Synthetic associations in the context of genome-wide association scan signals.

Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with complex traits, but these only explain a small proportion of the total heritability. It has been recently proposed that rare variants can create 'synthetic association' signals in GWAS, by occurring more often in association with one of the alleles of a common tag single nucleotide polymorphism. While the ultimate evaluation of this hypothesis will require the completion of large-scale sequencing studies, it is informative to place it in the broader context of what is known about the genetic architecture of complex disease. In this review, we draw from empirical and theoretical data to summarize evidence showing that synthetic associations do not underlie many reported GWAS associations.

GENOME-WIDE ASSOCIATION STUDIES AND ‘THE MISSING HERITABILITY'

Numerous common human diseases and phenotypic traits are believed to arise from a combination of genetic and environmental factors. The unravelling of the genetic predisposition to complex traits is a major challenge, and it could lead to better prevention, diagnosis and treatment of disease.

Recently, advances in genotyping technologies, reduction in genotyping costs and the availability of data regarding genome-wide sequence variation through the International HapMap Project and 1000 genomes project have made genome-wide association studies (GWAS) possible. GWAS have emerged as a powerful tool for identifying genetic variants associated with complex traits. In the past few years, more than 500 loci have been found to be associated with human common diseases and traits (1). GWAS have proven to be much more successful than linkage studies, which were underpowered to detect variants of modest effect (2), and candidate gene studies, which are non-systematic and biased due to our limited knowledge of the biological pathways implicated in disease pathogenesis (3).

GWAS are based on the common disease–common variant (CDCV) hypothesis (4), which states that relatively common genetic variants (MAF > 5%) of relatively low penetrance are important contributors to the genetic susceptibility to common diseases. Well-powered GWAS, which capture a substantial majority of common variation in the genome, have been now conducted for many common diseases. However, for the majority of these diseases, common variants explain only a small proportion of heritability (5), due to small individual effect sizes. It has been estimated that only 13% of all identified susceptibility loci have odds ratios (OR) above 2, and only 1% have OR above 10 (6).  For example, if we consider a total estimated sibling recurrence risk ratio (λs) of 5–10 for rheumatoid arthritis (RA) (7), 15 for type 1 diabetes (T1D) (8), 17–35 for Crohn's disease (CD) (9) and 3 for type 2 diabetes (T2D) (10), their established susceptibility loci would contribute ∼33–47%, 55.6%, 10–12.6% and 11.9% of the total heritability, respectively (Table 1).

Table 1.

