Genetic Study of Complex Diseases in the Post-GWAS Era

Available online at www.sciencedirect.com

ScienceDirect Journal of Genetics and Genomics 42 (2015) 87e98

JGG REVIEW

Genetic Study of Complex Diseases in the Post-GWAS Era Qingyang Huang* College of Life Sciences, Central China Normal University, Wuhan 430079, China Received 16 October 2014; revised 1 February 2015; accepted 3 February 2015 Available online 13 February 2015

ABSTRACT Genome-wide association studies (GWASs) have identified thousands of genes and genetic variants (mainly SNPs) that contribute to complex diseases in humans. Functional characterization and mechanistic elucidation of these SNPs and genes action are the next major challenge. It has been well established that SNPs altering the amino acids of protein-coding genes can drastically impact protein function, and play an important role in molecular pathogenesis. Functions of regulatory SNPs can be complex and elusive, and involve gene expression regulation through the effect on RNA splicing, transcription factor binding, DNA methylation and miRNA recruitment. In the present review, we summarize the recent progress in our understanding of functional consequences of GWAS-associated non-coding regulatory SNPs, and discuss the application of systems genetics and network biology in the interpretation of GWAS findings. KEYWORDS: SNP; Genome-wide association study; Complex diseases; Systems genetics; Network biology

INTRODUCTION Genome-wide association studies (GWASs) have identified lots of loci that contribute to complex diseases in humans. The National Human Genome Research Institute Catalog of published GWAS indicates that by 2013, 1751 curated publications reported 11,912 SNPs associations with P < 1
105 (Welter et al. 2014). The greatest challenge in the ‘postGWAS’ era is to understand the functional consequences of these SNPs and to accurately elucidate the biological mechanism by which these genes and SNPs act. Different from simple Mendelian diseases that result from individual variant primarily in coding regions, the vast majority of SNPs that have been identified for common complex diseases map to non-coding intergenic and intronic regulatory regions (Hindorff et al., 2009; Maurano et al., 2012), are enriched for expression quantitative trait loci (eQTLs) (Nicolae et al., 2010), DNase I hypersensitive sites sequencing (DHSseq) peaks, and chromatin immunoprecipitation sequencing (ChIPseq) peaks (ENCODE Project Consortium et al., 2012; * Tel: þ86 27 6786 7221. E-mail address: [email protected].

Maurano et al., 2012). SNPs altering the amino acids of protein-coding genes can drastically influence protein function, and play a vital role in disease pathophysiology. By contrast, regulatory SNPs (rSNPs) show modest effects that might modify gene function more subtly. rSNPs can modulate gene expression through multiple mechanisms involving RNA splicing, transcription factor binding, DNA methylation and miRNA recruitment (Table 1). In this review, we first summarize the recent progress in functional mechanism studies of GWAS-associated SNPs, with a focus on non-coding rSNPs, and then discuss the integration of systems genetics and network biology with GWAS findings.

FUNCTIONAL MECHANISMS OF GWASASSOCIATED SNPs SNPs affect RNA splicing It has been estimated that nearly 90% of protein-coding genes are subject to alternative splicing in humans. Splicing of mRNA can be regulated by rSNPs occurring within branch sites, 50 and 30 splice sites, and intronic and exonic splicing

http://dx.doi.org/10.1016/j.jgg.2015.02.001 1673-8527/Copyright Ó 2015, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, and Genetics Society of China. Published by Elsevier Limited and Science Press. All rights reserved.

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Table 1 Functional mechanisms of GWAS-associated SNPs Functional category

