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… In an influential paper, Benjamini and Hochberg (1995) introduced the concept of false discovery rate (FDR) as a way to allow inference when many tests are being conducted. Lower FWERs restrict the proportion of false positives at the expense of reducing the power to detect association when it truly exists. In such cases, one can apply a continuous generalization of the Bonferroni correction by employing Bayesian logic to relate the effective number of trials, Holm or Hochberg). {\displaystyle m} GWAS uses a stringent genome-wide statistical significance level for meta-analysis of the combined data to correct for a large number of tests across the genome, e.g., using the Bonferroni correction (Visscher et al., 2017). In other words, it adjusts the alpha value from a = 0.05 to a =(0.05/k) where k is the number of statistical tests conducted. 2. To address this concern, Yekutieli and Benjamini (1999) introduced the FDR-adjustment, in which monotonicity is enforced, and which definition is compatible with the original FDR definition. These model fits yield a p-value for each SNP, and then multiple comparison correction such as the Bonferroni correction or the Benjamini and Hochberg FDR correction is used to obtain a set of significant SNPs. α Although many common association test statistics are asymptotically multivariate normal, use of the asymptotic distribution requires reasonably large sample sizes and may not be appropriate in all cases—for example, dominant or recessive models with a rare minor allele. â¢Solutions include: â¢Bonferroni correction: By assuming markers are independent we Alternatively, we can plot out the empirical distribution of p values (using a histogram) and see if there is any departures from a uniform distribution. The procedure proposed by Dunn[2] (not to be confused with the Dunn procedure for rank-based analysis of variance) can be used to adjust confidence intervals. Statistically this can be achieved by applying resampling methods such as bootstrapping and permutations methods, admitted that they are computationally intensive. A Weighted-Holm Procedure Accounting for Allele Frequencies in Genomewide Association Studies. If the tests are independent then the Bonferroni bound provides a slightly conservative bound. = {\displaystyle m} For example, in the example above, with 20 tests and = 0:05, youâd only reject a null hypothesis if the p-value is less than 0.0025. Corrections âBonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inferenceâ Perneger (1998) â¢Counter-intuitive: interpretation of ï¬nding depends on the number of other tests performed â¢The general null hypothesis (that all the null hypotheses are That’s just it! of 0.05 could be maintained by conducting one test at 0.04 and the other at 0.01. − Usually, the plink software can give you raw and permuted p-values, although it uses (by default) an adaptive testing strategy with a sliding window that allows to stop running all permutations (say 1000 per SNP) if it appears that the SNP under consideration is not “interesting”; it also has option for computing maxT, see the online help. Setting beta in a power calculation is an arbitrary procedure. Dudbridge F, Koeleman BP (2004) Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies. We found support for an association of 15 of the 20 candidate variants (Table 6 ), but not for SNP rs4961252 (which does not map to the MHC region), the MHC class I alleles, or the HLA-DRB1*11 alleles. 1 Broberg, P. A comparative review of estimates of the proportion unchanged genes and the false discovery rate. 1 Bayes factors have also been proposed for the measurement of significance6. For example, after the Bonferroni correction, for the larger SCZ data set with 36,989 cases and 113,075 controls, our method applied to the gene body and enhancer regions identified 27 novel genes and 11 novel KEGG pathways to be significant, all missed by the transcriptome-wide association study (TWAS) approach. Conneely KN, Boehnke M: So many correlated tests, so little time! ... (SNP) analysis or haplotype analysis with Bonferroni correction for multiple comparisons. Next, we will perform pairwise t-tests using Bonferroniâs correction for the p=values to calculate pairwise differences between the exam scores of each group. The BH procedure has found many applications across different fields, including neuroimaging, as introduced by Genovese et al. The Bonferroni correction rejects the null hypothesis for each = Moreover, to reduce false positive due to multiple testing Bonferroni correction criteria (Corrected p-value = p-value × number of genes in the test; should be lower than the level of significance, 0.01 considered during the present study) was used. A typical permutation test on GWAS results is achieved by randomly reassigning the phenotypes of each individual to another individual (i.e., swapping the trait labels among the subjects) in the dataset, effectively breaking the genotype-phenotype relationship while keeping the LD structure of the dataset. 