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Comparison of classification methods for detecting associations between SNPs and chick mortality
- Nanye Long^{1}Email author,
- Daniel Gianola^{1, 2},
- Guilherme JM Rosa^{2},
- Kent A Weigel^{2} and
- Santiago Avendaño^{3}
https://doi.org/10.1186/1297-9686-41-18
© Long et al; licensee BioMed Central Ltd. 2009
- Received: 17 December 2008
- Accepted: 23 January 2009
- Published: 23 January 2009
Abstract
Multi-category classification methods were used to detect SNP-mortality associations in broilers. The objective was to select a subset of whole genome SNPs associated with chick mortality. This was done by categorizing mortality rates and using a filter-wrapper feature selection procedure in each of the classification methods evaluated. Different numbers of categories (2, 3, 4, 5 and 10) and three classification algorithms (naïve Bayes classifiers, Bayesian networks and neural networks) were compared, using early and late chick mortality rates in low and high hygiene environments. Evaluation of SNPs selected by each classification method was done by predicted residual sum of squares and a significance test-related metric. A naïve Bayes classifier, coupled with discretization into two or three categories generated the SNP subset with greatest predictive ability. Further, an alternative categorization scheme, which used only two extreme portions of the empirical distribution of mortality rates, was considered. This scheme selected SNPs with greater predictive ability than those chosen by the methods described previously. Use of extreme samples seems to enhance the ability of feature selection procedures to select influential SNPs in genetic association studies.
Keywords
- Bayesian Network
- Random Forest
- Hide Node
- Generalize Linear Mixed Model
- Forward Selection
Introduction
In genetic association studies of complex traits, assessing many loci jointly may be more informative than testing associations at individual markers. Firstly, the complexity of biological processes underlying a complex trait makes it probable that many loci residing on different chromosomes are involved [1, 2]. Secondly, carrying out thousands of dependent single marker tests tends to produce many false positives. Even when significance thresholds are stringent, "significant" markers that are detected sometimes explain less than 1% of the phenotypic variation [3].
Standard regression models have problems when fitting effects of a much larger number of SNPs (and, possibly, their interactions) than the number of observations available. To address this difficulty, a reasonable solution could be pre-selection of a small number of SNPs, followed by modeling of associations between these SNPs and the phenotype [4]. Other strategies include stepwise selection [5], Bayesian shrinkage methods [6], and semiparametric procedures, such as mixed models with kernel regressions [7, 8].
Machine learning methods are alternatives to traditional statistical approaches. Machine learning is a branch of artificial intelligence that "learns" from past examples, and then uses the learned rules to classify new data [9]. Their typical use is in a classification framework, e.g., disease classification. For example, Sebastiani et al. [10] applied Bayesian networks to predict strokes using SNP information, as well as to uncover complex relationships between diseases and genetic variants. Typically, classification is into two classes, such as "unaffected" and "affected". Multi-category classification has been studied, for example, by Khan et al. [11] and Li et al. [12]. It is more difficult than binary assignment, and classification accuracy drops as the number of categories increases. For instance, the error rate of random classification is 50% and 90% when 2 and 10 categories are used, respectively.
In a previous study of SNP-mortality association in broilers [13], the problem was cast as a case-control binary classification by assigning sires in the upper and lower tails of the empirical mortality rate distribution, into high or low mortality classes. Arguably, there was a loss of information about the distribution, because intermediate sires were not used. In the present work, SNP-mortality associations were studied as a multi-category classification problem, followed by a filter-wrapper SNP selection procedure [13] and SNP evaluations. All sire family mortality rates were classified into specific categories based on their phenotypes, and the number of categories was varied (2, 3, 4, 5 or 10). The objectives were: 1) to choose an integrated SNP selection technique by comparing three classification algorithms, naïve Bayes classifier (NB), Bayesian network (BN) and neural network (NN), with different numbers of categories, and 2) to ascertain the most appropriate use of the sire samples available.
