Guimin Gao

Methylome-Wide Association Study of Schizophrenia: Identifying Blood Biomarker Signatures of Environmental Insults

IMPORTANCE Epigenetic studies present unique opportunities to advance schizophrenia research because they can potentially account for many of its clinical features and suggest novel strategies to improve disease management. OBJECTIVE To identify schizophrenia DNA methylation biomarkers in blood. DESIGN, SETTING, AND PARTICIPANTS The sample consisted of 759 schizophrenia cases and 738 controls (N = 1497) collected in Sweden. We used methyl-CpG-binding domain protein-enriched genome sequencing of the methylated genomic fraction, followed by next-generation DNA sequencing. We obtained a mean (SD) number of 68 (26.8) million reads per sample. This massive data set was processed using a specifically designed data analysis pipeline. Critical top findings from our methylome-wide association study (MWAS) were replicated in independent case-control participants using targeted pyrosequencing of bisulfite-converted DNA. MAIN OUTCOMES AND MEASURES Status of schizophrenia cases and controls. RESULTS Our MWAS suggested a considerable number of effects, with 25 sites passing the highly conservative Bonferroni correction and 139 sites significant at a false discovery rate of 0.01. Our top MWAS finding, which was located in FAM63B, replicated with P = 2.3 × 10-10. It was part of the networks regulated by microRNA that can be linked to neuronal differentiation and dopaminergic gene expression. Many other top MWAS results could be linked to hypoxia and, to a lesser extent, infection, suggesting that a record of pathogenic events may be preserved in the methylome. Our findings also implicated a site in RELN, one of the most frequently studied candidates in methylation studies of schizophrenia. CONCLUSIONS AND RELEVANCE To our knowledge, the present study is one of the first MWASs of disease with a large sample size using a technology that provides good coverage of methylation sites across the genome. Our results demonstrated one of the unique features of methylation studies that can capture signatures of environmental insults in peripheral tissues. Our MWAS suggested testable hypotheses about disease mechanisms and yielded biomarkers that can potentially be used to improve disease management.

Pharmacogenetic association of the APOA1/C3/A4/A5 gene cluster and lipid responses to fenofibrate: the genetics of lipid-lowering drugs and diet network study.

Background The apolipoproteins (APOA1/C3/A4/A5) are key components in modulating lipoprotein metabolism. It is unknown whether variants at the APOA1/C3/A4/A5 gene cluster are associated with lipid response to pharmacologic intervention. Methods and results Plasma triglycerides (TGs) and high-density lipoprotein (HDL) levels were measured in 861 Genetics of Lipid-Lowering Drugs and Diet Network study participants who underwent a 3-week fenofibrate trial. We examined 18 common single nucleotide polymorphisms (SNPs) spanning the APOA1/C3/A4/A5 genes to investigate the effects of variants at the gene cluster on lipid response to fenofibrate treatment. We found that the minor alleles of the SNPs rs3135506 (APOA5_S19W), rs5104 (APOA4_N147S), rs4520 (APOC3_G34G), and rs5128 (APOC3_3U386) were associated with enhanced TG response to fenofibrate treatment (P= 0.0004–0.018). The minor allele of SNP rs2854117 (APOC3_M482) was associated with reduced rather than enhanced TG response (P= 0.026). The SNP rs3135506 (APOA5_S19W) was associated with HDL response, with minor allele related to reduced HDL response to fenofibrate (P= 0.002). Association analyses on haplotype provided corroborative evidence to single SNP association analyses. The common haplotypes H2, H3, and H5 were significantly associated with reduced TG response to fenofibrate. Conclusion The genetic variants at APOA1/C3/A4/A5 gene cluster may be useful markers to predict response of lipid-lowering therapy with fenofibrate. Further studies to replicate/confirm our findings are warranted.

Apolipoprotein B genetic variants modify the response to fenofibrate: a GOLDN study.

Hypertriglyceridemia, defined as a triglyceride measurement > 150 mg/dl, occurs in up to 34% of adults. Fenofibrate is a commonly used drug to treat hypertriglyceridemia, but response to fenofibrate varies considerably among individuals. We sought to determine if genetic variation in apolipoprotein B (APOB), an essential core of triglyceride-rich lipoprotein formation, may account for some of the inter-individual differences observed in triglyceride (TG) response to fenofibrate treatment. Participants (N = 958) from the Genetics of Lipid Lowering Drugs and Diet Network study completed a three-week intervention with fenofibrate 160 mg/day. Associations of four APOB gene single nucleotide polymorphisms (SNP) (rs934197, rs693, rs676210, and rs1042031) were tested for association with the TG response to fenofibrate using a mixed growth curve model where the familial structure was modeled as a random effect and cardiovascular risk factors were included as covariates. Three of these four SNPs changed the amino acid sequence of APOB, and the fourth was in the promoter region. TG response to fenofibrate treatment was associated with one APOB SNP, rs676210 (Pro2739Leu), such that participants with the TT genotype of rs676210 had greater TG lowering than those with the CC genotype (additive model, P = 0.0017). We conclude the rs676210 variant may identify individuals who respond best to fenofibrate for TG reduction.

