Dataset Information

Making Decisions with Trees: Examining Marijuana Outcomes among College Students using Recursive Partitioning.

ABSTRACT: Exploratory analyses were used to identify a unique constellations of variables that are associated with marijuana use outcomes among college students. We used recursive partitioning to examine over 100 putative antecedents of lifetime marijuana user status, past-month marijuana user status, and negative marijuana-related consequences. Participants (N=8141) completed measures online across 11 sites in the USA. Norms (descriptive, injunctive, and internalized norms) and marijuana identity best distinguished marijuana users from non-users (i.e., lifetime/past month), whereas marijuana use frequency, use of protective behavioral strategies, and positive/negative urgency best distinguished the degree to which users reported negative consequences. Our results demonstrate that tree-based modeling is a useful methodological tool in the selection of targets for future clinical research. Additional research is needed to determine if these factors are causal antecedents, rather than consequences or epiphenomena. We hope this large sample study provides the impetus to develop intervention strategies targeting these factors.

SUBMITTER: Ad W

PROVIDER: S-EPMC6516071 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Publications

Making Decisions with Trees: Examining Marijuana Outcomes among College Students using Recursive Partitioning.

Ad Wilson W Ks Montes M Aj Bravo B Bt Conner C Mr Pearson P

Clinical psychological science : a journal of the Association for Psychological Science 20180907 5

Exploratory analyses were used to identify a unique constellations of variables that are associated with marijuana use outcomes among college students. We used recursive partitioning to examine over 100 putative antecedents of lifetime marijuana user status, past-month marijuana user status, and negative marijuana-related consequences. Participants (<i>N</i>=8141) completed measures online across 11 sites in the USA. Norms (descriptive, injunctive, and internalized norms) and marijuana identity ...[more]

PMID: 31106039

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Project description:The HapMap (haplotype map) projects have produced valuable genetic resources in life science research communities, allowing researchers to investigate sequence variations and conduct genome-wide association study (GWAS) analyses. A typical HapMap project may require sequencing hundreds, even thousands, of individual lines or accessions within a species. Due to limitations in current sequencing technology, the genotype values for some accessions cannot be clearly called. Additionally, allelic heterozygosity can be very high in some lines, causing genetic and sometimes phenotypic segregation in their descendants. Genetic and phenotypic segregation degrades the original accession's specificity and makes it difficult to distinguish one accession from another. Therefore, it is vitally important to determine and validate HapMap accessions before one conducts a GWAS analysis. However, to the best of our knowledge, there are no prior methodologies or tools that can readily distinguish or validate multiple accessions in a HapMap population. We devised a bioinformatics approach to distinguish multiple HapMap accessions using only a minimum number of genetic markers. First, we assign each candidate marker with a distinguishing score (DS), which measures its capability in distinguishing accessions. The DS score prioritizes those markers with higher percentages of homozygous genotypes (allele combinations), as they can be stably passed on to offspring. Next, we apply the "set-partitioning" concept to select optimal markers by recursively partitioning accession sets. Subsequently, we build a hierarchical decision tree in which a specific path represents the selected markers and the homogenous genotypes that can be used to distinguish one accession from others in the HapMap population. Based on these algorithms, we developed a web tool named MAD-HiDTree (Multiple Accession Distinguishment-Hierarchical Decision Tree), designed to analyze a user-input genotype matrix and construct a hierarchical decision tree. Using genetic marker data extracted from the Medicago truncatula HapMap population, we successfully constructed hierarchical decision trees by which the original 262 M. truncatula accessions could be efficiently distinguished. PCR experiments verified our proposed method, confirming that MAD-HiDTree can be used for the identification of a specific accession. MAD-HiDTree was developed in C/C++ in Linux. Both the source code and test data are publicly available at https://bioinfo.noble.org/MAD-HiDTree/.

Dataset Information

Making Decisions with Trees: Examining Marijuana Outcomes among College Students using Recursive Partitioning.

Publications

Making Decisions with Trees: Examining Marijuana Outcomes among College Students using Recursive Partitioning.

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