Experiment-wise Level Research Articles

Epigenetic variation in OPRM1 gene in opioid-exposed mother-infant dyads.

Neonatal abstinence syndrome (NAS) due to in-utero opioid exposure has significant variability of severity. Preliminary studies have suggested that epigenetic variation within the μ-opioid receptor (OPRM1) gene impacts NAS. We aimed to determine if DNA methylation in OPRM1 within opioid-exposed mother-infant dyads is associated with differences in NAS severity in an independent cohort. Full-term opioid-exposed newborns and their mothers (N = 68 pairs) were studied. A DNA sample was obtained and then assessed for level of DNA methylation at 20 CpG sites within the OPRM1 promoter region by next-generation sequencing. Infants were monitored for NAS and treated with replacement opioids according to institutional protocol. The association between DNA methylation level at each CpG site with NAS outcome measures was evaluated using linear and logistic regression models. Higher methylation levels within the infants at the -18 (11.4% vs 4.4%, P = .0001), -14 (46.1% vs 24.0%, P = .002) and +23 (26.3% vs 12.9%, P = .008) CpG sites were associated with higher rates of infant pharmacologic treatment. Higher levels of methylation within the mothers at the -169 (R = 0.43, P = .008), -152 (R = 0.40, P = .002) and +84 (R = 0.44, P = .006) sites were associated point-wise with longer infant length of stay. Maternal associations remained significant point-wise for -169 (β = 0.07, P = .007) and on an experiment-wise level for +84 (β = -0.10, P = .003) using regression models. These results suggest an association of higher levels of OPRM1 methylation at specific CpG sites and increased NAS severity, replicating prior findings. These findings have important implications for personalized treatment regimens for infants at high risk for severe NAS.

Perfunctory analysis of variance in agronomy, and its consequences in experimental results interpretation

Analysis of variance (ANOVA) is based on two main assumptions, i.e., normality and homogeneity of the variances of the populations samples are collected from. In order to verify the correct application of ANOVA in agronomic research, we revised the two most recent years of two high ranked journals concerning agronomy: European Journal of Agronomy and Field Crops Research. The main issues considered were: presence of tests for normality and homogeneity of variance, and eventually the possibility of identifying problems due to analysis carried out on data not matching these assumptions and to incorrect applications of multiple comparisons. Forty-six percent of the reviewed papers uses ANOVA and, in 60% of these papers, assumptions are not verified at all (and frequently there are evidences that assumptions are not met), or there is a misuse of multiple comparison tests. We also pointed out that the more relevant risk of transmitting erroneous information to the scientific community comes from the use of wrong techniques for multiple comparisons, in particular when protected least significant difference (LSD) test is used. This was demonstrated through exemplifications carried out using Monte Carlo simulations that showed an unacceptable rate of type-III errors found with the protected LSD methods for means separation. We think this study could represent a useful warning on how to avoid misleading conclusions from agronomic experiments due to the incorrect application of classical statistical techniques (i.e., procedures not fully controlling the type-I error rate at experimentwise level).

Analyses of porcine public SNPs in coding-gene regions by re-sequencing and phenotypic association studies

The Porcine SNP database has a huge number of SNPs, but these SNPs are mostly found by computer data-mining procedures and have not been well characterized. We re-sequenced 1,439 porcine public SNPs from four commercial pig breeds and one Korean domestic breed (Korean Native pig, KNP) by using two DNA pools from eight unrelated animals in each breed. These SNPs were from 419 protein-coding genes covering the 18 autosomes, and the re-sequencing in breeds confirmed 690 public SNPs (47.9%) and 226 novel mutations (173 SNPs and 53 insertions/deletions). Thus, totally, 916 variations were found from our study. Of the 916 variations, 148 SNPs (16.2%) were found across all the five breeds, and 199 SNPs (21.7%) were breed specific polymorphisms. According to the SNP locations in the gene sequences, these 916 variations were categorized into 802 non-coding SNPs (785 in intron, 17 in 3'-UTR) and 114 coding SNPs (86 synonymous SNPs, 28 non-synonymous SNPs). The nucleotide substitution analyses for these SNPs revealed that 70.2% were from transitions, 20.0% from transversions, and the remaining 5.79% were deletions or insertions. Subsequently, we genotyped 261 SNPs from 180 genes in an experimental KNP × Landrace F2 cross by the Sequenom MassARRAY system. A total of 33 traits including growth, carcass composition and meat quality were analyzed for the phenotypic association tests using the 132 SNPs in 108 genes with minor allele frequency (MAF)>0.2. The association results showed that five marker-trait combinations were significant at the 5% experiment-wise level (ADCK4 for rear leg, MYH3 for rear leg, Hunter B, Loin weight and Shearforce) and four at the 10% experiment-wise level (DHX38 for average daily gain at live weight, LGALS9 for crude lipid, NGEF for front leg and LIFR for pH at 24 h). In addition, 49 SNPs in 44 genes showing significant association with the traits were detected at the 1% comparison-wise level. A large number of genes that function as enzymes, transcription factors or signalling molecules were considered as genetic markers for pig growth (RNF103, TSPAN31, DHX38, ABCF1, ABCC10, SCD5, KIAA0999 and FKBP10), muscling (HSPA5, PTPRM, NUP88, ADCK4, PLOD1, DLX1 and GRM8), fatness (PTGIS, IDH3B, RYR2 and NOL4) and meat quality traits (DUSP4, LIFR, NGEF, EWSR1, ACTN2, PLXND1, DLX3, LGALS9, ENO3, EPRS, TRIM29, EHMT2, RBM42, SESN2 and RAB4B). The SNPs or genes reported here may be beneficial to future marker assisted selection breeding in pigs.

