760:(T2D), an extremely common metabolic disorder, has demonstrated interplay between many environmental and genetic risk factors leading to disease onset. A number of risk assessment models incorporating a number of demographic, environmental and clinical risk factors are already shown to elicit reasonable discrimination in case-control studies; it has been proposed that identifying genetic variants that contribute to T2D as for standalone prediction or in conjunction with current risk models can improve prediction of T2D risk, if current models lack sufficient coverage of the full effect of an individual's genotype. Approximately 20 associated SNPs have been replicated in T2D; however, their effect sizes do not seem to be substantial: OR 1.37 for SNPs in the
120:, complex diseases and traits are affected by a number of gene loci and genetic variants with varying risk. A precursor to the development of preventative, prognostic and diagnostic tools in these diseases requires mapping genetic loci in disease etiology and discovering causal mutations. Creating a ‘genomic profile’ of individuals with the number of variants at the genome-wide level facilitates not only the prediction of disease prior to onset, but also serves as a primer to increasing the knowledge of causal variants.
2215:
791:(T2D)). With particular attention to T2D, Evans et al. were able to discern a marginal increase in AUC (+0.04) based on genome-wide information with respect to known susceptible variants. However, non-genetic based tests such as the Cambridge and Framingham offspring risk scores have been purported to perform better than genetic-risk models with 20 loci. Moreover, the addition of genetic risk with these phenotypical models did not produce statistically significant AUC results.
100:
863:
the number of observations may be used before modelling disease risk. Hayes et al. states that population size must be >100,000 in order to achieve high accuracy under their model assumptions; the exception is the case where there is a small effective population size. Furthermore, ethnic specific GWA studies show that each group has varied detectability of variants in terms of: frequency,
806:(HLA) genes are strongly implicated in CD development and HLA testing is undertaken in clinical practice. However, although there are serological and histological tests available for CD, these clinical screenings have been found to generate false positives. In 2014, Abraham et al. used a genomic risk score (GRS) generated over 6 cohorts with an AUC of 0.86 to 0.90.
850:
theoretical genetic heritability. It has been demonstrated by Goudey et al. that both 2-way and 3-way interactions between SNPs are able to explain trait variance relative to single SNPs. Goudey et al. also states that the barrier to expansion of higher order interactions has been limited by the intractability of exhaustive search techniques (see
819:(Area Under the ROC) is the de facto metric in comparing and evaluating the performance of predictive models, there is no consensus as to what kind of score is sufficient for clinical use. Jakobsdottir et al. states that 0.75 AUC is sufficient for discriminating between clear cases and controls; however, this is still arbitrary. The
1394:
Manolio, T. A.; Collins, F. S.; Cox, N. J.; Goldstein, D. B.; Hindorff, L. A.; Hunter, D. J.; McCarthy, M. I.; Ramos, E. M.; Cardon, L. R.; Chakravarti, A.; Cho, J. H.; Guttmacher, A. E.; Kong, A.; Kruglyak, L.; Mardis, E.; Rotimi, C. N.; Slatkin, M.; Valle, D.; Whittemore, A. S.; Boehnke, M.; Clark,
849:
Currently, the prevailing standard of risk models focus on univariate analysis rather than focusing upon interactions of higher order. Therefore, although typical GWA studies are able to detect a number of statistically significant loci, they have not been sufficient to fully explain the estimates of
875:
Predictive genomics has not been constrained to prediction of complex diseases. For instance, Hayes et al. uses genomic prediction for livestock, crop and forage species selection, where predicted results are currently in use. Furthermore, Kambouris et al. discusses the use of ‘genomic profiles’ for
862:
The issues surrounding sample size and number of variants become exacerbated particularly when GWA studies consider variants of volume in the order of millions. Therefore, due to the current constraints in the curse of dimensionality, prior screening methods that decrease the number of loci to below
123:
The foremost difficulty in achieving this goal is to understand the functionality of these variants with respect to areas of physiological and molecular importance in conjunction with phenotype. If associated variants are mapped to sequences with unknown function, then this restricts the ability for
880:
in forensics as a beneficiary of the genomic revolution. Functionally validating novel genetic findings is crucial in rare disease. However, the analysis of individual genetic variants often requires several years of work. Variants that are most likely to occur and present as disease-causing can be
840:
stratified across different ethnicities demonstrating better, although marginal, improvement of CNVs over SNPs for prediction. Furthermore, a comparison of over 10 complex disorders in prediction with respect to family history and SNPs for prediction did not suggest better discrimination with SNPs.