Methods
Data
Genotypes and phenotypes came from the Genomics Initiative Project at Aviagen Ltd. (Newbridge, Scotland, UK). Phenotypes consisted of early (0–14d) and late (14–42d) age mortality status (dead or alive) of 333,483 chicks. Birds were raised in either high (H) or low (L) hygiene conditions: 251,539 birds in the H environment and 81,944 in the L environment. The H and L environments were representative of those in selection nucleus and commercial levels, respectively, in broiler breeding. Information included sire, dam, dam's age, hatch and sex of each bird. There were 5,523 SNPs genotyped on 253 sires. Each SNP was bi-allelic (e.g., "A" or "G" alleles) and genotypes were arbitrarily coded as 0 (AA), 1 (AG) or 2 (GG). A detailed description of these SNPs is given in Long et al. [13].
The entire data set was divided into four strata, each representing an age-hygiene environment combination. For example, records of early mortality status of birds raised in low hygiene conditions formed one stratum, denoted as EL (early age-low hygiene). Similarly, the other three strata were EH (early age-high hygiene), LL (late age-low hygiene) and LH (late age-high hygiene). Adjusted sire mortality means were constructed by fitting a generalized linear mixed model (with fixed effect of dam's age and random effect of hatch) to data (dead or alive) from individual birds, to get a residual for each bird, and then averaging progeny residuals for each sire (see Appendix). After removing SNPs with missing values, the numbers of sires and SNPs genotyped per sire were: EL and LL: 222 sires and 5,119 SNPs; EH and LH: 232 sires and 5,166 SNPs. Means and standard deviations (in parentheses) of adjusted sire means were 0.0021 (0.051), -0.00021 (0.033), -0.0058 (0.058) and 0.00027 (0.049) for EL, EH, LL and LH, respectively. Subsequently, SNP selection and evaluation were carried out in each of the four strata in the same way.
Categorization of adjusted sire mortality means
Sire mortality means were categorized into K classes (K = 2, 3, 4, 5 or 10). The adjusted sire means were ordered, and each was assigned to one of K equal-sized classes in order to keep a balance between sizes of training samples falling into each category. For example, with K = 3, the thresholds determining categories were the 1/3 and 2/3 quantiles of the empirical distribution of sire means. This is just one of the many possible forms of categorization, and it does not make assumptions about the form of the distribution.
"Filter-wrapper" SNP selection
A two-step feature selection method, "filter-wrapper", described in Long et al. [13] was used. There, upper and lower tails of the distribution of sire means were used as case-control samples, and the classification algorithm used in the wrapper step was naïve Bayes. In the present study, all sires were used in a multi-category classification problem, and three classification algorithms (NB, BN and NN) were compared.
"Filter" step
A collection of 50 "informative" SNPs was chosen in this step. It was based on information gain [9], a measure of how strongly a SNP is associated with the category distinction of sire mortality means. Briefly, information gain is the difference between entropy of the mortality rate distribution before and after observing the genotype at a given SNP locus. The larger the information gain, the more the SNP reduces uncertainty about mortality rate. As noted earlier, the 50 top scoring SNPs with respect to their information gain were retained for further optimization in the wrapper step. The filter procedure was coded in Java.
"Wrapper" step
This procedure is an iterative search-and-evaluate process, using a specific classification algorithm to evaluate a subset of SNPs (relative to the full set of 50 SNPs) searched [14]. Three classification algorithms, NB, BN and NN, were compared in terms of the cross-validation classification accuracy of the chosen subset of SNPs. Two widely used search methods are forward selection (FS) and backward elimination (BE) [15]. FS starts from an empty set and progressively adds SNPs one at time; BE starts with the full set, and removes SNPs one at a time. The search methods stop when there is no further improvement in classification accuracy. In general, BE produces larger SNP sets and better classification accuracy than FS [13, 16], but it is more time-consuming. Differences in computation time between BE and FS were large when the classification algorithm was BN, which was computationally intensive. However, the difference between FS and BE in terms of classification accuracies of the chosen SNP subsets was small (Appendix). Hence, FS was adopted for BN. For NB and NN the search method was BE. The wrapper procedure was carried out on the Weka platform [16]. Computing time for running wrapper using the search method selected for each of NB, BN and NN was 1 min for NB, 3 min for BN and 8.2 h for NN. These were benchmarked on a dataset with 222 sires and 50 SNPs, which was typical for each stratum.
