Interpreting non-coding variation in complex disease genetics

Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has primarily focused on protein-coding variants, due to the difficulty of interpreting non-coding mutations. This picture has changed with advances in the systematic annotation of functional non-coding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs, and molecular quantitative trait loci all provide complementary information about non-coding function. These functional maps can help prioritize variants on risk haplotypes, filter mutations encountered in the clinic, and perform systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable dataset integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis, and treatment.

for experimental work. Assigning a prior probability to the deleteriousness of a noncoding mutation is challenging 136 . To address this challenge, non-coding sequence is being annotated at a rapid pace through systematic efforts such as the ENCODE Project 21 and the Roadmap Epigenomics Mapping Consortium 22 , and through studies of the impact of common variants on genomewide molecular phenotypes, discussed below.
Detection of rare variants. Significant loci tend to additively explain only a small proportion of the narrow-sense heritability of phenotypes 12 , suggesting that rare rather than common variants may underlie their genetics, which will only be discovered through whole-exome and whole-genome sequencing or family-based studies 13 . Many explanations for "hidden heritability" among the discovered common-variant associations have been proposed 12 . The relative importance of rare and common variants is a topic of intense debate 137 , ranging from arguments that associations with common variants are in fact driven by synthetic associations with large-effect rare variants in long-range LD 138 , that common associations of weak effect contribute to heritability well beyond the threshold of statistical significance 139 , and that narrow-sense heritability may be overestimated in many twin studies due to epistasis disguised as additivity 98 .
Reproducibility. GWAS sometimes do not replicate across studies or populations 140 , leading to the report of false positives and suspicion of the validity of novel associations, especially when they are non-coding. This could be partly due to the difficulties both in imputing genotypes, which will benefit from an increased understanding of common human variation, and to the poor definition of organismal phenotypes 140 , which can benefit from molecular disease biomarkers discussed below. Moreover, while the specific loci involved may differ across populations, they may reflect the same underlying molecular pathways, and thus regulatory annotations may be more reproducible across populations. Focusing on molecular phenotypes may improve reproducibility by isolating potential socio-economic or other environmental factors that occur downstream of molecular phenotypes and can strongly affect organismal phenotypes.
In this review, we discuss both the computational challenges and the opportunities presented by the large number of non-coding disease-associated variants being discovered through GWAS and medical resequencing. We first survey the types of regulatory annotations available, including those from functional and comparative genomics as well as quantitative trait loci (QTLs) and allele-specific events, and the ways in which these can be used to dissect disease-associated haplotypes to identify the most promising causal variants at a locus. We then discuss the utility of these regulatory annotations to perform systems-level analysis of GWAS and allelic spectra, revealing relevant cell types and regulatory mechanisms. Finally, we present a variety of bioinformatics hurdles and computational challenges that lie ahead for the field, such as discovering epistatic interactions, connections between molecular and organismal phenotype, and patterns that must be mined from potentially sensitive medical data.

Systematic annotation of the non-coding genome
Interpretation of the molecular mechanisms of disease-associated loci can be a great challenge. Even though protein biochemistry has been used to characterize missense and nonsense coding mutations that most often underlie monogenic traits, the frequency with which loss-of-function mutations and rare coding variants are being discovered in healthy individuals 16,17 suggests our understanding is far from complete. The challenge of interpretation is even greater for non-coding variants, given the diversity of non-coding functions, the incomplete annotation of regulatory elements, and potentially still unknown mechanisms of regulatory control. Several pioneering studies have provided a model for the types of systematic regulatory annotations needed, by revealing the diverse mechanisms of action underlying human disease, including at the transcriptional, splicing, and translational level ( Table 3).
In each of these cases, extensive experimental follow-up was needed to uncover the molecular mechanisms responsible for the disease association signal, and many more disease-associated variants remain uncharacterized, emphasizing the need for systematic methods for annotating regulatory regions, their functional nucleotides, and their interconnections.
Recognizing the need for systematic interpretation of non-coding disease-associated variants, several large-scale projects are currently underway to enhance the annotation of the non-coding genome (Fig. 2). These rely on reference annotation maps using both functional genomics and comparative genomics, and can dramatically increase the annotation of regulatory elements, which can have a strong impact for interpreting both existing GWAS and individual personal genomes.

