Joanna M. Biernacka

Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics : The PDGene database

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinsons disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P<5×10−8) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10−8). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.

Heterozygosity for a POMC-Null Mutation and Increased Obesity Risk in Humans

Congenital deficiency of proopiomelanocortin (POMC) results in a syndrome of hypoadrenalism, severe obesity, and altered skin and hair pigmentation. The concept that subtle variation in POMC expression and/or function might contribute to common obesity is suggested by studies reporting linkage of obesity-related traits to a locus on chromosome 2p22 encompassing the POMC gene. We identified a novel homozygous frameshift (C6906del) mutation in POMC in a child of Turkish origin with severe obesity and hypoadrenalism. This mutation would be predicted to lead to the loss of all POMC-derived peptides. The availability of a large extended pedigree provided the opportunity to address whether loss of one copy of the POMC gene was sufficient to alter obesity risk. Twelve relatives were heterozygous for the mutation and 7 were wild type. Of the heterozygotes, 11 of 12 heterozygotes were obese or overweight compared with only 1 of 7 of the wild-type relatives. The mean BMI SD score was 1.7 ± 0.5 in heterozygotes and 0.4 ± 0.4 in the wild-type relatives. Parametric linkage analysis of the trait “overweight” provided statistically significant evidence of linkage with this locus, with a maximum “location score” (comparable with multipoint logarithm of odds scores) of 3.191. We conclude that loss of one copy of the POMC gene predisposes to obesity in humans. Thus, genetic variants having relatively subtle effects on POMC expression and function could influence susceptibility to obesity.

SLC6A4 variation and citalopram response.

The influence of genetic variations in SLC6A4 (serotonin transporter gene) on citalopram treatment of depression using the Sequenced Treatment to Relieve Depression (STAR*D) sample was assessed. Of primary interest were three previously studied polymorphisms: 1) the VNTR variation of the second intron, 2) the indel promoter polymorphism (5HTTLPR or SERT), and 3) a single nucleotide polymorphism (SNP) rs25531. Additionally, SLC6A4 was resequenced to identify new SNPs for exploratory analyses. DNA from 1914 subjects in the STAR*D study were genotyped for the intron 2 VNTR region, the indel promoter polymorphism, and rs25531. Associations of these variants with remission of depressive symptoms were evaluated following citalopram treatment. In white non‐Hispanic subjects, variations in the intron 2 VNTR (point‐wise P = 0.041) and the indel promoter polymorphism (point‐wise P = 0.039) were associated with remission following treatment with citalopram. The haplotype composed of the three candidate loci was also associated with remission, with a global p‐value of 0.040 and a maximum statistic simulation p‐value of 0.0031 for the S‐a‐12 haplotype, under a dominant model. One SNP identified through re‐sequencing the SLC6A4 gene, Intron7‐83‐TC, showed point‐wise evidence of association, which did not remain significant after correction for the number of SNPs evaluated in this exploratory analysis. No associations between these SLC6A4 variations and remission were found in the white Hispanic or black subjects. These findings suggest that multiple variations in the SLC6A4 gene are associated with remission in white non‐Hispanic depressed adults treated with citalopram. The mechanism of action of these variants remains to be determined.

Glycine and a glycine dehydrogenase (GLDC) SNP as citalopram/escitalopram response biomarkers in depression: pharmacometabolomics-informed pharmacogenomics.

Major depressive disorder (MDD) is a common psychiatric disease. Selective serotonin reuptake inhibitors (SSRIs) are an important class of drugs used in the treatment of MDD. However, many patients do not respond adequately to SSRI therapy. We used a pharmacometabolomics‐informed pharmacogenomic research strategy to identify citalopram/escitalopram treatment outcome biomarkers. Metabolomic assay of plasma samples from 20 escitalopram remitters and 20 nonremitters showed that glycine was negatively associated with treatment outcome (P = 0.0054). This observation was pursued by genotyping tag single‐nucleotide polymorphisms (SNPs) for genes encoding glycine synthesis and degradation enzymes, using 529 DNA samples from SSRI‐treated MDD patients. The rs10975641 SNP in the glycine dehydrogenase (GLDC) gene was associated with treatment outcome phenotypes. Genotyping for rs10975641 was carried out in 1,245 MDD patients in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, and its presence was significant (P = 0.02) in DNA taken from these patients. These results highlight a possible role for glycine in SSRI response and illustrate the use of pharmacometabolomics to “inform” pharmacogenomics.

