Hon-Cheong So

Evaluating the heritability explained by known susceptibility variants: a survey of ten complex diseases

Recently, an increasing number of susceptibility variants have been identified for complex diseases. At the same time, the concern of “missing heritability” has also emerged. There is however no unified way to assess the heritability explained by individual genetic variants for binary outcomes. A systemic and quantitative assessment of the degree of “missing heritability” for complex diseases is lacking. In this study, we measure the variance in liability explained by individual variants, which can be directly interpreted as the locus‐specific heritability. The method is extended to deal with haplotypes, multi‐allelic markers, multi‐locus genotypes, and markers in linkage disequilibrium. Methods to estimate the standard error and confidence interval are proposed. To assess our current level of understanding of the genetic basis of complex diseases, we conducted a survey of 10 diseases, evaluating the total variance explained by the known variants. The diseases under evaluation included Alzheimers disease, bipolar disorder, breast cancer, coronary artery disease, Crohns disease, prostate cancer, schizophrenia, systemic lupus erythematosus (SLE), type 1 diabetes and type 2 diabetes. The median total variance explained across the 10 diseases was 9.81%, while the median variance explained per associated SNP was around 0.25%. Our results suggest that a substantial proportion of heritability remains unexplained for the diseases under study. Programs to implement the methodologies described in this paper are available at http://sites.google.com/site/honcheongso/software/varexp. Genet. Epidemiol. 2011.

Uncovering the Total Heritability Explained by All True Susceptibility Variants in a Genome-Wide Association Study

Genome‐wide association studies (GWAS) have become increasingly popular recently and contributed to the discovery of many susceptibility variants. However, a large proportion of the heritability still remained unexplained. This observation raises queries regarding the ability of GWAS to uncover the genetic basis of complex diseases. In this study, we propose a simple and fast statistical framework to estimate the total heritability explained by all true susceptibility variants in a GWAS. It is expected that many true risk variants will not be detected in a GWAS due to limited power. The proposed framework aims at recovering the “hidden” heritability. Importantly, only the summary z‐statistics are required as input and no raw genotype data are needed. The strategy is to recover the true effect sizes from the observed z‐statistics. The methodology does not rely on any distributional assumptions of the effect sizes of variants. Both binary and quantitative traits can be handled and covariates may be included. Population‐based or family‐based designs are allowed as long as the summary statistics are available. Simulations were conducted and showed satisfactory performance of the proposed approach. Application to real data (Crohns disease, HDL, LDL, and triglycerides) reveals that at least around 10–20% of variance in liability or phenotype can be explained by GWAS panels. This translates to around 10–40% of the total heritability for the studied traits. Genet. Epidemiol. 2011.

Risk prediction of complex diseases from family history and known susceptibility loci, with applications for cancer screening.

Risk prediction based on genomic profiles has raised a lot of attention recently. However, family history is usually ignored in genetic risk prediction. In this study we proposed a statistical framework for risk prediction given an individuals genotype profile and family history. Genotype information about the relatives can also be incorporated. We allow risk prediction given the current age and follow-up period and consider competing risks of mortality. The framework allows easy extension to any family size and structure. In addition, the predicted risk at any percentile and the risk distribution graphs can be computed analytically. We applied the method to risk prediction for breast and prostate cancers by using known susceptibility loci from genome-wide association studies. For breast cancer, in the population the 10-year risk at age 50 ranged from 1.1% at the 5th percentile to 4.7% at the 95th percentile. If we consider the average 10-year risk at age 50 (2.39%) as the threshold for screening, the screening age ranged from 62 at the 20th percentile to 38 at the 95th percentile (and some never reach the threshold). For women with one affected first-degree relative, the 10-year risks ranged from 2.6% (at the 5th percentile) to 8.1% (at the 95th percentile). For prostate cancer, the corresponding 10-year risks at age 60 varied from 1.8% to 14.9% in the population and from 4.2% to 23.2% in those with an affected first-degree relative. We suggest that for some diseases genetic testing that incorporates family history can stratify people into diverse risk categories and might be useful in targeted prevention and screening.