Reference functional genomics and chromatin state maps
Massively parallel short-read sequencing technologies have obviated the need for the extremely expensive tiling microarrays previously used to map biochemically active regions of the human genome. This has enabled chromatin immunoprecipitation followed by highthroughput sequencing (ChIP-seq) applied to map transcription factor binding, chromatin regulators, or histone modification marks 18 , mapping of DNA methylation using bisulfite sequencing (BS-Seq) 19 and mapping of accessible chromatin regions by DNase hypersensitivity analysis (DNase-Seq) 20 . Computational integration of these datasets through supervised or unsupervised machine learning enables mapping of functional noncoding elements such as distal enhancers, transcription factor binding sites, and regulatory RNA genes on a genome-wide scale. For example, the Encyclopedia of DNA Elements (ENCODE) project is releasing comprehensive maps of chromatin states, TF binding, and transcription for a selection of cell lines and DNase maps for many primary cells 21 , and the NIH Epigenomics Roadmap Project 22 and BluePrint project 23 both aim to construct reference epigenome maps of hundreds of primary cells and cultured cells. Regulatory maps can then guide the way towards the most likely causal regulators on a haplotype (Fig. 2a).

Nucleotide-resolution regulatory annotations
While maps of regulatory regions can be highly informative, increasing their resolution from hundreds of nucleotides to single nucleotides requires additional computational or experimental developments. This can leverage systematic efforts that seek to elucidate the binding specificities of transcription factors 24,25 and splicing regulators 26,27 , and to also discover regulatory motifs genome-wide based on their enrichment and conservation properties 28,29 . Similarly, new technologies have been applied to enhance existing techniques, such as digital genomic footprinting using DNAse-seq 30 , dynamic application of micrococcal nuclease (MNase) 31 , or the use of lambda exonuclease (ChIP-exo) 32 , dramatically increasing the mapping resolution of regulatory elements even without knowledge of the specific motifs involved.

Predictive models of variant effects
Even when the functional elements and motifs are known, we need models to distinguish how mutations in different positions of a regulatory motif or element will affects its function. These models can be used to distinguish silent from deleterious mutations, as is possible within protein-coding regions. This requires integrative models of sequence motifs, chromatin state, and expression patterns 24,[33][34][35][36] , which can be trained on experimentally tractable tissues or through in vitro experiments and applied to predict the effect of newlyobserved rare and private mutations. The massive scale of regulatory predictions, encompassing hundreds of regulators and millions of regulatory motif instances, demands correspondingly massively parallel methods to validate them. Such methods exploit emerging large-scale synthesis and sequencing technologies are being developed both in model organisms and cultured human cells 37-39 , and enable testing mechanistic hypotheses about causal variants at unprecedented scales (Fig. 2b).

Comparative genomics between related species
Even when a regulatory element is rarely used and its activity unobserved in the cell types and tissues sampled, its effect on fitness can still be recognized based on its preferential conservation across multiple related species. Genome-wide comparative analysis of many mammals has revealed a high-resolution map of constrained elements spanning 4.5% of the human genome 40,41 , revealing millions of likely new elements, including individual transcription factor binding sites, whose nucleotides have been preserved across evolutionary time. Beyond the overall level of evolutionary constraint, the specific evolutionary signatures encoded in the patterns of substitutions, insertions and deletions across related species can provide information for the type of molecular function likely encoded by the constrained elements 41-44 . Together, constraint and evolutionary signatures can pinpoint functional transcription factor binding motifs and individual binding sites (Fig.  2c), non-coding RNA genes and structures, microRNAs and their targets, and yet uncharacterized sequence elements that confer a selective advantage.

Evolutionarily conserved biochemical activity
Even in absence of conserved sequence, the conservation of biochemical activity can be indicative of conserved functional elements, even when the corresponding sequence features are not detectable by traditional alignment and constraint measures due to turnover 45,46 . Because some fraction of protein binding and RNA transcription may be nonfunctional "noise," cross-species analysis of transcription factor binding 47 or gene expression 48 can help reveal the subset of elements that are most likely to be functional. However, lineagespecific elements may nevertheless be important and not captured through this method.

Interpreting variants using functional genomic annotations
For protein-coding mutations, knowledge of protein structure and function, and the unambiguous nature of the genetic code, has allowed the development of a class of predictive algorithms that can score the severity of missense and nonsense variants 49-52 . Reference annotations are needed to bring functional datasets to bear on understanding the molecular roles of disease-associated common variants in individual regions, especially for non-coding variants (Fig. 2). In addition, new methods are needed to define the relationship between global genetic architectures and genome-wide functional landscapes.