Gene set analysis of SNP data: benefits, challenges, and future directions

The last decade of human genetic research witnessed the completion of hundreds of genome-wide association studies (GWASs). However, the genetic variants discovered through these efforts account for only a small proportion of the heritability of complex traits. One explanation for the missing heritability is that the common analysis approach, assessing the effect of each single-nucleotide polymorphism (SNP) individually, is not well suited to the detection of small effects of multiple SNPs. Gene set analysis (GSA) is one of several approaches that may contribute to the discovery of additional genetic risk factors for complex traits. Complex phenotypes are thought to be controlled by networks of interacting biochemical and physiological pathways influenced by the products of sets of genes. By assessing the overall evidence of association of a phenotype with all measured variation in a set of genes, GSA may identify functionally relevant sets of genes corresponding to relevant biomolecular pathways, which will enable more focused studies of genetic risk factors. This approach may thus contribute to the discovery of genetic variants responsible for some of the missing heritability. With the increased use of these approaches for the secondary analysis of data from GWAS, it is important to understand the different GSA methods and their strengths and weaknesses, and consider challenges inherent in these types of analyses. This paper provides an overview of GSA, highlighting the key challenges, potential solutions, and directions for ongoing research.

CYP2C19 Variation and Citalopram Response

Objective Variations in cytochrome P450 (CYP) genes have been shown to be associated with both accelerated and delayed pharmacokinetic clearance of many psychotropic medications. Citalopram is metabolized by three CYP enzymes. CYP2C19 and CYP3A4 play a primary role in citalopram metabolism, whereas CYP2D6 plays a secondary role. Methods The Sequenced Treatment Alternatives to Relieve Depression sample was used to examine the relationship between variations in the CYP2C19 and CYP2D6 genes and remission of depressive symptoms and tolerance to treatment with citalopram. The primary analyses were of the White non-Hispanic patients adherent to the study protocol (n=1074). Results Generally, patients who had CYP2C19 genotypes associated with decreased metabolism were less likely to tolerate citalopram than those with increased metabolism, although this difference was not statistically significant (P=0.06). However, patients with the inactive 2C19*2 allele had significantly lower odds of tolerance (P=0.02). Patients with the poor metabolism CYP2C19 genotype-based category who were classified as citalopram tolerant were more likely to experience remission (P=0.03). No relationship between CYP2D6 genotype-based categories and either remission or tolerance was identified, although exploratory analyses identified a potential interaction between CYP2C19 and CYP2D6 effects. Conclusion Despite several limitations including the lack of serum drug levels, this study showed that variations in CYP2C19 were associated with tolerance and remission in a large sample of White non-Hispanic patients treated with citalopram.

Machine learning in genome-wide association studies.

Recently, genome‐wide association studies have substantially expanded our knowledge about genetic variants that influence the susceptibility to complex diseases. Although standard statistical tests for each single‐nucleotide polymorphism (SNP) separately are able to capture main genetic effects, different approaches are necessary to identify SNPs that influence disease risk jointly or in complex interactions. Experimental and simulated genome‐wide SNP data provided by the Genetic Analysis Workshop 16 afforded an opportunity to analyze the applicability and benefit of several machine learning methods. Penalized regression, ensemble methods, and network analyses resulted in several new findings while known and simulated genetic risk variants were also identified. In conclusion, machine learning approaches are promising complements to standard single‐and multi‐SNP analysis methods for understanding the overall genetic architecture of complex human diseases. However, because they are not optimized for genome‐wide SNP data, improved implementations and new variable selection procedures are required. Genet. Epidemiol. 33 (Suppl. 1):S51–S57, 2009.