Naïve Bayes
Pr(C = c) can be estimated from training data and Pr(X_{1} = x_{1},..., X_{ p }= x_{ p }) is irrelevant for class allocation; the predicted value is the class that maximizes Pr(X_{1} = x_{1},..., X_{ p }= x_{ p }| C = c). NB assumes that X_{1},..., X_{ p }are conditionally independent given C, so that Pr(X_{1} = x_{1},..., X_{ p }= x_{ p }| C = c) can be decomposed as Pr(X_{1} = x_{1} | C = c) × ⋯ × Pr(X_{ p }= x_{ p }| C = c). Although the strong assumption of feature independence given class is often violated, NB often exhibits good performance when applied to data sets from various domains, including those with dependent features [17, 18]. The probabilities, e.g., Pr(X_{1} = x_{1} | C = c), are estimated using the ratio between the number of sires with genotype x_{1} that are in class c, and the total number of sires in class c.
Bayesian networks
Neural networks
SNP subset evaluation
Comparison of the three classification algorithms (NB, BN and NN) yielded a best algorithm in terms of classification accuracy. Using the best classification algorithm, there were five optimum SNP subsets selected in the wrapper step in each stratum, corresponding to the 2, 3, 4, 5 or 10-category classification situation, respectively. The SNP subset evaluation refers to comparing the five best SNP subsets in a certain stratum (EL, EH, LL or LH). Two measures were used as criteria; one was the cross-validation predicted residual sum of squares (PRESS), and the other was the proportion of significant SNPs. In what follows, the two measures are denoted as A and B. Briefly, for measure A, a smaller value indicates a better subset; for measure B, a larger value indicates a better subset.
Measure A
Here, M_{ i }is predicted using all sire means except the i th (i = 1, 2,..., N) sire, and this predicted mean is denoted by ${\widehat{M}}_{(i)}$. A subset of SNPs was considered "best" if it produced the smallest PRESS when employing this subset as predictors. A SAS^{®} macro was written to generate PRESS statistics and it was embedded in SAS^{®} PROC GLIMMIX (SAS^{®} 9.1.3, SAS^{®} Institute Inc., Cary, NC).
Measure B
This procedure involved calculating how many SNPs in a subset were significantly associated with the mortality phenotype. Given a subset of SNPs, an F-statistic (in the ANOVA sense) was computed for each SNP. Subsequently, given an individual SNP's F- statistic, its p-value was approximated by shuffling phenotypes across all sires 200 times, while keeping the sires' genotypes for this SNP fixed. Then, the proportion of the 200 replicate samples in which a particular F-statistic exceeded that of the original sample was calculated. This proportion was taken as the SNP's p-value. After obtaining p-values for all SNPs in the subset, significant SNPs were chosen by controlling the false discovery rate at level 0.05 [26]. The proportion of significant SNPs in a subset was the end-point.
Comparison of using extreme sires vs. using all sires
This comparison addressed whether or not the loss of information from using only two extreme tails of the sample, as in Long et al. [13], affected the "goodness" of the SNP subset selected. Therefore, SNP selection was also performed by an alternative categorization method based on using only two extreme portions of the entire sample of sire means. The two thresholds used were determined by α, such that one was the 100 × α% quantile of the distribution of sire mortality means, and the other was the 100×(1-α)% quantile. SNP selection was based on the filter-wrapper method, as for the multi-category classification, with NB adopted in the wrapper step. Four α values, 0.05, 0.20, 0.35 and 0.50, were considered, and each yielded one partition of sire samples and, correspondingly, one selected SNP subset.