Robust Association Tests Under Different Genetic Models, Allowing for Binary or Quantitative Traits and Covariates

The association of genetic variants with outcomes is usually assessed under an additive model, for example by the trend test. However, misspecification of the genetic model will lead to a reduction in power. More robust tests for association might therefore be preferred. A useful approach is to consider the maximum of the three test statistics under additive, dominant and recessive models (MAX3). The p-value however has to be adjusted to maintain the type I error rate. Previous studies and software on robust association tests have focused on binary traits without covariates. In this study we developed an analytic approach to robust association tests using MAX3, allowing for quantitative or binary traits as well as covariates. The p-values from our theoretical calculations match very well with those from a bootstrap resampling procedure. The methodology is implemented in the R package RobustSNP which is able to handle both small-scale studies and GWAS. The package and documentation are available at http://sites.google.com/site/honcheongso/software/robustsnp.

Common Variants on Xq28 Conferring Risk of Schizophrenia in Han Chinese

Schizophrenia is a highly heritable, severe psychiatric disorder affecting approximately 1% of the world population. A substantial portion of heritability is still unexplained and the pathophysiology of schizophrenia remains to be elucidated. To identify more schizophrenia susceptibility loci, we performed a genome-wide association study (GWAS) on 498 patients with schizophrenia and 2025 controls from the Han Chinese population, and a follow-up study on 1027 cases and 1005 controls. In the follow-up study, we included 384 single nucleotide polymorphisms (SNPs) which were selected from the top hits in our GWAS (130 SNPs) and from previously implicated loci for schizophrenia based on the SZGene database, NHGRI GWAS Catalog, copy number variation studies, GWAS meta-analysis results from the international Psychiatric Genomics Consortium (PGC) and candidate genes from plausible biological pathways (254 SNPs). Within the chromosomal region Xq28, SNP rs2269372 in RENBP achieved genome-wide significance with a combined P value of 3.98 × 10(-8) (OR of allele A = 1.31). SNPs with suggestive P values were identified within 2 genes that have been previously implicated in schizophrenia, MECP2 (rs2734647, P combined = 8.78 × 10(-7), OR = 1.28; rs2239464, P combined = 6.71 × 10(-6), OR = 1.26) and ARHGAP4 (rs2269368, P combined = 4.74 × 10(-7), OR = 1.25). In addition, the patient sample in our follow-up study showed a significantly greater burden for pre-defined risk alleles based on the SNPs selected than the controls. This indicates the existence of schizophrenia susceptibility loci among the SNPs we selected. This also further supports multigenic inheritance in schizophrenia. Our findings identified a new schizophrenia susceptibility locus on Xq28, which harbor the genes RENBP, MECP2, and ARHGAP4.

An Association Study of RGS4 Polymorphisms With Clinical Phenotypes of Schizophrenia in a Chinese Population

The regulator of G‐protein signaling 4 (RGS4) has been suggested as a candidate gene for schizophrenia. However, following an initial positive report, subsequent association studies between RGS4 and schizophrenia have yielded inconclusive results. Also, few studies have investigated the association of RGS4 polymorphisms with the phenotypic subgroups of schizophrenia. To further clarify the role of RGS4 in this disease, we performed a case‐control study (504 cases and 531 controls of Han Chinese descent) to examine the association of RGS4 with schizophrenia and with clinical and neurocognitive profiles. The four markers (SNPs 1, 4, 7, and 18) implicated in the original association study were genotyped. We detected significant association of four‐marker haplotypes with schizophrenia (UNPHASED: global P = 0.037; PHASE: global P = 0.048). The haplotype G‐G‐G‐G, which was implicated in at least three previous studies, was the major risk haplotype (UNPHASED: P = 0.019; PHASE: P = 0.010). Regarding the clinical phenotypes, the Wechsler Adult Intelligence Test (WAIS) information subtest score was associated with SNP4 genotypes (P = 0.001). PANSS total and global psychopathology scores were also associated with SNP4, but may not reliably reflect the general severity of disease as the scores may be affected by confounders like medication response. Our study provides further support for a role of RGS4 in the pathogenesis of schizophrenia. We identified G‐G‐G‐G as the risk haplotype in our Chinese sample. The association with information subtest score suggests an effect of RGS4 on premorbid functioning, which may be related to neurodevelopmental processes. Further independent studies are required to verify our findings.

A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait.