1617:
Fritsche, L. G.; Chen, W.; Schu, M.; Yaspan, B. L.; Yu, Y.; Thorleifsson, G.; Zack, D. J.; Arakawa, S.; Cipriani, V.; Ripke, S.; Igo, R. P.; Buitendijk, G. L. H. S.; Sim, X.; Weeks, D. E.; Guymer, R. H.; Merriam, J. E.; Francis, P. J.; Hannum, G.; Agarwal, A.; Armbrecht, A. M.; Audo, I.; Aung, T.;
709:
In the applications of predictive genomics below, these complex diseases either lack or are lacking reliable diagnostics for disease. Given the medical consequences of these diseases, the economic impact is also significant. However, none of the use cases below has been translated into the clinic.
831:
SNPs identified in GWA studies are considered to give better predictive performance if they have high effect sizes of Odds Ratios (OR). A case study involving 5 use cases of genomic prediction demonstrate that SNPs with extremely small p-values, and by implication extreme OR do not give extreme
1288:
Swanton, C.; Larkin, J. M.; Gerlinger, M.; Eklund, A. C.; Howell, M.; Stamp, G.; Downward, J.; Gore, M.; Futreal, P. A.; Escudier, B.; Andre, F.; Albiges, L.; Beuselinck, B.; Oudard, S.; Hoffmann, J.; Gyorffy, B. Z.; Torrance, C. J.; Boehme, K. A.; Volkmer, H.; Toschi, L.; Nicke, B.; Beck, M.;
136:
The identification of causal variants, genes and pathways leads to opportunities that bridge the divide between research and clinical usage. If successful, the subsequent discovery of therapeutic targets within implicated biological pathways have consequences for both treatment and prevention.
149:
The significance of translation from research to clinical usage relates to use of the complete knowledge of an individual to develop personalised approaches to disease management. The caveat with this is that there have been difficulties in both prediction and inference for complex diseases.
83:
A number of short- and long-term goals exist for predictive genomics. The identification of associated variants underpin all other downstream endeavors that point toward better data-cum-knowledge outcomes. In particular, those outcomes that facilitate clinical improvement and individualised
729:(OR) have been reported (greater than 2.0 per allele in some cases). In 2013, a comprehensive case-control GWA study with approximately 77,000 observations involving 18 international research groups from the International AMD Genetics Consortium implicated 19 gene loci and 9
745:- according to Jakobsdottir et al., 0.75 AUC is sufficient to distinguish between extreme cases and controls. In particular, of the 19 associated gene loci, there were 7 newly discovered loci, which the authors point to as additional entry points into AMD etiology and
814:
For predictive genomics to address their objectives, there must be an improvement in the accuracy of prediction through added methods or improvements to current techniques and to demonstrate that there is bonafide improvement in patient outcomes. Currently, although
725:) gene in 2005 motivating the search for more genetic variants in the disease. Over the past decade, a number of models have been proposed to assess individual risk to AMD. The genetic predisposition of AMD risk varies from 45% to 71% where highly effectual
70:
has progressively improved the fidelity of sequence determination, the overbearing complexity of the genome hinders the identification of associated or ultimately causal variants. In particular, there are likely to be a large number of implicated
721:(AMD) is one of the flagship complex diseases from the genomic revolution with over 19 associated genetic loci replicated in GWA studies. In particular, the first significant genetic risk variant was identified in the complement factor H(
1244:
McCarthy, M. I.; Abecasis, G. A. R.; Cardon, L. R.; Goldstein, D. B.; Little, J.; Ioannidis, J. P. A.; Hirschhorn, J. N. (2008). "Genome-wide association studies for complex traits: Consensus, uncertainty and challenges".