Tools for prioritizing variants
An immediate concern for practitioners of GWAS is the interpretation and prioritization of non-coding variants 53 . A number of resources, including HaploReg 54 (L.D.W. and M.K.), RegulomeDB 55 , and ENSEMBL's Variant Effect Predictor 56 aim to annotate non-coding common variants from association studies using conservation, functional genomics, and regulatory motif data. Databases such as ANNOVAR 57 and VAAST 58 are specialized for annotating whole-genome/exome sequencing data, and leverage population-level negative selection to identify extremely rare coding alleles that are most likely to be functional. None of these tools presently brings together all of the available annotation resources listed in the previous section, however, and they will need to be continuously updated to reflect the exponential growth of regulatory knowledge (Table 4).

Gene set enrichment analysis
Prior knowledge of gene interrelationships has been leveraged in studies of gene expression to discover differentially-regulated pathways even where single genes in those pathways change expression too little to rise to statistical significance 59 . These methods for gene set enrichment analysis (GSEA) are being applied to GWAS, where similarly, genetic risk is expected to be concentrated along biological pathways and multiple testing diminishes the statistical significance of associations considered individually. Dozens of methods have been developed to use prior knowledge from gene functional annotation databases to perform pathway analysis on GWAS 60,61 (Fig. 3a).

Regulatory element enrichment analysis
A recent study used chromatin state maps to discover an enrichment of cell type-specific enhancers among the top associations in several GWAS 62 (L.D.W., M.K., and colleagues), demonstrating the utility of high-resolution functional genomics maps to serve as a type of pathway annotation. Similar results have been seen using DNase hypersensitivity maps across a large number of cell types 63 , and by examining concordance between expression quantitative trait loci (eQTLs) and GWAS 64,65 . These approaches have demonstrated the power of reference epigenomes to identify relevant tissues for further study (Fig. 3b). Another way to use prior knowledge about variant function is to incorporate the information into the association study itself through Bayesian methods 61,[66][67][68][69] or using boosting to prioritize disease networks 70 . However, it is difficult to evaluate the utility of these weighting schemes, which essentially discard loci about which there is the least functional data.

Burden tests; dealing with heterogeneity
For potentially causal rare variants discovered through whole-genome sequencing, a class of techniques has been developed that deal successfully with allelic heterogeneity and low allele frequencies by pooling mutations across individuals by genes, pathways, or other functional annotations and filters 71 ; the additional use of functional genomic maps has recently been proposed 72 . Improved annotation of non-coding regions will obviously empower this type of analysis (Fig. 3c). Table 5 lists examples of new insights from computational methods integrating regulatory elements with GWAS.

Interpreting variants using population variation in molecular phenotypes
While until this point we have discussed regulatory annotations from reference cell lines, biochemical activity is itself genotype-dependent, and thus a single reference annotation fails to capture the complexity of the regulatory genome. Moreover, we treated LD as a property of the human genome, while it is in fact population specific, and patterns of LD and selection have varied across both geography and time. This increased complexity can in fact be leveraged to gain additional insights into genome regulation, and provide additional power for the aforementioned analyses.

Genotype-associated molecular activity
Two powerful tools have emerged to identify non-coding loci that affect molecular phenotypes: association studies and allele-specificity studies. Association studies ( Fig. 1b) have been used to discover non-coding cis regulators of methylation (meQTLs) 73 , DNase I sensitivity (dsQTLs) 74 , transcription factor binding 75 , gene expression (eQTLs) 76 , and alternative splicing 77 . In the same manner as GWAS on organism-level quantitative traits, these studies consider a phenotype associated with a particular genomic locus (such as steady-state mRNA level corresponding to a gene) in the same cell type isolated across unrelated individuals, and search for genetic regulators of those molecular processes. A recent related study used eQTL data to reveal selective signatures of epistasis between deleterious coding variants and the regulatory variants that modulate their penetrance 78 , a method which should be broadly applicable to testing hypotheses about cis regulatory interactions from genomics models.

Allele-specificity activity
In contrast, allele specificity tests look at heterozygous sites in individuals and look for a skew in the molecular signal towards one of the alleles (Fig. 1c). Allele-specific methylation 79 , histone modification 80 , DNAse I sensitivity 81 , protein binding 82 , and expression 83 have been surveyed genomewide. While association studies have the advantage of identifying regulatory variants that may be acting at some genetic distance from the regulated locus, and can include homozygous individuals in the sample, allele-specific studies can be performed on single individuals, and inherently control for possible transregulatory differences caused by individuals' genetic background.