Assessment of Response to Lithium Maintenance Treatment in Bipolar Disorder: A Consortium on Lithium Genetics (ConLiGen) Report

Objective The assessment of response to lithium maintenance treatment in bipolar disorder (BD) is complicated by variable length of treatment, unpredictable clinical course, and often inconsistent compliance. Prospective and retrospective methods of assessment of lithium response have been proposed in the literature. In this study we report the key phenotypic measures of the “Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder” scale currently used in the Consortium on Lithium Genetics (ConLiGen) study. Materials and Methods Twenty-nine ConLiGen sites took part in a two-stage case-vignette rating procedure to examine inter-rater agreement [Kappa (κ)] and reliability [intra-class correlation coefficient (ICC)] of lithium response. Annotated first-round vignettes and rating guidelines were circulated to expert research clinicians for training purposes between the two stages. Further, we analyzed the distributional properties of the treatment response scores available for 1,308 patients using mixture modeling. Results Substantial and moderate agreement was shown across sites in the first and second sets of vignettes (κ = 0.66 and κ = 0.54, respectively), without significant improvement from training. However, definition of response using the A score as a quantitative trait and selecting cases with B criteria of 4 or less showed an improvement between the two stages (ICC1 = 0.71 and ICC2 = 0.75, respectively). Mixture modeling of score distribution indicated three subpopulations (full responders, partial responders, non responders). Conclusions We identified two definitions of lithium response, one dichotomous and the other continuous, with moderate to substantial inter-rater agreement and reliability. Accurate phenotypic measurement of lithium response is crucial for the ongoing ConLiGen pharmacogenomic study.

SNP interaction detection with Random Forests in high-dimensional genetic data

BackgroundIdentifying variants associated with complex human traits in high-dimensional data is a central goal of genome-wide association studies. However, complicated etiologies such as gene-gene interactions are ignored by the univariate analysis usually applied in these studies. Random Forests (RF) are a popular data-mining technique that can accommodate a large number of predictor variables and allow for complex models with interactions. RF analysis produces measures of variable importance that can be used to rank the predictor variables. Thus, single nucleotide polymorphism (SNP) analysis using RFs is gaining popularity as a potential filter approach that considers interactions in high-dimensional data. However, the impact of data dimensionality on the power of RF to identify interactions has not been thoroughly explored. We investigate the ability of rankings from variable importance measures to detect gene-gene interaction effects and their potential effectiveness as filters compared to p-values from univariate logistic regression, particularly as the data becomes increasingly high-dimensional.ResultsRF effectively identifies interactions in low dimensional data. As the total number of predictor variables increases, probability of detection declines more rapidly for interacting SNPs than for non-interacting SNPs, indicating that in high-dimensional data the RF variable importance measures are capturing marginal effects rather than capturing the effects of interactions.ConclusionsWhile RF remains a promising data-mining technique that extends univariate methods to condition on multiple variables simultaneously, RF variable importance measures fail to detect interaction effects in high-dimensional data in the absence of a strong marginal component, and therefore may not be useful as a filter technique that allows for interaction effects in genome-wide data.

Self-Contained Gene-Set Analysis of Expression Data: An Evaluation of Existing and Novel Methods

Gene set methods aim to assess the overall evidence of association of a set of genes with a phenotype, such as disease or a quantitative trait. Multiple approaches for gene set analysis of expression data have been proposed. They can be divided into two types: competitive and self-contained. Benefits of self-contained methods include that they can be used for genome-wide, candidate gene, or pathway studies, and have been reported to be more powerful than competitive methods. We therefore investigated ten self-contained methods that can be used for continuous, discrete and time-to-event phenotypes. To assess the power and type I error rate for the various previously proposed and novel approaches, an extensive simulation study was completed in which the scenarios varied according to: number of genes in a gene set, number of genes associated with the phenotype, effect sizes, correlation between expression of genes within a gene set, and the sample size. In addition to the simulated data, the various methods were applied to a pharmacogenomic study of the drug gemcitabine. Simulation results demonstrated that overall Fishers method and the global model with random effects have the highest power for a wide range of scenarios, while the analysis based on the first principal component and Kolmogorov-Smirnov test tended to have lowest power. The methods investigated here are likely to play an important role in identifying pathways that contribute to complex traits.