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1.  Common variations in ALG9 are not associated with bipolar I disorder: a family-based study 
A mannosyltransferase gene (ALG9, DIBD1) at chromosome band 11q23 was previously identified to be disrupted by a balanced chromosomal translocation t(9;11)(p24;q23) co-segregating with bipolar affective disorder in a small family. Inborn ALG9 deficiency (congenital disorders of glycosylation type IL) is associated with progressive microcephaly, seizures, developmental delay, and hepatomegaly. It is unknown whether common variations of ALG9 predispose to bipolar affective disorder.
We tested five polymorphic markers spanning ALG9 (three intragenic and one upstream microsatellite repeats and one common missense variation, V289I (rs10502151) for their association with bipolar I disorder in two pedigree series. The NIMH (National Institute of Mental Health) pedigrees had a total of 166 families showing transmissions to 250 affected offspring, whereas The PITT (The University of Pittsburgh) pedigrees had a total of 129 families showing transmissions to 135 cases. We used transmission disequilibrium test for the association analyses.
We identified three common and distinct haplotypes spanning the ALG9 gene. We found no statistically-significant evidence of transmission disequilibrium of marker alleles or multi-marker haplotypes to the affected offspring with bipolar I disorder.
These results suggest that common variations in ALG9 do not play a major role in predisposition to bipolar affective disorder.
PMCID: PMC1569366  PMID: 16859551
2.  An abundance of rare functional variants in 202 drug target genes sequenced in 14,002 people 
Science (New York, N.Y.)  2012;337(6090):100-104.
Rare genetic variants contribute to complex disease risk; however, the abundance of rare variants in human populations remains unknown. We explored this spectrum of variation by sequencing 202 genes encoding drug targets in 14,002 individuals. We find rare variants are abundant (one every 17 bases) and geographically localized, such that even with large sample sizes, rare variant catalogs will be largely incomplete. We used the observed patterns of variation to estimate population growth parameters, the proportion of variants in a given frequency class that are putatively deleterious, and mutation rates for each gene. Overall we conclude that, due to rapid population growth and weak purifying selection, human populations harbor an abundance of rare variants, many of which are deleterious and have relevance to understanding disease risk.
PMCID: PMC4319976  PMID: 22604722
3.  Molecular Validation of the Schizophrenia Spectrum 
Schizophrenia Bulletin  2013;40(1):60-65.
Background: Early descriptive work and controlled family and adoption studies support the hypothesis that a range of personality and nonschizophrenic psychotic disorders aggregate in families of schizophrenic probands. Can we validate, using molecular polygene scores from genome-wide association studies (GWAS), this schizophrenia spectrum? Methods: The predictive value of polygenic findings reported by the Psychiatric GWAS Consortium (PGC) was applied to 4 groups of relatives from the Irish Study of High-Density Schizophrenia Families (ISHDSF; N = 836) differing on their assignment within the schizophrenia spectrum. Genome-wide single nucleotide polymorphism data for affected and unaffected relatives were used to construct per-individual polygene risk scores based on the PGC stage-I results. We compared mean polygene scores in the ISHDSF with mean scores in ethnically matched population controls (N = 929). Results: The schizophrenia polygene score differed significantly across diagnostic categories and was highest in those with narrow schizophrenia spectrum, lowest in those with no psychiatric illness, and in-between in those classified in the intermediate, broad, and very broad schizophrenia spectrum. Relatives of all of these groups of affected subjects, including those with no diagnosis, had schizophrenia polygene scores significantly higher than the control sample. Conclusions: In the relatives of high-density families, the observed pattern of enrichment of molecular indices of schizophrenia risk suggests an underlying, continuous liability distribution and validates, using aggregate common risk alleles, a genetic basis for the schizophrenia spectrum disorders. In addition, as predicted by genetic theory, nonpsychotic members of multiply-affected schizophrenia families are significantly enriched for replicated, polygenic risk variants compared with the general population.
PMCID: PMC3885304  PMID: 23970557
schizophrenia; schizophrenia spectrum; polygene score; GWAS
4.  The genetic overlap between schizophrenia and height 
Schizophrenia research  2013;151(0):226-228.
Epidemiological studies suggest that height and schizophrenia risk are inversely correlated. These findings might arise because i) height and schizophrenia share genetic variants and ii) the effects of these shared variants are in opposite direction for the two traits. We use genome wide association data to empirically evaluate these hypotheses. We find that variants which impact on height and risk for schizophrenia are distributed across several genomic regions and the directions of effect vary, some consistent and others inconsistent with the direction expected from the phenotypic data. Moreover, signals that were in and not in accord with the phenotypic data aggregated in distinct biological pathways.
