Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome data.

Accurate imputation of tissue-specific gene expression can be a powerful tool for understanding the biological mechanisms underlying human complex traits. Existing imputation methods can be grouped into two categories according to the types of predictors used. The first category uses genotype data, while the second category uses whole-blood expression data. Both data types can be easily collected from blood, avoiding invasive tissue biopsies. In this study, we attempted to build an optimal predictive model for imputing tissue-specific gene expression by combining the genotype and whole-blood expression data. We first evaluated the imputation performance of each standalone model (using genotype data [GEN model] and using whole-blood expression data [WBE model]) using their respective data types across 47 human tissues. The WBE model outperformed the GEN model in most tissues by a large gain. Then, we developed several combined models that leverage both types of predictors to further improve imputation performance. We tried various strategies, including utilizing a merged dataset of the two data types (MERGED models) and integrating the imputation outcomes of the two standalone models (inverse variance-weighted [IVW] models). We found that one of the MERGED models noticeably outperformed the standalone models. This model involved a fixed ratio between the two regularization penalty factors for the two predictor types so that the contribution of the whole-blood transcriptome is upweighted compared with the genotype. Our study suggests that one can improve the imputation of tissue-specific gene expression by combining the genotype and whole-blood expression, but the improvement can be largely dependent on the combination strategy chosen.

PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics.

Identifying and interpreting pleiotropic loci is essential to understanding the shared etiology among diseases and complex traits. A common approach to mapping pleiotropic loci is to meta-analyze GWAS summary statistics across multiple traits. However, this strategy does not account for the complex genetic architectures of traits, such as genetic correlations and heritabilities. Furthermore, the interpretation is challenging because phenotypes often have different characteristics and units. We propose PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test), a summary-statistic-based framework to map and interpret pleiotropic loci in a joint analysis of multiple diseases and complex traits. Our method maximizes power by systematically accounting for genetic correlations and heritabilities of the traits in the association test. Any set of related phenotypes, binary or quantitative traits with different units, can be combined seamlessly. In addition, our framework offers interpretation and visualization tools to help downstream analyses. Using our method, we combined 18 traits related to cardiovascular disease and identified 13 pleiotropic loci, which showed four different patterns of associations.

Amino acid position 37 of HLA-DRß1 affects susceptibility to Crohn's disease in Asians.

Crohn's disease (CD) and ulcerative colitis (UC) are the major types of chronic inflammatory bowel disease (IBD) characterized by recurring episodes of inflammation of the gastrointestinal tract. Although it is well established that human leukocyte antigen (HLA) is a major risk factor for IBD, it is yet to be determined which HLA alleles or amino acids drive the risks of CD and UC in Asians. To define the roles of HLA for IBD in Asians, we fine-mapped HLA in 12 568 individuals from Korea and Japan (3294 patients with CD, 1522 patients with UC and 7752 controls). We identified that the amino acid position 37 of HLA-DRß1 plays a key role in the susceptibility to CD (presence of serine being protective, P = 3.6 × 10-67, OR = 0.48 [0.45-0.52]). For UC, we confirmed the known association of the haplotype spanning HLA-C*12:02, HLA-B*52:01 and HLA-DRB1*1502 (P = 1.2 × 10-28, OR = 4.01 [3.14-5.12]).

An Intergenic Variant rs9268877 Between HLA-DRA and HLA-DRB Contributes to the Clinical Course and Long-term Outcome of Ulcerative Colitis.

BACKGROUND AND AIMS: The genetic contribution to the prognosis of ulcerative colitis [UC] is poorly understood, and most currently known susceptibility loci are not associated with prognosis. To identify genetic variants influencing the prognosis of UC, we performed an Immunochip-based study using an extreme phenotype approach. METHODS: Based on the finding that the only association, Pdiscovery-meta <1 × 10-4, was located in the human leukocyte antigen [HLA], we focused our analyses on the HLA region. We performed the analysis using HLA imputation data from three independent discovery cohorts of 607 UC patients [243 poor-prognosis and 364 good-prognosis], followed by replication in 274 UC patients [145 poor-prognosis and 129 good-prognosis]. RESULTS: We found that rs9268877, located between HLA-DRA and HLA-DRB, was associated with poor-prognosis of UC at genome-wide significance (odds ratio [ORdiscovery] = 1.82; ORreplication = 1.55; ORcombined-meta = 1.72, pcombined-meta = 1.04 × 10-8), with effect size [OR] increasing incrementally according to worsening of prognosis in each of the three independent discovery cohorts and the replication cohort. However, rs9268877 showed no association with UC susceptibility [ORcombined-meta = 1.07, pcombined-meta = 0.135]; rs9268877 influenced 30-year clinical outcomes, and the presence of the rs9268877 risk allele had a sensitivity of 80.0% and specificity of 38.1% for colectomy. CONCLUSIONS: Our results provide new insights into prognosis-associated genetic variation in UC, which appears to be distinct from the genetic contribution to disease susceptibility. These findings could be useful in identifying poor-prognosis patients who might benefit from early aggressive therapy.

