the relative weight of liver which significantly (P < 0.05) increased with 25 g of neem oil/kg, the relative weight of organs was not markedly affected by the dietary treatments.

Digestive organs
The development of digestive organs of chickens as affected by the graded levels of neem oil is summarized in Table 5. The intestine density significantly (P < 0.05) increased with 20 g of neem oil/kg as compared to antibiotic, while the intestine weight decreased (P < 0.05) with antibiotic as compared to the negative control and the treatments containing neem oil. Means with different superscript for the same parameters in each row are significantly different (P < 0.05). R 0 À: 0 g of neem oil/kg, R 0 +: R 0 À + 1 g of Doxycicline/kg, R 15 : R 0 À + 15 g of neem oil/kg, R 20 : R 0 À + 20 g neem oil/kg, R 25 : R 0 À + 25 g of neem oil/kg, P: Probability.  (Table 6). Urea and LDL-cholesterol serum content significantly (P < 0.05) decreased with 20 g of neem oil/kg feed as compared to the negative and positive control rations, while the triglyceride content significantly (P < 0.05) increased with the highest doses of oil (20 and 25 g/kg) as compared to other treatments.

Discussion
The present results showed a non-significant effect of graded levels of neem oil on feed intake during the entire period of the study. These results are consistent with the report of Landy et al. (2011) that indicated no significant effect of dietary inclusion of neem leaves powder at the rate of 7 and 12 g/kg of broiler chickens cumulative feed intake at 42 days. Singh et al. (2014), also depicted that there are no significant differences between treatments in the feed intake when adding neem leaves powder to the layer diet at a rate of 1, 2 and 3 g/kg. During the starter phase (days 1 to 21), feeding broilers with 25 g of neem oil/kg of feed resulted in a marked (P < 0.05) decrease in feed intake as compared to other treatments. This finding may be explained by the bitter taste of neem oil. During the entire period of the study, the average live body weight and the weight gain decreased with increasing levels of neem oil in the ration. This result contradicted the finding of Manwar et al. (2007), who supplemented broilers feed with increasing level of neem leaves powder and reported a significant increase in live body weight when compared with the control group. The present result is not consistent with the report of Onyimonyi et al. (2009), which revealed a significant improvement in the live body weight and weight gain when adding neem leaves powder to broiler diet at a Means with different superscript for the same parameters in each row are significantly different (P < 0.05). R 0 À: 0 g of neem oil/kg, R 0 +: R 0 À + 1 g of Doxycicline/kg, R 15 : R 0 À + 15 g of neem oil/kg, R 20 : R 0 À + 20 g neem oil/kg, R 25 : R 0 À + 25 g of neem oil/kg, P: Probability. Means with different superscript for the same parameters in each row are significantly different (P < 0.05). R 0 À: 0 g of neem oil/kg, R 0 +: R 0 À + 1 g of Doxycicline/kg, R 15 : R 0 À + 15 g of neem oil/kg, R 20 : R 0 À + 20 g neem oil/kg, R 25 : R 0 À + 25 g of neem oil/kg, P: Probability. rate of 0.5%. The decrease in weight gain could be possibly due to the presence of the various antinutritional factors found in neem seed oil. The highest level of neem oil inclusion (25 g/kg) reduced the growth rate in broilers which could be attributed to the different bio-active components of neem oil. This is similar to the adverse effect on growth as shown in White Leghorn chicks fed de-oiled neem seed meal at or above 5% level for 8 weeks (Subbarayudu & Reddy 1975). Salawut et al. (1994) also found that the lowest dose of neem oil in the diet of rabbits was better for growth performance than higher levels. Incorporation of graded levels of neem oil in the ration had no significant (P > 0.05) effect on the carcass yield of broilers. These results are in agreement with the findings of Kharde & Soujanya (2014), who reported no significant differences between treatments on carcass parameters when adding neem leaves and garlic powder to broiler diet at a rate of 1.2 g/kg and 0.5 g/kg, respectively. The relative weight of liver significantly (P < 0.05) increased in birds fed diets supplemented with 25 g of neem oil/ kg. These results are consistent with the study of Durrani et al. (2008) that indicated a significant improvement in relative weight when adding 40 mg of neem leaves powder/L of drinking water. In this study, the increase recorded in liver weight can be explained by the intense activity of this organ due to the presence of the antinutritional factors contained in this oil. Indeed, the phenolic compounds and flavonoids contained in this oil have an antioxidant potential with a strong protective activity of liver enzymes. This study revealed that neem oil contained Azadirachtin A (3.46 mg/kg) and Azadirachtin B (0.55 mg/kg) which are the most frequent triterpenoids in the group of flavonols. It is well-established that azadirachtin is a more potent antioxidant than other antioxidant nutrients such as Vitamin C, vitamin E and b-carotene (Rice-Evans et al. 1995).
Serum content in protein increased with diet supplemented with neem oil/kg as compared to the negative control. Serum protein actually depends on the availability of dietary protein. This suggests that the proteins of neem oil diets were available to the birds. The present result is in line with the findings of Samarth et al. (2003), which revealed that incorporation of herbs in the ration increased the serum protein in broilers. The present result suggests that the inclusion of neem oil in the poultry diets did not significantly alter dietary protein utilization and synthesis in the liver or suppress the immune system of the birds. Serum protein is involved in the transport of important body substances and maintenance of normal distribution of water between blood and tissues through osmotic pressure (Polat et al. 2011). Low protein is evidence of malnutrition, impaired protein synthesis in the liver or excessive protein catabolism while elevated protein levels may result from dehydration.
Inclusion of 20 g of neem oil/kg of poultry feed induced a significant decrease in LDL-cholesterol as compared to other treatments. This is consistent with It is presumed that those compounds either partially or wholly may be responsible for anti-hyperlipidermic activity of neem oil. The fall in LDL-cholesterol level of broilers fed neem oil diets probably suggests a general decrease in lipid mobilization; thus indicating that neem oil has indirect inhibitory effects exerted at the levels of HMG-CoA reductase, a key enzyme in cholesterol biosynthesis.
In the assessment of liver functioning, the activities of AST and ALT in serum were not significantly affected by the graded levels of neem oil. The AST is a cytoplasmic enzyme while ALT is found in both cytoplasmic and mitochondria. According to Bhatti & Dil (2005), alteration in serum enzymes activity under stress conditions occurs due to malfunctioning of liver as degenerating and necrotic cells leak enzymes from cytoplasm. In this study, birds were quite healthy as shown low concentration of liver enzymes. These results corroborate with Ansari et al. (2012) reports on the decreasing of the same parameters in birds fed on neem leaf meal as phytogenic feed additive. The non-hepatoxic nature of neem was reported in the previous study by Haque et al. (2006) who recorded unaltered and normal activities of serum AST. In the present study, serum content in AST increased with 20 g of neem oil/kg. Values recorded are range within the reference value (50-350 UI/L) for healthy chickens (Ansari et al. 2012). Neem oil is a promising hepatoprotective agent and this protective activity may be due to its antioxidant and normalization of impaired membrane function activity (Mohamed et al. 2010).