In each situation (using all sires vs. extreme sires only), the best subset was chosen by the PRESS criterion, as well as by its significance level. That is, the smallest PRESS was selected as long as it was significant at a predefined level (e.g., p = 0.01); otherwise, the second smallest PRESS was examined. This guaranteed that PRESS values of the best SNP subsets were not obtained by chance. Significance level of an observed PRESS statistic was assessed by shuffling phenotypes across all sires 1000 times, while keeping unchanged sires' genotypes at the set of SNPs under consideration. This procedure broke the association between SNPs and phenotype, if any, and produced a distribution of PRESS values under the hypothesis of no association. The proportion of the 1000 permutation samples with smaller PRESS than the observed one was taken as its p-value.
Results
Comparison of NB, BN and NN
Classification error rates using naïve Bayes (NB), Bayesian networks (BN) and neural networks (NN) in five categorization schemes (K = 2, 3, 4, 5 and 10), based on the final SNP subsets selected
Number of categories (K) | ||||||
---|---|---|---|---|---|---|
Stratum^{a} | Classifier | K = 2 | K = 3 | K = 4 | K = 5 | K = 10 |
EL | NB | 0.124 | 0.225 | 0.314 | 0.329 | 0.523 |
BN | 0.207 | 0.437 | 0.649 | 0.674 | 0.813 | |
NN | 0.270 | 0.295 | 0.543 | 0.662 | 0.813 | |
EH | NB | 0.116 | 0.212 | 0.330 | 0.397 | 0.506 |
BN | 0.228 | 0.422 | 0.653 | 0.688 | 0.820 | |
NN | 0.185 | 0.364 | 0.560 | 0.623 | 0.827 | |
LL | NB | 0.132 | 0.221 | 0.375 | 0.408 | 0.523 |
BN | 0.225 | 0.403 | 0.545 | 0.709 | 0.824 | |
NN | 0.221 | 0.401 | 0.588 | 0.610 | 0.831 | |
LH | NB | 0.151 | 0.252 | 0.338 | 0.405 | 0.494 |
BN | 0.261 | 0.438 | 0.532 | 0.681 | 0.816 | |
NN | 0.278 | 0.381 | 0.530 | 0.534 | 0.793 |
Evaluation of SNP subsets
Evaluating SNP subsets using predicted residual sum of squares (A) and proportion of significant SNPs (B)
Number of categories (K) | ||||||
---|---|---|---|---|---|---|
Stratum^{a} | Measure | K = 2 | K = 3 | K = 4 | K = 5 | K = 10 |
EL | A | 0.672 | 0.781 | 0.747 | 0.807 | 0.964 |
B | 0.53 | 0.47 | 0.52 | 0.42 | 0.52 | |
EH | A | 0.377 | 0.378 | 0.490 | 0.444 | 0.624 |
B | 0.03 | 0.16 | 0.05 | 0.02 | 0.05 | |
LL | A | 0.519 | 0.534 | 0.591 | 0.552 | 0.608 |
B | 0 | 0.33 | 0.34 | 0.05 | 0.12 | |
LH | A | 0.547 | 0.470 | 0.605 | 0.655 | 0.642 |
B | 0.27 | 0.24 | 0.15 | 0.25 | 0.23 |
SNP subsets selected under the five categorization schemes were compared with each other, to see if there were common ones. This led to a total of 10 pair-wise comparisons. The numbers of SNPs in these subsets differed, but were all less than 50, the full set size for "wrapper". As a result, the number of common SNPs ranged from 5 to 14 for stratum EL, 2 to 9 for EH, 2 to 13 for LH and 7 to 16 for LL.
