Genome-wide association studies: inherent limitations and future challenges

Abstract

Genome-wide association studies (GWAS) have achieved great success in identifying genetic variants related to complex human diseases such as cancer and have provided valuable insights into their genetic architecture. Recently, GWAS is quite the fashion in China. However, there are issues related to its nature. Enormous work needs to be done in the post-GWAS era. Deep sequencing followed by functional studies will be needed to elucidate the underpinning biological mechanisms and further translate GWAS findings into medical practice. Along with pharmacogenomics, the success of GWAS in identifying genetic risk factors and genetic differences in drug response has been gradually enabling personalized medicine. In this article, we used hepatocellular carcinoma (HCC) as an example to demonstrate some of the inherent limitations and summarized future challenges of GWAS.

Keywords

genome-wide association studies (GWAS) / genetic variant / cancer / limitation / challenge

Introduction

The completion of the Human Genome Project, the multi-national collaboration in the International HapMap Project, the well-established criteria for genetic association studies, and the fast advancement of genotyping technologies have made it possible to study the associations of genetic risk factors with complex diseases such as cancer. So far, genome-wide association studies (GWAS) have identified over 150 genetic loci for various cancers. However, the first wave of GWAS was rarely conducted in Chinese population. In the past few years, huge resources and efforts have been spent on GWAS in China. Many projects concerning different complex diseases such as cancer have been conducted. It is highly productive and the results are flourishing. Publications of GWAS results from the Chinese population have been appearing in leading journals at a very frequent pace [1-10]. GWAS has been claiming a lot of resources and attentions, just for different types of cancers alone there are about ten GWAS in the past three years. Table 1 summarizes recent results of cancer GWAS conducted in Chinese population. These findings have provided new insights into the possible mechanism of carcinogenesis. How to follow up these findings is important to elucidate the underpinning biologic basis and further translate GWAS findings into medical practice. For each new susceptibility region, additional genotyping and sequence analysis should be applied to comprehensively catalog the susceptibility alleles, followed by functional validation in cell lines or model systems, and then assess the interactions of genetic variations with environmental exposures.

An editorial entitled “Milestone in Anhui”[11] in Nature Genetics reported the first Nature Conference held in China (May 19th-21st, 2011). It summarized the achievements to date, demonstrated the current research environment, and oversaw the future cooperation and opportunity in the GWAS field in China. Nevertheless, GWAS is not the solution to major health problems. In developing countries such as China with inadequate health supplies and numerous public health problems, how much resource GWAS should claim is debatable. In this article we discussed some of the inherent limitations and future challenges of GWAS.

The successes and limitations of GWAS

The remarkable accomplishment of GWAS is exciting. GWAS have guided researchers to discover new biologic insights into many cancers and have proven the detective value for risk associated variants. For example, the nicotinic acetylcholine receptor subunit genes on 15q25 identified by lung cancer GWAS have shed light on the association between genetic determinants of nicotine addiction and lung cancer [12,13].

While with all its benefits, does that mean the future of genetic epidemiology will solely rely on these kinds of immense projects? The nature of GWAS has determined its inherent limitations. First of all, GWAS applies a non-candidate-gene approach, and it is hypothesis-free. In a typical GWAS analysis, the genotype-phenotype association is assessed for millions of markers one by one, false positive results may easily arise due to the multiple comparisons conducted. Therefore, a very large sample size is needed to achieve the optimal statistical power and minimize the spurious associations. Furthermore, the replication of the significant loci in independent populations is necessary according to the GWAS criteria. Hence, multi-center international collaboration is usually recommended if not always required. Setting up unified and high standards to conduct GWAS is the prerequisite for facilitating the formation of GWAS consortium to promote collaboration. GWAS is still in its very initial phase in China, efforts should be made to encourage collaboration, trust, and transparent databasing among Chinese GWAS researchers.

Secondly, association doesn’t mean causality. Most of the associated SNPs identified by GWAS are intergenetic or in the intron region. These SNPs may be the causal ones, or in linkage disequilibrium with the real functional SNPs. Case-control design of GWAS only indicate the association, rather than causation. Therefore, follow-up deep sequencing and functional studies are required to ascertain the biologic mechanisms. These studies require further investment. Different groups with common interest should work together to best utilize existing data and carry out next step research.