Computational tool

Experimental approach

RNA splicing

ESE finder, RESCUE-ESE

RNA-seq

Transcription factor binding

TRANSFAC, JASPER, MAPPER2

EMSAs ChIP, eQTL, reporter assays

DNA methylation

MethDB, EpiGraph, ENCODE

MeDIP-seq, MRE-seq, methylation array, bisulphate sequencing

miRNAs-mRNA interaction

miRNASNP, mrSNP, SNPinfo, MirSNP, miRdSNP

Reporter assays, Western blot, RNAi

LncRNA expression or structure

LincSNP, LncRNASNP 3dRNA

RNA-seq, RIP, microarray

enhancers and silencers. Disruption of normal splicing has been implicated in disease pathophysiology. Although the majority of Mendelian disease causing point mutations may exert their effects by altering splicing (Lo´pez-Bigas et al. 2005), there are relatively few examples of SNPs that alter splicing and drive complex diseases. The SNP rs10069690 was associated with breast cancer 1 (BRCA1) mutation carrier breast cancer, prostate cancer and invasive ovarian cancer. The risk minor allele of rs10069690 generates a severely truncated telomerase reverse transcriptase (TERT) splice variant INS1b that is likely to affect telomerase activity (Bojesen et al., 2013). The disrupted in schizophrenia 1 (DISC1) gene is a susceptibility gene for schizophrenia. More than 50 splice variants in the DISC1 gene have been identified in the brain. These isoforms are associated with schizophrenia risk-associated SNPs in the DISC1 gene (Nakata et al., 2009). Similarly, the risk allele of the Alzheimer’s disease associated SNP rs9331888 in the clusterin (CLU ) gene increases the relative abundance of transcript NM 203339 (Szymanski et al., 2011). Rs12459419, a proxy SNP for Alzheimer’s disease associated rs3865444 in the promoter of CD33, modulates the splicing efficiency of CD33 exon 2 (Malik et al., 2013). The SNP rs13438494 in intron 24 of piccolo presynaptic cytomatrix protein (PCLO) gene that was associated with bipolar disorder in a meta-analysis of GWAS alters splicing efficiency by creating or disrupting a splicing motif that binds splicing regulatory proteins (Seo et al., 2013). For multiple sclerosis, the SNP rs1800693 is located proximal to the exon 6/intron 6 boundary of the tumor necrosis factor receptor superfamily, member 1A (TNFRSF1A) gene and affects splicing of the TNFRSF1A mRNA. The risk G allele generates the shorter tumor necrosis factor receptor superfamily, member 1a (O6-TNFR1) protein that can antagonize TNF signaling to promote multiple sclerosis (Gregory et al., 2012). Protein kinase C a (PRKCa) gene was repeatedly associated with multiple sclerosis. Exon 3* can be present in two different versions in PRKCa mRNAs, out-offrame 61 bp or in-frame 66 bp long. The GGTG insertion shifts splicing towards the 66 bp isoform and downregulates 3* inclusion. Dysregulated PRKCa 3* inclusion is relevant to multiple sclerosis susceptibility (Paraboschi et al., 2014).

Likewise, SNPs in CDK5 regulatory subunit associated protein 1-like 1 (CDKAL1) gene have been associated with the risk of type 2 diabetes (T2D). A deficit of 2-methylthio modification of adenosine at position 37 of tRNALys(UUU) catalyzed by CDKAL1 causes aberrant protein synthesis, and is associated with impairment of insulin secretion in both mouse model and human. CDKAL1-v1, a specific non-coding splicing variant of CDKAL1, is expressed lower in individuals carrying risk SNPs of CDKAL1. Interestingly, CDKAL1-v1 competitively binds to a CDKAL1-targeting miRNA and increases the CDKAL1 level (Zhou et al., 2014). In addition, SNPs rs7703051, rs12654264, and rs3846663 in the 3-hydroxy-3methylglutaryl-CoA reductase (HMGCR) gene are associated with low-density lipoprotein cholesterol (LDL-C) in a GWAS. The SNP rs3846662 in intron 13, which was in linkage disequilibrium (LD) with these SNPs, regulates alternative splicing of HMGCR exon 13 by modulating heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1) binding (Burkhardt et al., 2008; Yu et al., 2014). SNPs affect transcription factor binding rSNPs in the DNA functional elements including promoters, enhancers, silencers and insulators can destroy, create, alter or modify motif sequences for transcription factors, resulting in abrogated, induced, increased or decreased binding. Thus, rSNPs in transcription factors binding sites may lead to changes in the localization, timing and level of gene expression, and ultimately affect disease susceptibility. Various bioinformatic tools have been developed to estimate the difference of transcription factor binding affinities between alleles by calculating the log-odds of binding probabilities for paired DNA motif sequences containing SNP (Matys et al., 2003; Sandelin et al., 2004; Macintyre et al., 2010; ThomasChollier et al., 2011). Standard techniques such as electrophoretic shift assays (EMSAs), chromatin immunoprecipitation (ChIP) and luciferase reporter constructs are widely applied to assess effects of rSNPs on gene transcription and binding. Chromatin conformation capture (3C) and fluorescence in situ hybridization-related methods are used as the supplementary experiment to directly verify the SNP effect on three dimensions, such as enhancer-promoter interaction. Additionally, the causal relationship between SNP and its functional products in vivo can be tested by correlating the genotype with target gene/protein expression in human samples (Civelek and Lusis., 2014). The final proof for the role of an SNP or gene in a disease needs the experimental evidence of animal models (Edwards et al., 2013). Many diseases can be attributed to the allele-specific transcription factor binding. Enhancers are the most abundant regulatory sequences, and are often cell-type specific. SNPs commonly target enhancers bound by transcription factors. For example, GWAS showed that SNPs rs11986220, rs1859962 and rs339331 are associated with prostate cancer. Risk allele of rs11986220 facilitates both stronger forkhead box A1 (FOXA1) binding and stronger androgen responsiveness (Jia et al., 2009). The rs1859962 risk LD block of the SOX9