Established susceptibility loci for RA, T1D, CD and T2D

Chromosome	SNP	Position	Region/gene	RAF	OR	λs	Reference
Rheumatoid arthritis
1p36	rs3890745	2553624	TNFSFR14	0.68	1.12	1.003	(16)
1p13	rs2476601	114377568	PTPN22	0.10	1.94	1.068	(16)
1p13	rs11586238	117263138	CD2, CD58	0.24	1.13	1.003	(16)
1q23	rs12746613	161467042	FCGR2A	0.12	1.13	1.002	(16)
1q31	rs10919563	198700442	PTPRC	0.87	1.14	1.002	(16)
2p16	rs13031237	61136129	REL	0.37	1.13	1.004	(16)
2p14	rs934734	65595586	SPRED2	0.49	1.13	1.004	(16)
2q11	rs10865035	100835734	AFF3	0.47	1.12	1.003	(16)
2q32	rs7574865	191964633	STAT4	0.22	1.16	1.004	(16)
2q33	rs1980422	204610396	CD28	0.24	1.12	1.002	(16)
2q33	rs3087243	204738919	CTLA4	0.56	1.15	1.005	(16)
3p14	rs13315591	58556841	PXK	0.09	1.29	1.007	(16)
4p15	rs874040	26108197	RBPJ	0.30	1.14	1.004	(16)
4q27	rs6822844	123509421	IL2, IL21	0.82	1.11	1.002	(16)
5q11	rs6859219	55438580	ANKRD55, IL6ST	0.79	1.28	1.009	(16)
5q21	rs26232	102596720	C5orf13	0.68	1.14	1.004	(16)
6p21			HLA			1.800	(70,71)
6q21	rs548234	106568034	PRDM1	0.33	1.10	1.002	(16)
6q23	rs10499194	138002637	TNFAIP3	0.73	1.10	1.002	(16)
6q23	rs6920220	138006504	TNFAIP3	0.22	1.22	1.008	(16)
6q23	rs5029937	138195151	TNFAIP3	0.04	1.40	1.006	(16)
6q25	rs394581	159482521	TAGAP	0.70	1.10	1.002	(16)
6q27	rs3093023	167534290	CCR6	0.43	1.13	1.004	(16)
7q32	rs10488631	128594183	IRF5	0.11	1.19	1.003	(16)
8p23	rs2736340	11343973	BLK	0.25	1.12	1.003	(16)
9p13	rs2812378	34710260	CCL21	0.34	1.10	1.002	(16)
9q33	rs3761847	123690239	TRAF1, C5	0.43	1.13	1.004	(16)
10p15	rs2104286	6099045	IL2RA	0.73	1.09	1.001	(16)
10p15	rs4750316	6393260	PRKCQ	0.81	1.15	1.003	(16)
11p12	rs540386	36525293	TRAF6	0.86	1.14	1.002	(16)
12q13	rs1678542	57968715	KIF5A	0.62	1.10	1.002	(16)
20q13	rs4810485	44747947	CD40	0.75	1.18	1.005	(16)
22q12	rs3218253	37544810	IL2RB	0.26	1.09	1.001	(16)
Type 1 diabetes
1p31	rs2269241	64108771	PGM1	0.19	1.10	1.001	(15)
1p13	rs2476601	114377568	PTPN22	0.14	2.05	1.104	(15)
1q31	rs2816316	192536813	RGS1	0.82	1.12	1.002	(15)
1q32	rs3024505	206939904	IL10	0.83	1.19	1.004	(15)
2p25	rs1534422	12640741	2p25	0.46	1.08	1.001	(15)
2q12	rs917997	103070568	IL18RAP	0.78	1.20	1.005	(15)
2q24	rs1990760	163124051	IFIH1	0.60	1.16	1.005	(15)
2q33	rs3087243	204738919	CTLA4	0.55	1.14	1.004	(15)
3p21	rs333	46345611	CCR5	0.88	1.18	1.003	(15)
4p15	rs10517086	26085511	4p15	0.30	1.09	1.002	(15)
4q27	rs17388568	123132492	IL2	0.26	1.26	1.011	(15)
5p13	rs6897932	35874575	IL7R	0.73	1.12	1.002	(15)
6p21			MHC			3.058	(72)
6q15	rs11755527	90958231	BACH2	0.47	1.13	1.004	(15)
6q22	rs9388489	126698719	C6orf173	0.45	1.17	1.006	(15)
6q23	rs6920220	137973068	TNFAIP3	0.22	1.09	1.001	(15)
6q25	rs1738074	159465977	TAGAP	0.56	1.09	1.002	(15)
7p15	rs7804356	26891665	7p15	0.76	1.14	1.003	(15)
7p12	rs4948088	51027194	COBL	0.95	1.30	1.002	(15)
9p24	rs7020673	4291747	GLIS3	0.50	1.14	1.004	(15)
10p15	rs11594656	6,62015	IL2RA	0.75	1.19	1.005	(15)
10p15	rs12722495	6137289	IL2RA	0.89	1.59	1.015	(15)
10p15	rs947474	6430456	PRKCQ	0.81	1.10	1.001	(15)
10q23	rs10509540	90023033	C10orf59	0.72	1.33	1.015	(15)
11p15	rs689	2138800	INS	0.71	2.30	1.096	(15)
12p13	rs4763879	9910164	CD69	0.37	1.09	1.002	(15)
12q13	rs2292239	56482180	ERBB3	0.34	1.31	1.018	(15)
12q13	rs1678536	57979190	Multiple	0.72	1.12	1.002	(15)
12q24	rs3184504	111884608	SH2B3	0.49	1.28	1.015	(15)
14q24	rs1465788	69263599	14q24	0.71	1.16	1.004	(15)
14q32	rs4900384	98498951	14q32	0.29	1.09	1.002	(15)
15q25	rs3825932	79235446	CTSH	0.68	1.16	1.005	(15)
16p13	rs12708716	11179873	CLEC16A	0.65	1.23	1.009	(15)
16p12	rs12444268	20342572	16p12	0.30	1.10	1.002	(15)
16p11	rs4788084	28539848	IL27	0.58	1.16	1.005	(15)
16q23	rs7202877	75247245	16q23	0.10	1.13	1.001	(15)
17p13	rs16956936	7633692	17p13	0.87	1.09	1.001	(15)
17q12	rs2290400	38066240	ORMDL3	0.51	1.15	1.005	(15)
17q21	rs7221109	38770286	17q21	0.65	1.05	1.001	(15)
18p11	rs1893217	12809340	PTPN2	0.17	1.13	1.002	(15)