Importance of population-specific effects
Causal variants within associated haplotypes should be identified not only for further research, but also for genetic counseling; because of variations in LD patterns, a SNP that marks a risk haplotype efficiently in one population may not in another 84 . Computational methods that explicitly model ethnic background in admixed populations can increase their power by exploiting their shared ancestry 85 .

Population differentiation and positive selection
Haplotype structure and allele frequencies from the HapMap project 9 and 1000 Genomes project 86 provide evidence of both positive and negative selection currently acting on the human lineage. Although the relative importance of population structure and selective sweeps in recent human history is debated 87-89 , many non-coding loci show multiple lines of evidence for local adaptation 90 .

Utilizing population structure and relatedness
Ultimately, linkage analysis and GWAS are sensitive to complementary genetic architectures, but a wide spectrum of diseases likely exhibit both locus and allele heterogeneity. Because the genomically-distributed signals of association with complex disease are weak, the potential confounding effects of population stratification and cryptic relatedness become especially important to control. Family-based methods such as linkage analysis and the transmission disequilibrium test (TDT) are free of these complications, and have been combined with association tests in a new class of methods 91 . In addition, new methods in phylogenomics and ancestral recombination graph reconstruction provide an opportunity to enhance association studies by explicitly taking population structure and region-specific relatedness into account 92,93 .

Aggregate measures of purifying selection
Modeling of allele frequency data 94,95 and sequence divergence data 46 suggests that a large amount of negative selection is occurring outside of mammalian conserved elements, evidence for widespread non-coding function. These same forces can maintain diseaseassociated alleles at lower frequency in the population dependent on their penetrance and expressivity.

Identifying higher-order relationships between variants
Even when considering genome-wide enrichments of functional annotations in diseaseassociated regions, the aforementioned methods have so far considered each locus as acting independently and considered their effects as additive. Functional genomics should enable us to consider higher-order interactions between these individual loci, by leveraging functional and variation information to build interaction and regulatory networks. These networks can then guide the search for epistatic effects.

Detecting epistasis de novo
Substantial disagreement exists over the relative importance of epistasis in the genetic basis of complex disease [96][97][98] . While genetic interactions have been systematically mapped in yeast 99 and cases have been identified in human 66 , testing for all possible interactions remains impossible; understandably, detecting epistasis in association studies is an area of intense theoretical interest 66,100,101 . One method 102 successfully discovered epistasis between two taste receptor genes affecting nicotine dependence by using a multifactor dimensionality reduction (MDR) method integrated with linkage information from a pedigree disequilibrium test, similar to the hybrid linkage-association studies described previously 91 .

Guiding search for epistasis
Some methods propose to limit the search space for interactions by only searching among the most significant independently-associated loci; this method failed to discover any interactions among the 180 loci reported to be associated with height 103 . Another proposed limit on the search space is with prior knowledge from gene annotations and protein-protein interactions [104][105][106] . Again, epigenomic maps and improved regulatory annotation holds promise for zeroing in on relevant combinations of SNPs that might be expected to interact.

Linking enhancers to their target genes using physical interaction data
Unlike promoters, enhancers pose the dual challenge of both pinpointing their location in vast nonfunctional sequences, and linking them to their target genes. These distal regulatory elements often interact physically with promoters, and technologies to detect these interactions, such as chromatin conformation capture (3C, Hi-C) 107,108 and chromatin interaction paired-end tagging (ChIA-PET) 109 are advancing rapidly.

Linking enhancers to their target genes using cell-to-cell variability
Another way of detecting enhancer-gene relationships is to measure the correlation of these elements' activity with expression across multiple cell types and conditions. This technique is being used to infer gene regulatory networks in human 35 and model organisms 99,110 . While protein-protein interaction and metabolic networks are the most common types of prior knowledge integrated into existing algorithms, these regulatory networks may provide a more useful starting point in the search for epistasis.

Inferring networks from individual-to-individual variability
Molecular QTL data discovered from inter-individual variation can also being used to help infer regulatory networks 111 , which unlike evidence learned solely from expression patterns provide unambiguous directionality for causality.

