Biology subjects

Lee, S. H.

Publications and source records attributed to Lee, S. H..

14 recordsLinked to original sources

bioRxiv2018

The genetic relationship between female reproductive traits and six psychiatric disorders

Female reproductive behaviors have an important implication in evolutionary fitness and health of offspring. Previous studies have shown that age at first birth of women (AFB) is genetically associated with schizophrenia (SCZ). However, for most other psychiatric disorders and reproductive traits, the latent shared genetic architecture is largely unknown. Here we used the second wave of UK Biobank data (N=220,685) to evaluate the association between five female reproductive traits and polygenetic risk scores (PRS) projected from genome-wide association study summary statistics of six psychiatric disorders (N=429,178). We found that the PRS of attention-deficit/hyperactivity disorder (ADHD) were strongly associated with AFB (genetic correlation of -0.68 {+/-} 0.03 with p-value = 1.86E-89), age at first sexual intercourse (AFS) (-0.56 {+/-} 0.03 with p-value = 3.42E-60), number of live births (NLB) (0.36 {+/-} 0.04 with p-value = 4.01E-17) and age at menopause (-0.27 {+/-} 0.04 with p-value = 5.71E-13). There were also robustly significant associations between the PRS of eating disorder (ED) and AFB (genetic correlation of 0.35 {+/-} 0.06), ED and AFS (0.19 0.06), Major depressive disorder (MDD) and AFB (-0.27 {+/-} 0.07), MDD and AFS (- 0.27 {+/-} 0.03) and SCZ and AFS (-0.10 {+/-} 0.03). Our findings reveal the shared genetic architecture between the five reproductive traits in women and six psychiatric disorders, which have a potential implication that helps to improve reproductive health in women, hence better child outcomes. Our findings may also explain, at least in part, an evolutionary hypothesis that causal mutations underlying psychiatric disorders have positive effects on reproductive success.

genetics↗

bioRxiv2018

Protect TUDCA stimulated CKD-derived hMSCs against the CKD-Ischemic disease via upregulation of PrPC

Although autologous human mesenchymal stem cells (hMSCs) are a promising source for regenerative stem cell therapy, the barriers associated with pathophysiological conditions in this disease limit therapeutic applicability to patients. We proved treatment of CKD-hMSCs with TUDCA enhanced the mitochondrial function of these cells and increased complex I & IV enzymatic activity, increasing PINK1 expression and decreasing mitochondrial O2*- and mitochondrial fusion in a PrPC-dependent pathway. Moreover, TH-1 cells enhanced viability when co-cultured in vitro with TUDCA-treated CKD-hMSC. In vivo, tail vein injection of TUDCA-treated CKD-hMSCs into the mouse model of CKD associated with hindlimb ischemia enhanced kidney recovery, the blood perfusion ratio, vessel formation, and prevented limb loss, and foot necrosis along with restored expression of PrPC in the blood serum of the mice. These data suggest that TUDCA-treated CKD-hMSCs are a promising new autologous stem cell therapeutic intervention that dually treats cardiovascular problems and CKD in patients.

cell biology↗

bioRxiv2018

Walking along chromosomes with super-resolution imaging, contact maps, and integrative modeling

Chromosome structure is thought to be crucial for proper functioning of the nucleus. Here, we present a method for visualizing chromosomal DNA at super-resolution and then integrating Hi-C data to produce three-dimensional models of chromosome organization. We begin by applying Oligopaint probes and the single-molecule localization microscopy methods of OligoSTORM and OligoDNA-PAINT to image 8 megabases of human chromosome 19, discovering that chromosomal regions contributing to compartments can form distinct structures. Intriguingly, our data also suggest that homologous maternal and paternal regions may be differentially organized. Finally, we integrate imaging data with Hi-C and restraint-based modeling using a method called integrative modeling of genomic regions (IMGR) to increase the genomic resolution of our traces to 10 kb.\n\nOne Sentence SummarySuper-resolution genome tracing, contact maps, and integrative modeling enable 10 kb resolution glimpses of chromosome folding.

