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05.02.2014
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Results from numerous linkage and association studies have greatly deepened scientists’ understanding of the genetic basis of many human diseases, yet some important questions remain unanswered. As fruitful as GWAS is, it still leaves many intriguing questions unanswered, top on which are the following two problems.
The rapidly developing NGS technology promises many opportunities to answer some of these questions. Next-generation sequencing can be used to identify not only single nucleotide variants (SNVs), but also SVs and epigenetic variations. By its study design, GWAS works ideally under the common-disease common-variant (CDCV) hypothesis (Visscher et al., 2012). Instead of using LD to detect signals near the probed SNVs, NGS allows researchers to study all the SNVs in each individual directly. Due to the still high cost of WGS, targeted sequencing is more commonly used as a cost-effective study design.
Re-sequencing GWAS loci of autoimmune diseases have yielded rich positive results (Hunt et al., 2013). Relatively fewer studies use population-based WGS for complex disorders or traits due to the high cost. Whole-exome sequencing and WGS allow a revisit to monogenic diseases that are traditionally studied using linkage analysis, and bring the opportunities of finding genetic causes for intractable patients with no previously known causes.
The major strategy used in GWAS analysis is to evaluate each SNP individually with a univariate statistic. All of our training programs are committed to insuring a range of diversity among our training classes. If confirmed, she would become the Assistant Secretary of Veterans Affairs for Policy and Planning. This means that you will not need to remember your user name and password in the future and you will be able to login with the account you choose to sync, with the click of a button. This page doesn't support Internet Explorer 6, 7 and 8.Please upgrade your browser or activate Google Chrome Frame to improve your experience.
For example, although a large number of disease-associated loci have been identified from genome-wide association studies in the past 10 years, it is challenging to interpret these results as most disease-associated markers have no clear functional roles in disease etiology, and all the identified genomic factors only explain a small portion of disease heritability. From linkage studies to genome-wide association studies (GWAS) to the extensive application of next-generation sequencing (NGS), technological developments have improved study designs, deepened our understanding of disease etiology, and led to numerous scientific discoveries (Figure 1).
Ultimately, it has the potential to provide further biological insight into disease etiology, which may lead to important clinical applications including disease prevention, diagnosis, and treatment. However, the CDCV hypothesis may not hold for many common diseases as recent studies have suggested a substantial contribution of rare variants to many diseases and traits, as reviewed in Gibson (2012).
However, it will also reveal an overwhelmingly large number of rare variants, most of which have no functional relevance. However, several pioneer studies have demonstrated the potential of WGS in understanding the genetic architecture of complex diseases, especially for discovering the contribution of rare variants and variants in non-coding regions. For rare monogenic diseases, causal variants may be identified even with a small sample size.
Rare diseases are usually diagnosed or defined by symptoms, whereas the same symptom can be induced by different mechanisms. Individuals with the same disease may have different causal variants from the same gene or different genes in the disease pathway(s).
Even if there is a substantial contribution from rare variants, it remains challenging to detect rare variant associations due to low statistical power. However, standard individual variant tests are underpowered to detect rare variant effects due to the low minor allele frequency (MAF) unless effect sizes or sample sizes are very large. This can be done either through an indicator of whether an individual has any rare variants, or through summing up the total number of rare alleles (Morris and Zeggini, 2010). With the help of next-generation sequencing (NGS), diverse types of genomic and epigenetic variations can be detected with high accuracy.


GWAS benefits from the array technology that allows millions of markers to be genotyped at reasonable cost and speed with high accuracy.
This is partly due to our limited understanding of genomic function, especially for non-coding regions, in which considerably many disease-associated loci have been identified. GWAS will likely fail to detect signals from rare variants in these cases unless the effect size is very large or the causal variants are in strong LD with genotyped markers. Therefore, it is non-trivial to identify the causal variants even after accurate variant calling.
Although WES misses the entire non-coding genome and sometimes part of the coding regions, numerous scientific discoveries have been made using WES (Majewski et al., 2011). By re-sequencing 55 ulcerative colitis GWAS loci in 200 cases and 150 controls, variants with low frequencies were detected in CARD9, IL23R, and RNF186 (Beaudoin et al., 2013). However, it remains challenging to identify causal alleles for most common diseases as well as some rare diseases with genetic heterogeneity (McClellan and King, 2010), which typically require a larger sample size. These studies not only identified de novo causal rare variants, but also demonstrated how large their contributions are to neurodevelopmental disorders, which brings new insight into the genetic etiology.
Many statistical methods have been proposed to increase the signal or reduce the noise in testing variant-disease association using sequencing data.
Moreover, rare variant association studies usually involve extreme multiple testing due to the large number of rare variants in each individual. Both schemes completely ignore the effect of common variants and weight all rare variants equally, independent of their allele frequency. More importantly, instead of using linkage disequilibrium to detect association signals based on a set of pre-set probes, NGS allows researchers to directly study all the variants in each individual, therefore promises opportunities for identifying functional variants and a more comprehensive dissection of disease heritability.
The vertical axis indicates the number of publications and the horizontal axis indicates different years. The correlation structure among neighboring variants, often referred to as linkage disequilibrium (LD), also impacts our ability to interpret the results. We also summarize study designs, statistical methods for analyzing sequencing data, current findings, and challenges. SVs in the human genome include copy number variants, copy-number neutral (balanced) translocations, and inversions of various sizes. Moreover, due to the relatively high cost of sequencing compared to other technological options today, most sequencing studies do not have a very large sample size to have adequate statistical power to detect signals through traditional univariate statistical tests.
Finally, in terms of sequencing target, WGS, WES, and candidate-gene sequencing all have been applied.
The synthetic association hypothesis (Dickson et al., 2010) provides the theoretical support for re-sequencing GWAS candidate genes in search of causal rare variants. However, re-sequencing 25 GWAS loci in a very large sample (24,892 cases for six autoimmune disease phenotypes and 17,019 controls) did not yield a supportive result for the synthetic association (Hunt et al., 2013).
As the sequencing cost continues to drop (now less than $1000 per genome at 30x coverage with the announcement of Illumina Hiseq X Ten system), we can expect more population-based WGS studies in the future. The identification of causal variants for those diseases generally requires larger sample sizes than monogenic diseases. On the other hand, individuals with the same causal genetic factors may not manifest the same phenotype due to incomplete penetrance, interaction with other genetic, epigenetic, or environmental factors. For example, according to twin studies, autism disorder has an estimated broad-sense heritability of over 0.9 (for the narrow phenotype of autism), while GWAS loci can only explain a small part of the heritability (Freitag, 2007). We group these methods into three general categories: group-based association test, meta-analysis, and functional annotation. Although the current scale of NGS studies is still limited due to the high cost, the success of several recent studies suggests the great potential for applying NGS in genomic epidemiology, especially as the cost of sequencing continues to drop. The three colors correspond to genetic linkage studies, GWAS, and next generation sequencing, respectively.
In fact, it is the haplotype blocks that a GWAS actually identifies, not the real functional variants (Cooper and Shendure, 2011).
First, if GWAS does detect the signal (likely to be weak) due to LD structures, the implicated genomic regions can be re-sequenced to uncover the candidates of causal variants. In order to tackle this problem, various strategies in terms of study designs and statistical models have been proposed and implemented. Although cost and budget are important factors in choosing a study design, it is crucial to choose the most appropriate study design according to the underlying genetic etiology in order to make meaningful scientific discoveries. The greater interpretability for variants in the coding regions, the increased statistical power due to less severe multiple testing and the larger sample size due to the much lower cost altogether make WES and candidate-gene studies popular choices. The contribution of rare variants in coding regions is negligible compared with that of common variants.