Inferring networks from systematic perturbations
Chemical perturbations of cultured cells have been used for network inference. These experiments are useful not only for their relevance to understanding pharmacological mechanisms, but also for revealing the difference in network topology between normal and cancerous cells 112 , including gene-gene and gene-drug interactions relevant to interpreting genetic architecture of cancer.

Artificial selection and drug response experiments in model organisms
While human genetic history and selective pressures are closely intertwined, model organisms offer an opportunity to measure the global effects of selection and the resulting genetic interactions in a controlled setting 113,114 . Model organisms have also proven useful for testing gene-gene 99 and gene-drug 115 interactions on a scale that is impossible in humans.

Functional genomics in a medical setting
While genotyping and sequencing is already becoming commonplace for discovery of disease loci and increasingly for diagnostics in a clinical setting, in the future the democratization of genome-wide molecular profiling technologies will further enable cohort-level molecular association studies and personal functional genomics in a medical setting. These can complement existing genetic and chemical biomarkers with molecularlevel diagnostics of disease state.

Functional genomics of disease cohorts
One of the major clinical applications of DNA microarrays was to identify disease-involved genes and to classify disease subtypes by genome-wide expression signatures 116 , and disease-associated gene sets from microarrays and now RNA-seq can be used to define biological pathways, such as those in the Molecular Signatures Database (MSigDB) 117 . Similarly, chromatin maps can be compared across lineages or between disease and normal tissue to define sets of regulating loci (Fig. 1d). These sets can be used for enrichment and pathway analysis of GWAS, as described previously.

Epigenome-phenotype association
Microarray-based assays for methylation are now allowing for the first time "epigenomewide association studies" (EWAS) 118 , which identify differentially-methylated sites associated with disease without taking into account genotype (Fig. 1d). Such studies may bypass some of the environmental variability that lowers the penetrance of genetic factors 119 . Integrating family members into EWAS studies may be especially useful in order to test for imprinting and other parent-of-origin effects.

Genetic association with molecular phenotypes for determining causality
One important future use of molecular QTLs may be to empower Mendelian randomization studies 120,121 . Molecular traits -expression, epigenetic state, or biomarkers -can be important stepping stones between genetic variation and complex phenotypes, but the direction of causality can be unclear between the molecular trait and the organismal trait. A recent study used this method to challenge the idea that raising HDL cholesterol levels reduces risk of myocardial infarction, showing that alleles for higher HDL did not convey the genetic protection from heart disease that would be expected if cholesterol were causal 122 .

Predicting molecular consequences of rare and private mutations
Once these regulatory mechanisms are predicted from functional genomics and molecular variation, the next challenge is applying this knowledge to rare variants discovered by whole-genome sequencing (Figure 2d). A goal for regulatory genomics should be to develop models that predict the effect of novel regulatory variants with the same accuracy as existing methods for novel protein-coding variants.

Functional genomics of individuals
Some expression signatures of disease subtypes or progression are already being used clinically, and their use promises to grow. However, analogous to the problem of rare variants discovered through sequencing, clinical functional genomics samples will also exhibit patterns too rare in the population to have been correlated with disease. As a recent pilot study on an individual demonstrates 123 , there is both great power but also many challenges associated with interpreting such personal -omics profiling, and new computational models are needed that can generalize from the effects of common genetic and functional variation to personal genetics and functional genomics.

Hurdles in biomedical informatics and interoperability
In addition to these conceptual challenges of statistical and computational integration of disparate datasets, each of these topics has relied on extensive data sharing between genomics and medical genetics researchers. However, sharing is still limited due to privacy concerns and informatics challenges of database interoperability. These challenges are even greater for non-genomic datasets such as medical records and drug response, resulting in treasure troves of information remaining unused. To complete the integration of genomics into the drug discovery and target validation pipelines, several additional hurdles need to be overcome:

GWAS P-value sharing
In order to facilitate integrative analysis, GWAS investigators should report the association of all variants, not just those that are most significant. The editorial board of Nature Genetics recently articulated a policy to this effect 124 , but concerns remain about sufficiently deidentifying association results in order to protect subject privacy 125 . Procedures in place at central archives such as the NCBI's database of Genotypes and Phenotypes (dbGaP) and the European Genome-Phenome Archive (EGA) are crucial to balancing the rights of human subjects with the principles of scientific openness.

Database integration
The interoperability of databases remains paramount to integrative analysis. Continuing efforts by the UCSC Genome Browser and the ENSEMBL Genome Browser have facilitated integration of epigenomic and variation data, but better connections to domainspecific knowledge bases such as the GTex eQTL Browser, dbGaP analyses, and the NHGRI GWAS Catalog 11 would broaden the scope of connections available to geneticists.

