Biology subjects

Kendler, K.

Publications and source records attributed to Kendler, K..

13 recordsLinked to original sources

bioRxiv2022

Phenotype integration improves power and preserves specificity in biobank-based genetic studies of MDD

Biobanks often contain several phenotypes relevant to a given disorder, and researchers face complex tradeoffs between shallow phenotypes (high sample size, low specificity and sensitivity) and deep phenotypes (low sample size, high specificity and sensitivity). Here, we study an extreme case: Major Depressive Disorder (MDD) in UK Biobank. Previous studies found that shallow and deep MDD phenotypes have qualitatively distinct genetic architectures, but it remains unclear which are optimal for scientific study or clinical prediction. We propose a new framework to get the best of both worlds by integrating together information across hundreds of MDD-relevant phenotypes. First, we use phenotype imputation to increase sample size for the deepest available MDD phenotype, which dramatically improves GWAS power (increases #loci ~10 fold) and PRS accuracy (increases R2 ~2 fold). Further, we show the genetic architecture of the imputed phenotype remains specific to MDD using genetic correlation, PRS prediction in external clinical cohorts, and a novel PRS-based pleiotropy metric. We also develop a complementary approach to improve specificity of GWAS on shallow MDD phenotypes by adjusting for phenome-wide PCs. Finally, we study phenotype integration at the level of GWAS summary statistics, which can increase GWAS and PRS power but introduces non-MDD-specific signals. Our work provides a simple and scalable recipe to improve genetic studies in large biobanks by combining the sample size of shallow phenotypes with the sensitivity and specificity of deep phenotypes.

genetics↗

bioRxiv2022

Deep Learning-based Phenotype Imputation on Population-scale Biobank Data Increases Genetic Discoveries

Biobanks that collect deep phenotypic and genomic data across large numbers of individuals have emerged as a key resource for human genetic research. However, phenotypes acquired as part of Biobanks are often missing across many individuals, limiting the utility of these datasets. The ability to accurately impute or "fill-in" missing phenotypes is critical to harness the power of population-scale Biobank datasets. We propose AutoComplete, a deep learning-based imputation method which can accurately impute missing phenotypes in population-scale Biobank datasets. When applied to collections of phenotypes measured across {approx} 300K individuals from the UK Biobank, AutoComplete improved imputation accuracy over existing 2 methods (average improvement in r2 of 18% for all phenotypes and 42% for binary phenotypes). We explored the utility of phenotype imputation for improving the power of genome-wide association studies (GWAS) by applying our method to a group of five clinically relevant traits with an average missigness rate of 83% (67% to 94%) leading to an an increase in effective sample size of {approx}2-fold on average (0.5 to 3.3-fold across the phenotypes). GWAS on the resulting imputed phenotypes led to an increase in the total number of loci significantly associated to the traits from four to 129. Our results demonstrate the utility of deep-learning based imputation to increase power for genetic discoveries in existing biobank data sets.

bioinformatics↗

bioRxiv2022

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latinx participants (32%). The multi-ancestry GWAS identified 190 significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic/Latinx group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with [≤]5 variants increased from 3 to 12. A transcriptome-wide association study identified 354 significantly associated genes, 205 of them novel. Mendelian Randomisation showed a bidirectional relationship with BMI exclusively in samples of European ancestry. This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

genetics↗

bioRxiv2022

Cross-trait assortative mating is widespread and inflates genetic correlation estimates

The observation of genetic correlations between disparate traits has been interpreted as evidence of widespread pleiotropy, altered theories of human genetic architecture, and spurred considerable research activity across the natural and social sciences. Here, we introduce cross-trait assortative mating (xAM) as an alternative explanation for observed genetic correlations. We observe that xAM is common across a broad array of phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates (R2 = 76%). Then, we present theoretical and simulation-based results demonstrating that, under xAM, genetic correlation estimators yield significant estimates even for traits with entirely distinct genetic bases. We demonstrate that existing xAM plausibly accounts for substantial fractions of genetic correlation estimates in two large samples (N = 827,960). For example, previously reported genetic correlation estimates between many pairs of psychiatric disorders are fully consistent with xAM alone. Finally, we provide evidence for a history of xAM at the genetic level using a novel approach based on cross-trait even/odd chromosome polygenic score correlations. Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.

genetics↗

bioRxiv2019

The Impact on Estimations of Genetic Correlations of the Use, in Genome Wide Case-Control Studies, of Super-Normal, Unscreened and Family-History Screened Controls