137:
Furthermore, the downstream effect of identifying disease-relevant biomarkers allow for improvements to monitoring disease progression and response-to-treatment, where the implementation of these results into
881:
predicted; distinct from and supplementary to pathogenicity prediction. This application guides research to test the effect of top candidate variants in preparation for novel disease cases.
154:, there is a limit to the predictive accuracy of their ‘genomic profiles’. However, preliminary examples of predictive genomics for personalising healthcare include: using an individual's
124:
specific targeting in areas of interest. Therefore, the ability for predictive genomics to succeed also depends upon other related areas such as the functional annotation of the genome (
141:(CDSS) facilitate personalised medicine and outcomes. Even if only marginally effectual, the repeated replication of associated variants can offer significant translational value.
1867:
Meigs, J. B.; Shrader, P.; Sullivan, L. M.; McAteer, J. B.; Fox, C. S.; Dupuis, J. E.; Manning, A. K.; Florez, J. C.; Wilson, P. W. F.; d'Agostino, R. B.; Cupples, L. A. (2008).
307:
220:
876:
the performance of elite athletes noting individualised and personalised training regimens for both dietary and physical aspects. Additionally, Kayser et al. point to
832:
differences in discrimination. They point out that use of significantly associated genetic variants does not necessarily lead to better classification. Alternatively,
2170:"Genetic Architecture of Complex Traits and Accuracy of Genomic Prediction: Coat Colour, Milk-Fat Percentage, and Type in Holstein Cattle as Contrasting Model Traits"
334:
246:
274:
188:
2014:
Peterson, R. E.; Maes, H. H.; Lin, P.; Kramer, J. R.; Hesselbrock, V. M.; Bauer, L. O.; Nurnberger, J. I.; Edenberg, H. J.; Dick, D. M.; Webb, B. T. (2014).
158:
data to monitor progress to treatment, or using the genomic profile of the P450 drug metabolising system of individuals to assist dosage and selection.
43:
in humans. To date, the success of predictive genomics has been dependent on the genetic framework underlying these applications, typically explored in
2352:
Lawless, D; Lango Allen, H; Thaventhiran, J; NIHR BioResource–Rare
Diseases Consortium; Hodel, F; Anwar, R; Fellay, J; Walter, J; Savic, S (2019).
820:
1684:
1342:"A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine"
2116:
Thornton-Wells, T. A.; Moore, J. H.; Haines, J. L. (2004). "Genetics, statistics and human disease: Analytical retooling for complexity".
170:(SNP) based models reflecting known genetic factors for a European population (subject to change as more associations are discovered).
166:
In the table below is a performance comparison of diseases selected on disease frequency and known heritability estimates, with use of
2065:
Goudey, B.; Rawlinson, D.; Wang, Q.; Shi, F.; Ferra, H.; Campbell, R. M.; Stern, L.; Inouye, M. T.; Ong, C. S.; Kowalczyk, A. (2013).
1921:"Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk"
925:
816:
1818:
Talmud, P. J.; Hingorani, A. D.; Cooper, J. A.; Marmot, M. G.; Brunner, E. J.; Kumari, M.; Kivimaki, M.; Humphries, S. E. (2010).
900:
138:
1715:"Interpretation of Genetic Association Studies: Markers with Replicated Highly Significant Odds Ratios May Be Poor Classifiers"
940:
167:
48:
2309:
Kayser, M.; De Knijff, P. (2011). "Improving human forensics through advances in genetics, genomics and molecular biology".
1618:
Barile, G. R.; Benchaboune, M.; Bird, A. C.; Bishop, P. N.; Branham, K. E.; Brooks, M.; Brucker, A. J.; et al. (2013).
44:
1820:"Utility of genetic and non-genetic risk factors in prediction of type 2 diabetes: Whitehall II prospective cohort study"
2274:
Kambouris, M; Ntalouka, F; Ziogas, G; Maffulli, N (2012). "Predictive genomics DNA profiling for athletic performance".