There is also the challenge of entangling the large amount of data acquired. Analyzing GWAS data is computationally demanding and requires its own methodology and philosophy. New methods incorporating different aspects of science including information technology and mathematical and physical modeling are being developed to serve this purpose. The systems-level approach of combing these large amounts of data sets with known cellular network information is particularly valuable in gaining biologic insights of cancer initiation, progression and metastasis.

In addition, many significant SNPs identified by GWAS only have relatively moderate or even weak effects, with the odds ratio (OR) generally between 1.1 and 1.4 [14]. Compared to the huge effects of certain environmental factors causing more than 2 fold increases in cancer risk, an OR of 1.33 on average might be easily missed if not controlling environmental factors. Although GWAS is successful in the discovery of many novel disease-associated variants, the statistically significant variants typically account for minimal portion of the genetic variance (Missing Heritability) [15], even for highly heritable traits such as height [16]. However, despite the relatively weak effects, in combination with other clinical and pathology predictors, genotyping these susceptibility SNPs could be a useful addition to assess disease risk and progression.

Last but not least, the cost of conducting GWAS is extremely high (usually more than 20 millions RMB per study) compared to other types of studies such as candidate gene association studies, and the results produced cannot be put into immediate medical practice. As GWAS is relatively easy to be published in high impact journals, these high-cost studies are suspected to be publication-oriented, rather than public health-oriented. Beneficiary should be those Chip companies, rather than Chinese citizens who actually pay the bills. China has a large population base with various existing (e.g. malnutrition in remote areas), and emerging public health problems (e.g., infectious diseases), hence how to best utilize the limited resources and control research costs needs careful consideration. With the same amount of input, dramatic changes can be made in certain public health areas, for example, vaccination in rural areas to reduce the rates of infectious diseases.

GWAS: bring personalized medicine into hepatocellular carcinoma (HCC)

Genetic factors are now very useful in clinical practice for predicting treatment outcome and adverse reactions in liver diseases [17]. Results from recent HCC GWAS make it possible to perform risk stratification based on genetic background as well as environmental (viral) factors of each patient.

Chronic infection with hepatitis B virus (HBV) and/or hepatitis C virus (HCV), exposure to toxins such as aflatoxin B1, alcohol consumption, no-alcoholic fatty liver disease, diabetes mellitus, and obesity are associated with an increased risk of HCC [18]. In mainland China, HCC is one of the leading causes of cancer mortality. Unlike in many developed countries, chronic HBV infection is the major cause of HCC in China [19]. The relative risk of HCC among HBV-infected individuals ranges from 5 to 49 in case-control studies and from 7 to 98 in cohort studies [20]. HBV genotype C, high HBV viral load (≥10⁴ copies/ml), HBx, and HBV mutations, such as C1653T, T1753V, A1762T/G1764A, preS deletion, C2964A, and C3116T are associated with increased risks of HCC [21-25]. Our meta-analysis has also shown that HBV preS deletions, A1762T/G1764A, T1753V, and C1653T are important risk factors for HCC, each has an OR of greater than 2 [23]. These associations are relatively strong, and also have relatively high sensitivities and specificities, and can be used for HCC surveillance in chronic HBV infected populations.

However, even though HBV infection is the major risk factor of HCC, only a fraction of chronic HBV carriers develop HCC. Viral mutation/variations, non-viral environmental factors, and genetic factors contribute synergistically to HCC occurrence in HBV-infected subjects. Multiple host genetic factors contribute to HBV-related HCC development. However, the host genetic factors have been incompletely characterized so far. Recently, three virus-related GWAS results have been reported. Zhang et al. identified a SNP within the KIF1B locus (rs17401966) associated with HBV-HCC risk [2]. Two GWAS carried out in Japan reported genetic factors, MICA locus (rs2596542) and DEPDC5 locus (rs1012068), associated with HCV-HCC [26,27]. For HBV and/or HCV patients who are unable to clear the viruses, screening of susceptible SNPs may help improve prognosis and target high-risk patients for more rigorous surveillance. When carrying out GWAS, it is crucial to select appropriate controls to avoid confounding risk factors. It is also crucial to integrate SNP interactions, environmental and viral factors in the HBV-related HCC GWAS in order to discover real associations. If not taking comprehensive consideration of these important risk factors when conducting GWAS, misleading results would occur. The GWAS by Clifford et al. didn’t consider the strong sex disequilibrium between the two disease status (HCC vs. liver cirrhosis), and produced the misleading result of pseudovariant rs2880301 in TPTE2 gene for HCC risk [28,29].