PMCID: PMC3939673  PMID: 24239283
Shrinkage; Suggestive signals; Network analysis
5.  Association Testing Strategy for Data from Dense Marker Panels 
PLoS ONE  2013;8(11):e80540.
Genome wide association studies have been usually analyzed in a univariate manner. The commonly used univariate tests have one degree of freedom and assume an additive mode of inheritance. The experiment-wise significance of these univariate statistics is obtained by adjusting for multiple testing. Next generation sequencing studies, which assay 10-20 million variants, are beginning to come online. For these studies, the strategy of additive univariate testing and multiple testing adjustment is likely to result in a loss of power due to (1) the substantial multiple testing burden and (2) the possibility of a non-additive causal mode of inheritance. To reduce the power loss we propose: a new method (1) to summarize in a single statistic the strength of the association signals coming from all not-very-rare variants in a linkage disequilibrium block and (2) to incorporate, in any linkage disequilibrium block statistic, the strength of the association signals under multiple modes of inheritance. The proposed linkage disequilibrium block test consists of the sum of squares of nominally significant univariate statistics. We compare the performance of this method to the performance of existing linkage disequilibrium block/gene-based methods. Simulations show that (1) extending methods to combine testing for multiple modes of inheritance leads to substantial power gains, especially for a recessive mode of inheritance, and (2) the proposed method has a good overall performance. Based on simulation results, we provide practical advice on choosing suitable methods for applied analyses.
PMCID: PMC3827222  PMID: 24265830
6.  DIST: direct imputation of summary statistics for unmeasured SNPs 
Bioinformatics  2013;29(22):2925-2927.
Motivation: Genotype imputation methods are used to enhance the resolution of genome-wide association studies, and thus increase the detection rate for genetic signals. Although most studies report all univariate summary statistics, many of them limit the access to subject-level genotypes. Because such an access is required by all genotype imputation methods, it is helpful to develop methods that impute summary statistics without going through the interim step of imputing genotypes. Even when subject-level genotypes are available, due to the substantial computational cost of the typical genotype imputation, there is a need for faster imputation methods.
Results: Direct Imputation of summary STatistics (DIST) imputes the summary statistics of untyped variants without first imputing their subject-level genotypes. This is achieved by (i) using the conditional expectation formula for multivariate normal variates and (ii) using the correlation structure from a relevant reference population. When compared with genotype imputation methods, DIST (i) requires only a fraction of their computational resources, (ii) has comparable imputation accuracy for independent subjects and (iii) is readily applicable to the imputation of association statistics coming from large pedigree data. Thus, the proposed application is useful for a fast imputation of summary results for (i) studies of unrelated subjects, which (a) do not provide subject-level genotypes or (b) have a large size and (ii) family association studies.
Availability and implementation: Pre-compiled executables built under commonly used operating systems are publicly available at
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3810851  PMID: 23990413
8.  RNA-Seq analysis implicates dysregulation of the immune system in schizophrenia 
BMC Genomics  2012;13(Suppl 8):S2.
While genome-wide association studies identified some promising candidates for schizophrenia, the majority of risk genes remained unknown. We were interested in testing whether integration gene expression and other functional information could facilitate the identification of susceptibility genes and related biological pathways.
We conducted high throughput sequencing analyses to evaluate mRNA expression in blood samples isolated from 3 schizophrenia patients and 3 healthy controls. We also conducted pooled sequencing of 10 schizophrenic patients and matched controls. Differentially expressed genes were identified by t-test. In the individually sequenced dataset, we identified 198 genes differentially expressed between cases and controls, of them 19 had been verified by the pooled sequencing dataset and 21 reached nominal significance in gene-based association analyses of a genome wide association dataset. Pathway analysis of these differentially expressed genes revealed that they were highly enriched in the immune related pathways. Two genes, S100A8 and TYROBP, had consistent changes in expression in both individual and pooled sequencing datasets and were nominally significant in gene-based association analysis.
Integration of gene expression and pathway analyses with genome-wide association may be an efficient approach to identify risk genes for schizophrenia.
PMCID: PMC3535722  PMID: 23282246
9.  Comparison of Statistical Tests for Association between Rare Variants and Binary Traits 
PLoS ONE  2012;7(8):e42530.