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gene including SNPs rs8072254 and rs1859961 harbors a prostate cancer-specific enhancer. The variant allele of rs8072254 facilitates androgen receptor binding and increases enhancer activity. The variant allele of rs1859961 decreases FOXA1 binding and increases AP-1 binding to modulate the activity of the same enhancer (Zhang et al., 2012). The SNP rs12653946 at 5p15, which is associated with prostate cancer susceptibility, regulates IRX4 expression and could suppress prostate cancer growth by interacting with the vitamin D receptor (Nguyen et al., 2012). Likewise, the SNP rs6983267 at 8q24 in colorectal cancer also resides within a transcriptional enhancer. Alleles of rs6983267 differentially bind transcription factor 7-like 2 (TCF7L2) and form a long-range chromatin loop with the MYC proto-oncogene to physically interact (Pomerantz et al., 2009; Ahmadiyeh et al., 2010; Wasserman et al., 2010). Moreover, the rs6983267 G risk allele increases the binding affinity of transcription factor 4 (TCF4) compared to the reference T allele, resulting in higher levels of expression for the MYC oncogene (Tuupanen et al., 2009; Sotelo et al., 2010; Wright et al., 2010). The SNP rs16888589 acts as an allele-specific transcriptional repressor, and interacts with the promoter of the eukaryotic translation initiation factor 3, subunit H (EIF3H ) gene. Higher expression of the EIF3H gene increases colorectal cancer growth and invasiveness (Pittman et al., 2010). SNPs rs4784227 and rs554219 are associated with breast cancer. The risk allele of rs4784227 increases the binding affinity of the pioneer factor FOXA1 in breast cancer cells, favoring the recruitment of the Groucho/TLE repressive complex to an enhancer to repress expression of the TOX3 gene (Cowper-Sal lari et al., 2012), while the rs554219 G allele reduces the cyclin D1 (CCND1) protein levels and increases the risk of breast cancer by abolishing the binding of ELK4 (French et al., 2013). The 10q26 locus in the second intron of the fibroblast growth factor receptor 2 (FGFR2) gene is the most strongly associated with estrogen-receptor (ER)positive breast cancer in GWAS. SNPs rs2981578 and rs7895676 regulate FGFR2 expression by altering binding affinity for transcription factors Oct-1/Runx2 and C/EBP b (Meyer et al., 2008). FOXA1 preferentially binds to the riskassociated C allele of rs2981578 in MCF-7 and is able to recruit ERa in an allele-specific manner, whereas E2F transcription factor 1 preferentially binds to the risk variant of rs35054928 (Meyer et al., 2013). The SNP rs4442975 at the 2q35 is associated with oestrogen receptor-positive breast cancer and the G-allele increased breast cancer susceptibility through reducing IGFBP5 expression. Recently, Ghoussaini et al. (2014) demonstrated that the rs4442975 G-allele reduces FOXA1 binding. SNPs in a susceptibility locus 11q13.3 for renal cell carcinoma modulate the binding and function of hypoxia-inducible factor (HIF) at a transcriptional enhancer of CCND1 that is specific for renal cancers. The protective haplotype impairs binding of HIF-2, and affects a link between cell cycle control and hypoxia pathways (Scho¨del et al., 2012). Additionally, the rs339331 T allele at 6q22 increases binding of the homeobox B13 to a transcriptional enhancer, upregulating expression of the regulatory factor X6 gene, which

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correlates with metastasis, tumor progression and risk of biochemical relapse clinically (Huang et al., 2014). The SNP rs1801282 (Pro12Ala) in the peroxisome proliferator-activated receptor g (PPARg) gene is associated with BMI, fasting insulin, and insulin sensitivity (Voight et al., 2010). The SNP rs4684847 is located 6.5 kb upstream of the PPARg2-specific promoter and is in perfect LD with rs1801282. The rs4684847 C risk allele inhibits PPARg2 expression by binding of paired related homeobox 1 (PRRX1), whereas the rs4684847 T allele reduces the binding ability of PRRX1 and thus maintains a higher level of PPARG2 expression (Claussnitzer et al., 2014). SNPs within introns of FTO are associated with increased risk for obesity and T2D in multiple GWASs. It is worth noting that obesity-associated SNPs are associated with expression of IRX3, but not FTO, in human brains. A 25% to 30% reduction in body weight was observed in Irx3-deficient mice, whereas hypothalamic expression of a dominant-negative form of Irx3 reproduces the metabolic phenotypes of Irx3-deficient mice, indicating that IRX3 is a functional target of obesity-associated SNPs within FTO (Smemo et al., 2014). The SNP rs12740374 at the 1p13 locus, which is associated with a lower level of plasma LDLC, upregulates the hepatic expression level of the sortilin 1 (SORT1) gene by creating a C/EBP binding site (Musunuru et al., 2010). It is worth noting that SORT1 is the fourthclosest gene of the SNP rs12740374. Similarly, the risk allele of SNPs rs10811656 and rs10757278, which are associated with coronary artery disease (CAD), changes the same signal transducer and activator of transcription (STAT) DNA motif to decrease STAT1 binding in human vascular endothelial cells, resulting in the differential expression of the CDKN2B antisense RNA 1 gene (Harismendy et al., 2011). In addition, variant allele of the SNP rs1427407 associated with the fetal hemoglobin level, decreases the recruitment of the GATA binding protein 1(GATA1)/T cell acute lymphocytic leukemia 1 (TAL1) to the enhancer, resulting in the downregulation of the BCL11A gene (Bauer et al., 2013). The chronic obstructive pulmonary disease-associated rs1542725 C allele exhibits increased Sp3 binding and reduced hedgehog interacting protein (HHIP) expression through distal transcriptional regulation, leading to increased disease susceptibility (Zhou et al., 2012). The SNP rs12203592 within an intron of the interferon regulatory factor 4 (IRF4) gene that is strongly associated with sensitivity of skin to sun exposure, freckles, blue eyes, and brown hair color impairs binding of the transcription factor AP-2a. IRF4 cooperates with the melanocyte master regulator MITF to activate expression of tyrosinase, an essential enzyme in melanin synthesis, in zebrafish and mice (Praetorius et al., 2013). Common SNPs in chromosome 17q12-q21 alter the risk for type 1 diabetes (T1D), asthma, and Crohn’s disease. The SNP rs12936231 shows allele-specific differences of CCCTC-binding factor binding and altered domain-wide cis-regulation (Verlaan et al., 2009). The human pigmentation-associated SNP rs12913832 resides in an enhancer 21 kb upstream of the OCA2 pigment gene. Interestingly, the rs12913832 T allele favors chromatin loops to the OCA2 gene compared to the C allele and is