Medical record standardization
Medical records have been successfully mined to discover epidemiological patterns 126 , adverse drug reactions 127 , and disease risk factors and heterogeneity 128 . As electronic medical records become populated with genetic data, cooperation with clinicians will be needed in order to mine patient data for genetic associations with biomarkers and disease, and discover novel patterns of disease heterogeneity 129 .

Integration of medical and pharmacogenomics datasets
Ultimately, informatics challenges will need to be resolved in order to connect the resulting molecular predictions to patient records, environmental variables, drug screening and response databases, towards enabling genomics as commonplace for clinical practice.

CONCLUSIONS
Data from GWAS and whole-genome sequencing continue to expand the catalog of noncoding variants implicated in human disease, and data from epigenome mapping consortia complemented with regulatory modeling are needed to prioritize candidate causal variants and candidate affected tissues. Thoughtful integration of systematic and manual annotations of gene sets along with higher-resolution functional maps may hold the key to implicating pathways and cell types, both through joint consideration of the many weak additive associations discovered in GWAS as well as in the search for epistatic interactions between variants. Clinically relevant regulatory interactions may then be tested experimentally in the tissues or in vitro experimental conditions that are predicted to recapitulate the phenotype. In addition, an explosion of functional genomics data has been facilitated by high-throughput sequencing technology, allowing "intermediate" molecular phenotypes to be correlated with both organismal phenotype and with genotype. This new type of data can be combined with genetic associations to decipher the mechanisms underlying complex disease.  unlike molecular QTLs discovered across individuals, these studies control for variation in trans genetic background. In this example, the G allele is not only associated with the presence of a TF binding peak at that locus, but in heterozygous individuals is overrepresented in ChIP-seq reads originating from that locus, suggesting that the TF binds specifically to the G allele. (d) Functional genomics data can be directly compared between cases and controls to discover biomarkers for disease, without necessarily attributing genetic causes to these molecular changes. Indeed, these biomarkers may be caused by trans genetic factors, environmental factors, or by the disease itself.

Figure 2. Dissecting haplotypes discovered through association tests
These three examples are ways to annotate loci containing several linked SNPs (in this case, three) to discover those most likely to be causal. (a) Functional genomics techniques are being developed to discover putative regulatory elements and link these elements to their target genes. Here, the middle SNP lies in an enhancer in Tissue 1 and Tissue 3, and regulates a gene to its left. (b) Regulatory genomics information leads to prediction of sequence motifs active in classes of enhancers, and this can be combined with the motif creation/disruption caused by variants. In this case, the middle SNP deletes a match to motif B, which is predicted to be active in enhancers found in both Tissue 1 and Tissue 3. (c) Comparative genomics identifies regions of evolutionary constraint in non-coding sequence.
Here, sequence surrounding only the middle SNP is constrained across mammals. A typical analysis of GWAS results will compare the set of genes near associated loci with prior knowledge about those genes, leading to hypotheses about the pathways involved (in this example, process A but not process B). (b) Non-coding enrichment analysis of genetic architecture using regulatory annotations. High-resolution maps of diverse regulatory annotations can also be intersected with GWAS results. Examples are shown where tissueassociated enhancers, eQTLs, DNAse peaks, or allele-specific polymerase binding are enriched among the results of a GWAS. In addition, regulatory annotations can be combined with gene-based annotations and linking information, in this case discovering an enrichment for enhancers linked to the genes involved in process A. (c) Interpreting linked loci exhibiting high allelic heterogeneity. In some cases only rare mutations at a locus contribute to its genetic mechanism, and these regions will only be discovered through classical linkage analysis. These regions can now be interrogated through WES/WGS, and an imbalanced burden of putatively deleterious alleles can be observed in cases (as in the left example). With regulatory annotations, these burden tests can now be extended to noncoding regions (as in the right example.) (d) Interpreting causal variants in whole genomes. Personal genomes pose the challenge of exposing potentially causal variants that Ward and Kellis Page 25 were too rare or low-penetrance to have been associated with a phenotype through association or linkage studies. For coding alleles, prior knowledge is currently used in several ways when analyzing personal genomes: knowledge of the genetic code (to filter on nonsynonymous variants), inference of negative selection from population panels (to filter out common variants), and models developed from biophysical principles (to focus on those amino acid substitutions most likely to alter protein structure and function.) Similar pipelines will need to be developed for regulatory regions. We propose using both population-level and cross-species signals of selection (to filter out not only common variants, but those that are not constrained across mammals), and all of the regulatory models previously mentioned (predicted regulatory elements and the motifs active within them, molecular trait associations such as eQTLs, etc.) Such a pipeline will be crucial to interpreting the flood of sequencing data that will be collected in both clinical and research settings. Table 1 The diversity of genetic architectures underlying human phenotypes.