Challenges in the post-GWAS era

Initial GWAS has met success in identifying genetic variants associated with cancers. It is possible that the identification of susceptibility variants can be applied to individualized disease prevention. Along with pharmacogenomics, the success of GWAS in identifying genetic risk factors and genetic differences in drug response has been gradually enabling personalized medicine. However, it is far from an immediate course of action to translate GWAS findings into medical practice.

The genetic architecture of cancers remains elusive. The first stage of cancer GWAS has laid the foundation to discover risk associated regions and variants, the follow-up of these findings is essential to elucidate the underpinning biologic mechanisms. It is unclear how genetic determinants, environmental factors, and their complex interactions contribute to the disease development and progression. The typical GWAS analysis techniques tackle markers individually and do not have enough statistical power to detect gene-gene and gene-environmental interactions; however, cancers are unlikely to be caused by a single or even a combination of genetic variants. How to integrate germline susceptible variants to explore the underlying mechanisms of driver somatic mutations is one of the major challenges. Methods for system-level analysis of GWAS data, including multi-SNP GWAS analysis methods [30], pathway-based approaches [31], modeling epistasis of gene-gene interaction are needed. The new field of systems genetics, integrating the concept of systems biology to genetics, will greatly enrich our understanding of cancer genomics. Systems biology, combing empirical, mathematical and computational techniques, could help in building prediction models and enable the translation of GWAS findings. It is necessary to take genetic and environmental factors and their interactions into consideration when studying the associations of genetic factors with cancers, especially for infection-related cancers such as HCC.

With the accumulation of such large amount of data, conducting joint analyses of individual results is ideal [11]. In addition, combining GWAS data sets to perform meta-analysis is also very useful in increasing the statistical power to detect common alleles with small effect sizes (OR<1.2) [32]. However, population stratification is one of the concerns of genetic association studies. With so many different ethnic groups and diverse heritages, China’s large population is by no means homogeneous. How to control and analyze heterogeneity is another challenge encountered by Chinese GWAS researchers.

The GWAS results so far have indicated that the common SNPs are unable to account for the total heritability of a given complex disease such as cancer. The current available commercial genotyping arrays cover only a portion of the total genetic variants. Rare variants and non-SNP variants have not been intensively studied yet. The development of new generation sequencing technologies might enable researchers to study a broader spectrum of genetic variants, and bring about more biologic insights [15]. More efficient and accurate approaches are needed to interrogate the genetic variants and disease associations. These approaches should be cost-effective and be validated at the population level with a satisfactory statistical power.

Extending GWAS to longitudinal cohorts with the collection of large clinical resource including detailed environmental exposures and clinical histories will have profound effect on understanding cancer occurrence and progression [33]. The significant genetic variants from the case-control studies should be validated in prospective cohort studies for the following reasons. First, current GWAS only look at SNPs one at a time, unable to consider the impacts of other factors such as the cancer-associated inflammatory milieu on them due to limited statistical power. Somatic mutations in the circulating cancer cells and the cancer-associated inflammatory peripheral leukocytes might have confounding effects on the association of SNPs with cancers. Second, cohort studies are essential to establish temporal associations of genetic and environmental factors and their interactions with cancers and furthermore-causality. Finally, cohort study enables the research of cancer prognosis-related SNPs, which are likely to be driving forces of cancer evolution and development.

Conclusions

To summarize, GWAS, while accomplishing magnificently in the initial phase, have its inherent problems and challenges to face. The next phase of GWAS research will require more systematic, integrated and comprehensive methods. GWAS has proven its detective value for risk associated variants. However, in order to translate GWAS findings into medical practices, efficient genetic testing and prediction models need to be improved. Besides, GWAS requires large amount of input, both financially and intellectually, which are extremely costly and hardly available for many institutions and researchers. The application of GWAS should be carried out sensibly, with care for social, ethical and economic considerations. For most Chinese researchers, candidate gene approach is still a very useful tool and should be more appropriately utilized. The most important thing in conducting scientific research is always having the specific research questions in mind (cause of disease or response to treatment, etc.) and using the appropriate methods (good study design, well-conducted statistical analysis, etc.) to answer these questions. GWAS, like other research tools, are used for solving the problems; they cannot lead the study directions.

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