Haplotyping methods for pedigrees.

Haplotypes provide valuable information in the study of diseases, complex traits, population histories, and evolutionary genetics. With the dramatic increase in the number of available single nucleotide polymorphism (SNP) markers, haplotype inference (haplotyping) using observed genotype data has become an important component of genetic studies in general and of statistical gene mapping in particular. Existing haplotyping methods include (1) population-based methods, (2) methods for pooled DNA samples, and (3) methods for family and pedigree data. The methods and computer programs for population data and pooled DNA samples were reviewed recently in the literature. As several authors noted, family and pedigree datasets are abundant and have unique advantages. In the past twenty years, many haplotyping methods for family and pedigree data have been developed. Therefore, in this contribution we review haplotyping methods and the corresponding computer programs suitable for family and pedigree data and discuss their applications and limitations. We explore the connections among these methods, and describe the challenges that remain to be addressed.

Rare Variant Association Testing by Adaptive Combination of P-values

With the development of next-generation sequencing technology, there is a great demand for powerful statistical methods to detect rare variants (minor allele frequencies (MAFs)<1%) associated with diseases. Testing for each variant site individually is known to be underpowered, and therefore many methods have been proposed to test for the association of a group of variants with phenotypes, by pooling signals of the variants in a chromosomal region. However, this pooling strategy inevitably leads to the inclusion of a large proportion of neutral variants, which may compromise the power of association tests. To address this issue, we extend the -MidP method (Cheung et al., 2012, Genet Epidemiol 36: 675–685) and propose an approach (named ‘adaptive combination of P-values for rare variant association testing’, abbreviated as ‘ADA’) that adaptively combines per-site P-values with the weights based on MAFs. Before combining P-values, we first imposed a truncation threshold upon the per-site P-values, to guard against the noise caused by the inclusion of neutral variants. This ADA method is shown to outperform popular burden tests and non-burden tests under many scenarios. ADA is recommended for next-generation sequencing data analysis where many neutral variants may be included in a functional region.

Epigenetic Alterations and an Increased Frequency of Micronuclei in Women with Fibromyalgia

Fibromyalgia (FM), characterized by chronic widespread pain, fatigue, and cognitive/mood disturbances, leads to reduced workplace productivity and increased healthcare expenses. To determine if acquired epigenetic/genetic changes are associated with FM, we compared the frequency of spontaneously occurring micronuclei (MN) and genome-wide methylation patterns in women with FM (n = 10) to those seen in comparably aged healthy controls (n = 42 (MN); n = 8 (methylation)). The mean (sd) MN frequency of women with FM (51.4 (21.9)) was significantly higher than that of controls (15.8 (8.5)) (χ 2 = 45.552; df = 1; P = 1.49 × 10−11). Significant differences (n = 69 sites) in methylation patterns were observed between cases and controls considering a 5% false discovery rate. The majority of differentially methylated (DM) sites (91%) were attributable to increased values in the women with FM. The DM sites included significant biological clusters involved in neuron differentiation/nervous system development, skeletal/organ system development, and chromatin compaction. Genes associated with DM sites whose function has particular relevance to FM included BDNF, NAT15, HDAC4, PRKCA, RTN1, and PRKG1. Results support the need for future research to further examine the potential role of epigenetic and acquired chromosomal alterations as a possible biological mechanism underlying FM.

Haplotype-based methods for detecting uncommon causal variants with common SNPs.