Traditionally, in normal case-control studies of disorder A, individuals with disorder A are screened-out of controls. However, in genome wide association (GWA) studies, controls are sometimes unscreened or screened for disorder A and disorder B, producing super-normal controls. Using simulations, we examine how the observed genetic correlations between two disorders (A and B) are influenced by the use of unscreened, normal, and super-normal controls. Normal controls produce unbiased estimates of the genetic correlation. However, unscreened and super-normal controls both bias upward the genetic correlations. The strength of the bias increases with increasing population prevalences for the two disorders. With super-normal controls, the magnitude of bias is stronger when the true genetic correlation is low. The opposite is seen with the use of unscreened controls. Adding screening of first-degree relatives of controls substantially increases the bias in genetic correlations with super-normal controls but has minimal impact when normal controls are used.

genetics↗

bioRxiv2019

Not just one p: Multivariate GWAS of psychiatric disorders and their cardinal symptoms reveal two dimensions of cross-cutting genetic liabilities

Understanding which biological pathways are specific versus general across diagnostic categories and levels of symptom severity is critical to improving nosology and treatment of psychopathology. Here, we combine transdiagnostic and dimensional approaches to genetic discovery for the first time, conducting a novel multivariate genome-wide association study (GWAS) of eight psychiatric symptoms and disorders broadly related to mood disturbance and psychosis. We identify two transdiagnostic genetic liabilities that distinguish between common forms of mood disturbance (major depressive disorder, bipolar II, and self-reported symptoms of depression, mania, and psychosis) versus rarer forms of serious mental illness (bipolar I, schizoaffective disorder, and schizophrenia). Biological annotation revealed divergent genetic architectures that differentially implicated prenatal neurodevelopment and neuronal function and regulation. These findings inform psychiatric nosology and biological models of psychopathology, as they suggest the severity of mood and psychotic symptoms present in serious mental illness may reflect a difference in kind, rather than merely in degree.

genetics↗

bioRxiv2019

Assessing the role of long-noncoding RNA in nucleus accumbens in subjects with alcohol dependence

Recently, long noncoding RNA (lncRNA) were implicated in the etiology of alcohol dependence (AD). As lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step towards understanding lncRNA functions in AD. To that end, we profiled the expression of lncRNA and protein coding genes (PCG) in nucleus accumbens (NAc) from 41 subjects with AD and 41 controls. At false discovery rate (FDR) of 5%, we identified 69 and 309 differentially expressed lncRNA and PCG genes, respectively. Using co-expression network analyses, we identified three lncRNA and five PCG modules significantly correlated with AD at Bonferroni adj. p[≤]0.05. To better understand lncRNA functions, we integrated the lncRNA and PCG hubs from the significant AD modules; at FDR of 5%, we identified 3 151 positive and 2 255 negative correlations supporting the functional role of lncRNA in the development of AD. Gene enrichment analysis revealed that PCG significantly correlated with lncRNA are, among others, enriched for neuronal and immune related processes. To highlight the mechanisms, by which genetic variants contribute to AD, we integrated lncRNA and PCG hubs with genome-wide SNP data. At FDR[≤]0.3, we identified 276 expression quantitative trait loci (eQTL), affecting the expression of 20 and 256 lncRNA and PCG hubs, respectively. Our study is the first to profile lncRNA expression in nucleus accumbens in a large postmortem alcohol brain sample and our results may provide novel insights into the regulation of the brain transcriptome across disease.

neuroscience↗

bioRxiv2018

DISTMIX2: robust imputation of summary statistics for cosmopolitan cohorts using a large and diverse reference panel

Genotype imputation across populations of mixed ancestry is critical for optimal discovery in large-scale genome-wide association studies (GWAS). Methods for direct imputation of GWAS summary statistics were previously shown to be practically as accurate as summary statistics produced after raw genotype imputation, while incurring orders of magnitude lower computational burden. Given that direct imputation needs a precise estimation of linkage-disequilibrium (LD) and that most of the methods using a small reference panel e.g., ~2,500 subject coming from the 1000 Genome Project, there is a great need for much larger and more diverse reference panels. To accurately estimate the LD needed for an exhaustive analysis of any cosmopolitan cohort, we developed DISTMIX2. DISTMIX2: i) uses a much larger and more diverse reference panel and ii) estimates weights of ethnic mixture based solely on Z-scores (when AFs are not available). We applied DISTMIX2 to GWAS summary statistics from the Psychiatric Genetic Consortium (PGC). DISTMIX2 uncovered signals in numerous new regions, with most of these findings coming from the rarer variants. Rarer variants provide much sharper location for the signals compared with common variants, as the LD for rare variants extends over a lower distance than for common ones. For example, while the original PGC post-traumatic stress disorder (PTSD) study found only 3 marginal signals for common variants, we now uncover a very strong signal for a rare variant in PKN2, a gene associated with neuronal and hippocampal development. Thus, DISTMIX2 provides a robust and fast (re)imputation approach for most Psychiatric GWAS studies.

bioinformatics↗

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↗