Stability of variable importance scores and rankings using statistical learning tools on single-nucleotide polymorphisms and risk factors involved in gene × gene and gene × environment interactions

Risk of complex disorders is thought to be multifactorial, involving interactions between risk factors. However, many genetic studies assess association between disease status and markers one single-nucleotide polymorphism (SNP) at a time, due to the high-dimensional nature of the search space of all possible interactions. Three ensemble methods have been recently proposed for use in high-dimensional data (Monte Carlo logic regression, random forests, and generalized boosted regression). An intuitive way to detect an association between genetic markers and disease status is to use variable importance measures, even though the stability of these measures in the context of a whole-genome association study is unknown. For the simulated data of Problem 3 in the Genetic Analysis Workshop 15 (GAW15), we examined the variability of both rankings and magnitude of variable importance measures using 10 variables simulated to participate in gene × gene and gene × environment interactions. We conducted 500 analyses per method on one randomly selected replicate, tallying the rankings and importance measures for each of the 10 variables of interest. When the simulated effect size was strong, all three methods showed stable rankings and estimates of variable importance. However, under conditions more commonly expected to be encountered in complex diseases, random forests and generalized boosted regression showed more stable estimates of variable importance and variable rankings. Individuals endeavoring to apply statistical learning methods to detect interaction in complex disease studies should perform repeated analyses in order to assure variable importance measures and rankings do not vary greatly, even for statistical learning algorithms that are thought to be stable

An AUC-based Permutation Variable Importance Measure for Random Forests

The random forest (RF) method is a commonly used tool for classification with high dimensional data as well as for ranking candidate predictors based on the so-called random forest variable importance measures (VIMs). However the classification performance of RF is known to be suboptimal in case of strongly unbalanced data, i.e. data where response class sizes differ considerably. Suggestions were made to obtain better classification performance based either on sampling procedures or on cost sensitivity analyses. However to our knowledge the performance of the VIMs has not yet been examined in the case of unbalanced response classes. In this paper we explore the performance of the permutation VIM for unbalanced data settings and introduce an alternative permutation VIM based on the area under the curve (AUC) that is expected to be more robust towards class imbalance. We investigated the performance of the standard permutation VIM and of our novel AUC-based permutation VIM for different class imbalance levels using simulated data and real data. The results suggest that the standard permutation VIM loses its ability to discriminate between associated predictors and predictors not associated with the response for increasing class imbalance. It is outperformed by our new AUC-based permutation VIM for unbalanced data settings, while the performance of both VIMs is very similar in the case of balanced classes. The new AUC-based VIM is implemented in the R package party for the unbiased RF variant based on conditional inference trees. The codes implementing our study are available from the companion website: http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/070_drittmittel/janitza/index.html

The behaviour of random forest permutation-based variable importance measures under predictor correlation

<p>Abstract</p> <p>Background</p> <p>Random forests (RF) have been increasingly used in applications such as genome-wide association and microarray studies where predictor correlation is frequently observed. Recent works on permutation-based variable importance measures (VIMs) used in RF have come to apparently contradictory conclusions. We present an extended simulation study to synthesize results.</p> <p>Results</p> <p>In the case when both predictor correlation was present and predictors were associated with the outcome (H_A), the unconditional RF VIM attributed a higher share of importance to correlated predictors, while under the null hypothesis that no predictors are associated with the outcome (H₀) the unconditional RF VIM was unbiased. Conditional VIMs showed a decrease in VIM values for correlated predictors versus the unconditional VIMs under H_Aand was unbiased under H₀. Scaled VIMs were clearly biased under H_Aand H₀.</p> <p>Conclusions</p> <p>Unconditional unscaled VIMs are a computationally tractable choice for large datasets and are unbiased under the null hypothesis. Whether the observed increased VIMs for correlated predictors may be considered a "bias" - because they do not directly reflect the coefficients in the generating model - or if it is a beneficial attribute of these VIMs is dependent on the application. For example, in genetic association studies, where correlation between markers may help to localize the functionally relevant variant, the increased importance of correlated predictors may be an advantage. On the other hand, we show examples where this increased importance may result in spurious signals.</p

Tingkat Penggunaan Teknologi Informasi dan Dampaknya pada Kreativitas Pembelajaran Guru-guru Sekolah Luar Biasa di Provinsi Sulawesi Utara Indonesia

Penelitian ini bertujuan untuk menganalisis tingkat penggunaan teknologi informasi dan dampaknya pada kreativitas pembelajaran guru-guru sekolah luar biasa di Provinsi Sulawesi Utara. Penelitian ini menggunakan metode penelitian deskriptif. Metode ini dapat digunakan untuk mendeskripsikan, menginterpretasikan suatu fenomena. Data yang digunakan adalah data sekunder yang diambil dari teori-teori terkait. Berdasarkan hasil analisis dapat disimpulkan bahwa dari 335 guru SLB di Provinsi Sulawesi Utara hanya 263 orang guru yang menguasai IT, sementara sisanya 72 orang belum menguasai IT.&nbsp; Usia guru SLB di Prov. Sulawesi Utara terbanyak berusia kurang dari 30 tahun dengan jumlah perempuan 57 orang dan laki-laki 21 orang. Sementara persentase usia diatas 55 tahun sebanyak 16 orang perempuan dan 8 orang laki-laki. Hal ini menjukkan bahwa usia guru tingkat pemahaman IT oleh guru yang berusia lebih muda lebih mendominasi dikarenakan mereka lahir dan tumbuh di tengah-tengah perkembangan IT secara global