Detecting uncommon causal variants (minor allele frequency [MAF] < 5%) is difficult with commercial single‐nucleotide polymorphism (SNP) arrays that are designed to capture common variants (MAF > 5%). Haplotypes can provide insights into underlying linkage disequilibrium (LD) structure and can tag uncommon variants that are not well tagged by common variants. In this work, we propose a wei‐SIMc‐matching test that inversely weights haplotype similarities with the estimated standard deviation of haplotype counts to boost the power of similarity‐based approaches for detecting uncommon causal variants. We then compare the power of the wei‐SIMc‐matching test with that of several popular haplotype‐based tests, including four other similarity‐based tests, a global score test for haplotypes (global), a test based on the maximum score statistic over all haplotypes (max), and two newly proposed haplotype‐based tests for rare variant detection. With systematic simulations under a wide range of LD patterns, the results show that wei‐SIMc‐matching and global are the two most powerful tests. Among these two tests, wei‐SIMc‐matching has reliable asymptotic P‐values, whereas global needs permutations to obtain reliable P‐values when the frequencies of some haplotype categories are low or when the trait is skewed. Therefore, we recommend wei‐SIMc‐matching for detecting uncommon causal variants with surrounding common SNPs, in light of its power and computational feasibility.

The SCARB1 gene is associated with lipid response to dietary and pharmacological interventions

AbstractThe scavenger receptor class B type 1 (SCARB1) gene is a key component in the reverse cholesterol transport pathway and thus plays an important role in lipid metabolism. Studies suggest that the SCARB1 gene may contribute to variation in plasma lipid levels at fasting; however, the results have been inconsistent, and it is unclear whether SCARB1 may also influence lipid response to dietary and pharmacologic interventions. In this study, we examined genetic variation in the SCARB1 gene in participants of the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study for associations with basal lipid levels, changes in lipid measures after dietary fat intake, and fenofibrate treatment. We found that the exon 1 variant SCARB1_G2S was significantly associated with postfenofibrate change for triglycerides (TG) (P = 0.004). Subjects bearing SCARB1_G2S minor allele A tend to have higher responsiveness to fenofibrate in lowering TG. In summary, our study suggested that the SCARB1 gene may serve as a useful marker that predicts variation in baseline lipid levels, postprandial lipid response, and response to fenofibrate intervention.

Haplotype kernel association test as a powerful method to identify chromosomal regions harboring uncommon causal variants.

For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome‐wide association studies is minor. Although the so‐called “rare variants” (minor allele frequency [MAF] < 1%) have attracted increasing attention, they are unlikely to account for much of the “missing heritability” because very few people may carry these rare variants. The genetic variants that are likely to fill in the “missing heritability” include uncommon causal variants (MAF < 5%), which are generally untyped in association studies using tagging single‐nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome‐wide or exome‐wide next‐generation sequencing. In this work, we propose a haplotype kernel association test (HKAT) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of HKAT to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, HKAT is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.

Weighted Multiple Hypothesis Testing Procedures

Multiple hypothesis testing is commonly used in genome research such as genome-wide studies and gene expression data analysis (Lin, 2005). The widely used Bonferroni procedure controls the family-wise error rate (FWER) for multiple hypothesis testing, but has limited statistical power as the number of hypotheses tested increases. The power of multiple testing procedures can be increased by using weighted p-values (Genovese et al., 2006). The weights for the p-values can be estimated by using certain prior information. Wasserman and Roeder (2006) described a weighted Bonferroni procedure, which incorporates weighted p-values into the Bonferroni procedure, and Rubin et al. (2006) and Wasserman and Roeder (2006) estimated the optimal weights that maximize the power of the weighted Bonferroni procedure under the assumption that the means of the test statistics in the multiple testing are known (these weights are called optimal Bonferroni weights). This weighted Bonferroni procedure controls FWER and can have higher power than the Bonferroni procedure, especially when the optimal Bonferroni weights are used. To further improve the power of the weighted Bonferroni procedure, first we propose a weighted Šidák procedure that incorporates weighted p-values into the Šidák procedure, and then we estimate the optimal weights that maximize the average power of the weighted Šidák procedure under the assumption that the means of the test statistics in the multiple testing are known (these weights are called optimal Šidák weights). This weighted Šidák procedure can have higher power than the weighted Bonferroni procedure. Second, we develop a generalized sequential (GS) Šidák procedure that incorporates weighted p-values into the sequential Šidák procedure (Scherrer, 1984). This GS Šidák procedure is an extension of and has higher power than the GS Bonferroni procedure of Holm (1979). Finally, under the assumption that the means of the test statistics in the multiple testing are known, we incorporate the optimal Šidák weights and the optimal Bonferroni weights into the GS Šidák procedure and the GS Bonferroni procedure, respectively. Theoretical proof and/or simulation studies show that the GS Šidák procedure can have higher power than the GS Bonferroni procedure when their corresponding optimal weights are used, and that both of these GS procedures can have much higher power than the weighted Šidák and the weighted Bonferroni procedures. All proposed procedures control the FWER well and are useful when prior information is available to estimate the weights.