genetics↗

bioRxiv2018

Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model

The genomics era has brought useful tools to dissect the genetic architecture of complex traits. We propose a reaction norm model (RNM) to tackle genotype-environment correlation and interaction problems in the context of genome-wide association analyses of complex traits. In our approach, an environmental risk factor affecting the trait of interest can be modeled as dependent on a continuous covariate that is itself regulated by genetic as well as environmental factors. Our multivariate RNM approach allows the joint modelling of the relation between the genotype (G) and the covariate (C), so that both their correlation (association) and interaction (effect modification) can be estimated. Hence we jointly estimate genotype-covariate correlation and interaction (GCCI). We demonstrate using simulation that the proposed multivariate RNM performs better than the current state-of-the-art methods that ignore G-C correlation. We apply the method to data from the UK Biobank (N= 66,281) in analysis of body mass index using smoking quantity as a covariate. We find a highly significant G-C correlation, but a negligible G-C interaction. In contrast, when a conventional G-C interaction analysis is applied (i.e., G-C correlation is not included in the model), highly significant G-C interaction estimates are found. It is also notable that we find a significant heterogeneity in the estimated residual variances across different covariate levels probably due to residual-covariate interaction. Using simulation we also show that the residual variances estimated by genomic restricted maximum likelihood (GREML) or linkage disequilibrium score regression (LDSC) can be inflated in the presence of interactions, implying that the currently reported SNP-heritability estimates from these methods should be interpreted with caution. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses and that the failure to do so may lead to substantial biases in inferences relating to genetic architecture of complex traits, including estimated SNP-heritability.

genetics↗

bioRxiv2018

Effect of selection on bias and accuracy in genomic prediction of breeding values

Reference populations for genomic selection (GS) usually involve highly selected individuals, which may result in biased prediction of estimated genomic breeding values (GEBV). In the present study, bias and accuracy of GEBV were explored for various genetic models and prediction methods when using selected individuals for a reference. Data were simulated for an animal breeding program to compare Best Linear Unbiased Prediction of breeding values using pedigree based relationships (PBLUP), genomic relationships for genotyped animals only (GBLUP) and a Single Step approach (SSGBLUP), where information on genotyped individuals was used to infer a matrix H with relationships among all available genotyped and non-genotyped individuals that were linked through pedigree. In SSGBLUP, various weights (=0.95, 0.80, 0.50) for the genomic relationship matrix (G) relative to the numerator relationship matrix (A) were applied to construct H and in another version (SSGBLUP_F), inbreeding was accounted for while computing A-1. With GBLUP, accuracy of GEBV prediction increased linearly with an increase in the number of animals selected in reference. For the scenario with no-selection and random mating (RR) prediction was unbiased. For GBLUP, lower accuracy and bias observed in the scenarios with selection and random mating (SR) or selection and positive assortative mating (SA), in which prediction bias increased when a smaller and highly selected proportion genotyped. Bias disappeared when all individuals were genotyped. SSGBLUP_F showed higher accuracy compared to GBLUP and bias of prediction was negligible even with selective genotyping. However, PBLUP and SSGBLUP showed bias in SA owing to not fully accounting for allele frequency changes because of selection of quantitative trait loci (QTL) with larger effects and also due to high inbreeding rate. In genetic models with fewer QTL but each with larger effect, predictions were less accurate and more biased for selection scenarios. Results suggest that prediction accuracy and bias is affected by the genetic architecture of the trait. Selective genotyping lead to significant bias in GEBV prediction. SSGBLUP with appropriate scaling of A and G matrices can provide accurate and less biased prediction but scaling requires careful consideration in populations under selection and with high levels of inbreeding.

genomics↗

bioRxiv2018

CRISPR Cas9 searches for a protospacer adjacent motif by one-dimensional diffusion

Since its discovery, the CRISPR/Cas9 system has been at the focus of fundamental researchers, genome engineers, and the general public alike. Despite being in the spotlight for several years, aspects of the precise molecular mechanism of Cas9 activity remain ambiguous. We use single-molecule Foerster resonance energy transfer (smFRET) to reveal Cas9 target search mechanism with nanometer sensitivity. We have developed single-molecule assays to monitor transient interactions of Cas9 and DNA in real time. Our study shows that Cas9 interacts with the protospacer adjacent motif (PAM) sequence weakly, yet probing neighboring sequences via lateral diffusion. This dynamic mode of interactions leads to translocation of Cas9 to another PAM nearby and consequently an on-target sequence. We propose a model in which lateral diffusion competes with 3-dimensional diffusion and thus might aid PAM finding and consequently on-target binding.