950:
28:
2016:"On the association of common and rare genetic variation influencing body mass index: A combined SNP and CNV analysis"
1070:
Janssens, A. C. J. W.; Van Duijn, C. M. (2008). "Genome-based prediction of common diseases: Advances and prospects".
1764:
Lyssenko, V.; Almgren, P.; Anevski, D.; Orho-Melander, M.; Sjögren, M.; Saloranta, C.; Tuomi, T.; Groop, L. (2005).
1291:"Predictive biomarker discovery through the parallel integration of clinical trial and functional genomics datasets"
1108:
Hindorff, L. A.; Sethupathy, P.; Junkins, H. A.; Ramos, E. M.; Mehta, J. P.; Collins, F. S.; Manolio, T. A. (2009).
1027:
Hirschhorn, J. N.; Daly, M. J. (2005). "Genome-wide association studies for common diseases and complex traits".
1667:
Grassmann, F.; Heid, I. M.; Weber, B. H. F. (2014). "Genetic Risk Models in Age-Related
Macular Degeneration".
1110:"Potential etiologic and functional implications of genome-wide association loci for human diseases and traits"
788:
784:
757:
564:
356:
52:
1395:
A. G.; Eichler, E. E.; Gibson, G.; Haines, J. L.; MacKay, T. F. C.; McCarroll, S. A.; Visscher, P. M. (2009).
802:(CD) is a complex immune disorder that has been found to have strong genetic links in disease. In particular,
1454:"Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro"
51:(variation of a DNA sequence in a population) underpin GWA studies in complex diseases that have ranged from
803:
340:
113:
2214:
1206:
Ginsburg, G. S.; Willard, H. F. (2009). "Genomic and personalized medicine: Foundations and applications".
2125:
864:
580:
420:
945:
833:
93:
767:
In 2009, a study was conducted on the WTCCC (GWA study involving 7 cohorts with 7 diseases: including
1465:
1408:
1121:
910:
780:
742:
718:
500:
67:
56:
2130:
935:
890:
372:
20:
99:
2334:
2256:
2228:
2165:
1540:
1270:
1168:
1052:
973:
836:(copy number variants) have been proposed to usurp SNPs as better candidates for prediction with
730:
644:
532:
772:
692:
60:
2067:"GWIS - model-free, fast and exhaustive search for epistatic interactions in case-control GWAS"
280:
193:
2408:
2385:
2326:
2291:
2248:
2203:
2143:
2098:
2047:
1996:
1942:
1898:
1849:
1797:
1746:
1690:
1680:
1649:
1594:
1532:
1493:
1434:
1373:
1322:
1262:
1223:
1188:
1149:
1087:
1044:
1009:
905:
867: – the co-inheritance of SNPs through generations – and the actual loci themselves.
746:
596:
484:
85:
24:
2375:
2365:
2318:
2283:
2240:
2231:(2008). "Genomic selection: Prediction of accuracy and maximisation of long term response".
2193:
2183:
2135:
2088:
2078:
2037:
2027:
1986:
1976:
1932:
1888:
1880:
1839:
1831:
1787:
1777:
1736:
1726:
1672:
1639:
1631:
1584:
1574:
1524:
1483:
1473:
1424:
1416:
1363:
1353:
1312:
1302:
1254:
1215:
1180:
1139:
1129:
1079:
1036:
999:
991:
920:
895:
768:
741:
activity. The predictive performance of the full model including all 19 loci exhibited 0.74
734:
468:
72:
1963:
Abraham, G.; Tye-Din, J. A.; Bhalala, O. G.; Kowalczyk, A.; Zobel, J.; Inouye, M. (2014).
982:
837:
676:
404:
155:
117:
313:
225:
1469:
1412:
1125:
112:
Whilst the single-gene, single-disease hypothesis holds for
Mendelian disorders such as
2380:
2353:
2198:
2169:
2093:
2066:
2042:
2015:
1991:
1964:
1893:
1868:
1844:
1819:
1792:
1765:
1741:
1714:
1644:
1619:
1589:
1562:
1429:
1396:
1368:
1341:
1317:
1290:
1144:
1109:
1004:
978:"Prediction of individual genetic risk to disease from genome-wide association studies"
977:
799:
628:
612:
516:
259:
173:
1488:
1453:
1171:; Visscher, P. M. (2008). "Prediction of individual genetic risk of complex disease".