Conclusion
The present study revealed that feeding broiler chickens with Azadirachta indica seed oil has no detrimental effects on growth performance and hemato-biochemical parameters. However, the dietary supplementation of this oil up to 20 g/kg may lead to the production of low-cholesterol chicken meat as demand by health-conscious consumers. Considering the growing restrictions of antibiotics growth promoters, neem oil could be used as feed additive to mitigate the public concern about bacteria resistance issues as well as antibiotics residues in broiler chickens meat.


extfieldsofstudy: Physics Biology

text:
Analysis of matK, rbcL and trnL-trnF Intergenic Spacer Sequences on Durik-Durik (Syzygium sp)

Syzygium sp. with the local name of durik-durik, that grows in Riau Province, Indonesia, is one of many adapted plants to flooding stress in floodplain ecosystems, and plays a role for survival of the ecosystems. This study reports analysis of three DNA barcodes, i.e. matK, rbcL and trnL-trnF intergenic spacer on this plant. The fresh leaves of this plant were picked up from floodplain ecosystem in Pelalawan, Riau Province for DNA extraction requiring. In this study had been obtained DNA sequences of matK, rbcL and trnL-trnF intergenic spacer with the size of 686 bp, 641 bp and 828 bp, respectively. The BLASTn analysis based on those sequences showed that none of the accessions of the Syzygium genus were 100% similar to Syzygium sp. The phylogenetic tree showed that Syzygium sp. formed one group with the same species of Syzygium and separated from other genera in Myrtaceae family. These results confirmed that durik-durik was a member Syzygium. Those sequences had been deposited in GenBank and could be used for molecular identification of this plant.