biophysics↗

bioRxiv2018

Effect of cytochrome CYP2C19 metabolizing activity on antidepressant response and side effects: meta-analysis of data from genome-wide association studies

Cytochrome (CYP) P450 enzymes have a primary role in antidepressant metabolism and variants in these polymorphic genes are targets for pharmacogenetic investigation. This is the first meta-analysis to investigate how CYP2C19 polymorphisms predict citalopram/escitalopram efficacy and side effects.\n\nCYP2C19 phenotypes comprise poor metabolizers (PM), intermediate and intermediate+ metabolizers (IM; IM+), extensive and extensive+ metabolizers (EM [wild type]; EM+) and ultra-rapid metabolizers (UM) defined by the two most common CYP2C19 functional polymorphisms (rs4244285 and rs12248560) in Caucasians. These polymorphisms were genotyped or imputed from genome-wide data in four samples treated with citalopram or escitalopram (GENDEP, STAR*D, GenPod, PGRN-AMPS). Treatment efficacy was percentage symptom improvement and remission. Side effect data were available at weeks 2-4, 6 and 9 in three of the investigated samples. A fixed-effects meta-analysis was performed using EM as the reference group.\n\nAnalysis of 2558 patients for efficacy and 2037 patients for side effects showed that PMs had higher symptom improvement (SMD=0.43, CI=0.19-0.66) and higher remission rates (OR=1.55, CI=1.23-1.96) compared to EMs. At weeks 2-4, PMs showed higher risk of gastro-intestinal (OR=1.26, CI=1.08-1.47), neurological (OR=1.28, CI=1.07-1.53) and sexual side effects (OR=1.52, CI=1.23-1.87; week 6 values similar). No difference was seen at week 9 or in total side effect burden. PMs did not have higher risk of dropout at week 4 compared to EMs. Antidepressant dose was not different among CYP2C19 groups.\n\nCYP2C19 polymorphisms may provide helpful information for guiding citalopram/escitalopram treatment, despite PMs are relatively rare among Caucasians (~2%).

genetics↗

bioRxiv2018

Gradual repression of selenoprotein W ensures physiological bone remodelling

Selenoproteins containing selenium in the form of selenocysteine are critical for bone remodelling. However, their mechanism of action is not well understood. Here, we report the identification of selenoprotein W (SELENOW) through large-scale mRNA profiling of receptor activator of nuclear factor (NF)-{kappa}B ligand (RANKL)-induced osteoclast differentiation, as a protein that is downregulated via RANKL/RANK/tumour necrosis factor receptor-associated factor 6/p38 signalling. RNA sequencing analysis revealed that SELENOW regulates osteoclastogenic genes. SELENOW overexpression enhanced osteoclastogenesis in vitro via nuclear translocation of NF-{kappa}B and nuclear factor of activated T-cells cytoplasmic 1, whereas its loss suppressed osteoclast formation. SELENOW-deficient and SELENOW-overexpressing mice exhibited osteopetrosis and osteoporosis, respectively. Ectopic SELENOW expression stimulated cell-cell fusion critical for osteoclast maturation as well as bone resorption. Thus, RANKL-dependent repression of SELENOW maintains proper osteoclast differentiation and blocks osteoporosis caused by overactive osteoclasts. These findings demonstrate a biological link between selenium and bone metabolism.

physiology↗

bioRxiv2017

Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood

Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e. linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ~150,000 individuals give a higher accuracy than LDSC estimates based on ~400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-data sets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

genetics↗

bioRxiv2017

Age at first birth in women is genetically associated with increased risk of schizophrenia

Previous studies have shown an increased risk for a range of mental health issues in children born to both younger and older parents compared to children of average-aged parents. However, until recently, it was not clear if these increased risks are due to psychosocial factors associated with age or if parents at higher genetic risk for psychiatric disorders tend to have children at an earlier or later age. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age of mothers at the birth of their first child (AFB). Here, we use independent data from the UK Biobank (N=38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, end to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value=1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value=3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE=0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE=0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.

genetics↗

bioRxiv2017

TumorFusions: an integrative resource for reporting cancer-associated transcript fusions in 33 tumor types

