Biology subjects

Kendler, K.

Publications and source records attributed to Kendler, K..

5 recordsLinked to original sources

bioRxiv2018

Minimal phenotyping yields GWAS hits of low specificity for major depression

Minimal phenotyping refers to the reliance on the use of a small number of self-report items for disease case identification. This strategy has been applied to genome-wide association studies (GWAS) of major depressive disorder (MDD). Here we report that the genotype derived heritability (h2SNP) of depression defined by minimal phenotyping (14%, SE = 0.8%) is lower than strictly defined MDD (26%, SE = 2.2%). This cannot be explained by differences in prevalence between definitions or including cases of lower liability to MDD in minimal phenotyping definitions of depression, but can be explained by misdiagnosis of those without depression or with related conditions as cases of depression. Depression defined by minimal phenotyping is as genetically correlated with strictly defined MDD (rG = 0.81, SE = 0.03) as it is with the personality trait neuroticism (rG = 0.84, SE = 0.05), a trait not defined by the cardinal symptoms of depression. While they both show similar shared genetic liability with neuroticism, a greater proportion of the genome contributes to the minimal phenotyping definitions of depression (80.2%, SE = 0.6%) than to strictly defined MDD (65.8%, SE = 0.6%). We find that GWAS loci identified in minimal phenotyping definitions of depression are not specific to MDD: they also predispose to other psychiatric conditions. Finally, while highly predictive polygenic risk scores can be generated from minimal phenotyping definitions of MDD, the predictive power can be explained entirely by the sample size used to generate the polygenic risk score, rather than specificity for MDD. Our results reveal that genetic analysis of minimal phenotyping definitions of depression identifies non-specific genetic factors shared between MDD and other psychiatric conditions. Reliance on results from minimal phenotyping for MDD may thus bias views of the genetic architecture of MDD and may impede our ability to identify pathways specific to MDD.

genetics↗

bioRxiv2018

JEPEGMIX2-P: Novel pathway transcriptomic method greatly increases detection of molecular pathways in cosmopolitan cohorts

Genetic signal detection in genome-wide association studies (GWAS) is enhanced by pooling small signals from multiple Single Nucleotide Polymorphism (SNP), e.g. across genes and pathways. Because genes are believed to influence traits via gene expression, it is of interest to combine information from expression Quantitative Trait Loci (eQTLs) in a gene or genes in the same pathway. Such methods, widely referred as transcriptomic wide association analysis (TWAS), already exist for gene analysis. Due to the possibility of eliminating most of the confounding effect of linkage disequilibrium (LD) from TWAS gene statistics, pathway TWAS methods would be very useful in uncovering the true molecular bases of psychiatric disorders. However, such methods are not yet available for arbitrarily large pathways/gene sets. This is possibly due to it quadratic (in the number of SNPs) computational burden for computing LD across large regions. To overcome this obstacle, we propose JEPEGMIX2-P, a novel TWAS pathway method that i) has a linear computational burden, ii) uses a large and diverse reference panel (33K subjects), iii) is competitive (adjusts for background enrichment in gene TWAS statistics) and iv) is applicable as-is to ethnically mixed cohorts. To underline its potential for increasing the power to uncover genetic signals over the state-of-the-art and commonly used non-transcriptomics methods, e.g. MAGMA, we applied JEPEGMIX2-P to summary statistics of most large meta-analyses from Psychiatric Genetics Consortium (PGC). While our work is just the very first step toward clinical translation of psychiatric disorders, PGC anorexia results suggest a possible avenue for treatment.

bioinformatics↗

bioRxiv2017

Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes

Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a significant proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention and treatment. In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was significantly higher than that based on the average genomic region (91 regions, p = 1.2x10-6) while the inverse was not significant (19 regions, p=0.89). Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several significant correlations with: 1) SCZ subphenotypes: negative symptoms (SCZ, p=3.6x10-6) and manic symptoms (BD, p=2x10-5), 2) BD subphenotypes: psychotic features (SCZ p=1.2x10-10, BD p=5.3x10-5) and age of onset (SCZ p=7.9x10-4). Finally, we show that psychotic features in BD has significant SNP-heritability (h2 snp=0.15, SE=0.06), and a significant genetic correlation with SCZ (rg=0.34) in addition there is a significant sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p=0.0038, one-side binomial test). For the first time, we have identified specific loci pointing to a potential role of 4 genes (DARS2, ARFGEF2, DCAKD and GATAD2A) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.

genomics↗

bioRxiv2017

Genome-wide Association Study of Clinical Features in the Schizophrenia Psychiatric Genomics Consortium: Confirmation of Polygenic Effect on Negative Symptoms

Schizophrenia is a clinically heterogeneous disorder. Proposed revisions in DSM - 5 included dimensional measurement of different symptom domains. We sought to identify common genetic variants influencing these dimensions, and confirm a previous association between polygenic risk of schizophrenia and the severity of negative symptoms. The Psychiatric Genomics Consortium study of schizophrenia comprised 8,432 cases of European ancestry with available clinical phenotype data. Symptoms averaged over the course of illness were assessed using the OPCRIT, PANSS, LDPS, SCAN, SCID, and CASH. Factor analyses of each constituent PGC study identified positive, negative, manic, and depressive symptom dimensions. We examined the relationship between the resultant symptom dimensions and aggregate polygenic risk scores indexing risk of schizophrenia. We performed genome - wide association study (GWAS) of each quantitative traits using linear regression and adjusting for significant effects of sex and ancestry. The negative symptom factor was significantly associated with polygene risk scores for schizophrenia, confirming a previous, suggestive finding by our group in a smaller sample, though explaining only a small fraction of the variance. In subsequent GWAS, we observed the strongest evidence of association for the positive and negative symptom factors, with SNPs in RFX8 on 2q11.2 (P = 6.27x10-8) and upstream of WDR72 / UNC13C on 15q21.3 (P = 7.59x10-8), respectively. We report evidence of association of novel modifier loci for schizophrenia, though no single locus attained established genome - wide significance criteria. As this may have been due to insufficient statistical power, follow - up in additional samples is warranted. Importantly, we replicated our previous finding that polygenic risk explains at least some of the variance in negative symptoms, a core illness dimension.

genetics↗

bioRxiv2017

JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts.

MotivationTo increase detection power, researchers use gene level analysis methods to aggregate weak marker signals. Due to gene expression controlling biological processes, researchers proposed aggregating signals for expression Quantitative Trait Loci (eQTL). Most gene-level eQTL methods make statistical inferences based on i) summary statistics from genome-wide association studies (GWAS) and ii) linkage disequilibrium (LD) patterns from a relevant reference panel. While most such tools assume homogeneous cohorts, our Gene-level Joint Analysis of functional SNPs in Cosmopolitan Cohorts (JEPEGMIX) method accommodates cosmopolitan cohorts by using heterogeneous panels. However, JEPGMIX relies on brain eQTLs from older gene expression studies and does not adjust for background enrichment in GWAS signals.\n\nResultsWe propose JEPEGMIX2, an extension of JEPEGMIX. When compared to JPEGMIX, it uses i) cis-eQTL SNPs from the latest expression studies and ii) brains specific (sub)tissues and tissues other than brain. JEPEGMIX2 also i) avoids accumulating averagely enriched polygenic information by adjusting for background enrichment and ii), to avoid an increase in false positive rates for studies with numerous highly enriched (above the background) genes, it outputs gene q-values based on Holm adjustment of p-values.\n\nContactchris.chatzinakos@vcuhealth.org\n\nSupplementary informationSupplementary material is available at Bioinformatics online.

bioinformatics↗