An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures.

Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. However, current association testing approaches for dealing with sex, such as including sex as a covariate, cannot fully account for these phenomena and can be suboptimal in statistical power. We present a novel association mapping framework, MetaSex, that can comprehensively account for the genetic architecture differences between the sexes. Through simulations and applications to real data, we show that our framework has superior performance than previous approaches in association mapping.

FOLD: a method to optimize power in meta-analysis of genetic association studies with overlapping subjects.

MOTIVATION: In genetic association studies, meta-analyses are widely used to increase the statistical power by aggregating information from multiple studies. In meta-analyses, participating studies often share the same individuals due to the shared use of publicly available control data or accidental recruiting of the same subjects. As such overlapping can inflate false positive rate, overlapping subjects are traditionally split in the studies prior to meta-analysis, which requires access to genotype data and is not always possible. Fortunately, recently developed meta-analysis methods can systematically account for overlapping subjects at the summary statistics level. RESULTS: We identify and report a phenomenon that these methods for overlapping subjects can yield low power. For instance, in our simulation involving a meta-analysis of five studies that share 20% of individuals, whereas the traditional splitting method achieved 80% power, none of the new methods exceeded 32% power. We found that this low power resulted from the unaccounted differences between shared and unshared individuals in terms of their contributions towards the final statistic. Here, we propose an optimal summary-statistic-based method termed as FOLD that increases the power of meta-analysis involving studies with overlapping subjects. AVAILABILITY AND IMPLEMENTATION: Our method is available at http://software.buhmhan.com/FOLD. CONTACT: mail: buhm.han@amc.seoul.kr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Generation and molecular characterization of pancreatic cancer patient-derived xenografts reveals their heterologous nature.

Pancreatic ductal adenocarcinoma (PDAC) is the most challenging type of cancer to treat, with a 5-year survival rate of <10%. Furthermore, because of the large portion of the inoperable cases, it is difficult to obtain specimens to study the biology of the tumors. Therefore, a patient-derived xenograft (PDX) model is an attractive option for preserving and expanding these tumors for translational research. Here we report the generation and characterization of 20 PDX models of PDAC. The success rate of the initial graft was 74% and most tumors were re-transplantable. Histological analysis of the PDXs and primary tumors revealed a conserved expression pattern of p53 and SMAD4; an exome single nucleotide polymorphism (SNP) array and Comprehensive Cancer Panel showed that PDXs retained over 94% of cancer-associated variants. In addition, Polyphen2 and the Sorting Intolerant from Tolerant (SIFT) prediction identified 623 variants among the functional SNPs, highlighting the heterologous nature of pancreatic PDXs; an analysis of 409 tumor suppressor genes and oncogenes in Comprehensive Cancer Panel revealed heterologous cancer gene mutation profiles for each PDX-primary tumor pair. Altogether, we expect these PDX models are a promising platform for screening novel therapeutic agents and diagnostic markers for the detection and eradication of PDAC.

Identification of Loci at 1q21 and 16q23 That Affect Susceptibility to Inflammatory Bowel Disease in Koreans.

Recent genome-wide association studies have identified more than 200 regions that affect susceptibility to inflammatory bowel disease (IBD). However, identified common variants account for only a fraction of IBD heritability and largely have been identified in populations of European ancestry. We performed a genome-wide association study of susceptibility loci in Korean individuals, comprising a total of 1505 IBD patients and 4041 controls. We identified 2 new susceptibility loci for IBD at genome-wide significance: rs3766920 near PYGO2-SHC1 at 1q21 and rs16953946 in CDYL2 at 16q23. In addition, we confirmed associations, in Koreans, with 28 established IBD loci (P < 2.16 × 10-4). Our findings support the complementary value of genetic studies in different populations.