Fusion genes, particularly those involving kinases, have been demonstrated as drivers and are frequent therapeutic targets in cancer1. Here, we describe our results on detecting transcript fusions across 33 cancer types from The Cancer Genome Atlas (TCGA), totaling 9,966 cancer samples and 648 normal samples2. Preprocessing, including read alignment to both genome and transcriptome, and fusion detection were carried out using a uniform pipeline3. To validate the resultant fusions, we also called somatic structural variations for 561 cancers from whole genome sequencing data. A summary of the data used in this study is provided in Table S1. Our results can be accessed per our portal at http://www.tumorfusions.org.

cancer biology↗

bioRxiv2017

A genetic investigation of sex bias in the prevalence of attention deficit hyperactivity disorder

Attention-deficit/hyperactivity disorder (ADHD) shows substantial heritability and is 2-7 times more common in males than females. We examined two putative genetic mechanisms underlying this sex bias: sex-specific heterogeneity and higher burden of risk in female cases. We analyzed genome-wide common variants from the Psychiatric Genomics Consortium and iPSYCH Project (20,183 cases, 35,191 controls) and Swedish population-registry data (N=77,905 cases, N=1,874,637 population controls). We find strong genetic correlation for ADHD across sex and no mean difference in polygenic burden across sex. In contrast, siblings of female probands are at an increased risk of ADHD, compared to siblings of male probands. The results also suggest that females with ADHD are at especially high risk of comorbid developmental conditions. Overall, this study supports a greater familial burden of risk in females with ADHD and some clinical and etiological heterogeneity. However, autosomal common variants largely do not explain the sex bias in ADHD prevalence.

genetics↗

bioRxiv2017

Estimation Of Genomic Prediction Accuracy From Reference Populations With Varying Degrees Of Relationship

Genomic prediction is emerging in a wide range of fields including animal and plant breeding, risk prediction in human precision medicine and forensic. It is desirable to establish a theoretical framework for genomic prediction accuracy when the reference data consists of information sources with varying degrees of relationship to the target individuals. A reference set can contain both close and distant relatives as well as unrelated individuals from the wider population in the genomic prediction. The various sources of information were modeled as different populations with different effective population sizes (Ne). Both the effective number of chromosome segments (Me) and Ne are considered to be a function of the data used for prediction. We validate our theory with analyses of simulated as well as real data, and illustrate that the variation in genomic relationships with the target is a predictor of the information content of the reference set. With a similar amount of data available for each source, we show that close relatives can have a substantially larger effect on genomic prediction accuracy than lesser related individuals. We also illustrate that when prediction relies on closer relatives, there is less improvement in prediction accuracy with an increase in training data or marker panel density. We release software that can estimate the expected prediction accuracy and power when combining different reference sources with various degrees of relationship to the target, which is useful when planning genomic prediction (before or after collecting data) in animal, plant and human genetics.

genetics↗

bioRxiv2016

Super-resolution Imaging of Synaptic and Extra-synaptic Pools of AMPA Receptors with Different-sized Fluorescent Probes

Whether AMPA receptors (AMPARs) enter into neuronal synapses, by exocytosis from an internal pool, or by diffusion from an external membrane-bound pool, is hotly contested. 3D super-resolution fluorescent nanoscopy to measure the dynamics and placement of AMPAR is a powerful method for addressing this issue. However, probe size and accessibility to tightly packed spaces can be limiting. We have therefore labeled AMPARs with differently sized fluorophores: small organic fluorescent dyes (~ 4 nm), small quantum dots (sQD, ~10 nm in diameter), or big (commercial) quantum dots (bQD, ~ 20 nm in diameter). We then compared their diffusion rate, trajectories, and placement with respect to a postsynaptic density (PSD) protein, Homer 1c. Labeled with the small probes of sQDs or organic fluorophores, we find that AMPARs are located largely within PSDs (~73-93%), and generally reside in \"nanodomains\" with constrained diffusion. In contrast, when labeled with bQDs, only 5-10% of AMPARs are within PSDs. The results can be explained by relatively free access, or lack thereof, to synaptic clefts of the AMPARs when labeled with small or big probes, respectively. This implies that AMPARs primarily enter PSDs soon after their exocytosis and not from a large diffusive pool of extrasynaptic AMPARs.

neuroscience↗