10-year outcome study of an early intervention program for psychosis compared with standard care service

BACKGROUND Despite evidence on the short-term benefits of early intervention (EI) service for psychosis, long-term outcome studies are limited by inconsistent results. This study examined the 10-year outcomes of patients with first-episode psychosis who received 2-year territory-wide EI service compared to those who received standard care (SC) in Hong Kong using an historical control design. METHOD Consecutive patients who received the EI service between 1 July 2001 and 30 June 2002, and with diagnosis of schizophrenia-spectrum disorders, were identified and matched with patients who received SC first presented to the public psychiatric service from 1 July 2000 to 30 June 2001. In total, 148 matched pairs of patients were identified. Cross-sectional information on symptomatology and functioning was obtained through semi-structured interview; longitudinal information on hospitalization, functioning, suicide attempts, mortality and relapse over 10 years was obtained from clinical database. There were 70.3% (N = 104) of SC and 74.3% (N = 110) of EI patients interviewed. RESULTS Results suggested that EI patients had reduced suicide rate (χ2 (1) = 4.35, p = 0.037), fewer number [odds ratio (OR) 1.56, χ2 = 15.64, p < 0.0001] and shorter duration of hospitalization (OR 1.29, χ2 = 4.06, p = 0.04), longer employment periods (OR -0.28, χ2 = 14.64, p < 0.0001) and fewer suicide attempts (χ2 = 11.47, df = 1, p = 0.001) over 10 years. At 10 years, no difference was found in psychotic symptoms, symptomatic remission and functional recovery. CONCLUSIONS The short-term benefits of the EI service on number of hospitalizations and employment was sustained after service termination, but the differences narrowed down. This suggests the need to evaluate the optimal duration of the EI service.

Identification of Neuroglycan C and Interacting Partners as Potential Susceptibility Genes for Schizophrenia in a Southern Chinese Population

Chromosome 3p was reported by previous studies as one of the regions showing strong evidence of linkage with schizophrenia. We performed a fine‐mapping association study of a 6‐Mb high‐LD and gene‐rich region on 3p in a Southern Chinese sample of 489 schizophrenia patients and 519 controls to search for susceptibility genes. In the initial screen, 4 SNPs out of the 144 tag SNPs genotyped were nominally significant (P < 0.05). One of the most significant SNPs (rs3732530, P = 0.0048) was a non‐synonymous SNP in the neuroglycan C (NGC, also known as CSPG5) gene, which belongs to the neuregulin family. The gene prioritization program Endeavor ranked NGC 8th out of the 129 genes in the 6‐Mb region and the highest among the genes within the same LD block. Further genotyping of NGC revealed 3 more SNPs to be nominally associated with schizophrenia. Three other genes (NRG1, ErbB3, ErbB4) involved in the neuregulin pathways were subsequently genotyped. Interaction analysis by multifactor dimensionality reduction (MDR) revealed a significant two‐SNP interaction between NGC and NRG1 (P = 0.015) and three‐SNP interactions between NRG1 and ErbB4 (P = 0.009). The gene NGC is exclusively expressed in the brain. It is implicated in neurodevelopment in rats and was previously shown to promote neurite outgrowth. Methamphetamine, a drug that may induce psychotic symptoms, was reported to alter the expression of NGC. Taken together, these results suggest that NGC may be a novel candidate gene, and neuregulin signaling pathways may play an important role in schizophrenia.

Estimating the Total Number of Susceptibility Variants Underlying Complex Diseases from Genome-Wide Association Studies

Recently genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases. In this study we proposed several approaches to estimate the total number of variants underlying these diseases. We assume that the variance explained by genetic markers (Vg) follow an exponential distribution, which is justified by previous studies on theories of adaptation. Our aim is to fit the observed distribution of Vg from GWAS to its theoretical distribution. The number of variants is obtained by the heritability divided by the estimated mean of the exponential distribution. In practice, due to limited sample sizes, there is insufficient power to detect variants with small effects. Therefore the power was taken into account in fitting. Besides considering the most significant variants, we also tried to relax the significance threshold, allowing more markers to be fitted. The effects of false positive variants were removed by considering the local false discovery rates. In addition, we developed an alternative approach by directly fitting the z-statistics from GWAS to its theoretical distribution. In all cases, the “winners curse” effect was corrected analytically. Confidence intervals were also derived. Simulations were performed to compare and verify the performance of different estimators (which incorporates various means of winners curse correction) and the coverage of the proposed analytic confidence intervals. Our methodology only requires summary statistics and is able to handle both binary and continuous traits. Finally we applied the methods to a few real disease examples (lipid traits, type 2 diabetes and Crohns disease) and estimated that hundreds to nearly a thousand variants underlie these traits.