18q22	rs763361	67531642	CD226	0.47	1.16	1.006	(15)
19q13	rs425105	47208481	19q13	0.84	1.16	1.003	(15)
20p13	rs2281808	1610551	20p13	0.64	1.11	1.002	(15)
21q22	rs11203203	43836186	UBASH3A	0.43	1.13	1.004	(15)
22q12	rs5753037	30581722	22q12	0.39	1.10	1.002	(15)
22q13	rs229541	37591318	C1QTNF6	0.43	1.11	1.003	(15)
Xq28	rs2664170	153945602	Xq28	0.32	1.16	1.005	(15)
Crohn's disease
1p31	rs11465804	67475114	IL23R	0.93	2.50	1.025	(14)
1p13	rs2476601	114179091	PTPN22	0.90	1.31	1.005	(14)
1q23	rs2274910	159118670	ITLN1	0.68	1.14	1.004	(14)
1q24	rs9286879	171128857	1q24	0.24	1.19	1.006	(14)
1q32	rs11584383	199202489	1q32	0.70	1.18	1.005	(14)
2q27	rs3828309	233845149	ATG16L1	0.53	1.28	1.015	(14)
3p21	rs3197999	49696536	MST1	0.27	1.20	1.007	(14)
5p13	rs4613763	40428485	PTGER4	0.13	1.32	1.010	(14)
5q31	rs2188962	131798704	5q31	0.43	1.25	1.013	(14)
5q33	rs11747270	150239060	IRGM	0.09	1.33	1.008	(14)
5q33	rs10045431	158747111	IL12B	0.71	1.11	1.002	(14)
6p22	rs6908425	20836710	CDKAL1	0.78	1.21	1.006	(14)
6q21	rs7746082	106541962	6q21	0.29	1.17	1.005	(14)
6q27	rs2301436	167357978	CCR6	0.46	1.21	1.009	(14)
7p12	rs1456893	50240218	7p12	0.68	1.20	1.007	(14)
8q24	rs1551398	126609233	8q24	0.62	1.08	1.001	(14)
9p24	rs10758669	4971602	JAK2	0.35	1.12	1.003	(14)
9q32	rs4263839	116606261	TNFSF15	0.68	1.22	1.008	(14)
10p11	rs17582416	35327656	10p11	0.35	1.16	1.005	(14)
10q21	rs10995271	64108492	ZNF365	0.39	1.25	1.012	(14)
10q24	rs11190140	101281583	NKX2-3	0.48	1.20	1.008	(14)
11q13	rs7927894	75978964	C11orf30	0.39	1.16	1.005	(14)
12q12	rs11175593	38888207	LRRK2, MUC19	0.02	1.54	1.005	(14)
13q14	rs3764147	43355925	13q14	0.22	1.25	1.010	(14)
16q12	rs2066847	49321280	NOD2	0.02	3.99	1.147	(14)
17q21	rs2872507	35294289	ORMDL3	0.47	1.12	1.003	(14)
17q21	rs744166	37767727	STAT3	0.57	1.18	1.007	(14)
18p11	rs2542151	12769947	PTPN2	0.15	1.35	1.014	(14)
21q21	rs1736135	15727091	21q21	0.57	1.18	1.007	(14)
21q22	rs762421	44439989	ICOSLG	0.39	1.13	1.004	(14)
Type 2 diabetes
1p13–p11	rs10923931	120319482	NOTCH2	0.11	1.09	1.001	(73)
1q32	rs340874	212225879	PROX1	0.56	1.07	1.001	(18)
2p23	rs780094	27594741	GCKR	0.61	1.06	1.001	(18)
2p21	rs7578597	43586327	THADA	0.90	1.15	1.002	(73)
2p16	rs243021	60438323	BCL11A	0.46	1.08	1.001	(74)
2q26	rs7578326	226728897	IRS1	0.64	1.11	1.002	(74)
3p25	rs1801282	12368125	PPARG	0.85	1.23	1.005	(73)
3p14	rs4607103	64686944	ADAMTS9	0.76	1.10	1.002	(73)
3q13–q21	rs11708067	124548468	ADCY5	0.77	1.12	1.002	(18)
3q27	rs4402960	186994381	IGF2BP2	0.31	1.11	1.002	(73)
4p16	rs10010131	6343816	WFS1	0.59	1.14	1.004	(73)
5q13	rs4457053	76460705	ZBED3	0.26	1.08	1.001	(74)
6p22	rs10946398	20769013	CDKAL1	0.33	1.09	1.002	(73)
7p21	rs2191349	15030834	DGKB/TMEM195	0.47	1.06	1.001	(18)
7p15	rs864745	28147081	JAZF1	0.50	1.08	1.001	(73)
7p15	rs4607517	44202193	GCK	0.2	1.07	1.001	(18)
7q32	rs972283	130117394	KLF14	0.55	1.07	1.001	(74)
8q22	rs896854	96029687	TP53INP1	0.48	1.06	1.001	(74)
8q24	rs13266634	118253964	SLC30A8	0.68	1.12	1.003	(73)
9p21	rs10811661	22124094	CDKN2A/B	0.84	1.17	1.003	(73)
9q21	rs13292136	81141948	CHCHD9	0.93	1.11	1.001	(74)
10p13	rs12779790	12368016	CDC123/CAMK1D	0.18	1.11	1.002	(73)
10q23	rs5015480	94455539	HHEX/IDE	0.59	1.10	1.002	(73)
11p15	rs2334499	1653428	DUSP8	0.41	1.08	1.001	(32)
11p15	rs231362	2648047	KCNQ1	0.52	1.08	1.001	(74)
11p15	rs2237892	2796327	KCNQ1	0.34	1.42	1.031	(73)
11p15	rs5219	17366148	KCNJ11	0.39	1.15	1.005	(73)
11q13	rs1552224	72110746	CENTD2	0.88	1.14	1.002	(74)
11q21	rs10830963	92348358	MTNR1B	0.30	1.09	1.001	(73)
12q14	rs1531343	64461161	HMGA2	0.1	1.1	1.001	(74)
12q14–q21	rs7961581	69949369	TSPAN8/LGR5	0.27	1.06	1.001	(73)
12q24	rs7957197	119945069	HNF1A	0.85	1.07	1.001	(74)
15q25	rs11634397	78219277	ZFAND6	0.6	1.06	1.001	(74)
15q26	rs8042680	89322341	PRC1	0.22	1.07	1.001	(74)
16q12	rs8050136	52373776	FTO	0.38	1.21	1.009	(73)
17cen–q21.3	rs757210	33170628	HNF1B (TCF2)	0.38	1.10	1.002	(73)
Xq28	rs5945326	152553116	DUSP9	0.21	1.27	1.011	(74)

