scholarly article | Q13442814 |
P50 | author | Qiongshi Lu | Q56037067 |
Yiming Hu | Q59677627 | ||
P2093 | author name string | Mo Li | |
Wei Liu | |||
Hongyu Zhao | |||
Yuhua Zhang | |||
P2860 | cites work | Biological insights from 108 schizophrenia-associated genetic loci | Q24561833 |
Multiple common variants for celiac disease influencing immune gene expression | Q24608614 | ||
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder | Q28250609 | ||
New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk | Q28270700 | ||
Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes | Q28272915 | ||
The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies | Q28742793 | ||
Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease | Q29417023 | ||
Dissecting the genetics of complex traits using summary association statistics | Q30241340 | ||
A UNIFIED FRAMEWORK FOR VARIANCE COMPONENT ESTIMATION WITH SUMMARY STATISTICS IN GENOME-WIDE ASSOCIATION STUDIES. | Q52668235 | ||
Developing and evaluating polygenic risk prediction models for stratified disease prevention | Q30251440 | ||
Joint analysis of functional genomic data and genome-wide association studies of 18 human traits | Q30794057 | ||
A statistical framework to predict functional non-coding regions in the human genome through integrated analysis of annotation data | Q30959484 | ||
Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies | Q30981389 | ||
Genetic-based prediction of disease traits: prediction is very difficult, especially about the future | Q33693846 | ||
Leveraging functional annotations in genetic risk prediction for human complex diseases | Q33825680 | ||
MultiBLUP: improved SNP-based prediction for complex traits | Q34153795 | ||
Polygenic modeling with bayesian sparse linear mixed models | Q34585745 | ||
Estimating missing heritability for disease from genome-wide association studies | Q34687621 | ||
Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder | Q35059381 | ||
GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation | Q35418401 | ||
Risk Classification with an Adaptive Naive Bayes Kernel Machine Model | Q35902754 | ||
Integrative Tissue-Specific Functional Annotations in the Human Genome Provide Novel Insights on Many Complex Traits and Improve Signal Prioritization in Genome Wide Association Studies | Q35983658 | ||
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores | Q36133352 | ||
Partitioning heritability by functional annotation using genome-wide association summary statistics | Q36225291 | ||
Prediction of breast cancer risk based on profiling with common genetic variants | Q36583004 | ||
Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits | Q36670925 | ||
An atlas of genetic correlations across human diseases and traits | Q36701237 | ||
Joint Bayesian inference of risk variants and tissue-specific epigenomic enrichments across multiple complex human diseases | Q37336953 | ||
Improving genetic risk prediction by leveraging pleiotropy | Q37705235 | ||
Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States | Q39734134 | ||
Personalized risk prediction for type 2 diabetes: the potential of genetic risk scores | Q41706055 | ||
Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease | Q42105908 | ||
GenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation. | Q47602336 | ||
P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
P6216 | copyright status | copyrighted | Q50423863 |
P433 | issue | 6 | |
P304 | page(s) | e1006836 | |
P577 | publication date | 2017-06-09 | |
P1433 | published in | PLOS Genetics | Q1893441 |
P1476 | title | Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction | |
P478 | volume | 13 |
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Q52326277 | Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Penalized Gaussian Mixture Models. |
Q52311378 | Unitary Physiology. |
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