Shared Genetic Risk Factors of Intracranial, Abdominal, and Thoracic Aneurysms.

BACKGROUND: Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co-occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. METHODS AND RESULTS: We performed a mega-analysis of 1000 Genomes Project-imputed genome-wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA-, AAA-, and TAA-associated SNPs and tested these scores for association to case-control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium-score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single-nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1×10(-5)) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1×10(-3)). CONCLUSIONS: Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication.

A method to decipher pleiotropy by detecting underlying heterogeneity driven by hidden subgroups applied to autoimmune and neuropsychiatric diseases.

There is growing evidence of shared risk alleles for complex traits (pleiotropy), including autoimmune and neuropsychiatric diseases. This might be due to sharing among all individuals (whole-group pleiotropy) or a subset of individuals in a genetically heterogeneous cohort (subgroup heterogeneity). Here we describe the use of a well-powered statistic, BUHMBOX, to distinguish between those two situations using genotype data. We observed a shared genetic basis for 11 autoimmune diseases and type 1 diabetes (T1D; P < 1 × 10(-4)) and for 11 autoimmune diseases and rheumatoid arthritis (RA; P < 1 × 10(-3)). This sharing was not explained by subgroup heterogeneity (corrected PBUHMBOX > 0.2; 6,670 T1D cases and 7,279 RA cases). Genetic sharing between seronegative and seropostive RA (P < 1 × 10(-9)) had significant evidence of subgroup heterogeneity, suggesting a subgroup of seropositive-like cases within seronegative cases (PBUHMBOX = 0.008; 2,406 seronegative RA cases). We also observed a shared genetic basis for major depressive disorder (MDD) and schizophrenia (P < 1 × 10(-4)) that was not explained by subgroup heterogeneity (PBUHMBOX = 0.28; 9,238 MDD cases).

Comparison of Two Meta-Analysis Methods: Inverse-Variance-Weighted Average and Weighted Sum of Z-Scores.

The meta-analysis has become a widely used tool for many applications in bioinformatics, including genome-wide association studies. A commonly used approach for meta-analysis is the fixed effects model approach, for which there are two popular methods: the inverse variance-weighted average method and weighted sum of z-scores method. Although previous studies have shown that the two methods perform similarly, their characteristics and their relationship have not been thoroughly investigated. In this paper, we investigate the optimal characteristics of the two methods and show the connection between the two methods. We demonstrate that the each method is optimized for a unique goal, which gives us insight into the optimal weights for the weighted sum of z-scores method. We examine the connection between the two methods both analytically and empirically and show that their resulting statistics become equivalent under certain assumptions. Finally, we apply both methods to the Wellcome Trust Case Control Consortium data and demonstrate that the two methods can give distinct results in certain study designs.

Association of HLA Genotype and Fulminant Type 1 Diabetes in Koreans.

Fulminant type 1 diabetes (T1DM) is a distinct subtype of T1DM that is characterized by rapid onset hyperglycemia, ketoacidosis, absolute insulin deficiency, and near normal levels of glycated hemoglobin at initial presentation. Although it has been reported that class II human leukocyte antigen (HLA) genotype is associated with fulminant T1DM, the genetic predisposition is not fully understood. In this study we investigated the HLA genotype and haplotype in 11 Korean cases of fulminant T1DM using imputation of whole exome sequencing data and compared its frequencies with 413 participants of the Korean Reference Panel. The HLA-DRB1*04:05-HLA-DQB1*04:01 haplotype was significantly associated with increased risk of fulminant T1DM in Fisher's exact test (odds ratio [OR], 4.11; 95% confidence interval [CI], 1.56 to 10.86; p = 0.009). A histidine residue at HLA-DRß1 position 13 was marginally associated with increased risk of fulminant T1DM (OR, 2.45; 95% CI ,1.01 to 5.94; p = 0.054). Although we had limited statistical power, we provide evidence that HLA haplotype and amino acid change can be a genetic risk factor of fulminant T1DM in Koreans. Further large-scale research is required to confirm these findings.