Introduction
DNA barcode is a short sequence of genome in all organsims [1], that today it has been widely used for the purposes of molecular identification. For example, there are three DNA barcodes, i.e. matK, rbcL and trnL-trnF intergenic spacer, that has been used for plant molecular identification [2,3,4,5,6].
Those sequences are on plant chloroplast genome. The matK and rbcL sequences encode maturase K protein and ribulose bisphosphate carboxylase large chain respectively, while trnL-trnF intergenic spacer does not encode protein and only spacer between two genes such as trn-L and trn-F genes. Compared to matK and rbcL, the trnL-trnF intergenic spacer is easy to mutate and its variation very high among plants so that it can be better distinguish plant species [2].
Those DNA barcode sequences of Syzygium sp. are not yet available in GenBank and this can inhibit molecular identification of this plant. Molecular identification has many advantages compared to conventional identification based on morphological data such as 1) plants that are incomplete or damaged by organs can still be identified, 2) plants can be identified at any stage of development, and 3) molecular identification does not require expertise in taxonomic fields or in other words molecular identification can be carried out by everyone [1].
Molecular identification can be performed easily and quickly if DNA barcode sequence data is available in public databases such as GenBank and can be accessed easily [7,5,6]. Therefore, this study reports the first the tree DNA barcodes namely matK, rbcL, and trnL-trnF intergenic spacer on

Materials
There are about six Syzygium sp. in Kajuik Lake located in Langgam, Pelalawan Regency, Riau Province, Indonesia. The leaves of this plant were taken for analysis. The primer pairs used for the amplification of the matK and rbcL genes were designed based on the conserved regions of both genes in the Syzygium genus sequentially extracted from the GenBank database. Moreover, the primer pairs for the amplification of the exon trnL region and the trnL-trnF intergenic spacers were designed based on [8] (Table 1). Table 1. Primers for amplification of matK, rbcL and trnL-trnF intergenic spacer regions.

Methods
The total DNA molecule was extracted from fresh leaves using the DNeasy plant mini kit (Qiagen). The presence of total DNA was determined by electrophoresis technique. The total DNA molecule was then amplified using PCR technique. PCR components included 1X PCR buffers (plus Mg 2+ ), 0.1 mM dNTPs, 2.4 μM forward primer, 2.4 μM reverse primer, 2 U Dream Taq DNA polymerase (Thermo Scientific), 1 ng total DNA, and aquabidestilata up to 50 μl of total PCR volume [9]. The PCR procedure was performed as follows: 1 cycle at 94°C for 5 minutes, followed by 35 cycles consisting of three stages: denaturation at 94°C for 45 seconds, annealing at annealing temperature (Table 1) for 45 seconds, and elongation at 72°C for 1 minute. The process resumed at 1 cycle at 72°C for 10 minutes. The PCR products were then migrated on agarose gel electrophoresis. The PCR products are packaged for shipment to PT Genetika Science located in Jakarta and then sent to 1 st Base Malaysia for purification and two-way sequencing. The sequencing was conducted using the primer pairs for PCR ( Table 1). The sequences obtained were then analysis using BioEdit version 7.0.0 [10] software, BLASTn (Basic Local Alignment Search Tool) program at http://www.ncbi. nlm.nih.gov/BLAST [11] and MEGA version 6.06 (Build #: 6140226) software [12]. The DNA sequences from Eugenia uniflora and Psidium guajava which are also members of the Myrtaceae family were used as outgroups. The DNA sequences other than Syzygium sp. DNA sequence used for phenograms construction were taken from the GenBank database (ncbi.nih.nlm.gov).

Results and Discussion
The total DNA of Syzygium sp. had been obtained ( Figure 1) with good concentration and quality for use in the PCR process. The PCR products of the target genes such as matK, rbcL, trnL exon and trnL-trnF intergenic spacer had been obtained with the size of 700 bp, 650 bp, 600 bp, and 250 bp,