2402:
2174:
1965:"Accurate and Robust Genomic Prediction of Celiac Disease Using Statistical Learning"
1869:"Genotype Score in Addition to Common Risk Factors for Prediction of Type 2 Diabetes"
877:
660:
436:
1713:
Jakobsdottir, J.; Gorin, M. B.; Conley, Y. P.; Ferrell, R. E.; Weeks, D. E. (2009).
1544:
1274:
2338:
2260:
1056:
776:
738:
1671:. Advances in Experimental Medicine and Biology. Vol. 801. pp. 291–300.
2188:
1981:
1782:
1731:
1676:
1579:
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851:
452:
2370:
2287:
1219:
2244:
2139:
1563:"Comparison of Family History and SNPs for Predicting Risk of Complex Disease"
1184:
930:
915:
726:
150:
Therefore, unless individuals have an overwhelming high or low number of risk
36:
32:
2032:
1452:
Gunther, E. C.; Stone, D. J.; Gerwien, R. W.; Bento, P; Heyes, M. P. (2003).
1478:
1134:
89:
2389:
2330:
2295:
2252:
2207:
2147:
2102:
2051:
2000:
1946:
1902:
1853:
1801:
1750:
1694:
1653:
1598:
1536:
1497:
1438:
1377:
1358:
1326:
1266:
1227:
1192:
1153:
1091:
1048:
1013:
823:(PPV) must be high enough to avoid a higher prevalence of false-positives.
1884:
1937:
1920:
1083:
548:
1420:
1528:
995:
151:
40:
1835:
1340:
Kawamoto, K.; Lobach, D. F.; Willard, H. F.; Ginsburg, G. S. (2009).
388:
125:
2322:
1635:
1258:
1040:
1511:
De Leon, J; Susce, M. T.; Murray-Carmichael, E (2006). "The Ampli
1307:
98:
1620:"Seven new loci associated with age-related macular degeneration"
2164:
Hayes, B. J.; Pryce, J.; Chamberlain, A. J.; Bowman, P. J.;
1561:
Do, C. B.; Hinds, D. A.; Francke, U.; Eriksson, N. (2012).
1515:
CYP450 genotyping test: Integrating a new clinical tool".
31:. Specifically, predictive genomics deals with the future
2354:"Predicting the Occurrence of Variants in RAG1 and RAG2"
1397:"Finding the missing heritability of complex diseases"
316:
283:
262:
228:
196:
176:
1919:Evans, D. M.; Visscher, P. M.; Wray, N. R. (2009).
328:
301:
268:
240:
214:
182:
103:Objectives as a hierarchy for predictive genomics.
84:healthcare further lead to actionable measures in
47:(GWA) studies. The identification of associated
19:is at the intersection of multiple disciplines:
1458:Proceedings of the National Academy of Sciences
1114:Proceedings of the National Academy of Sciences
1766:"Genetic Prediction of Future Type 2 Diabetes"
1173:Current Opinion in Genetics & Development
827:Variants in prediction: SNPs and alternatives
764:gene purported to give highest genetic risk.
8:
1239:
1237:
1103:
1101:
1958:
1956:
1813:
1811:
1346:BMC Medical Informatics and Decision Making
845:Interacting variants: higher order analysis
2276:Recent Patents on DNA & Gene Sequences
1914:
1912:
1556:
1554:
1389:
1387:
967:
965:
250:
2379:
2369:
2197:
2187:
2129:
2092:
2082:
2041:
2031:
1990:
1980:
1936:
1892:
1843:
1791:
1781:
1740:
1730:
1643:
1612:
1610:
1608:
1588:
1578:
1487:
1477:
1428:
1367:
1357:
1316:
1306:
1143:
1133:
1003:
315:
293:
288:
282:
261:
227:
206:
201:
195:
175:
733:including the regulation of complement,
39:in areas such as complex multifactorial
1708:
1706:
1704:
961:
108:Identify associated variants to disease
2159:
2157:
162:Applications in complex human diseases
75:which exhibit small marginal effects.