Architecture Notes Role of computational and regulatory genomics
Classic monogenic traits The earliest human genes characterized were those leading to inborn errors in metabolism, which were shown by Garrod in the early 1900s to follow Mendelian inheritance 140,141 . The modern study of human disease genes began with the cloning of loci responsible for highpenetrance monogenic disorders with Mendelian inheritance patterns, such as phenylketonuria and cystic fibrosis 140,142,143 , that were most amenable to classical mapping approaches. Variants associated with monogenic traits were also the first to be identified through positional cloning in the 1980s, a classic success being the CFTR mutations responsible for most cases of cystic fibrosis 3,142,143 .
As the underlying mutations tend to alter protein structure, the computational challenge in predicting their effect lies in molecular modeling and structural studies.

Monogenic traits with multiple disease alleles
Even monogenetic diseases differ greatly in the extent to which a single risk allele predominates among affected individuals (allelic heterogeneity). On one end of the spectrum, the F508del allele of CFTR is found in about 70% of patients with cystic fibrosis 144 , even though thousands of alleles are known. In contrast, phenylketonuria is extremely heterogeneous, with different PAH alleles predominating among affected individuals in different populations 145 . A majority of mutations in this class are missense or nonsense coding mutations 3 .
As noted above, for protein-coding mutations, the relevant problem is predicting the biochemical effect of the amino acid substitution. In cases of allele heterogeneity, the observed substitutions may be too numerous to characterize experimentally, necessitating computational models (Fig. 3c).
Multiple loci with independent contributions ("oligogenetic") Many variants increase or decrease the risk of a disease, with the final phenotype relying on the genotype at many loci (locus heterogeneity). One example well-studied through linkage analysis is Hirschprung disease, a complex disorder with low sex-dependent penetrance for which at least ten genes are involved, including the tyrosine kinase receptor RET and the gene GDNF which encodes its ligand 146 . Interestingly, the most common variant in the main susceptibility gene RET is non-coding, a singlenucleotide polymorphism (SNP) in an enhancer. Both coding and non-coding variants are involved typically in one or a small number of well-defined pathways.
Oligogenetic traits, in which a handful of well-characterized loci contribute to the phenotype, may present the best opportunity to observe and quantify epistatic interactions. In cases where non-coding regions are implicated, these haplotypes can be functionally mapped to isolate the most likely causal variants (Fig. 2).
Large numbers of variants jointly contributing weakly to a complex trait GWAS on complex traits are also discovering many weaklycontributing loci. For example a recent meta-analysis of several height studies found 180 loci reaching genome-wide significance 15,103,139 , enriched near genes already known to underlie skeletal growth defects. In the height study and in a study of psychiatric disorders, it has been shown that polygenic association extends to thousands of common variants, extending far beyond genome-wide significant loci 135,139 In contrast to the variants underlying monogenic traits, the variants involved in complex traits are overwhelmingly not associated with missense or nonsense coding mutations, suggesting that their mechanisms are primarily regulatory 11 . Large sets of regulatory variants can be combined with reference annotations to elucidate relevant pathways and tissues (Fig. 3b, Table 5).
Variants regulating a "molecular trait" with unknown effect on organismal phenotype or fitness Variants are rapidly being discovered that directly affect molecular quantitative traits, such as gene expression or chromatin state, many of which may have no effect on organismal phenotype or fitness 38 .
QTL and allele-specific analyses are needed to characterize these variants (Fig. 1b,c). As the studies performed to date sample only a small fraction of the cell types in which a variant may have an effect, and variantexpression associations are highly tissuespecific 147 , it is possible that many such regulatory variants remain to be discovered.
Variants causing no known molecular phenotype and no effect on organismal phenotype or fitness The idea that the majority of mutations are neutral from an adaptive perspective was controversial when first proposed, and now is widely accepted [148][149][150] .
Although it is straightforward to calculate from the genetic code what fraction of protein-coding mutations will cause an amino acid change, an analogous estimate for other molecular phenotypes is far more challenging and requires comprehensive regulatory models at the nucleotide level.
Private and somatic variants Somatic mutations within an organism are frequent driver mutations selected in cancer formation 151 .
The interpretation of private and somatic variations (Fig 3d) will also benefit Ward and Kellis Page 29 Table 2 Computational tools for association analyses.

