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It has been reported that about half of biological discoveries are irreproducible. These irreproducible discoveries were partially attributed to poor statistical power. The poor powers are majorly owned to small sample sizes. However, in molecular biology and medicine, due to the limit of biological resources and budget, most molecular biological experiments have been conducted with small samples. Two-sample t-test controls bias by using a degree of freedom. However, this also implicates that t-test has low power in small samples. A discovery found with low statistical power suggests that it has a poor reproducibility. So, promotion of statistical power is not a feasible way to enhance reproducibility in small-sample experiments. An alternative way is to reduce type I error rate. For doing so, a so-called tα-test was developed. Both theoretical analysis and simulation study demonstrate that tα-test much outperforms t-test. However, tα-test is reduced to t-test when sample sizes are over 15. Large-scale simulation studies and real experiment data show that tα-test significantly reduced type I error rate compared to t-test and Wilcoxon test in small-sample experiments. tα-test had almost the same empirical power with t-test. Null p-value density distribution explains why tα-test had so lower type I error rate than t-test. One real experimental dataset provides a typical example to show that tα-test outperforms t-test and a microarray dataset showed that tα-test had the best performance among five statistical methods. In addition, the density distribution and probability cumulative function of tα-statistic were given in mathematics and the theoretical and observed distributions are well matched. [ABSTRACT FROM AUTHOR]

Although most high-density linkage maps have been constructed from codominant markers such as single-nucleotide polymorphisms (SNPs) and microsatellites due to their high linkage information, dominant markers can be expected to be even more significant as proteomic technique becomes widely applicable to generate protein polymorphism data from large samples. However, for dominant markers, two possible linkage phases between a pair of markers complicate the estimation of recombination fractions between markers and consequently the construction of linkage maps. The low linkage information of the repulsion phase and high linkage information of coupling phase have led geneticists to construct two separate but related linkage maps. To circumvent this problem, we proposed a new method for estimating the recombination fraction between markers, which greatly improves the accuracy of estimation through distinction between the coupling phase and the repulsion phase of the linked loci. The results obtained from both real and simulated [F.sub.2] dominant marker data indicate that the recombination fractions estimated by the new method contain a large amount of linkage information for constructing a complete linkage map. In addition, the new method is also applicable to data with mixed types of markers (dominant and codominant) with unknown linkage phase.

The goal of linkage mapping is to find the true order of loci from a chromosome. Since the number of possible orders is large even for a modest number of loci, the problem of finding the optimal solution is known as a NP-hard problem or traveling salesman problem (TSP). Although a number of algorithms are available, many either are low in the accuracy of recovering the true order of loci or require tremendous amounts of computational resources, thus making them difficult to use for reconstructing a large-scale map. We developed in this article a novel method called unidirectional growth (UG) to help solve this problem. The UG algorithm sequentially constructs the linkage map on the basis of novel results about additive distance. It not only is fast but also has a very high accuracy in recovering the true order of loci according to our simulation studies. Since the UG method requires n - 1 cycles to estimate the ordering of n loci, it is particularly useful for estimating linkage maps consisting of hundreds or even thousands of linked codominant loci on a chromosome.

Abstract Background Microarray technology provides an efficient means for globally exploring physiological processes governed by the coordinated expression of multiple genes. However, identification of genes differentially expressed in microarray experiments is challenging because of their potentially high type I error rate. Methods for large-scale statistical analyses have been developed but most of them are applicable to two-sample or two-condition data. Results We developed a large-scale multiple-group F-test based method, named ranking analysis of F-statistics (RAF), which is an extension of ranking analysis of microarray data (RAM) for two-sample t-test. In this method, we proposed a novel random splitting approach to generate the null distribution instead of using permutation, which may not be appropriate for microarray data. We also implemented a two-simulation strategy to estimate the false discovery rate. Simulation results suggested that it has higher efficiency in finding differentially expressed genes among multiple classes at a lower false discovery rate than some commonly used methods. By applying our method to the experimental data, we found 107 genes having significantly differential expressions among 4 treatments at Conclusion Our method is suitable to identify differentially expressed genes among multiple groups, in particular, when sample size is small.