7:
222:denotes heritability of liability,
248:denotes area under the ROC curve.
14:
1517:Molecular Diagnosis & Therapy
926:Receiver operating characteristic
139:clinical decision support systems
132:Translation: research to clinical
2213:
901:Clinical decision support system
719:Age-related macular degeneration
714:Age-related macular degeneration
501:Age-related macular degeneration
57:Age-related macular degeneration
1873:New England Journal of Medicine
49:single-nucleotide polymorphisms
2358:Journal of Clinical Immunology
941:Single-nucleotide polymorphism
190:denotes lifetime morbid risk,
168:single-nucleotide polymorphism
1:
1669:Retinal Degenerative Diseases
2189:10.1371/journal.pgen.1001139
1982:10.1371/journal.pgen.1004137
1783:10.1371/journal.pmed.0020345
1732:10.1371/journal.pgen.1000337
1677:10.1007/978-1-4614-3209-8_37
1580:10.1371/journal.pgen.1002973
951:Translational bioinformatics
29:translational bioinformatics
2084:10.1186/1471-2164-14-S3-S10
2425:
2371:10.1007/s10875-019-00670-z
2288:10.2174/187221512802717321
1220:10.1016/j.trsl.2009.09.005
976:; Visscher, P. M. (2007).
858:Population: size and scope
145:Individualising healthcare
2245:10.1007/s10709-008-9308-0
2140:10.1016/j.tig.2004.09.007
1185:10.1016/j.gde.2008.07.006
821:positive predictive value
302:{\displaystyle h_{L}^{2}}
215:{\displaystyle h_{L}^{2}}
2033:10.1186/1471-2164-15-368
1925:Human Molecular Genetics
1072:Human Molecular Genetics
2311:Nature Reviews Genetics
1479:10.1073/pnas.1632587100
1247:Nature Reviews Genetics
1135:10.1073/pnas.0903103106
1029:Nature Reviews Genetics
804:human leukocyte antigen
341:Coronary artery disease
45:genome-wide association
1359:10.1186/1472-6947-9-17
1208:Translational Research
865:linkage disequilibrium
330:
303:
270:
242:
216:
184:
104:
1885:10.1056/NEJMoa0804742
1630:(4): 433–9, 439e1–2.
1289:Szallasi, Z. (2010).
946:Copy-number variation
331:
304:
271:
243:
217:
185:
102:
911:Human Genome Project
781:rheumatoid arthritis
314:
281:
260:
226:
194:
174:
114:Huntington's disease
68:Human Genome Project
1470:2003PNAS..100.9608G
1421:10.1038/nature08494
1413:2009Natur.461..747M
1126:2009PNAS..106.9362H
936:Logistic regression
891:Predictive medicine
731:biological pathways
373:Atrial fibrillation
329:{\displaystyle AUC}
298:
241:{\displaystyle AUC}
211:
21:predictive medicine
17:Predictive genomics
2118:Trends in Genetics
1938:10.1093/hmg/ddp295
1529:10.1007/bf03256453
1084:10.1093/hmg/ddn250
996:10.1101/gr.6665407
871:Other applications
645:Ulcerative colitis
533:Multiple sclerosis
326:
299:
284:
266:
238:
212:
197:
180:
105:
1836:10.1136/bmj.b4838
1686:978-1-4614-3208-1
1407:(7265): 747–753.
906:Personal genomics
707:
706:
597:Pancreatic cancer
581:Parkinson disease
485:Colorectal cancer
421:Alzheimer disease
269:{\displaystyle K}
183:{\displaystyle K}
25:personal genomics
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2077:(Suppl 3): S10.
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2011:
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1573:(10): e1002973.