The matK Sequence Analysis of Syzygium sp.
The sequenced Syzygium sp. matK fragments produced a DNA sequence of 686 nucleotides. The sequence had been registered and released in GenBank with accession number KY503028.1. The BLASTn analysis of the Syzygium sp sequence indicated that matK sequence from Syzygium sp. matched 99% resemblance to most species of the Syzygium genus. This result was supported by high max score and the total score (1308), high query cover value (100%), and low value (0.0) ( Table 2). The matK sequences analysis of Syzygium sp., some species of the Syzygium genus, and outgroups (Psidium guajava and Eugenia uniflora) indicated 39 variations or different nucleotides in the form of substitution among them. Nucleotides number 471 and 636 were critical nucleotides that distinguished Syzygium sp. with other analysed accessions ( Table 3). In that positions, Syzygium sp. had Adenine (A) and Guanine (G) nucleotides respectively, whereas the other accessions in that positions had nucleotides Cytosine (C) and Thymine (T), respectively (Table 3). Dendrogram based on the matK sequence showed that Syzygium sp. formed a group similar to the fellow members of the Syzygium genus (Group 1), separated from outgroups (Group II) (Figure 2). Phenogram also showed that the Syzygium sp. had a closer genetic relationship with S. tenuiflorum with the difference at two critical nucleotides (Table 3).

The rbcL Sequence Analysis of Syzygium sp.
The rbcL sequence obtained from Syzygium sp. was 641 bp in size. This sequence had been registered and released in GenBank with accession number KY503029.1. The BLASTn analysis showed that the rbcL sequence from Syzygium sp had a similarity of up to 100% with S. balsameum, but this result was not supported by the high query cover value (the query cover value was only 98%). On the other hand, some species of the genus Syzygium had a 100% query cover value but the similarity value was only 99% (Table 4). An analysis on the rbcL sequences presented 34 variations of nucleotides. The variations included substitution, insertion and deletion and no critical nucleotides distinguished Syzygium sp. from compared accessions.The grouping analysis based on the rbcL sequence indicated that Syzygium sp. belongs to one group (Group I) with fellow members of the Syzygium genus and distincted from other species outside the genus of Syzygium (Group II) which is still a Myrtaceae family (Figure 3).

The Intergenic Spacer trnL-trnF Intergenic Spacer Sequence Analysis of Syzygium sp.
The trnL-trnF intergenic spacer sequence of Syzygium sp. had been obtained with size of 828 bp. The sequence had been registered and released in GenBank with accession number MG6836254. The BLASTn analysis based on the sequence showed that Syzygium sp. had 94% -99% similarity with some species of the Syzygium genus and other genus in the Myrtaceae family. The query cover values were ranging 70% -100% and the E-values were low such as 0.0 (Table 5). Analysis of the trnL-trnF intergenic spacer sequences showed 145 variations of nucleotides in which 53 of them were critical nucleotides that differentiated Syzygium sp. from other comparable species (data not shown).The grouping analysis based on the trnL-trnF intergenic spacer sequences indicated the same grouping pattern to the matK and rbcL sequences. In other words, Syzygium sp. formed a similar group to the same species of the Syzygium genus and separated from other species of the Myrtaceae family (Figure 4). These results indicated that Syzygium sp. was closely related to the species of the Syzygium genus.

Results and Discussion
The DNA Barcode is a short-term DNA sequence of about 500 to 1500 bp that can be used to identify organisms [1,13,14,15,16]. In addition, the DNA barcode has also been applied for phylogenetic analysis in plants, animals, and microorganisms [17,18,19,20]. The ordo of Myrtales consists of 9 families, and one of them is the Myrtaceae family which is composed of 132 genus and 5920 species [21,22]. Syzygium Gaertn as one of the members of the Myrtaceae family consists of a group of woody plants with the largest number of species compared to other flowering plant genus. The number of species in this genus ranges from 1200-1800 species [23]. However, the amount of DNA sequence data from the genus Syzygium available in the public database is limited, i.e. 309 for matK, 252 for rbcL, and 18 for trnL-trnF intergenic spacers (updated by March 4, 2018). In line with this, [24] have also reported the limited amount of DNA sequence data from the Syzygium genus based on the sequence of ITS and psbA-trnH intergenic spacer regions. Thus, the availability of DNA sequence data in public databases becomes very important for the molecular identification using DNA barcoding techniques. Therefore, the absence of sequences of matK, rbcL, and trnL-trnF intergenic spacers available in GenBank databases of 100% resemblance and 100% query cover with sequences of matK, rbcL, and trnL-trnF intergenic spacers from Syzygium sp. indicates that the sequences obtained in this study are the first reported from this plant. Two critical nucleotides were found in the matK and 52 critical nucleotides in the trnL-trnF intergenic spacer that definitely determine Syzygium sp. On the other hand, no critical nucleotides were found in the rbcL sequence. These results indicate that the DNA barcodes of matK and trnL-trnF intergenic spacer are more varied than the rbcL sequence and both (matK and trnL-trnF intergenic spacer) can serve as a differentiator for plant species.