Interallelic relations among endosperm variants in sorghum

The inheritance of four endosperm variants of sorghum that exhibit xenia effects was studied in the F1, F2, and backcross seed of crosses among variants and with the normal type. It was concluded that the endosperm variants vanl, sugary (su), and high lysine (h1) are controlled by three Independent single recessive alleles and that dimpled endosperm is controlled by a single recessive allele (dp) allellc to that controlling the vanl endosperm trai

Comparison of type I error for multiple test corrections in large single-nucleotide polymorphism studies using principal components versus haplotype blocking algorithms

Although permutation testing has been the gold standard for assessing significance levels in studies using multiple markers, it is time-consuming. A Bonferroni correction to the nominal p-value that uses the underlying pair-wise linkage disequilibrium (LD) structure among the markers to determine the number of effectively independent tests has recently been proposed. We propose using the number of independent LD blocks plus the number of independent single-nucleotide polymorphisms for correction. Using the Collaborative Study on the Genetics of Alcoholism LD data for chromosome 21, we simulated 1,000 replicates of parent-child trio data under the null hypothesis with two levels of LD: moderate and high. Assuming haplotype blocks were independent, we calculated the number of independent statistical tests using 3 haplotype blocking algorithms. We then compared the type I error rates using a principal components-based method, the three blocking methods, a traditional Bonferroni correction, and the unadjusted p-values obtained from FBAT. Under high LD conditions, the PC method and one of the blocking methods were slightly conservative, whereas the 2 other blocking methods exceeded the target type I error rate. Under conditions of moderate LD, we show that the blocking algorithm corrections are closest to the desired type I error, although still slightly conservative, with the principal components-based method being almost as conservative as the traditional Bonferroni correction

Pengaruh kejut suhu terhadap proses pemijahan bulubabi Tripneustes gratilla pada media terkontrol

Perubahan lingkungan suhu perairan dapat menyebabkan stress pada biota perairan yang dapat mempengaruhi proses pemijahan, perkembangan dan pertumbuhan bulubabi T. gratilla, bahkan dapat menyebabkan kematian apabila perubahan yang terjadi melewati batas toleransi. Penelitian ini dilaksanakan dari bulan Juli sampai Agustus 2020, di Laboratorium Fakultas Kelautan dan Perikanan Universitas Nusa Cendana, Kupang. Penelitian ini bertujuan untuk mengetahui suhu yang efektif untuk merangsang pemijahan dan pengaruh kejut suhu terhadap persentase pemijahan bulubabi T. gratilla. Penelitian ini mengunakan kejut suhu selama 5 menit dengan empat perlakuan dan tiga ulangan dengan menaikan dan menurunkan suhu dari suhu pemeliharaan 27 0C untuk merangsang pemijahan pada induk bulubabi T. gratilla. Kejut suhu yang digunakan yaitu menaikan suhu 3 0C dan 6 0C menggunakan heater dan menurunkan suhu 6 0C dan 3 0C mengunakan es batu. Bulubabi yang digunakan diambil dari perairan Bolok dengan diameter 50-60 mm dan dipelihara dalam akuarim dengan kepadatan 5 ekor / akuarium, selama pemeliharaan bulubabi diberi pakan berbasis Enhalus 10% yang diberikan secara adlibitum hingga matang gonad. Hasil penelitian menunjukan suhu yang efektif yaitu penurunan suhu 3 0C dari suhu 27 0C menjadi 24 0C dengan persentase pemijahan 86,6% dan persentase telur terbuahi yaitu 35,57%. Hasil anova menunjukan kejut suhu berpengaruh signifikan terhadap pemijahan bulubabi T. gratilla (P &lt; 0.05). Metode kejut suhu dapat digunakan untuk merangsang pemijahan pada bulubabi T. gratilla. Kata kunci: bulubabi T. gratilla, kejut suhu, pemijahan

A genetic study of popping quality in Sorghum (Sorghum bicolor (L.) Moench)

Two crosses of sorghum, Sorghum bicolor (L.) Moench (IS 1054 × ICSV-1, and IS 5604 × IS 1054) were evaluated in parental, F1, F2, and backcross generations for the variation in their popping quality as measured by pop volume (ml). Dominance was in the direction of low pop volume. Dominance and additive gene effects, in that order, governed most of the variation, while significant dominance x dominance type of interaction effects could also be detected. There was no evidence for higher order gene interactions