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1137:
1105:
1096:
1095:
1067:
1061:
1060:
1024:
1018:
1017:
1007:
969:
921:Machine learning
896:Pharmacogenomics
789:Type II Diabetes
769:bipolar disorder
735:lipid metabolism
469:Bipolar disorder
335:
333:
332:
327:
308:
306:
305:
300:
297:
292:
275:
273:
272:
267:
251:
247:
245:
244:
239:
221:
219:
218:
213:
210:
205:
189:
187:
186:
181:
2424:
2423:
2419:
2418:
2417:
2415:
2414:
2413:
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2398:
2397:
2351:
2350:
2346:
2323:10.1038/nrg2952
2308:
2307:
2303:
2273:
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2268:
2227:
2226:
2222:
2212:
2182:(9): e1001139.
2163:
2162:
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2131:10.1.1.325.3919
2115:
2114:
2110:
2064:
2063:
2059:
2013:
2012:
2008:
1975:(2): e1004137.
1962:
1961:
1954:
1931:(18): 3525–31.
1918:
1917:
1910:
1879:(21): 2208–19.
1866:
1865:
1861:
1817:
1816:
1809:
1763:
1762:
1758:
1725:(2): e1000337.
1712:
1711:
1702:
1687:
1666:
1665:
1661:
1636:10.1038/ng.2578
1624:Nature Genetics
1616:
1615:
1606:
1560:
1559:
1552:
1510:
1509:
1505:
1464:(16): 9608–13.
1451:
1450:
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1392:
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1339:
1338:
1334:
1295:Genome Medicine
1287:
1286:
1282:
1259:10.1038/nrg2344
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1235:
1205:
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1200:
1166:
1165:
1161:
1107:
1106:
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1069:
1068:
1064:
1041:10.1038/nrg1521
1026:
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983:Genome Research
971:
970:
963:
959:
887:
873:
860:
847:
829:
812:
797:
785:Type I Diabetes
773:Crohn's disease
758:Type 2 diabetes
755:
753:Type 2 diabetes
716:
693:Crohn's disease
565:Type 1 diabetes
405:Prostate cancer
357:Type 2 diabetes
312:
311:
279:
278:
258:
257:
224:
223:
192:
191:
172:
171:
164:
156:gene expression
147:
134:
118:cystic fibrosis
110:
81:
61:Crohn's disease
53:Type 2 Diabetes
12:
11:
5:
2422:
2420:
2412:
2411:
2401:
2400:
2396:
2395:
2364:(7): 688–701.
2344:
2301:
2266:
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2166:Goddard, M. E.
2153:
2108:
2057:
2006:
1952:
1908:
1859:
1807:
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1685:
1659:
1604:
1550:
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1444:
1383:
1332:
1280:
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1198:
1179:(3): 257–263.
1169:Goddard, M. E.
1159:
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1097:
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1019:
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974:Goddard, M. E.
960:
958:
955:
954:
953:
948:
943:
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933:
928:
923:
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800:Celiac disease
796:
795:Celiac disease
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698:
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689:
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677:Celiac disease
673:
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669:
666:
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657:
656:
653:
650:
647:
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640:
637:
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631:
629:Thyroid cancer
625:
624:
621:
618:
615:
613:Ovarian cancer
609:
608:
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602:
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538:
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517:Bladder cancer
513:
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2282:(3): 229–39.
2281:
2277:
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2258:
2254:
2250:
2246:
2242:
2239:(2): 245–57.
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2175:PLOS Genetics
2171:
2167:
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2124:(12): 640–7.
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2080:
2076:
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2068:
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1969:PLOS Genetics
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1959:
1957:
1953:
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1939:
1934:
1930:
1926:
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1767:
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1564:
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1526:
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1467:
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1217:
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1213:
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1167:Wray, N. R.;
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972:Wray, N. R.;
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878:DNA profiling
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2071:BMC Genomics
2070:
2060:
2023:
2020:BMC Genomics
2019:
2009:
1972:
1968:
1928:
1924:
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1627:
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761:
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747:drug targets
722:
717:
708:
165:
148:
135:
122:
111:
82:
73:genetic loci
65:
16:
15:
2229:Goddard, M.
852:NP-complete
810:Limitations
727:odds ratios
453:Lung cancer
2026:(1): 368.