0 Determine GWAS Significance Threshold Bonferroni threshold can be determined to identify significantly markers associated with the trait by using the below equation: P â¤ 1/N (Î± =0.05) For example: If there are 13,169 sites (markers), then Bonferroni threshold would be = â¦ {\displaystyle H_{i}} {\displaystyle \alpha =0.05/20=0.0025} Thus, applying FDR across multiple correlated loci (due to LD) for a single trait is dubious. Otherwise, if the collection of tests contains some alternative hypotheses mixed in with true nulls, the distribution tends to be a mixture, with fraction n0/n being draws from a uniform and (1 - n0/n) from some other distribution. It rejects any hypothesis with p-value â¤ Î±/m. This method does not assume independence among tests but assumes the asymptotic joint distribution of all association test statistics is a multivariate normal distribution with known covariance matrix. To give an extreme example, when all the p-values are the same (as in a case of perfect dependence), the cutoff value for the Bonferroni procedure (assume it is alpha/number of markers) should be just alpha. Each random reassignment of the data represents one possible sampling of individuals under the null hypothesis, and this process is repeated a predefined number of times N to generate an empirical distribution with resolution N, so a permutation procedure with an N of 1000 gives an empirical pvalue within 1/1000th of a decimal place. However, in the context of GWAS, permutation is likely to require too much computation time, so computationally efficient alternatives are desirable. Wei, Z, Sun, W, Wang, K, and Hakonarson, H. Multiple Testing in Genome-Wide Association Studies via Hidden Markov Models. If we want the overall Type I error (i.e., family-wide error rate) to remain at 5% we need to lower the significance level at each locus. Genetics 173.4 (2006): 2371-2381. If the tests are correlated then the bound becomes more conservative. Bonferroni correction is based on the fact that $$ P(\cup_{j=1}^{k} A_j) \leq \sum_{j=1}^{k} P(A_j) $$ for events $\{ A_j \}_{j=1}^{k}$, which can be a poor upper bound when the events are not disjoint. Dependence in the noise of the statistics creates problems only for deriving FDR-controlling procedures and therefore can be addressed (Benjamini and Yekutieli, 2001), as long as the dependence structure meets certain constraint. For multiple testing problems this is almost certainly the case. {\displaystyle \leq \alpha } Stat. the number of true null hypotheses. In such cases, even sequential Bonferroni correction are likely too stringent, resulting in too many false negatives (i.e., less power). α Regarding p-value adjustment for multiple comparisons, permutation testing is widely considered the gold standard that other estimators and tests can be compared with. Thus, we are often more concerned with Type-II errors, as the penalty of including a test that is from the null may be less than the penalty of excluding a test that is not from the null. "Relaxed significance criteria for linkage analysis." [1] Numerous corrections have been proposed in the literature, including the most commonly applied FWER Bonferroni correction (most stringentâbut with higher probability of false negatives, due to erroneously adjudging a truly significant result to be false, which is due to a more conservative pâvalue cutâoff), sequential Bonferroni corrections (Holm, 1979) and the False Discovery Rate (FDR) correction â¦ Foundations (Version 26 March, 2014). For a typical GWAS using 500,000 SNPs, statistical significance of a SNP association would be set at 1e-7. As mentioned, a multiple-comparison problem makes statistical inferences based on the joint distribution of p-values from all the tests. among Europeans). To perform pairwise t-tests with Bonferroniâs correction in R we can use the pairwise.t.test() function, which uses the following syntax: Food Raw.p Bonferroni BH Holm Hochberg Hommel BY 20 Total_calories 0.001 0.025 0.0250000 0.025 0.025 0.025 0.09539895 12 Olive_oil 0.008 0.200 0.1000000 0.192 0.192 0.192 0.38159582 In order to adjust for them, I searched for a way in R and realized that implementing a multiple testing adjustment is easier than I thought/remembered. The nature of the problem is made clear in this post (the XKCD “Green jelly bean” cartoon). For GWA studies of dense SNPs and resequence data, a standard genome-wide significance threshold of 7.2e−8 for the UK Caucasian population has been proposed by Dudbridge and Gusnanto21. The Bonferroni correction compensates for that increase by testing each individual hypothesis at a significance level of {\displaystyle \alpha /m} If each additional G allele increases odds of disease by 1.2, and 1618 cases and 3413 The Bonferroni correction for an error rate of 0.05 is the standard, which implies that a p-value in the order of 10â8 10 â 8 is needed to reach genome-wide significance. As mentioned, the decision to control the false