Class of analysis Tool Notes
Genome-wide association between genotype and phenotype (GWAS) SNPTEST 155 Incorporates imputation  Mechanisms through which non-coding variants influence human disease.

Non-coding element disrupted Molecular function and effect of mutations. Disease association
Splice-junction and splicing-enhancer Splicing is constitutive for some transcripts and highly tissue-specific for others, relying on both canonical sequences at the exon-intron junction as well as weaklyspecified sequence motifs distributed throughout the transcript. Mutations affecting constitutive splice sites can have an effect similar to nonsense or missense mutations, resulting in aberrantly included introns or skipped exons, sometimes resulting in nonsense-mediated decay (NMD).
Splicing regulatory variants are implicated in several diseases 164,165 .
A recent analysis suggests that the majority of disease-causing point mutations in OMIM may exert their effects through splicing 166 .
Alternative splice site variants in the WT1 gene are involved in Frasier Syndrome (FS) 167 Skipping of exon 7 of the SMN gene is involved in spinal muscular atrophy (SMA) 168 Sequences regulating translation, stability, and localization Sequences in the 5′-untranslated regions (UTRs) of mRNAs can influence translation regulation, such as upstream ORFs, premature AUG or AUC codons, and palindromic sequences that form inhibitory stem loops 169 . Sequence motifs in the 3′-UTR are recognized by microRNAs and RNA-binding proteins (RBPs).
Loss-of-function mutations in the 5′-UTR of CDKN2A predispose individuals to melanoma 170 .
A rare mutation that creates a binding site for the miRNA hs-miR-189 in the transcript of the gene SLITRK1 is associated with Tourette's syndrome 171 .
Genes encoding trans-regulatory RNA Non-coding RNAs participate in a panoply of regulatory functions, ranging from the well-understood transfer and ribosomal RNA to the recently-discovered long non-coding RNAs 172,173 .
Both rare and common mutations in the gene RMRP encoding an RNA component of the mitochondrial RNA processing ribonuclease have been associated with cartilage-hair hypoplasia 174 Non-coding RNA mutations can cause many other diseases 175 .

Promoter
Promoter regions are an essential component of transcription initiation and the assembly of RNA polymerase and associated regulators. Mutations can affect binding of activators or repressors, chromatin state, nucleosome positioning, and also looping contacts of promoters with distal regulatory elements. Genes with coding disease mutations can also harbor independently-associated regulatory variants that correlate with expression, are bound by proteins in an allele-specific manner, and disrupt or create regulatory motifs 176 .
Mutations in the promoter of the HIV1-progression associated gene CCR5, are correlated with expression of the receptor it encodes and bind differentially to at least three transcription factors 177,178 APOE promoter mutations are associated with Alzheimer's disease 179,180 Heme oxygenase-1 (HO-1) promoter mutations lead to expression changes and are associated with many diseases 181

Enhancer
Enhancers are distal regulatory elements that often lie 10,000 to 100,000 nucleotides from the start of their target gene. Mutations within them can disrupt sequence motifs for sequence-specific transcription factors, chromatin regulators, and nucleosome positioning signals. Structural variants including inversions and translocations can disrupt their regulatory activity by moving them away from their targets, disrupting local chromatin conformation, or creating interactions with insulators or repressors that can hinder their action. While it is thought that looping interactions with promoter regions play a role, the rules of enhancer-gene targeting are still poorly understood.
The role of distal enhancers in disease was suggested even before GWAS by many Mendelian disorders for which some patients had translocations or other structural variants far from the promoter [182][183][184] .
In one early study, point mutations were mapped in an unlinked locus in the intron of a neighboring gene, a million nucleotides away from the developmental gene Shh 185 ; this distal locus acted as an enhancer of Ward and Kellis Page 32 Table 4 Comparison of recent tools to systematically annotate variants Many such tools have been released as databases or software in the past decade; listed below are a sampling of the most recent.  Table 5 Examples of regulatory enrichment analyses of genetic associations.