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associated with a darker pigmentation in melanocytes (Visser et al., 2012), providing a novel functional mechanism by which SNP alters chromatin loop formation bridging enhancers and promoters. SNPs within promoters that function as local eQTLs can also alter transcription factor binding leading to differential target gene expression. For example, SNPs rs1552224 and rs11603334, which are in complete LD with each other, are strongly associated with fasting proinsulin and T2D. The proinsulin-decreasing and T2D-risk C allele of rs11603334, located near one of the ARAP1 promoters, decreases binding of pancreatic b cell transcriptional regulators PAX4 and PAX6 and exhibits 2-fold higher transcriptional activity than the T allele. Moreover, human pancreatic islet samples heterozygous for rs11603334 have higher ARAP1 expression (Kulzer et al., 2014). Common SNPs in the CDC123/CAMK1D locus are strongly associated with T2D in East Asians, South Asians and Europeans. The risk T allele of the lead SNP rs11257655 presents greater transcriptional activity than the non-risk allele C in T2D-relevant cell types, and allele-specific binding to FOXA1 and FOXA2, providing a novel molecular mechanism at this GWAS locus (Fogarty et al., 2014). SNP195 is associated with the a-thalassemia blood disorder. The risk C allele creates a GATA1 motif that decreases the expression of the aglobin genes, which promotes a-thalassemia (De Gobbi et al., 2006). SNPs affect DNA methylation at promoters DNA methylation is the most extensively studied epigenetic modification. Aberrant DNA methylation is associated with human diseases (Robertson, 2005). For example, in hereditary nonpolyposis colorectal cancer, an SNP within MLH1 50 -UTR gave rise to MLH1 hypermethylation and transcriptional silencing (Hitchins et al., 2011). Similarly, a point mutation upstream of DAPK1 promoter resulted in heritable methylation in individuals with familial chronic lymphocytic leukemia (Raval et al., 2007). Moreover, altered DNA methylation of distal transcriptional enhancers might also play an important role in cancer predisposition (Aran et al., 2013). Evaluating the link among SNPs, disease predisposition and DNA methylation are currently a very active research area. Cytosines in CpG pairs are usually methylated in the mammalian genome and some proteins bind only to methylated DNA. Therefore, introduction or removal of CpG dinucleotides can affect gene function. The rs143383 located in the 50 -UTR of the growth differentiation factor-5 (GDF5) gene is a risk factor for knee-osteoarthritis (OA), lumbar disc degeneration, congenital hip dysplasia and Achilles tendon pathology. The GDF5 50 -UTR is demethylated in OA knee cartilage. The rs143383 C creates a CpG site. Methylation regulates GDF5 expression and the functional effect of the rs143383 by altering binding of transcriptional repressors SP1, SP3 and DEAF1, which is particularly striking for knee cartilage relative to hip cartilage. This may explain why the rs143383 OA association is more pronounced at the knee (Reynard et al. 2011; Syddall et al. 2013; Reynard et al. 2014).