positives versus false discoveries hinges to a large extent on the fraction of the tests that are true nulls (n0/n), which is usually unknown. The general idea is that when we reject a hypothesis, there remain one fewer tests, and the multiple comparison correction should take this into account. Randomly permuting and reanalyzing the data many times and comparing the permutation-based results with the original results allows estimation of the probability of observing a P value as in the original result, given the correlation between tests. α We will use the subset of the mice data in the BGLR R package. Chen, Lin, and John D. Storey. When tests are independent, the Sidak correction is exact; however, in GWA studies comprising dense sets of markers, this is unlikely to be true and both corrections are then very conservative. i Multiple testing correction â¢In GWAS a large number of marker tests are conducted, which leads to a multiple testing problem. So how are the methods we have introduced doing in this context? If the tests are independent then the Bonferroni bound provides a slightly conservative bound. is the number of hypotheses. [2], When searching for a signal in a continuous parameter space there can also be a problem of multiple comparisons, or look-elsewhere effect. This is usually far too stringent and results in an enormous loss of power, producing many false negatives or Type II errors (failing to declare a test significant when the null is false). Recently, researchers have accepted the linear mixed model (LMM) as standard practice for performing GWAS. For example, some researchers suggest calculating the effective number of independent SNPs in a gene region and using this value in the Bonferroni correction (Cheverud 2001; Dudbridge & Gusnanto, 2008; Galwey 2009; Gao et al., 2008; Li and Ji, 2004; Nyholt 2004). Bonferroni) and sequential adjustment (e.g. Other research has also come to about the same numbers and recommendations, and loci identified with this stringent p-value tend to hold up across different experiments. = This chapter provides a practical overview of the statistical analysis using R [1] and genotype by sequencing (GBS) markers for genome-wide association studies (GWAS) in oats. 20 m With respect to FWER control, the Bonferroni correction can be conservative if there are a large number of tests and/or the test statistics are positively correlated. The Bonferroni correction, which aims to control the probability of having at least one false positive finding, calculates the adjusted p value threshold with the formula 0.05/n, with n â¦ With regard to GWAS, in a typical study there are hundreds of thousands to millions of tests simultaneously conducted, each for a single marker and with its own false positive probability. their corresponding p-values. {\displaystyle 1-\alpha } a hypothesis test involving a multiple regression model Mapping analysis - an association analysis Linkage disequilibrium (LD) mapping - an association analysis (we will define LD next lecture) Thus, we have 0.05/1e6 = 5e-8. For association tests applied at each of n SNPs, per-test significance levels of α* for a given FWER of α can be simply approximated using Bonferroni (α* = α/n) or Sidak15,16 (α* = 1 − (1 – α)1/n) adjustments. Let 0.05 An appropriate method to correct for ... identify nonindependence of phenotypes using GWAS results from samples with only a â¦ The familywise error rate (FWER) is the probability of rejecting at least one true Bonferroni: The standard Bonferroni correction, simply using the total number of SNPs tested in the genome-wide significance level calculation, was 7.1 × 10-8, which corresponded to a genome-wide significance level of Î± â 0.05 when compared with PRESTO (see Table 1).While a permutation test may not result in a large improvement in the corresponding genome-wide â¦ BMC Bioinformatics 2005 6: 199. Although Bonferroni correction has been widely applied in GWAS, analytical and simulation studies by Sabatti and others (2003) have shown that the FDR procedure of Benjamini and Hochberg (1995) can effectively control the FDR for the dependent tests encountered in caseâcontrol association studies and increase power over more traditional methods. The problem with the FDR-correction is that q_{(i)} is not a monotonic function of p_{(i)}. Hi pfs, it would help if you could elaborate on why Bonferroni (over other methods) is the method-of-choice here. This makes the new methods universal, since there is no need to consider the specifics of samples and traits. Thus, applying Bonferroni correction often gives us the most conservative p-value threshold (maybe as a lower bound?) These methods require Bonferroni correction for multiple tests, which often is too conservative when the number of markers is extremely large. [2], Statistical hypothesis testing is based on rejecting the null hypothesis if the likelihood of the observed data under the null hypotheses is low. Multiple hypothesis testing is an essential step in GWAS analysis. Genome-wide association studies (GWAS) have been widely used in genetic dissection of complex traits. 