RA, rheumatoid arthritis; T1D, type 1 diabetes; CD, Crohn's disease; T2D, type 2 diabetes; RAF, risk allele frequency in controls; OR, odds ratio.

Sibling recurrence risk ratio (λs) was calculated using the formula:

where q is the risk allele frequency, p = 1 − q, and γ is the genotype relative risk under the additive model.

POSSIBLE CONTRIBUTORS TO THE UNEXPLAINED HERITABILITY

Explaining this ‘missing heritability' of complex diseases (11–13) is an area of active research, and there are likely to be multiple contributing factors. Part of the explanation is likely to be an underestimate of the contribution made by the types of variants targeted by GWAS. For instance, it might be that there are large numbers of variants of very small effect, which early GWAS were underpowered to detect, yet to be found. This idea is supported by the observation that meta-analyses of published GWAS are discovering a substantial number of new susceptibility loci (14–25). In addition, for most loci, causal variants and potential independent additional markers within the region have not been identified yet. New ways of analysing the genetic architecture of complex traits using GWAS data are suggesting that indeed a large proportion of heritability can be explained by common variants and that larger GWAS will yield many more validated loci for complex traits (26,27).

Of course, GWAS only interrogate a portion of the types of variation that could underlie disease risk. Analysis of GWAS data has been mainly focused on single nucleotide polymorphisms (SNPs), but there are other types of genetic variation, such as structural variants, that have not been studied in depth. However, recent studies of common (MAF > 5%) copy number variants (CNVs) have shown that they seem unlikely to account for a substantial proportion of the ‘missing heritability’ (28). Similarly, the analysis of gene–environment and gene–gene interactions (epistasis) might improve the fraction of heritability explained by loci documented thus far. Several epistatic interactions have been indentified in humans [e.g. between the RET protooncogene and endothelin receptor type B genes in Hirschsprung disease (29), the interleukin 4 receptor variants and interleukin 13 promoter variants in asthma (30) and the alpha- and beta-adrenergic receptors in congestive heart failure (31)], although they have not been replicated. However, this phenomenon has not been thoroughly explored through large-scale analysis of genome-wide SNP interactions, first due to the fact that current sample sizes are underpowered to detect modest interaction effects and secondly due to the paucity of sample collections with genetic and detailed environmental exposure data. Complex patterns of inheritance, such as parent of origin effects (32), as well as inherited epigenetic modifications of the genome, the presence of phenotype heterogeneity in the cohorts used in the first wave of GWAS, or even an initial over-estimation of the heritability of complex traits (33) can also contribute to the missing heritability.