Results from those studies may greatly deepen our understanding of disease architecture, due to their unparalleled coverage of the human genome. All of these scenarios may exist simultaneously among patients of a complex disease, making it difficult to characterize the genetic etiology. A recent WES study on more than 2500 simplex families showed that 12% of autism diagnoses can be explained by 13% of de novo missense mutations, and 9% of autism diagnoses can be explained by 43% of de novo likely gene-disruption mutations (Iossifov et al., 2014).
However, despite using very different techniques, these three categories are closely related to each other and are often used in combination.
In this review, we discuss several pioneer applications of NGS, summarize scientific discoveries for rare and complex diseases, and compare various study designs including targeted sequencing and whole-genome sequencing using population-based and family-based cohorts. Second, if the disease risk is driven by rare variants independent from the genotyped SNVs, then GWAS will completely miss the signal.
Notably, although this study has a very large sample size, only the exons of 25 GWAS loci were re-sequenced. GWAS can only identify common variants with reasonable effect sizes, which are usually non-causal.
Finally, we highlight recent advancements in statistical methods proposed for sequencing analysis, including group-based association tests, meta-analysis techniques, and annotation tools for variant prioritization. This has been partly resolved by estimating the chip heritability using linear mixed models and restricted maximum-likelihood estimation in genome-wide complex trait analysis (Yang et al., 2010, 2011).
In this case, WES or WGS could be used to scan the exome or the genome and detect rare-variant associations in a hypothesis-free manner.
This type of studies usually significantly reduces the cost of sequencing so that larger samples may be analyzed.
Prior knowledge of diseases can guide researchers to make the best use of NGS when designing studies. Therefore, the contribution of rare variants to Crohn’s disease still awaits further assessment with genome-wide screening. These results demonstrate the power of NGS in identifying causal variants for monogenic diseases even with very small sample size.
Notably, although contribution from de novo mutation to population-level phenotypic variation is small compared with common variants, de novo mutations are very important for individual phenotype, and thus detecting those causal de novo mutations is important and may lead to improvements in disease risk prediction and personalized treatment. Therefore, many group-based association tests that assess the cumulative effects of multiple variants have been proposed for sequencing studies. Second, the associated loci discovered from GWAS could serve as risk predictors for some diseases, provided large enough GWAS discovery sample size (Wray et al., 2008).
The second strategy is to focus on a certain type of variants that are more likely to be causal by properly choosing the control group. There are certain complications, though, caused by potential unnoticed environmental risk factors (Weatherall, 2001) and existence of functionally redundant paralogs of disease genes (Chen et al., 2013). Moreover, the fact that the detected de novo mutations tend to come from certain pathways further reveals the pathological mechanisms of those disorders, which may lead to novel treatment strategies. Those genetic risk predictors, either used alone or combined with traditional non-genetic risk prediction, have the potential to improve risk-prediction accuracy, which might benefit clinical diagnose and personalized treatment (Jostins and Barrett, 2011). Epigenetic variations are the modifications on DNA or chromatin without altering the DNA sequence. All these genetic and non-genetic factors may have substantial contributions to the etiology for some diseases and disorders.
Now, with the help of NGS, epigenetic variations can be detected with ultra high resolution. These statistical methods are crucial when applying NGS in population-based association studies.
Since many types of genomic and epigenetic variations can be detected with improved coverage and accuracy using sequencing data, NGS has the potential to partly recover the missing heritability. Therefore, a more comprehensive view of the decomposition of phenotypic variance is expected from applications of NGS in genetic epidemiology.
However, although the variant-calling accuracy of structural and epigenetic variations has been significantly improved, it is still relatively low compared to that of SNVs, especially for earlier sequencing technology with shorter reads.
It is also particularly challenging to call SVs using whole-exome sequencing (WES) compared with whole-genome sequencing (WGS).



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