Comparison of using extreme sires vs. using all sires
Comparison of SNP selection using sires with extreme phenotypes vs. using all sires, in terms of predicted residual sum of squares of the best SNP subsets
Extreme sires | All sires | |||||||
---|---|---|---|---|---|---|---|---|
Stratum^{a} | PRESS^{b} | p-value^{c} | α^{d} | #SNPs^{f} | PRESS^{b} | p-value^{c} | K ^{e} | #SNPs^{f} |
EL | 0.638 | 0.001 | 0.35 | 34 | 0.672 | 0.001 | 2 | 36 |
EH | 0.313 | 0.001 | 0.35 | 35 | 0.377 | 0.002 | 2 | 33 |
LL | 0.454 | 0.001 | 0.35 | 29 | 0.519 | 0.001 | 2 | 46 |
LH | 0.510 | 0.007 | 0.20 | 28 | 0.470 | 0.001 | 3 | 45 |
Discussion
Arguably, the conditional independence assumption of NB, i.e., independence of SNPs given class, is often violated. However, it greatly simplifies the learning process, since the probabilities of each SNP genotype, given class, can be estimated separately. Here, NB clearly outperformed the two more elaborate methods (BN and NN). One reason could be that, although simple decomposition using the independence assumption results in poor estimates of Pr(C = c | X_{1} = x_{1},..., X_{ p }= x_{ p }), the correct class still has the highest estimated probability, leading to high classification accuracy of NB [17]. Another reason might be overfitting in BN and NN, especially in the current study, where there were slightly over 200 sires in total. Overfitting can lead to imprecise estimates of coefficients in NN, and imprecise inference about network structure and associated probabilities in BN. In this sense, a simpler algorithm, such as NB, seems more robust to noisy data than complex models, since the latter may fit the noise. The best way to avoid overfitting is to increase size of training data, so that it is sufficiently large relative to the number of model parameters (e.g., 5 times as many training cases as parameters). If sample size is fixed, approaches for reducing model complexity have to be used. In the case of NN, one can reduce the number of hidden nodes or use regularization (weight decay), to control magnitude of weights [27]. For BN, the number of parent nodes for each node can be limited in advance, to reduce the number of conditional probability distributions involved in the network. One can also choose a network quality measure that contains a penalty for network size, for example, the Bayesian information criterion [28] and the minimal description length [29]. These measures trade off "goodness-of-fit" with complexity of the model. Finally, one may consider other classifiers that are less prone to overfitting, such as support vector machines (SVMs) [30]. Guyon et al. [31] presented a recursive feature elimination-based SVM (SVM-RFE) method for selecting discriminant genes, by using the weights of a SVM classifier to rank genes. Unlike ranking which is based on individual gene's relevance, SVM-RFE ranking is a gene subset ranking and takes into account complementary relationship between genes.
An alternative to the filter-wrapper approach for handling a large number of genetic markers is the random forests methodology [32], which uses ensembles of trees. Each tree is built on a bootstrap sample of the original training data. Within each tree, the best splitting SNP (predictor) at each node is chosen from a random set of all SNPs. For prediction, votes from each single tree are averaged. Random forests does not require a pre-selection step, and ranks SNPs by a variable importance measure, which is the difference in prediction accuracy before and after permuting a SNP. Unlike a univariate one-by-one screening method, which may miss SNPs with small main effects but large interaction effects, ranking in random forests takes into account each SNP's interaction with others. Thus, random forests have gained attention in large scale genetic association studies, for example, for selecting interacting SNPs [33]. In fact, the wrapper is designed to address the same problem, by evaluating a subset of SNPs rather than a single SNP at a time. However, it cannot accommodate the initial pool of a large number of SNPs due to computational burden, so a pre-selection stage is required. In this sense, wrapper is not as efficient as random forests. In the case when correlated predictors exist, Strobl et al. [34] pointed out that the variable importance measures used in ordinary random forests may lead to biased selection of non-influential predictors correlated to influential ones, and proposed a conditional permutation scheme that could better reflect the true importance of predictors.
The number of top scoring SNPs (50) was set based on a previous study [13], where it was found that, starting with different numbers (50, 100, 150, 200 and 250) of SNPs, a naïve Bayes wrapper led to similar classification performances. To reduce model complexity and to save computational time, a smaller number of SNPs is preferred. To examine whether the 50 SNPs were related to each other or not, a redundancy measure was computed, to measure similarity between all pairs of the 50 SNPs (1225 pairs in total). Redundancy is based on mutual information between two SNPs, and ranges from 0 to 0.5, as in Long et al. [13]. Redundancies were low and under 0.05 for almost all pairs. For example, in stratum EL-3-category classification, 1222 out of 1225 pairs had values under 0.05. This indicates that SNP colinearity was unlikely in the subsequent wrapper step, which involved training classifiers using the SNP inputs.