While the above-mentioned plausible contributors seem unlikely to play a substantial role in explaining missing heritability, rare variants are increasingly thought to account for a large proportion of it (34–36). Contrary to the CDCV hypothesis, the multiple rare variant (MRV) hypothesis argues that the summation of the effects of low-frequency polymorphisms, each conferring an intermediate increase in risk (i.e. incompletely penetrant, but greater than those observed for common variants), can explain a significant proportion of the genetic susceptibility to common diseases and traits. Some studies analysing rare variants using GWAS data have been carried out, but these have proven to be underpowered to detect robust associations. Re-sequencing approaches are more suitable for rare variant analysis, and, as these are becoming more cost-effective and new analysis methods are being developed (37,38), they will soon be applied to large-scale studies of rare variants. Indeed, several targeted sequencing studies have already proven successful for the identification of associations between rare variants and some human diseases and disease-related phenotypes (39–43). The same argument can also be extended to other forms of genetic variation, and it has been recently proposed that rare CNVs may be responsible for some fraction of the missing heritability of complex traits (44,45).

SYNTHETIC ASSOCIATIONS HYPOTHESIS

It has been recently proposed that GWAS signals that have been credited to common variants could instead reflect the effect of MRVs. Dickson et al. (46) argue that rare variants can create ‘synthetic association' signals in GWAS, by occurring more often in association with one of the alleles of a common tag SNP (Fig. 1), which would thus synthetically confer an increased risk for disease. This might also mean that the causal variants could be megabases away from the common variants detected in GWAS, and that the real effect size could be much stronger than that implied by the common tag SNP. If true, the synthetic association hypothesis would suggest that follow-up studies from GWAS hits should encompass a much larger region than the linkage disequilibrium region surrounding the detected common variant (6).

Figure 1.

Simplified view of genetic variation at the NOD2 locus, a well-documented example of a synthetic association. The left-hand side shows a genealogical tree representing six SNPs in this region after discarding rare recombinant events. The right-hand side shows the resulting haplotypes and their population frequencies (48), with coloured circles representing common GWAS SNPs, and starbursts representing previously identified low-frequency coding variants responsible for association between NOD2 and CD. While none of the GWAS SNPs is strongly correlated with any individual causal allele, the three coding variants create a synthetic association because they cluster by chance on the side of the tree marked by the green GWAS SNP (rs2076756).

There are very few documented examples showing that MRVs may be responsible for a common variant GWAS signal (47). It therefore seems sensible to evaluate this hypothesis in the broader context of human disease genetics, including historical study designs, functional annotations of GWAS regions and experiments in human populations with diverse ancestry. While sequencing experiments currently underway or in planning will ultimately resolve the role of synthetic association, the balance of evidence available today is already illuminating.

LINKAGE EVIDENCE SUGGESTS SYNTHETIC ASSOCIATIONS ARE RARE

One line of evidence that suggests that synthetic associations do not underlie many reported GWAS associations is provided by linkage scans that have been conducted in the past. The genetic model that underpins synthetic association (allelic heterogeneity caused by several low-frequency variants with larger effects than commonly seen in GWAS) is highly tractable by linkage analysis, which combines information from all causal variants at a particular locus. This relationship is highlighted by the widely replicated linkage between the NOD2 gene and CD, which is driven by three independent, low-frequency causal variants (48–50) which cause a synthetic association signal in GWAS of CD (Fig. 1). NOD2 is the exception that proves the rule that, despite many attempts, very few replicable linkages to complex diseases have been discovered (51). This dearth of findings is informative when considering the likelihood of synthetic associations because it rules out a class of genetic models from playing a substantial role in complex disease.

