Genetic Predictors of Lithium Response

Response Scale ◽

Single Nucleotide ◽

Genetic Predictors ◽

Genome Wide ◽

Lithium Response ◽

Initial Genome

Lithium remains a first-line pharmacological treatment of bipolar disorder (BD). However, treatment response is heterogeneous, with several lines of evidence implicating genetic factors. Unfortunately, neither hypothesis-driven approaches nor initial genome-wide association studies (GWAS) were successful in identifying genetic drivers of response heterogeneity, probably due to low statistical power and different phenotype measurements. Recently, a GWAS of the Consortium of Lithium Genetics (ConLiGen) has identified four single nucleotide polymorphisms (SNPs) mediating response to lithium, located in genes for two long non-coding RNAs. This success was only possible by international collaboration and the use of an established lithium response scale. The findings await further replication.

Modeling diseases in multiple mouse strains for precision medicine studies

Physiological Genomics ◽

10.1152/physiolgenomics.00123.2016 ◽

2017 ◽

Vol 49 (3) ◽

pp. 177-179 ◽

Cited By ~ 3

Author(s):

Andrés D. Klein

Keyword(s):

Statistical Power ◽

Association Studies ◽

Phenotypic Variability ◽

Inbred Strains ◽

Mouse Strains ◽

Single Nucleotide ◽

Genome Wide ◽

Number Of Patients

The genetic basis of the phenotypic variability observed in patients can be studied in mice by generating disease models through genetic or chemical interventions in many genetic backgrounds where the clinical phenotypes can be assessed and used for genome-wide association studies (GWAS). This is particularly relevant for rare disorders, where patients sharing identical mutations can present with a wide variety of symptoms, but there are not enough number of patients to ensure statistical power of GWAS. Inbred strains are homozygous for each loci, and their single nucleotide polymorphisms catalogs are known and freely available, facilitating the bioinformatics and reducing the costs of the study, since it is not required to genotype every mouse. This kind of approach can be applied to pharmacogenomics studies as well.

AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2015-0074 ◽

2016 ◽

Vol 15 (2) ◽

Cited By ~ 4

Author(s):

Mathieu Emily

Keyword(s):

Statistical Power ◽

Association Studies ◽

Case Control ◽

Single Nucleotide ◽

Genome Wide ◽

Biological Interpretation ◽

Limited Power ◽

Control Association

AbstractAmong the large of number of statistical methods that have been proposed to identify gene-gene interactions in case-control genome-wide association studies (GWAS), gene-based methods have recently grown in popularity as they confer advantage in both statistical power and biological interpretation. All of the gene-based methods jointly model the distribution of single nucleotide polymorphisms (SNPs) sets prior to the statistical test, leading to a limited power to detect sums of SNP-SNP signals. In this paper, we instead propose a gene-based method that first performs SNP-SNP interaction tests before aggregating the obtained

Pharmacogenomics of Lithium Response in Bipolar Disorder

Pharmaceuticals ◽

10.3390/ph14040287 ◽

2021 ◽

Vol 14 (4) ◽

pp. 287

Author(s):

Courtney M. Vecera ◽

Gabriel R. Fries ◽

Lokesh R. Shahani ◽

Jair C. Soares ◽

Rodrigo Machado-Vieira

Keyword(s):

Bipolar Disorder ◽

Association Studies ◽

Mood Stabilizer ◽

Non Coding Rna ◽

Genome Wide ◽

Lithium Response ◽

Genetic Loading ◽

Long Non Coding Rna

Despite being the most widely studied mood stabilizer, researchers have not confirmed a mechanism for lithium’s therapeutic efficacy in Bipolar Disorder (BD). Pharmacogenomic applications may be clinically useful in the future for identifying lithium-responsive patients and facilitating personalized treatment. Six genome-wide association studies (GWAS) reviewed here present evidence of genetic variations related to lithium responsivity and side effect expression. Variants were found on genes regulating the glutamate system, including GAD-like gene 1 (GADL1) and GRIA2 gene, a mutually-regulated target of lithium. In addition, single nucleotide polymorphisms (SNPs) discovered on SESTD1 may account for lithium’s exceptional ability to permeate cell membranes and mediate autoimmune and renal effects. Studies also corroborated the importance of epigenetics and stress regulation on lithium response, finding variants on long, non-coding RNA genes and associations between response and genetic loading for psychiatric comorbidities. Overall, the precision medicine model of stratifying patients based on phenotype seems to derive genotypic support of a separate clinical subtype of lithium-responsive BD. Results have yet to be expounded upon and should therefore be interpreted with caution.

Common genetic variants with fetal effects on birth weight are enriched for proximity to genes implicated in rare developmental disorders

Human Molecular Genetics ◽

10.1093/hmg/ddab060 ◽

2021 ◽

Author(s):

Robin N Beaumont ◽

Isabelle K Mayne ◽

Rachel M Freathy ◽

Caroline F Wright

Keyword(s):