1, 2 To assess â¦ The first step for the Bonferroni and Sidak tests used as a followup to ANOVA is to compute the Fisher LSD test. , When alternative hypothesis (as opposed to null) is correct, there will be an inflation of p values near zero and therefore a strong departure from linearity near one in the lower curve (p near zero, so 1 - p values near one). Genetically, we can model the dependency among the SNPs (i.e., LD) and incorporating this information into previous methods/procedures (e.g., Dalmasso et al., 2008; Wei et al., 2009; Broberg 2005; Need et al., 2009). Bonferroni correction simply divides the significance level at each locus by the number of tests. The significance threshold after the Bonferroni correction was 0.05/4572 = 1.09 × 10 â 5 for gene-based analysis, or 0.05 / 186 = 0.000268 for pathway-based analysis. , provided that the level of each test is determined before looking at the data. Simply, the Bonferroni correction, also known as the Bonferroni type adjustment, is one of the simplest methods use during multiple comparison testing. Then reject all H_{(i)}, i=1, 2, \ldots , k. The constant c(N) is not in the original publication, and appeared in Benjamini and Yekutieli (2001) for cases in which independence cannot be ascertained. Assume n is the total number of tests and n0 is the number of true nulls, taht means n0 is close to n. An alternate setting is that some substantial fraction of the tests are indeed expected to be false. This means that if someone uses any q_{(i)} to threshold the set of FDR-corrected values, the result is not the same as would be obtained by applying sequentially the B&H procedure for all these corresponding q levels. I describe the background to the Bonferroni correction (type 1 error and familywise error) as well as the two approaches to conducting a Bonferroni correction. For example, in the example above, with 20 tests and = 0:05, youâd only reject a null hypothesis if the p-value is less than 0.0025. There is an option to define the threshold of the significance of âlog 10 (p) of which Bonferroni can be selected. Given this caveat, we identified genome-wide significant associations after correcting for multiple testing within each bacterial abundance GWAS individually, either by Bonferroni correction or by q-value (considering significance thresholds at both q â¤ 0.1 and q â¤ 0.2 cutoffs). , to the prior-to-posterior volume ratio. Gao X, Starmer J, Martin ER: A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms. So-called gap statistics or sliding window have been proved successful in some case, but you’ll find a good review in (7) and (8). Originally developed by Benjamini and Hochberg (1995), FDR procedures essentially correct for the number of expected false discoveries, providing an estimate of the number of actual true results among those called significant. The Bonferroni correction [ 17] assumes independence among the association tests. Here, we used the Bonferroni correction for 20 SNPs and alleles, corresponding to a significance threshold of Î± = 2.5 × 10 â3. Most recently, the accepted standard is 5e−8 (ref. If these tests are independent, it is relatively easy to compute the joint distribution of p-values assuming the null hypothesis of each test is true (each p value is a random value following a uniform distribution). This association, however, did not remain significant after Bonferroni correction. GWAS summary statistics are normally published after all these issues have been fixed (Yang et al., 2018). Multiple significance tests and the Bonferroni correction If we test a null hypothesis which is in fact true, using 0.05 as the critical significance level, we have a probability of 0.95 of coming to a `not significant' (i.e. {\displaystyle \alpha /m} H hypotheses with a desired Some approaches were developed to help people visually detect the discrepancy/departure. And don’t forget that all these tests are conducted on one common data set (i.e., the same set of individuals) that may not have a large number of samples. (2002). None of the medical statisticians advertise this. , Alternatives to the FWER approach include false discovery rate (FDR) procedures18,19, which control for the expected proportion of false positives among those SNPs declared significant. {\displaystyle \alpha =0.05} Bonferroni correction would markedly overcorrect for the in-flated false-positive rate in such correlated datasets, resulting in a reduction in power. To determine the statistical significance threshold in GWAS, different statistical procedures accounting for multiple testing have been proposed, including the Bonferroni correction, Sidak correction, False Discovery Rate (FDR), permutation test, and Bayesian approaches. Multiple significance tests and the Bonferroni correction This is a section from my text book An Introduction to Medical Statistics, Third Edition.I hope that the topic will be useful in its own