Genome-wide association studies have found thousands of common genetic variants associated with a wide variety of diseases and other complex traits. However, a large portion of the predicted genetic contribution to many traits remains unknown. One plausible explanation is that some of the missing variation is due to the effects of rare variants. Nonetheless, the statistical analysis of rare variants is challenging. A commonly used method is to contrast, within the same region (gene), the frequency of minor alleles at rare variants between cases and controls. However, this strategy is most useful under the assumption that the tested variants have similar effects. We previously proposed a method that can accommodate heterogeneous effects in the analysis of quantitative traits. Here we extend this method to include binary traits that can accommodate covariates. We use simulations for a variety of causal and covariate impact scenarios to compare the performance of the proposed method to standard logistic regression, C-alpha, SKAT, and EREC. We found that i) logistic regression methods perform well when the heterogeneity of the effects is not extreme and ii) SKAT and EREC have good performance under all tested scenarios but they can be computationally intensive. Consequently, it would be more computationally desirable to use a two-step strategy by (i) selecting promising genes by faster methods and ii) analyzing selected genes using SKAT/EREC. To select promising genes one can use (1) regression methods when effect heterogeneity is assumed to be low and the covariates explain a non-negligible part of trait variability, (2) C-alpha when heterogeneity is assumed to be large and covariates explain a small fraction of trait’s variability and (3) the proposed trend and heterogeneity test when the heterogeneity is assumed to be non-trivial and the covariates explain a large fraction of trait variability.
PMCID: PMC3415421  PMID: 22912707
10.  Genome-Wide Gene-Environment Study Identifies Glutamate Receptor Gene GRIN2A as a Parkinson's Disease Modifier Gene via Interaction with Coffee 
PLoS Genetics  2011;7(8):e1002237.
Our aim was to identify genes that influence the inverse association of coffee with the risk of developing Parkinson's disease (PD). We used genome-wide genotype data and lifetime caffeinated-coffee-consumption data on 1,458 persons with PD and 931 without PD from the NeuroGenetics Research Consortium (NGRC), and we performed a genome-wide association and interaction study (GWAIS), testing each SNP's main-effect plus its interaction with coffee, adjusting for sex, age, and two principal components. We then stratified subjects as heavy or light coffee-drinkers and performed genome-wide association study (GWAS) in each group. We replicated the most significant SNP. Finally, we imputed the NGRC dataset, increasing genomic coverage to examine the region of interest in detail. The primary analyses (GWAIS, GWAS, Replication) were performed using genotyped data. In GWAIS, the most significant signal came from rs4998386 and the neighboring SNPs in GRIN2A. GRIN2A encodes an NMDA-glutamate-receptor subunit and regulates excitatory neurotransmission in the brain. Achieving P2df = 10−6, GRIN2A surpassed all known PD susceptibility genes in significance in the GWAIS. In stratified GWAS, the GRIN2A signal was present in heavy coffee-drinkers (OR = 0.43; P = 6×10−7) but not in light coffee-drinkers. The a priori Replication hypothesis that “Among heavy coffee-drinkers, rs4998386_T carriers have lower PD risk than rs4998386_CC carriers” was confirmed: ORReplication = 0.59, PReplication = 10−3; ORPooled = 0.51, PPooled = 7×10−8. Compared to light coffee-drinkers with rs4998386_CC genotype, heavy coffee-drinkers with rs4998386_CC genotype had 18% lower risk (P = 3×10−3), whereas heavy coffee-drinkers with rs4998386_TC genotype had 59% lower risk (P = 6×10−13). Imputation revealed a block of SNPs that achieved P2df<5×10−8 in GWAIS, and OR = 0.41, P = 3×10−8 in heavy coffee-drinkers. This study is proof of concept that inclusion of environmental factors can help identify genes that are missed in GWAS. Both adenosine antagonists (caffeine-like) and glutamate antagonists (GRIN2A-related) are being tested in clinical trials for treatment of PD. GRIN2A may be a useful pharmacogenetic marker for subdividing individuals in clinical trials to determine which medications might work best for which patients.