The SNP rs7405776 associated with invasive serous ovarian cancer is located within the promoter CpG island of the HNF1 homeobox B (HNF1B) gene and is related to higher DNA methylation level at the HNF1B promoter. The HNF1B gene is silenced by DNA methylation in serous ovarian tumors (Shen et al., 2013). Dayeh et al. (2013) reported that 19 of 40 T2D-associated SNPs remove or introduce a CpG site. Sixteen successfully analyzed CpG-SNPs representing genes TCF7L2, KCNQ1, SLC30A8, PPARG, HHEX, HMGA2, CDKN2A, WFS1, IRS1, DUSP9, CDKAL1, ADCY5, SRR, DUSP8, TSPAN8 and CHCHD9 are associated with differential DNA methylation in human islets. Recently, Hutchinson et al. (2014) showed that four cis-regulated allele-specific methylation SNPs are implicated in Crohn’s disease, IgA nephropathy and early-onset inflammatory bowel disease (rs713875), height (rs6569648), ulcerative colitis and AIDS progression disease (rs10491434) and celiac disease (rs2762051). SNPs affect miRNAs binding to its target mRNA miRNAs target mRNAs by recognizing their complementary sequences in 30 -UTRs. SNPs can affect miRNA repressive functions by changing complementary sequence on target mRNAs. Because a single miRNA can potentially regulate hundreds of different genes, the major challenge in exploring miRNA function is the identification of target genes. Influence of SNPs on miRNAs binding can be predicted by computational tools such as miRNASNP (http://www.bioguo.org/ miRNASNP2/online.php, Gong et al., 2012) and mrSNP (http://mrsnp.osu.edu, Deveci et al., 2014), and several databases (SNPinfo, http://www.niehs.nih.gov/snpinfo; MirSNP, http://cmbi.bjmu.edu.cn/mirsnp; miRdSNP, http://mirdsnp.ccr. buffalo.edu) have also been developed for linking SNPs in predicted miRNA-binding sites with complex phenotypes (Xu and Taylor, 2009; Bruno et al., 2012; Liu et al., 2012). It should be noted that prediction programs only assess the possibility of interaction, and prediction results need to be experimentally validated by luciferase reporter gene assay and other methods recently developed (Thomson et al., 2011; Hausser and Zavolan, 2014). Apolipoprotein A-V (APOA5) is strongly associated with plasma triglyceride (TG) levels and modulates the occurrence of both moderate and severe hypertriglyceridemia. The rare C allele of APOA5 creates a functional target site for liverexpressed miR-485-5p. In human embryonic kidney 293T cells cotransfected with an APOA5 30 -UTR luciferase reporter vector and a miR485-5p precursor, the expression of the rs2266788 C allele was significantly decreased. Moreover, in HuH-7 cells endogenously expressing miR-485-5p, luciferase activity in the presence of the rs2266788 C allele was significantly lower than that in the presence of the T allele, which was completely reversed by a miR-485-5p inhibitor (Caussy et al., 2014). rs12190287 in the 30 -UTR of the transcription factor 21 (TCF21) gene affects risk of coronary heart disease (CHD) in both East Asians and Caucasians, and alters the seed binding sequence for miR-224 by computational

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prediction. Significant imbalance of the TCF21 transcript in circulating leukocytes and human coronary artery smooth muscle cells (HCASMC) was correlated with genotype at rs12190287. The disease-associated C allele has reduced expression and the TCF21 C allelic transcript shows faster RNA-RNA complex formation and greater binding of miR224 (Miller et al., 2014). LPL rs13702, which is in LD with several SNPs identified by GWAS, is significantly associated with TG and LDL-C. 30 -UTR luciferase reporter carrying the rs13702 T allele was reduced by 40% in response to a miR-410 mimic (Richardson et al., 2013). Mutiple SNPs at human PLIN4 are associated with obesity-related phenotypes. The rs8887 minor A allele of the PLIN4 30 -UTR is predicted in silico to be a seed site for the human miR-522. A PLIN4 30 UTR luciferase reporter carrying the rs8887 A allele was reduced in response to miR-522 mimics compared to the G allele (Richardson et al., 2011). Similarly, alleles of XRCC1 rs1799782 and TGFb1 rs1982073 could modulate gene expression by differential interaction with miR-138 and miR187, contributing to cancer susceptibility (Nicoloso et al., 2010). The SNP rs5848 in the 30 -UTR of GRN is a major susceptibility factor for TAR DNA-binding protein 43-positive frontotemporal dementia. miR-659 binds more efficiently to the high risk T-allele of rs5848 resulting in augmented translational inhibition of GRN (Rademakers et al., 2008). HTR3E 30 -UTR SNP rs62625044 G>A associated with female diarrhea-predominant irritable bowel syndrome affects binding to miR-510 and causes elevated luciferase expression (Kapeller et al., 2008). The SNP of HLA-C 30 -UTR affects binding by miR-148 and results in differential cell-surface expression levels of HLA-C allotypes, providing the mechanism for a linked HIV-control associated SNP located 35 kb upstream of HLA-C (Kulkarni et al., 2011). SNPs affect long non-coding RNAs SNPs can affect long non-coding RNAs (lncRNAs) expression or tertiary structures that contribute to complex diseases. Several databases (LincSNP, http://http//bioinfo.hrbmu.edu.cn/ LincSNP; LncRNASNP, http://bioinfo.life.hust.edu.cn/ lncRNASNP) have been developed for integrating current lincRNA and GWAS SNP annotations (Ning et al., 2014). GWASs have identified the lincRNA gene ANRIL as a genetic susceptibility locus shared associated by coronary disease, T2D, intracranial aneurysm, gliomas and basal cell carcinomas (Pasmant et al., 2011). The pivotal role of ANRIL in regulating CDKN2A/B expression through a cis-acting mechanism and its implication in proliferation and senescence has been confirmed in a mouse model. Many of lincRNA ciseQTL SNPs are associated with complex diseases (Kumar et al., 2013). A GWAS of papillary thyroid carcinoma (PTC) pinpointed SNP rs944289 located 3.2 kb upstream of a lincRNA gene PTCSC3 that has the characteristics of a tumor suppressor. The PTCSC3 gene expression is strictly thyroidspecific and was strongly down-regulated in thyroid tumor tissue of PTC patients. The risk T allele of SNP rs944289 destroyed the binding site for the C/EBPa and b in silico,