As illustrated by the error rates found in the present study, multi-category classification gets harder as the number of categories (K) increases. This is because the baseline predictive power decreases with K, and average sample size for each category also decreases with K, which makes the trained model less reliable. To make a fair comparison among SNP subsets found with different K, the same evaluation procedure, neutral with respect to filter-wrapper and K, was uniformly applied to each setting. By using mortality means in their original form (a continuous response variable, as opposed to a discretized variable), two measures were used. Measure A (PRESS) evaluated a subset of SNPs from the perspective of predictive ability, while measure B estimated the proportion of SNPs in a subset that had a statistically significant association with mortality. Although the best SNP subset was measure-dependent, it was either with K = 2 or K = 3. Thus, it appears that classification into two or three categories is sufficient.
The comparison between SNP subsets selected using sires with extreme phenotypic values and those selected using all sire means indicated better performance of the former strategy of SNP detection. This is so, at least in part, because concern is about classification accuracy, and obtaining more informative samples for each class is more important than avoiding loss of information resulting from discarding some sire means. Perhaps including all sire samples brings noise, leading to a poorer predictive ability of the selected SNPs. In order to assess significance of the observed difference in PRESS between using "extreme" and "all" strategies, one can shuffle sire means over genotypes B (e.g., 1000) times, generating B permutation samples. For each of the B samples, apply "extreme" and "all" to get PRESS, and then take their difference. This would produce a null distribution, against which the observed difference in PRESS can be referred to. This was not done in this study, due to extra computational intensiveness.
In the context of selecting SNPs associated with chick mortality, two conclusions emerge. First, if one wishes to utilize all sire samples available, a good choice consists of a naïve Bayes classifier, coupled with a categorization of mortality rates into two or three classes. Second, one may want to use more extreme (hence more representative) samples, even at the cost of losing some information, to achieve better predictive ability on the selected SNPs.
In summary, and in the spirit of the studies of Long et al. [13, 35], a filter-wrapper two step feature selection method was used effectively to ascertain SNPs associated with quantitative traits. The sets of interacting SNPs identified in this procedure can then be used in statistical models for genomic-assisted prediction of quantitative traits [7, 8, 36–38].
Appendix
Calculation of adjusted sire means by strata
To create a response variable for each of the sires genotyped, effects of factors (dam's age and hatch) potentially affecting individual bird survival were removed via a generalized linear mixed model (GLMM) without sire and hygiene effects. For each bird, a residual derived from the GLMM was calculated. Birds were classified into two groups, corresponding to the hygiene environments (L and H) in which they had been raised. In each hygiene group, residuals of progeny of a sire were averaged, producing an adjusted progeny mortality mean as the response variable for each sire.
The individual record on each bird was binary (dead or alive), and the GLMM fitted was:
logit(p_{ ijk }) = μ + DA_{ i }+ H_{ j }, (1)
where p_{ ijk }is the death probability of bird k, progeny of a dam of age i and born in hatch j. Here, DA_{ i }stands for the fixed effect of the i th level of dam's age (i = 1, 2,..., 18); H_{ j }denotes the random effect of hatch j (j = 1, 2,..., 232), which was assumed normal, independent and identically distributed as H_{ j }~NIID(0, ${\sigma}_{H}^{2}$), where ${\sigma}_{H}^{2}$ was the variance between hatches. Let y_{ ijk }be the true binary status of bird ijk (0 = alive, 1 = dead) and ${\widehat{p}}_{ijk}$ be the fitted death probability using model (1); then, the residual for a given bird is r_{ ijk }= y_{ ijk }- ${\widehat{p}}_{ijk}$, with a sampling space of [-1,1]. GLMM was implemented in SAS^{®} PROC GLIMMIX (SAS^{®} 9.1.3, SAS^{®} Institute Inc., Cary, NC).
