scholarly article | Q13442814 |
P6179 | Dimensions Publication ID | 1034573097 |
P356 | DOI | 10.1186/S13073-016-0272-5 |
P932 | PMC publication ID | 4756503 |
P698 | PubMed publication ID | 26884246 |
P5875 | ResearchGate publication ID | 295503533 |
P50 | author | Robert C. Green | Q7342606 |
Joel Krier | Q96102116 | ||
P2093 | author name string | Peter Kraft | |
Richard Barfield | |||
P2860 | cites work | Comparison of family history and SNPs for predicting risk of complex disease | Q21144905 |
Potential etiologic and functional implications of genome-wide association loci for human diseases and traits | Q22066284 | ||
Why Most Discovered True Associations Are Inflated | Q24273233 | ||
Clinical assessment incorporating a personal genome | Q24612653 | ||
A map of human genome variation from population-scale sequencing | Q24617794 | ||
Genetic screening for the risk of type 2 diabetes: worthless or valuable? | Q26829897 | ||
The International HapMap Project | Q27860695 | ||
Common polygenic variation contributes to risk of schizophrenia and bipolar disorder | Q28250609 | ||
Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes | Q28272915 | ||
Genomewide association studies and assessment of the risk of disease | Q28288414 | ||
Genome-wide association study identifies new prostate cancer susceptibility loci | Q28924380 | ||
Large-scale genotyping identifies 41 new loci associated with breast cancer risk | Q29416989 | ||
Shattuck lecture--medical and societal consequences of the Human Genome Project. | Q48582472 | ||
Combining 33 genetic variants with prostate-specific antigen for prediction of prostate cancer: longitudinal study. | Q54387343 | ||
Evaluation of risk prediction updates from commercial genome-wide scans | Q57364888 | ||
Will Genetics Revolutionize Medicine? | Q57745587 | ||
Diagnostic clinical genome and exome sequencing | Q95515418 | ||
Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array | Q29417155 | ||
SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap | Q29614907 | ||
Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies | Q29614926 | ||
The MedSeq Project: a randomized trial of integrating whole genome sequencing into clinical medicine | Q30585056 | ||
Combining information from common type 2 diabetes risk polymorphisms improves disease prediction | Q33259329 | ||
The future of genetic counselling: an international perspective | Q33658958 | ||
Improving the prediction of complex diseases by testing for multiple disease-susceptibility genes | Q33904517 | ||
Clinical interpretation and implications of whole-genome sequencing | Q33989890 | ||
Metabolite traits and genetic risk provide complementary information for the prediction of future type 2 diabetes. | Q34073453 | ||
BioQ: tracing experimental origins in public genomic databases using a novel data provenance model | Q34198963 | ||
Genomic medicine--a primer | Q34992066 | ||
Problems with risk reclassification methods for evaluating prediction models | Q35116610 | ||
Common genetic variants in prostate cancer risk prediction--results from the NCI Breast and Prostate Cancer Cohort Consortium (BPC3) | Q35868793 | ||
Summarizing polygenic risks for complex diseases in a clinical whole-genome report | Q35990450 | ||
Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases | Q36017113 | ||
Estimation of effect size distribution from genome-wide association studies and implications for future discoveries | Q36191121 | ||
Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement | Q36393193 | ||
Genome-wide association and replication studies identify four variants associated with prostate cancer susceptibility | Q36582802 | ||
Prediction of breast cancer risk based on profiling with common genetic variants | Q36583004 | ||
Large-scale association analysis identifies new risk loci for coronary artery disease | Q36921066 | ||
Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies | Q37059182 | ||
The predictive capacity of personal genome sequencing | Q37090585 | ||
The future of direct-to-consumer clinical genetic tests | Q37148870 | ||
Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review | Q37220830 | ||
Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status | Q37223335 | ||
Curses--winner's and otherwise--in genetic epidemiology | Q37243708 | ||
Variation in predictive ability of common genetic variants by established strata: the example of breast cancer and age. | Q37370221 | ||
Genome-based prediction of breast cancer risk in the general population: a modeling study based on meta-analyses of genetic associations | Q37826562 | ||
Variations in predicted risks in personal genome testing for common complex diseases | Q39395332 | ||
Literature-based genetic risk scores for coronary heart disease: the Cardiovascular Registry Maastricht (CAREMA) prospective cohort study | Q39646988 | ||
Does genotyping of risk-associated single nucleotide polymorphisms improve patient selection for prostate biopsy when combined with a prostate cancer risk calculator? | Q40152616 | ||
Use and misuse of the receiver operating characteristic curve in risk prediction | Q40241086 | ||
Prediction of individual genetic risk to prostate cancer using a polygenic score | Q41584352 | ||
Public awareness and use of direct-to-consumer personal genomic tests from four state population-based surveys, and implications for clinical and public health practice | Q41889955 | ||
Further insight into the incremental value of new markers: the interpretation of performance measures and the importance of clinical context | Q42138242 | ||
Impact of direct-to-consumer genomic testing at long term follow-up | Q42695084 | ||
Regulation: The FDA is overcautious on consumer genomics | Q45214163 | ||
Assessing improvement in disease prediction using net reclassification improvement: impact of risk cut-offs and number of risk categories | Q45232818 | ||
P433 | issue | 1 | |
P921 | main subject | genome-wide association study | Q1098876 |
P304 | page(s) | 20 | |
P577 | publication date | 2016-02-17 | |
P1433 | published in | Genome Medicine | Q15816848 |
P1476 | title | Reclassification of genetic-based risk predictions as GWAS data accumulate | |
P478 | volume | 8 |
Q60925818 | Big data hurdles in precision medicine and precision public health |
Q43258142 | Cancer Prevention and Treatment by Wholistic Nutrition |
Q26746219 | Clinical validity and utility of genetic risk scores in prostate cancer |
Q47562094 | Functional assessment of the BMPR2 gene in lymphoblastoid cell lines from Graves' disease patients. |
Q47945968 | Genetic Test, Risk Prediction, and Counseling. |
Q37344774 | Genomic sequencing in clinical practice: applications, challenges, and opportunities |
Q39114110 | Personal Genomic Testing for Cancer Risk: Results From the Impact of Personal Genomics Study |
Q40137394 | Reclassification of prostate cancer risk using sequentially identified SNPs: Results from the REDUCE trial. |
Q64278528 | Returning a Genomic Result for an Adult-Onset Condition to the Parents of a Newborn: Insights From the BabySeq Project |
Q57306568 | The BabySeq project: implementing genomic sequencing in newborns |
Q38371452 | The genomic potential of the Aspirin in Reducing Events in the Elderly and Statins in Reducing Events in the Elderly studies. |
Q30391847 | Towards precision medicine |
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