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showing the strongest suppression (Jendrzejewski et al., 2012). The risk allele of the SNP rs35955962 in the MIAT lncRNA increases its affinity for nuclear proteins compared to the nonrisk allele (Ishii et al., 2006). SYSTEMS GENETICS STUDY OF COMPLEX DISEASES Systems genetics integrates intermediate phenotypes (transcript, protein or metabolite levels) to understand the flow of biological information, and is useful for the identification of genes, pathways and networks that underlie complex diseases (Civelek and Lusis., 2014). Integration of gene expression with human GWASs successfully identified SORT1 (Musunuru et al., 2010) and KLF14 (Small et al., 2011) genes as the causal gene in the GWAS locus. Bell et al. (2010) identified variant-CpG-restricted haplotype-specific methylation within the FTO obesity susceptibility locus by combining GWAS and epigenome-wide association study data. The main difficulty of carrying out systems genetics studies directly in humans is obtaining samples from relevant tissues. Animal models have proven invaluable for systems genetics studies of complex diseases (Quigley and Balmain, 2009). As an example, mouse genome-wide association and co-expression network analysis successfully identified Asxl2 as a regulator of osteoclastogenesis and bone mineral density (Farber et al., 2011). Complex diseases are caused by perturbations of biological networks, not isolated genes or proteins. Single genetic variant is unlikely to explain complex disease phenotypes. Biological network analysis focuses on identifying the interacting genes and proteins that lead to disease pathogenesis, providing a systems-level understanding of the mechanisms underlying complex diseases. Network biology has emerged as a systemslevel and integrative approach to aid in the interpretation of GWAS findings (Furlong, 2013). Zhang et al. (2013) constructed gene-regulatory networks in 1647 postmortem brain tissues from late-onset Alzheimer’s disease (LOAD) patients and nondemented subjects. An integrative network-based analysis highlights an immune- and microglia-specific module that is involved in pathogen phagocytosis and is upregulated in LOAD. Mouse microglia cells overexpressing intact or truncated key regulator TYRO protein tyrosine kinase binding protein (TYROBP) revealed expression changes that significantly overlapped the human brain TYROBP network. Integration of 14 GWASs from the CARDIoGRAM Consortium and two GWASs from the Ottawa Heart Institute with 1) metabolic and signaling pathways from public databases, 2) genetics of gene expression studies of CAD-relevant tissues in humans, and 3) data-driven, tissue-specific gene networks from a multitude of human and mouse experiments, not only detected CAD-associated gene networks of lipid metabolism, immunity, coagulation, and additional networks with no clear functional annotation, but also revealed key driver genes for each CAD network (Ma¨kinen et al. 2014). The key driver genes glyoxalase I (GLO1) and peptidylprolyl isomerase-like 1 (PPIL1) of a gene network that is involved in antigen processing and strongly associated with CAD have been verified

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as regulatory genes by siRNA experiments in human aortic endothelial cells. Network analysis of 233 candidate genes in the largest genetic study of CAD generated five interaction networks. Analysis of subnetwork overlaps with canonical pathways implicated crosstalk between lipid metabolism and inflammatory pathways as underlying the pathogenesis of CAD (The CARDIoGRAMplusC4D Consortium, 2013). Huan

et al. (2013) surveyed CHD-associated molecular interactions by constructing coexpression networks using whole blood gene expression profiles from 188 CHD cases and 188 sexand age-matched controls. Twenty-four coexpression modules were identified, including one control-specific and one casespecific differential modules (DM) that were enriched for genes involved in immune response, B-cell activation, and ion