Power calculations comparing a large-scale linkage scan (52) with the largest GWAS considered by Dickson et al. (46) show that only a small fraction of the genetic models which can give rise to synthetic associations would not be detected by linkage. Furthermore, the scenario where synthetic associations could have escaped linkage comprises models with a small number of causal variants with genotype relative risk <2.5 (53). While these observations do not entirely rule out synthetic associations, they seriously confine the parameter space in which they might exist. In addition, comparisons of even modest linkage signals with GWAS regions have shown only a few overlaps, and even these are largely driven by atypically large effects like the MHC in autoimmunity. In addition, attempts to explicitly use linkage information to boost the power of GWAS (54) have not been successful. This contrast between largely overlapping genetic models that linkage and synthetic association are well powered to detect and almost completely non-overlapping results from linkage and GWAS strongly suggests that synthetic associations do not underlie many GWAS signals.

PATHWAY ANALYSES IMPLY GWAS ARE POINTING TO KEY FUNCTIONAL ELEMENTS

Another prediction made by the synthetic association hypothesis is that the most significantly associated common variant identified by GWAS might be located several megabases away from the underlying low-frequency functional variants. The empirical properties of linkage disequilibrium between low-frequency and common variants are not fully understood, although the complete 1000 Genomes project (http://www.1000genomes.org/) will soon provide information necessary to evaluate this question directly. Nevertheless, two indirect pieces of evidence suggest that most GWAS hit SNPs are within a few hundred kilobases (and many within tens of kilobases) of their tagged functional alleles. First, a large number of GWAS signals across a variety of traits are nearby to genes previously established to cause Mendelian forms of the same trait (55). Secondly, genes involved in key pathways repeatedly arise in GWAS of some diseases. For example, 8 of 10 proteins involved in the Th17-differentiation signalling pathway have been associated with one or more auto-inflammatory diseases (56). As with many aspects relating to the evaluation of the prevalence of synthetic associations, deeper sequence data sets will be needed to fully answer the question of the distance between GWAS tag SNPs and causal variants, but these early patterns imply that the tag SNP often resides in the proximity of the relevant functional genomic element.

TRANS-ETHNIC ASSOCIATIONS ARE WIDESPREAD

Under the synthetic associations model, common variant signals reflecting single or multiple rare alleles are unlikely to be consistent across populations of different ancestry. This is based on the fact that many of these rare variants would have arisen recently and will therefore not be shared across diverged populations. The majority of GWAS to date have focused on populations of European descent. However, data on more diverse populations are now starting to arise. For example, a study from early 2010 clearly demonstrated that common variant signals for T2D are reproducible and have similar effect sizes across East Asian populations including Chinese, Malays and Asian-Indians in Singapore (57). In fact, T2D-associated variants have been found to be associated with disease in diverse populations (ranging from African-Americans to Chinese) by several studies (58–62). Similarly, in RA, the STAT4 locus, as an example, has shown reproducible association with disease in the USA (63), UK (64), Spanish, Swedish, Dutch (65), Korean (66), Colombian (67), Japanese (68) and Greek (69) populations.

FUTURE DIRECTIONS FOR GWAS AND THE SEARCH FOR GENETIC CAUSES OF COMMON DISEASE

Although synthetic associations explaining common GWAS signals for complex polygenic traits are certainly plausible and can occur under specific circumstances (e.g. NOD2 in CD), results from studies thus far suggest that these scenarios are actually a rarity. The idea that MRVs at a particular locus may be associated with complex traits of interest has been around for over a decade. We are now starting to accrue a growing body of empirical evidence in support of this hypothesis. The field of complex trait genetics has over the last few months engaged in discussions on the controversial topic of synthetic associations, but it transpires that there is little evidence to support this as a widespread scenario.

Empowered by advances in sequencing technologies, attention is currently shifting towards the comprehensive study of low-frequency and rare variants. Resources such as the 1000 genomes project and emerging large-scale studies like the UK10k project will undoubtedly facilitate the examination of variants at this end of the allele frequency spectrum. In parallel, improved strategies for accurate imputation and powerful analysis of low-frequency and rare variants in aggregate are being further developed and fine-tuned to the needs of these next generation truly genome-wide scans for association.

Conflict of Interest statement. None declared.

FUNDING

G.O. is funded by the European Union (Marie Curie IEF Fellowship PIEF-GA-2009-235662). E.Z. and J.C.B. are supported by the Wellcome Trust (WT088885/Z/09/Z, WT089120/Z/09/Z). Funding to pay the Open Access Charge was provided by The Wellcome Trust.