Model (1) was fitted to both early and late mortality data. Subsequently, birds were divided into the two hygiene groups, and progeny residuals were averaged for each sire, as described above. Thus, four strata of age-hygiene combinations were formed, with each stratum containing the adjusted progeny mortality means calculated for: 1) birds of early age raised in low hygiene (EL); 2) birds of early age raised in high hygiene (EH); 3) birds of late age raised in low hygiene (LL); and 4) birds of late age raised in high hygiene (LH).
Choice of search method for BN-based wrapper
Declarations
Acknowledgements
Support by the Wisconsin Agriculture Experiment Station, and by grants NRICGP/USDA 2003-35205-12833, NSF DEB-0089742 and NSF DMS-044371 is acknowledged. Prof. William G. Hill is thanked for suggesting the permutation test employed for generating the null distribution of PRESS values.
Authors’ Affiliations
References
- Blakey JD: Looking for a bit of co-action?. Thorax. 2007, 62: 196-197.PubMed CentralView ArticlePubMedGoogle Scholar
- Dekkers JCM, Hospital F: The use of molecular genetics in the improvement of agricultural populations. Nat Rev Genet. 2002, 3: 22-32.View ArticlePubMedGoogle Scholar
- Maier LM, Howson JMM, Walker N, Spickett GP, Jones RW, Ring SM, McArdle WL, Lowe CE, Bailey R, Payne F, et al: Association of IL13 with total IgE: Evidence against an inverse association of atopy and diabetes. J Allergy Clin Immunol. 2006, 117: 1306-1313.View ArticlePubMedGoogle Scholar
- Hoh J, Wille A, Zee R, Cheng S, Reynolds R, Lindpaintner K, Ott J: Selecting SNPs in two-stage analysis of disease association data: a model-free approach. Ann Hum Genet. 2000, 64: 413-417.View ArticlePubMedGoogle Scholar
- Cordell HJ, Clayton DG: A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes. Am J Hum Genet. 2002, 70: 124-141.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang H, Zhang Y-M, Li X, Masinde GL, Mohan S, Baylink DJ, Xu S: Bayesian shrinkage estimation of quantitative trait loci parameters. Genetics. 2005, 170: 465-480.PubMed CentralView ArticlePubMedGoogle Scholar
- Gianola D, Fernando RL, Stella A: Genomic-assisted prediction of genetic value with semiparametric procedures. Genetics. 2006, 173: 1761-1776.PubMed CentralView ArticlePubMedGoogle Scholar
- Gianola D, van Kaam JBCHM: Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics. 2008, 178: 2289-2303.PubMed CentralView ArticlePubMedGoogle Scholar
- Mitchell TM: Machine Learning. 1997, Hightstown, NJ: McGraw-HillGoogle Scholar
- Sebastiani P, Ramoni MF, Nolan V, Baldwin CT, Steinberg MH: Genetic dissection and prognostic modeling of overt stroke in sickle cell anemia. Nat Genet. 2005, 37: 435-440.PubMed CentralView ArticlePubMedGoogle Scholar
- Khan J, Wei JS, Ringner M, Saal LH, Ladanyi M, Westermann F, Berthold F, Schwab M, Antonescu CR, Peterson C, Meltzer PS: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med. 2001, 7: 673-679.PubMed CentralView ArticlePubMedGoogle Scholar
- Li T, Zhang C, Ogihara M: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics. 2004, 20: 2429-2437.View ArticlePubMedGoogle Scholar
- Long N, Gianola D, Rosa GJM, Weigel KA, Avendano S: Machine learning classification procedure for selecting SNPs in genomic selection: application to early mortality in broilers. J Anim Breed Genet. 2007, 124: 377-389.View ArticlePubMedGoogle Scholar
- Kohavi R, John GH: Wrappers for feature subset selection. Artificial Intelligence. 1997, 97: 273-324.View ArticleGoogle Scholar