Table 2 Successfully characterized GWAS functional SNPs Disease or phenotype

SNP

Functiona

Prostate cancer

rs11986220

FOXA1

Prostate cancer

rs8072254/ rs1859961

AR FOXA1/AP-1

Prostate cancer

rs12653946

Colorectal cancer

rs6983267

Colorectal cancer

rs6983267

Colorectal cancer Breast cancer

Target gene of SNP

Reference

SOX9 SOX9

Zhang et al., 2012

IRX4

Nguyen et al., 2012

TCF7L2

MYC

Wasserman et al, 2010

TCF4

MYC

Wright et al, 2010

rs16888589

STAT

EIF3H

Pittman et al., 2010

rs10069690

Splicing

TRET

Bojesen et al., 2013

Breast cancer

rs4784227

FOXA1

TOX3

Cowper-Sal lari et al., 2012

Breast cancer

rs4442975

FOXA1

IGFBP5

Ghoussaini et al. 2014

Breast cancer

rs554219

ELK4

CCND1

French et al., 2013

Breast cancer

rs7895676

Oct-1/Runx2

FGFR2

Meyer et al., 2008

Breast cancer

rs2981578

C/EBPb

FGFR2

Meyer et al., 2008

Breast cancer

rs2981578

FOXA1

FGFR2

Meyer et al., 2013

Breast cancer

rs35054928

E2F1

FGFR2

Meyer et al., 2013

Renal cancer

11q13.3

HIF-2

CCND1

Scho¨del et al, 2012

Prostate cancer

rs339331

HOXB13

RFX6

Huang et al., 2014

T2D

rs10946398

Splicing

CDKAL1

Zhou et al., 2014

T2D/BMI

rs4684847

PRRX1

PPARG2

Claussnitzer et al, 2014

T2D

rs11603334

PAX6/4

ARAP1

Kulzer et al., 2014

T2D

rs11257655

FOXA1/2

BMI

rs8887

miR-522

PLIN4

Richardson et al., 2011

LDL

rs3846662

HNRNPA1

HMGCR

Yu et al., 2014

LDL

rs12740374

C/EBP

SORT1

Musunuru et al., 2010

LDL/TG

rs13702

miR-410

LPL

Richardson et al., 2013

Jia et al., 2009

Fogarty et al, 2014

TG

rs2266788

miR485-5p

APOA5

Caussy et al., 2014

CHD

rs12190287

miR224

TCF21

Miller et al., 2014

CAD

rs10811656/ rs10757278

STAT1

ANRIL

Harismendy et al., 2011

Alzheimer’s disease

rs9331888

Splicing

CLU

Malik et al., 2013

Alzheimer’s disease

rs12459419

Splicing

CD33

Malik et al., 2013

Bipolar disorder

rs13438494

Splicing

PCLO

Seo et al., 2013

Multiple sclerosis

rs1800693

Splicing

TNFRSF1A

Gregory et al., 2012

Asthma

rs12936231

CTCF

ZPBP2/GSDMB ORMDL3

Verlaan et al. 2009

Hemoglobin level

rs1427407

GATA1/TAL1

BCL11A

Bauer et al., 2013

Chronic obstructive pulmonary

rs1542725

Sp3

HHIP

Zhou et al., 2012

Pigmentation

rs12203592

TFAP2A

IRF4

Praetorius et al., 2013

a

Transcription factor or miRNA the SNP affects.

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transport. By integrating the DMs with gene expressionassociated SNPs and with results of GWASs of CHD and its risk factors, the control-specific DM was implicated as CHD causal based on its significant enrichment for both CHD and lipid expression-associated SNPs. This causal DM was further integrated with tissue-specific Bayesian networks and proteineprotein interaction networks, and multi-tissue key drivers (spi-B transcription factor (SPIB) and tumor necrosis factor receptor superfamily, member 13C (TNFRSF13C )) and tissuespecific key drivers (e.g., early B-cell factor 1) were identified. Additionally, Bergholdt et al. (2012) combined proteineprotein interaction network overlap with genes located at GWAS risk loci and subnetwork-based enrichment for differential expression to identify new candidate T1D disease genes. These successful examples indicate that network analysis can shed light on mechanistic link between genotype and complex diseases. CONCLUDING REMARKS GWASs and whole-genome sequencing have robustly identified a large number of SNPs and genes associated with complex diseases. So far, only a small fraction of SNPs/genes and their functional mechanisms were successfully characterized (Table 2), mirroring the difficulty of genetic studies of complex diseases. The functional interpretation of SNPs and genes remains challenging. First, the true functional/causal variants are still unknown for many GWAS loci. A strong association with a disease does not necessarily mean that the SNP is the actual causal variant. Furthermore, SNPs are unlikely to act alone, and combinations and interactions of SNPs are crucial for complex diseases. It is hard to unequivocally prove that a common SNP is the direct cause of a given association. Second, computational approaches are powerful and essential for post-GWAS studies, which generate hypotheses to test candidate-SNP effects. Integration of computational and experimental approaches is an effective method to reveal functions of SNPs. Although various bioinformatics tools are available for SNP function prediction, prediction results could be a false positive hit due to the limited power and conditiondependent gene regulation. Third, the identification of the target genes of rSNPs is the key to understanding the mechanism by which GWAS-associated SNPs act. Notably, the nearest gene might not be the target of a given GWAS SNP. Moreover, rSNPs might affect multiple promoters and regulate the expression of more than one gene in a long distance. Finally, although network biology offers a powerful method to uncover the pathogenic mechanisms underlying complex diseases at a systems-wide level, currently no approach is available to evaluate the consequences of SNP at the network level. With the current availability of functional genomics data generated by projects such as ENCODE along with rapidly evolving functional evaluation techniques, it can be anticipated that the shift from statistic association to functionality and causality of human complex diseases will be accelerated. Hopefully, functional characterization and biological insights of genes and SNPs identified by GWAS can be clinically