The silkworm (Bombyx mori L.) is a lepidopteran insect with a long history of significant agricultural value. We have constructed the first amplified fragment length polymorphism (AFLP) genetic linkage map of the silkworm B. mori at a LOD score of 2.5. The mapping AFLP markers were genotyped in 47 progeny from a backcross population of the cross no. 782 x od100. A total of 1248 (60.7%) polymorphic AFLP markers were detected with 35 PstI/TaqI primer combinations. Each of the primer combinations generated an average of 35.7 polymorphic AFLP markers. A total of 545 (44%) polymorphic markers are consistent with the expected segregation ratio of 1:1 at the significance level of P = 0.05. Of the 545 polymorphic markers, 356 were assigned to 30 linkage groups. The number of markers on linkage groups ranged from 4 to 36. There were 21 major linkage groups with 7-36 markers and 9 relatively small linkage groups with 4-6 markers. The 30 linkage groups varied in length from 37.4 to 691.0 cM. The total length of this AFLP linkage map was 6512 cM. Genetic distances between two neighboring markers on the same linkage group ranged from 0.2 to 47 cM with an average of 18.2 cM. The sex-linked gene od was located between the markers P1T3B40 and P3T3B27 at the end of group 3, indicating that AFLP linkage group 3 was the Z (sex) chromosome. This work provides an essential basic map for constructing a denser linkage map and for mapping genes underlying agronomically important traits in the silkworm B. mori L.

It has been reported that about half of biological discoveries are irreproducible. These irreproducible discoveries were partially attributed to poor statistical power. The poor powers are majorly owned to small sample sizes. However, in molecular biology and medicine, due to the limit of biological resources and budget, most molecular biological experiments have been conducted with small samples. Two-sample t -test controls bias by using a degree of freedom. However, this also implicates that t -test has low power in small samples. A discovery found with low statistical power suggests that it has a poor reproducibility. So, promotion of statistical power is not a feasible way to enhance reproducibility in small-sample experiments. An alternative way is to reduce type I error rate. For doing so, a so-called t α -test was developed. Both theoretical analysis and simulation study demonstrate that t α -test much outperforms t -test. However, t α -test is reduced to t -test when sample sizes are over 15. Large-scale simulation studies and real experiment data show that t α -test significantly reduced type I error rate compared to t -test and Wilcoxon test in small-sample experiments. t α -test had almost the same empirical power with t -test. Null p-value density distribution explains why t α -test had so lower type I error rate than t -test. One real experimental dataset provides a typical example to show that t α -test outperforms t -test and a microarray dataset showed that t α -test had the best performance among five statistical methods. In addition, the density distribution and probability cumulative function of t α -statistic were given in mathematics and the theoretical and observed distributions are well matched., (© 2022 Walter de Gruyter GmbH, Berlin/Boston.)

Gastrointestinal microflora is a key component in the maintenance of health and longevity across many species. In humans and mice, nonpathogenic viruses present in the gastrointestinal tract enhance the effects of the native bacterial microbiota. However, it is unclear whether nonpathogenic gastrointestinal viruses, such as Nora virus that infects Drosophila melanogaster , lead to similar observations. Longevity analysis of Nora virus infected (NV+) and uninfected (NV-) D. melanogaster in relationship to presence (B+) or absence (B-) of the native gut bacteria using four different treatment groups, NV+/B+, NV+/B-, NV-/B+, and NV-/B-, was conducted. Data from the longevity results were tested via Kaplan-Meier analysis and demonstrated that Nora virus can be detrimental to the longevity of the organism, whereas bacterial presence is beneficial. These data led to the hypothesis that gastrointestinal bacterial composition varies from NV+ to NV- flies. To test this, NV+ and NV- virgin female flies were collected and aged for 4 days. Surface sterilization followed by dissections of the fat body and the gastrointestinal tract, divided into crop (foregut), midgut, and hindgut, were performed. Ribosomal 16S DNA samples were sequenced to determine the bacterial communities that comprise the microflora in the gastrointestinal tract of NV+ and NV- D. melanogaster . When analyzing operational taxonomic units (OTUs), the data demonstrate that the NV+ samples consist of more OTUs than NV- samples. The NV+ samples were both more rich and diverse in OTUs compared to NV-. When comparing whole body samples to specific organs and organ sections, the whole fly was more diverse in OTUs, whereas the crop was the most rich. These novel data are pertinent in describing where Nora virus infection may be occurring within the gastrointestinal tract, as well as continuing discussion between the relationship of persistent viral and bacterial interaction., Competing Interests: Conflict of interest: All authors declare no conflict of interest in this paper., (© 2021 the Author(s), licensee AIMS Press.)