957:References
931:Odds ratio
916:Statistics
787:(T1D) and
739:angiogenic
94:prevention
79:Objectives
59:(AMD) and
37:prediction
33:phenotypic
2126:CiteSeerX
1830:: b4838.
1301:(8): 53.
90:prognosis
86:diagnosis
2409:Genomics
2403:Category
2390:31388879
2331:21331090
2296:22827597
2253:18704696
2233:Genetica
2208:20927186
2168:(2010).
2148:15522460
2103:23819779
2052:24884913
2001:24550740
1947:19553258
1903:19020323
1854:20075150
1802:17570749
1751:19197355
1695:24664710
1654:23455636
1599:23071447
1545:27626247
1537:16771600
1498:12869696
1439:19812666
1378:19309514
1327:20701793
1275:15032294
1267:18398418
1228:19931193
1193:18682292
1154:19474294
1092:18852206
1049:15716906
1014:17785532
885:See also
549:Melanoma
41:diseases
2381:6754361
2339:6448781
2261:1780250
2199:2944788
2094:3665501
2043:4035084
1992:3923679
1894:2746946
1845:2806945
1793:1274281
1742:2629574
1645:3739472
1590:3469463
1466:Bibcode
1430:2831613
1409:Bibcode
1369:2666673
1318:2945010
1145:2687147
1122:Bibcode
1057:2813666
1005:1987352
254:Disease
152:alleles
55:(T2D),
2388:
2378:
2337:
2329:
2294:
2259:
2251:
2206:
2196:
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1401:Nature
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687:0.733
671:0.540
655:0.666
639:0.614
623:0.548
607:0.557
591:0.592
575:0.638
559:0.640
543:0.622
527:0.577
511:0.758
495:0.564
479:0.550
463:0.525
447:0.586
431:0.648
415:0.614
399:0.528
389:Stroke
383:0.593
367:0.592
351:0.584
126:ENCODE
2335:S2CID
2257:S2CID
1541:S2CID
1271:S2CID
1053:S2CID
697:0.005
681:0.007
665:0.007
649:0.009
633:0.010
617:0.014
601:0.015
585:0.016
569:0.018
553:0.020
537:0.020
521:0.024
505:0.047
489:0.051
473:0.051
457:0.069
441:0.123
425:0.132
409:0.165
393:0.190
377:0.245
361:0.339
345:0.402
2386:PMID
2327:PMID
2292:PMID
2249:PMID
2204:PMID
2144:PMID
2099:PMID
2048:PMID
1997:PMID
1943:PMID
1899:PMID
1850:PMID
1798:PMID
1747:PMID
1691:PMID
1681:ISBN
1650:PMID
1595:PMID
1533:PMID
1513:Chip
1494:PMID
1435:PMID
1374:PMID
1323:PMID
1263:PMID
1224:PMID
1189:PMID
1150:PMID
1088:PMID
1045:PMID
1010:PMID
737:and
700:0.56
684:0.75
668:0.66
652:0.53
636:0.53
620:0.22
604:0.36
588:0.27
572:0.87
556:0.21
540:0.51
524:0.08
508:0.71
492:0.13
476:0.60
460:0.08
444:0.25
428:0.79
412:0.42
396:0.17
380:0.62
364:0.30
348:0.49
116:and
92:and
27:and
2376:PMC
2366:doi
2319:doi
2284:doi
2241:doi
2237:136
2194:PMC
2184:doi
2136:doi
2089:PMC
2079:doi
2038:PMC
2028:doi
1987:PMC
1977:doi
1933:doi
1889:PMC
1881:doi
1877:359
1840:PMC
1832:doi
1828:340
1824:BMJ
1788:PMC
1778:doi
1737:PMC
1727:doi
1673:doi
1640:PMC
1632:doi
1585:PMC
1575:doi
1525:doi
1484:PMC
1474:doi
1462:100
1425:PMC
1417:doi
1405:461
1364:PMC
1354:doi
1313:PMC
1303:doi
1255:doi
1216:doi
1212:154
1181:doi
1140:PMC
1130:doi
1118:106
1080:doi
1037:doi
1000:PMC
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854:).
838:BMI
834:CNV
817:AUC
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