Author Summary
Parkinson's disease (PD), like most common disorders, involves interactions between genetic make-up and environmental exposures that are unique to each individual. Caffeinated-coffee consumption may protect some people from developing PD, although not all benefit equally. In a genome-wide search, we discovered that variations in the glutamate-receptor gene GRIN2A modulate the risk of developing PD in heavy coffee drinkers. The study was hypothesis-free, that is, we cast a net across the entire genome allowing statistical significance to point us to a genetic variant, regardless of whether it fell in a genomic desert or an important gene. Fortuitously, the most significant finding was in a well-known gene, GRIN2A, which regulates brain signals that control movement and behavior. Our finding is important for three reasons: First, it is a proof of concept that studying genes and environment on the whole-genome scale is feasible, and this approach can identify important genes that are missed when environmental exposures are ignored. Second, the knowledge of interaction between GRIN2A, which is involved in neurotransmission in the brain, and caffeine, which is an adenosine-A2A-receptor antagonist, will stimulate new research towards understanding the cause and progression of PD. Third, the results may lead to personalized prevention of and treatment for PD.
PMCID: PMC3158052  PMID: 21876681
11.  Linkage analysis of anorexia and bulimia nervosa cohorts using selected behavioral phenotypes as quantitative traits or covariates 
To increase the likelihood of finding genetic variation conferring liability to eating disorders, we measured over 100 attributes thought to be related to liability to eating disorders on affected individuals from multiplex families and two cohorts: one recruited through a proband with anorexia nervosa (AN; AN cohort); the other recruited through a proband with bulimia nervosa (BN; BN cohort). By a multilayer decision process based on expert evaluation and statistical analysis, six traits were selected for linkage analysis (1): obsessionality (OBS), age at menarche (MENAR) and anxiety (ANX) for quantitative trait locus (QTL) linkage analysis; and lifetime minimum Body Mass Index (BMI), concern over mistakes (CM) and food-related obsessions (OBF) for covariate-based linkage analysis. The BN cohort produced the largest linkage signals: for QTL linkage analysis, four suggestive signals: (for MENAR, at 10p13; for ANX, at 1q31.1, 4q35.2, and 8q13.1); for covariate-based linkage analyses, both significant and suggestive linkages (for BMI, one significant [4q21.1] and three suggestive [3p23, 10p13, 5p15.3]; for CM, two significant [16p13.3, 14q21.1] and three suggestive [4p15.33, 8q11.23, 10p11.21]; and for OBF, one significant [14q21.1] and five suggestive [4p16.1, 10p13.1, 8q11.23, 16p13.3, 18p11.31]). Results from the AN cohort were far less compelling: for QTL linkage analysis, two suggestive signals (for OBS at 6q21 and for ANX at 9p21.3); for covariate-based linkage analysis, five suggestive signals (for BMI at 4q13.1, for CM at 11p11.2 and 17q25.1, and for OBF at 17q25.1 and 15q26.2). Overlap between the two cohorts was minimal for substantial linkage signals.
PMCID: PMC2590774  PMID: 16152574
Complex disease; endophenotype; liability; mixture model; regression
12.  Selection of eating-disorder phenotypes for linkage analysis 
Vulnerability to anorexia nervosa (AN) and bulimia nervosa (BN) arise from the interplay of genetic and environmental factors. To explore the genetic contribution, we measured over 100 psychiatric, personality and temperament phenotypes of individuals with eating disorders from 154 multiplex families accessed through an AN proband (AN cohort) and 244 multiplex families accessed through a BN proband (BN cohort). To select a parsimonious subset of these attributes for linkage analysis, we subjected the variables to a multilayer decision process based on expert evaluation and statistical analysis. Criteria for trait choice included relevance to eating disorders pathology, published evidence for heritability, and results from our data. Based on these criteria, we chose six traits to analyze for linkage. Obsessionality, Age-at-Menarche, and a composite Anxiety measure displayed features of heritable quantitative traits, such as normal distribution and familial correlation, and thus appeared ideal for quantitative trait locus (QTL) linkage analysis. By contrast, some families showed highly concordant and extreme values for three variables — lifetime minimum Body Mass Index (lowest BMI attained during the course of illness), concern over mistakes, and food-related obsessions — whereas others did not. These distributions are consistent with a mixture of populations, and thus the variables were matched with covariate linkage analysis. Linkage results appear in a subsequent report. Our report lays out a systematic roadmap for utilizing a rich set of phenotypes for genetic analyses, including the selection of linkage methods paired to those phenotypes.
PMCID: PMC2560991  PMID: 16152575
Complex disease; endophenotype; liability; clinical judgment; covariate selection; mixture model; regression

Results 1-12 (12)