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Text S1 - Regularized Machine Learning in the Genetic Prediction of Complex Traits

Figshare2015-12-02 更新2026-04-29 收录

数据链接：

https://figshare.com/articles/dataset/_Regularized_Machine_Learning_in_the_Genetic_Prediction_of_Complex_Traits_/1239994 数据链接链接失效反馈

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资源简介：

Implementation details for a range of regularized machine learning models. (PDF)

应用场景：

创建时间：

2015-12-02

多效性分析

复杂性状遗传学

Data set and analysis results from our paper "A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics

NIAID Data Ecosystem90

Genes annotated to candidate SNPs identified via GEMMA analysis of body weight across the Americas.

遗传关联分析

复杂性状遗传学

Functional summarization is based primarily on MGI Mammalian Phenotype annotations and is not exhaustive. For additional detail on GEMMA results, see S14 Table.

NIAID Data Ecosystem60

Additional file 1 of Next-Gen GWAS: full 2D epistatic interaction maps retrieve part of missing heritability and improve phenotypic prediction

上位性分析

复杂性状遗传学

Additional file 1. Additional text, figures and table.

Figshare2024-03-25 更新60

Characteristics of the associated SNP rs6532023 in the total group of subjects.

遗传关联分析

复杂性状遗传学

Data are shown as mean ± SD, BMI body mass index, BMD bone mineral density.

Figshare2015-12-02 更新60

Top three newly identified candidate genes reported by gene- ε for the six quantitative traits studied in the UK Biobank (using imputed genotypes with gene boundaries defined by the NCBI’s RefSeq database in the UCSC Genome Browser [27]).

复杂性状遗传学

候选基因筛选

We call these novel candidate genes because they are not listed as being associated with the trait of interest in either the GWAS catalog or dbGaP, and they have top posterior enrichment probabilities

NIAID Data Ecosystem60

Text S1 - Regularized Machine Learning in the Genetic Prediction of Complex Traits

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