Quantitative trait loci analysis for growth and carcass traits in a half-sib family of purebred Japanese Black (Wagyu) cattle1

We used a half-sib family of purebred Japanese Black (Wagyu) cattle to locate economically important quantitative trait loci. The family was composed of 348 fattened steers, 236 of which were genotyped for 342 microsatellite markers spanning 2,664 cM of 29 bovine autosomes. The genome scan revealed evidence of 15 significant QTL (<5% chromosome-wise level) affecting growth and carcass traits. Of the 15 QTL, six QTL were significant at the 5% experiment-wise level and were located in bovine chromosomes (BTA) 4, 5, and 14. We analyzed these three chromosomes in more detail in the 348 steers, with an average marker interval of 1.2 cM. The second scan revealed that the same haplotype of the BTA 4 region (52 to 67 cM) positively affected LM area and marbling. We confirmed the QTL for carcass yield estimate on BTA 5 in the region of 45 to 54 cM. Five growth-related QTL located on BTA 14, including slaughter and carcass weights, were positively affected by the same region of the haplotype of BTA 14 (29-51 cM). These data should provide a useful reference for further marker-assisted selection in the family and positional cloning research. The research indicates that progeny design with moderate genotyping efforts is a powerful method for detecting QTL in a purebred half-sib family.

A Global View on Parametric and Nonparametric Approaches to the Analysis of Ordered Categorical Data

AbstractRank approaches are very common in the analysis of ordered categorical data but can only be interpreted on an experiment‐wise level. Therefore, parametric tests from linear models, although based on metric structures, are used frequently to analyze this type of data. So the questions arise 1. what parametric tests measure in this context and 2. whether the rank approach could be modified to achieve a global level of interpretation. A possible solution to question 2. offers the so called ridit approach, which is based on known reference distributions. In this paper we discuss a global view that shows how rank analysis and ridit analyses are related and how parametric procedures fit into the same framework. The use of the uniform distribution as a reference in the ridit approach gives an explanation to question 1. The asymptotic multivariate normality of the effect estimators is shown and robust test statistics are discussed. Type I and type II error rates are examined in simulation studies and the approach is applied to a toxicological example. (© 2004 WILEY‐VCH Verlag GmbH &amp; Co. KGaA, Weinheim)

Short Communication: Quantitative Trait Loci Analysis on 17 Nonproduction Traits in the New Zealand Dairy Population

Seven New Zealand Holstein-Friesian families and two Jersey families, a total of 274 sires, were analyzed in a granddaughter design for marker-quantitative trait loci associations. For 17 nonproduction traits (management, size, and conformation traits), an acrossfamily analysis was undertaken using multimarker regression procedures. Threshold levels were set empirically by permuting the data. A quantitative trait locus for stature was identified on chromosome 14, which was significant at the 15% experimentwise level (suggestive linkage). The quantitative trait locus was identified to be segregating in three of the nine families.

On the modified s-method and directional errors

Scheffe (1970) introduced a modified S-method for testing all linear contrasts, which is a stagewise rejective two stage test procedure. If a contrast hypothesis α'μ = 0 is rejected using this method, the problem arises whether or not one can decide for α'μ 0 without violating the multiple level α. We give a general proof that the modified S-method still controls the experimentwise level of significance α. Furthermore, we show that this procedure can also be used as a very powerful peocedure for the special case of comparing three means with unequal sample size, which is of great practical importance. Finally we note that a similar result holds true for various closed directional range procedures for the comparison of three means.