Background: Dominant markers in an F 2 population or a hybrid population have much less linkage information in repulsion phase than in coupling phase. Linkage analysis produces two separate complementary marker linkage maps that have little use in disease association analysis and breeding. There is a need to develop efficient statistical methods and computational algorithms to construct or merge a complete linkage dominant marker maps. The key for doing so is to efficiently estimate recombination fractions between dominant markers in repulsion phases., Result: We proposed an expectation least square (ELS) algorithm and binomial analysis of three-point gametes (BAT) for estimating gamete frequencies from F 2 dominant and codominant marker data, respectively. The results obtained from simulated and real genotype datasets showed that the ELS algorithm was able to accurately estimate frequencies of gametes and outperformed the EM algorithm in estimating recombination fractions between dominant loci and recovering true linkage maps of 6 dominant loci in coupling and unknown linkage phases. Our BAT method also had smaller variances in estimation of two-point recombination fractions than the EM algorithm., Conclusion: ELS is a powerful method for accurate estimation of gamete frequencies in dominant three-locus system in an F 2 population and BAT is a computationally efficient and fast method for estimating frequencies of three-point codominant gametes.

Because of the high operation costs involved in microarray experiments, the determination of the number of replicates required to detect a gene significantly differentially expressed in a given multiple-testing procedure is of considerable significance. Calculation of power/replicate numbers required in multiple-testing procedures provides design guidance for microarray experiments. Based on this model and by choice of a multiple-testing procedure, expression noises based on permutation resampling can be considerably minimized. The method for mixture distribution model is suitable to various microarray data types obtained from single noise sources, or from multiple noise sources. By using the biological replicate number required in microarray experiments for a given power or by determining the power required to detect a gene significantly differentially expressed, given the sample size, or the best multiple-testing method can be chosen. As an example, a single-distribution model of t-statistic was fitted to an observed microarray dataset of 3 000 genes responsive to stroke in rat, and then used to calculate powers of four popular multiple-testing procedures to detect a gene of an expression change D. The results show that the B-procedure had the lowest power to detect a gene of small change among the multiple-testing procedures, whereas the BH-procedure had the highest power. However, all multiple-testing procedures had the same power to identify a gene having the largest change. Similar to a single test, the power of the BH-procedure to detect a small change does not vary as the number of genes increases, but powers of the other three multiple-testing procedures decline as the number of genes increases.

There is a complex- and multi-effect for interdependent survival between intestinal- microorganisms and hosts. The symbiosis or coevolution that results from this effect for interdependent survival is used to reveal the phylogenies of hosts as well as intestinal microorganisms. The symbiosis or coevolution between intestinal microorganisms and hosts has been generated by interactive natural selection occurred between them. The symbiosis information that has been formed by interactive natural selection during a long evolutionary process must be recorded in DNA sequences. According to this point of view,we analyzed the phylogeny of 9 intestinal bacteria genera using their contents in intestines of 8 Cyrinidate species. At the same time,we fetched the 16S rRNA gene DNA sequences of 43 intestinal bacteria species being included in these nine genera of six intestinal families from GeneBank and constructed phylogenetic trees by NJ and MP methods. The NJ tree and MP tree have the same topologic configuration and are identical with the classical phylogenetic tree. Both the trees of 16S rRNA gene separated 43 bacteria species into gram-negative bacteria group and the gram-positive bacteria group,which are the first branches. Each of the first branches (groups) made again 6 subbranches (subgroups) where each subbranch is a family.Especially,the subbranch (subgroup) of enterobacteriaceace made again four small branches as genus taxon. This tree also shows that bacilliform bacterium is distinct from each other in the NJ and MP trees. After all species on the tree are merged,the topological configuration of the unrooted tree of 16S gene is closed to that of the host range unrooted tree.However,the position of bacillus is greatly changed on both the unrooted trees. The difference can be found if we increase the examination level and extend the hosts examed.