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translated to effective strategies for disease diagnosis, prevention and treatment in the near future. ACKNOWLEDGEMENTS This work was supported by the National Natural Science Foundation of China (Nos. 31371275 and 30971635), the National Basic Research Program of China (973 Program) (No. 2011CB504004) and self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (No. CCNU14Z01003). REFERENCES Ahmadiyeh, N., Pomerantz, M.M., Grisanzio, C., Herman, P., Jia, L., Almendro, V., He, H.H., Brown, M., Liu, X.S., Davis, M., Caswell, J.L., Beckwith, C.A., Hills, A., Macconaill, L., Coetzee, G.A., Regan, M.M., Freedman, M.L., 2010. 8q24 prostate, breast, and colon cancer risk loci show tissue-specific long-range interaction with MYC. Proc. Natl. Acad. Sci. USA 107, 9742e9746. Aran, D., Sabato, S., Hellman, A., 2013. DNA methylation of distal regulatory sites characterizes dysregulation of cancer genes. Genome Biol. 14, R21. Bauer, D.E., Kamran, S.C., Lessard, S., Xu, J., Fujiwara, Y., Lin, C., Shao, Z., Canver, M.C., Smith, E.C., Pinello, L., Sabo, P.J., Vierstra, J., Voit, R.A., Yuan, G.C., Porteus, M.H., Stamatoyannopoulo, J.A., Lettre, G., Orkin, S.H., 2013. An erythroid enhancer of BCL11A subject to genetic variation determines fetal hemoglobin level. Science 342, 253e257. Bell, C.G., Finer, S., Lindgren, C.M., Wilson, G.A., Rakyan, V.K., Teschendorff, A.E., Akan, P., Stupka, E., Down, T.A., Prokopenko, I., Morison, I.M., Mill, J., Pidsley, R., International Type 2 Diabetes 1q Consortium, Deloukas, P., Frayling, T.M., Hattersley, A.T., McCarthy, M.I., Beck, S., Hitman, G.A., 2010. Integrated genetic and epigenetic analysis identifies haplotype-specific methylation in the FTO type 2 diabetes and obesity susceptibility locus. PLoS One 5, e14040. Bergholdt, R., Brorsson, C., Palleja, A., Berchtold, L.A., Floyel, T., BangBerthelsen, C.H., Frederiksen, K.S., Jensen, L.J., Storling, J., Pociot, F., 2012. Identification of novel type 1 diabetes candidate genes by integrating genome-wide association data, proteineprotein interactions, and human pancreatic islet gene expression. Diabetes 61, 954e962. Bojesen, S.E., Pooley, K.A., Johnatty, S.E., Beesley, J., Michailidou, K., Tyrer, J.P., Edwards, S.L., Pickett, H.A., Shen, H.C., Smart, C.E., Hillman, K.M., Mai, P.L., Lawrenson, K., Stutz, M.D., Lu, Y., Karevan, R., Woods, N., Johnston, R.L., French, J.D., Chen, X., Weischer, M., Nielsen, S.F., Maranian, M.J., Ghoussaini, M., Ahmed, S., Baynes, C., Bolla, M.K., Wang, Q., Dennis, J., McGuffog, L., Barrowdale, D., Lee, A., Healey, S., Lush, M., Tessier, D.C., Vincent, D., Bacot, F., Australian Cancer Study, Australian Ovarian Cancer Study, Kathleen Cuningham Foundation Consortium for Research into Familial BreastCancer (kConFab), Gene Environment Interaction and Breast Cancer (GENICA), Swedish Breast Cancer Study(SWE-BRCA), Hereditary Breastand Ovarian Cancer Research Group Netherlands (HEBON), Epidemiological study of BRCA1 & BRCA2 Mutation Carriers (EMBRACE), Genetic Modifiers of Cancer Risk in BRCA1/2 Mutation Carriers (GEMO), Vergote, I., Lambrechts, S., Despierre, E., Risch, H.A., Gonza´lezNeira, A., Rossing, M.A., Pita, G., Doherty, J.A., Alvarez, N., Larson, M.C., Fridley, B.L., Schoof, N., Chang-Claude, J., Cicek, M.S., Peto, J., Kalli, K.R., Broeks, A., Armasu, S.M., Schmidt, M.K., Braaf, L.M., Winterhoff, B., Nevanlinna, H., Konecny, G.E., Lambrechts, D., Rogmann, L., Gue´nel, P., Teoman, A., Milne, R.L., Garcia, J.J., Cox, A., Shridhar, V., Burwinkel, B., Marme, F., Hein, R., Sawyer, E.J., Haiman, C.A., Wang-Gohrke, S., Andrulis, I.L., Moysich, K.B., Hopper, J.L., Odunsi, K., Lindblom, A., Giles, G.G., Brenner, H., Simard, J., Lurie, G., Fasching, P.A., Carney, M.E., Radice, P., Wilkens, L.R., Swerdlow, A., Goodman, M.T., Brauch, H., GarciaClosas, M., Hillemanns, P., Winqvist, R., Du¨rst, M., Devilee, P.,

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