Power Differences between Pairwise Multiple Comparisons

Abstract A number of multiple comparison procedures are studied whose maximum Type I error rate, experimentwise, is limited to a fixed value, the experimentwise level. Some of these procedures are slight revisions of existing methods. The procedures are compared for all-pairs power; i.e., for the probability of simultaneous significance for all pairs which are truly unequal. Results of Monte Carlo simulation show that a revision of Peritz's F method is uniformly the most powerful among all the procedures studied. The revised Peritz F method is substantially more powerful than Tukey's method with a power advantage as high as 0.50. A method due to Welsch (1972, 1977) is also quite powerful and may sometimes be recommended for greater simplicity.

Power Differences Between Pairwise Multiple Comparisons

Abstract A number of multiple comparison procedures are studied whose maximum Type I error rate, experimentwise, is limited to a fixed value, the experimentwise level. Some of these procedures are slight revisions of existing methods. The procedures are compared for all-pairs power; i.e., for the probability of simultaneous significance for all pairs which are truly unequal. Results of Monte Carlo simulation show that a revision of Peritz's F method is uniformly the most powerful among all the procedures studied. The revised Peritz F method is substantially more powerful than Tukey's method with a power advantage as high as 0.50. A method due to Welsch (1972, 1977) is also quite powerful and may sometimes be recommended for greater simplicity.

Expository Notes On The Problem Of Making Multiple Comparisons In A Completely Randomized Design

MANY OF the current tests and reference books used by educational researchers are quite sharp in their condemnation of the practice of performing t-tests on each pair of means as a method of testing the hypothesis that, in a completely randomized de sign, jUj. = ju2 = ... = Lij. whenk>2. The prac tice generally recommended as a suitable alterna tive, however, has but little more merit. The authors of these works quite rightly indi cate that when a given critical value is set for test ing all the possible pairs of differences between a number of means in this way, the probability of at least one false significant difference will be great er than the critical value. The practice sometimes suggested and followed today involves applying the F-test for analysis of variance and following that with t-tests for all possible comparisons between means. Such a procedure ignores one of the prin cipal arguments against applying the t-test in the first place. As Steel and Torrie (19) point out, with three treatments the observed value of t for the greatest difference will exceed the tabled .05 level about 13% of the time even if the treatment popula tions truly do not have differing means. This change in significance level is progressive, and for six treatments the value of t for the greatest dif ference will be greater than the tables . 05 level about 40% of the time. It is true that some protection against the er ror of rejecting a true hypothesis of no difference is provided if specific comparisons are made only after the application of the F-test and only if the result indicates rejection of the hypothesis that ?i = M2 = MkIt would seem, however, that at this point it would be appropriate to turn to one of a number of procedures for specific comparisons among treatment means which have been developed in recent years. These procedures, in general, are designed to set an experiment-wise error rate or level of significance. A number of the more apparently useful meth ods of comparison have received some attention in recent years (5, 9,10,11,15,19, 22). In an article in the September 1955 issue of Psychological Bulletin, McHugh (9) discussed the problem of examining comparisons between means which are suggested by the data. A method pro posed by Scheff? (13) was applied to two examples of this type of situation. Additional interest in such a posteriori or post-mortem analyses was stirred by a paper prepared by Stanley (15) and also pub lished in the Psychological Bulletin. Stanley point ed out that Scheff?'s test, while offering an experi ment-wise level of significance, was not as power ful as some other tests proposed. A procedure de veloped by Tukey (20) was offered as an alternative. For the instance in which comparisons of each ex perimental group with a control only are desired, Stanley offered the procedure suggested by Dun nett (4). More recently, Thomas A. Ryan (11) discussed the questions of logic which arise in choosing methods of dealing with multiple comparisons and other multiple statistical tests. He contended that the decision with respect to which methods of com parison are correct should not revolve around whether or not the comparisons being made were specified in advance of data collection (a priori) or were suggested by the data (a posteriori). Rather, wrote Ryan, the fact is that the classical methods are inappropriate for multiple comparisons under any circumstances, and should be replaced by the newer methods in either instance. It should be noted that Ryan also presented a very comprehen sive discussion of the considerations involved in determining the actual error rate or level of sig nificance in a given experiment involving multiple comparisons. A resulting exchange by Gaito (6) and Ryan (12) resulted in further clarification of the problem and proposed solutions. Nevertheless, the question of whether the appropriate error rate should be per comparison or per experiment con tinues to be debated (13, 21), with the weight of evidence seeming to fall on the side of those who espouse the use of an experiment-wise error rate. One point raised by Ryan (12) with respect to the newer methods for multiple comparisons should be made quite clear. The Scheff?, Tukey, Dunnett and certain other of the available methods may be applied without the use of an initial F-test of the