right, as well as giving a flavour of the book. m Hi pfs, it would help if you could elaborate on why Bonferroni (over other methods) is the method-of-choice here. Let be a family of hypotheses and Although Bonferroni and Sidak corrections provide a simple way to adjust for multiple testing by assuming independence between markers, permutation testing is considered to be the ‘gold standard’ for accurate correction20. Stat Med 9: 811–818. Am J Hum Genet. Under a uniform distribution, the result is a straight line passing through the origin and the point (1, n), as the upper curve shown below. Genetics 2008 180(1): 697–702. Rather than testing each hypothesis at the Using GenStat, GWAS can be done either GLM or MLM models with population structure correction to control genetic relatedness by PCA or Kinship. An extension of the method to confidence intervals was proposed by Olive Jean Dunn. {\displaystyle m=20} Bonferroni correction is to control the overall type I errors when all tests are independent. A uniform distribution results in a flat histogram, whereas any shift will lead to a skew towards 0 or 1. Genet Epidemiol 2008; 32: 361–369. â¢Using a 5% significance threshold, we would expect 5% of the markers that have true marker effects of 0 to be significant. / Statistical technique used to correct for multiple comparisons, Bonferroni, C. E., Teoria statistica delle classi e calcolo delle probabilitГ , Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze 1936, Familywise error rate В§ Controlling procedures, Journal of the American Statistical Association, "The look-elsewhere effect from a unified Bayesian and frequentist perspective", Journal of Cosmology and Astroparticle Physics, "Are per-family Type I error rates relevant in social and behavioral science? m Likewise, when constructing multiple confidence intervals the same phenomenon appears. m {\displaystyle \alpha } 2. Genome-Wide Association Studies Caitlin Collins, Thibaut Jombart Imperial College London MRC Centre for Outbreak Analysis and Modelling August 6, 2015 For example, the HolmвЂ“Bonferroni method and the Е idГЎk correction are universally more powerful procedures than the Bonferroni correction, meaning that they are always at least as powerful. Setting beta in a power calculation is an arbitrary procedure. For example, a physicist might be looking to discover a particle of unknown mass by considering a large range of masses; this was the case during the Nobel Prize winning detection of the Higgs boson. None of the medical statisticians advertise this. A further method (Conneely & Boehnke 2007) uses extreme tail theory to explicitly calculate the probability of detecting an extreme test statistic (i.e., beyond the predefined thresholds) and it is typically thousands of times faster than permutation-based p values at a given level of precision. The threshold p value after Bonferroni correction was 0.05/N = 7.45 × 10 â 8, where N is the number of SNPs. is the desired overall alpha level and Ann. An example of this kind of correction is the Bonferroni correction. ADD COMMENT â¢ link written 2.4 years ago by pfs â¢ 280. Because the permutation schemes preserve the correlational structure between SNPs, this provides a less stringent correction for multiple testing in comparison to the Bonferroni, which assumes all tests are independent. To demonstrate Recall also that many discussions on role of power testing and Bonferroni type corrections involve only speculations on the nature of unknowable alternate distribtions. {\displaystyle \alpha } Benjamini, Y., and D. Yekutieli, 2001. Several approaches are useful in correcting these potential pitfalls, including Bonferroni correction and permutation test. Statistical analysis is performed by R package rrBLUP [2] and issues associated with the analysis are addressed along with t â¦ Genet Epidemiol 32: 227–234. So, when doing corrections, simply multiply the nominal p-value by m to get the adjusted p-values. Bonferroni correction uses all n single nucleotide polymorphisms (SNPs) across the genome, but this approach is highly conservative and would “overcorrect” for SNPs that are not truly independent. Corrections âBonferroni adjustments are, at best, unnecessary and, at worst, deleterious to sound statistical inferenceâ Perneger (1998) â¢Counter-intuitive: interpretation of ï¬nding depends on the number of other tests performed â¢The general null hypothesis (that all the null hypotheses are Bonferroni correction is based on the fact that $$ P(\cup_{j=1}^{k} A_j) \leq \sum_{j=1}^{k} P(A_j) $$ for events $\{ A_j \}_{j=1}^{k}$, which can be a poor upper bound when the events are not disjoint. , thereby controlling the FWER at α As SNP associations on CP are entirely mediated by the Heredity 2001; 87: 52–58. For example, testing 100,000 loci for association at the 5% level is expected to have about 5,000 false positives. Sort the p-values in ascending order, p_{(1)} \leq p_{(2)} \leq \ldots \leq p_{(N)} and denote H_{(i)} the hypothesis corresponding to p_{(i)}.