- Caruana R, Freitag D: Greedy attribute selection. International Conference on Machine Learning. 1994, 28-36.Google Scholar
- Witten IH, Frank E: Data Mining: Practical Machine Learning Tools and Techniques. 2005, San Francisco, CA: Morgan KaufmannGoogle Scholar
- Domingos P, Pazzani MJ: Beyond independence: conditions for the optimality of the simple Bayesian classifier. International Conference on Machine Learning. 1996, 105-112.Google Scholar
- Kelemen A, Zhou H, Lawhead P, Liang Y: Naive Bayesian classifier for microarray data. Proceedings of the International Joint Conference on Neural Networks. 2003, 1769-1773.Google Scholar
- Jensen FV: Bayesian Networks and Decision Graphs. 2001, New York, NY: Springer-VerlagView ArticleGoogle Scholar
- Helman P, Veroff R, Atlas SR, Willman C: A Bayesian network classification methodology for gene expression data. J Comput Biol. 2004, 11: 581-615.View ArticlePubMedGoogle Scholar
- Friedman N, Linial M, Nachman I, Pe'er D: Using Bayesian networks to analyze expression data. J Comput Biol. 2000, 7: 601-620.View ArticlePubMedGoogle Scholar
- Cooper GF, Herskovits E: A Bayesian method for the induction of probabilistic networks from data. Machine Learning. 1992, 9: 309-347.Google Scholar
- Russell SJ, Norvig P: Artificial Intelligence: A Modern Approach. 2002, Upper Saddle River, NJ: Prentice HallGoogle Scholar
- Warner B, Misra M: Understanding neural networks as statistical tools. Am Stat. 1996, 50: 284-293.Google Scholar
- Ruppert D, Wand MP, Carroll RJ: Semiparametric Regression. 2003, Cambridge, UK: Cambridge University PressView ArticleGoogle Scholar
- Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc (B). 1995, 57: 289-300.Google Scholar
- MacKay DJC: Probable networks and plausible predictions — A review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems. 1995, 6: 469-505.View ArticleGoogle Scholar
- Schwarz G: Estimating the dimension of a model. Ann Stat. 1978, 6: 461-464.View ArticleGoogle Scholar
- Rissanen J: Modeling by shortest data description. Automatica. 1978, 14: 465-471.View ArticleGoogle Scholar
- Vapnik V: The Nature of Statistical Learning Theory. 2000, Red Bank: Springer, 2View ArticleGoogle Scholar
- Guyon I, Weston J, Barnhill S, Vapnik V: Gene selection for cancer classification using support vector machines. Machine Learning. 2002, 46: 389-422.View ArticleGoogle Scholar
- Breiman L: Random forests. Machine Learning. 2001, 45: 5-22.View ArticleGoogle Scholar
- Lunetta K, Hayward LB, Segal J, Van Eerdewegh P: Screening large-scale association study data: exploiting interactions using random forests. BMC Genet. 2004, 5: 5-32.View ArticleGoogle Scholar
- Strobl C, Boulesteix A-L, Kneib T, Augustin T, Zeileis A: Conditional variable importance for random forests. BMC Bioinformatics. 2008, 9: 307-PubMed CentralView ArticlePubMedGoogle Scholar
- Long N, Gianola D, Rosa GJM, Weigel KA, Avendano S: A marker-assisted assessment of genotype by environment interaction: SNP-mortality association in broilers in two hygiene environments. J Anim Sci. 2008, 86: 3358-3366.View ArticlePubMedGoogle Scholar
- Gianola D, Perez-Enciso M, Toro MA: On marker-assisted prediction of genetic value: Beyond the ridge. Genetics. 2003, 163: 347-365.PubMed CentralPubMedGoogle Scholar
- Meuwissen THE, Hayes BJ, Goddard ME: Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001, 157: 1819-1829.PubMed CentralPubMedGoogle Scholar
- Xu S: Estimating polygenic effects using markers of the entire genome. Genetics. 2003, 163: 789-801.PubMed CentralPubMedGoogle Scholar
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