Birth Weight ◽

Statistical Power ◽

Developmental Disorders ◽

Association Studies ◽

Later Life ◽

Genome Wide ◽

Common Genetic Variants ◽

Causal Genes

Abstract Birth weight is an important factor in newborn survival; both low and high birth weights are associated with adverse later-life health outcomes. Genome-wide association studies (GWAS) have identified 190 loci associated with maternal or fetal effects on birth weight. Knowledge of the underlying causal genes is crucial to understand how these loci influence birth weight and the links between infant and adult morbidity. Numerous monogenic developmental syndromes are associated with birth weights at the extreme ends of the distribution. Genes implicated in those syndromes may provide valuable information to prioritize candidate genes at the GWAS loci. We examined the proximity of genes implicated in developmental disorders (DDs) to birth weight GWAS loci using simulations to test whether they fall disproportionately close to the GWAS loci. We found birth weight GWAS single nucleotide polymorphisms (SNPs) fall closer to such genes than expected both when the DD gene is the nearest gene to the birth weight SNP and also when examining all genes within 258 kb of the SNP. This enrichment was driven by genes causing monogenic DDs with dominant modes of inheritance. We found examples of SNPs in the intron of one gene marking plausible effects via different nearby genes, highlighting the closest gene to the SNP not necessarily being the functionally relevant gene. This is the first application of this approach to birth weight, which has helped identify GWAS loci likely to have direct fetal effects on birth weight, which could not previously be classified as fetal or maternal owing to insufficient statistical power.

Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2016.0569 ◽

2016 ◽

Vol 283 (1835) ◽

pp. 20160569 ◽

Cited By ~ 52

Author(s):

M. E. Goddard ◽

K. E. Kemper ◽

I. M. MacLeod ◽

A. J. Chamberlain ◽

B. J. Hayes

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Quantitative Traits ◽

Association Studies ◽

Crop Breeding ◽

Single Nucleotide ◽

Genome Wide ◽

Phenotype Identification

Complex or quantitative traits are important in medicine, agriculture and evolution, yet, until recently, few of the polymorphisms that cause variation in these traits were known. Genome-wide association studies (GWAS), based on the ability to assay thousands of single nucleotide polymorphisms (SNPs), have revolutionized our understanding of the genetics of complex traits. We advocate the analysis of GWAS data by a statistical method that fits all SNP effects simultaneously, assuming that these effects are drawn from a prior distribution. We illustrate how this method can be used to predict future phenotypes, to map and identify the causal mutations, and to study the genetic architecture of complex traits. The genetic architecture of complex traits is even more complex than previously thought: in almost every trait studied there are thousands of polymorphisms that explain genetic variation. Methods of predicting future phenotypes, collectively known as genomic selection or genomic prediction, have been widely adopted in livestock and crop breeding, leading to increased rates of genetic improvement.

Impact of Pre and Post Variant Filtration Strategies on Imputation

10.21203/rs.3.rs-128366/v1 ◽

2020 ◽

Author(s):

Celine Charon ◽

Rodrigue Allodji ◽

Vincent Meyer ◽

Jean-François Deleuze

Keyword(s):

Quality Control ◽

Rare Variants ◽

Association Studies ◽

Direct Effects ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Genome Wide ◽

Conservative Post

Abstract Quality control methods for genome-wide association studies and fine mapping are commonly used for imputation, however, they result in loss of many single nucleotide polymorphisms (SNPs). To investigate the consequences of filtration on imputation, we studied the direct effects on the number of markers, their allele frequencies, imputation quality scores and post-filtration events. We pre-phrased 1,031 genotyped individuals from diverse ethnicities and compared the imputed variants to 1,089 NCBI recorded individuals for additional validation.Without variant pre-filtration based on quality control (QC), we observed no impairment in the imputation of SNPs that failed QC whereas with pre-filtration there was an overall loss of information. Significant differences between frequencies with and without pre-filtration were found only in the range of very rare (5E-04-1E-03) and rare variants (1E-03-5E-03) (p < 1E-04). Increasing the post-filtration imputation quality score from 0.3 to 0.8 reduced the number of single nucleotide variants (SNVs) <0.001 2.5 fold with or without QC pre-filtration and halved the number of very rare variants (5E-04). As a result, to maintain confidence and enough SNVs, we propose here a 2-step post-filtration approach to increase the number of very rare and rare variants compared to conservative post-filtration methods.

Genome-Wide Association Studies for the Concentration of Albumin in Colostrum and Serum in Chinese Holstein

Animals ◽

10.3390/ani10122211 ◽

2020 ◽

Vol 10 (12) ◽

pp. 2211

Author(s):

Shan Lin ◽

Zihui Wan ◽

Junnan Zhang ◽

Lingna Xu ◽

Bo Han ◽

...

Keyword(s):

Association Studies ◽

Significant Snps ◽

Albumin Concentration ◽

Single Nucleotide ◽

Chinese Holstein ◽

Genome Wide ◽

Chinese Holstein Cows ◽

Newborn Calves

Albumin can be of particular benefit in fighting infections for newborn calves due to its anti-inflammatory and anti-oxidative stress properties. To identify the candidate genes related to the concentration of albumin in colostrum and serum, we collected the colostrum and blood samples from 572 Chinese Holstein cows within 24 h after calving and measured the concentration of albumin in the colostrum and serum using the ELISA methods. The cows were genotyped with GeneSeek 150 K chips (containing 140,668 single nucleotide polymorphisms; SNPs). After quality control, we performed GWASs via GCTA software with 91,620 SNPs and 563 cows. Consequently, 9 and 7 genome-wide significant SNPs (false discovery rate (FDR) at 1%) were identified. Correspondingly, 42 and 206 functional genes that contained or were approximate to (±1 Mbp) the significant SNPs were acquired. Integrating the biological process of these genes and the reported QTLs for immune and inflammation traits in cattle, 3 and 12 genes were identified as candidates for the concentration of colostrum and serum albumin, respectively; these are RUNX1, CBR1, OTULIN,CDK6, SHARPIN, CYC1, EXOSC4, PARP10, NRBP2, GFUS, PYCR3, EEF1D, GSDMD, PYCR2 and CXCL12. Our findings provide important information for revealing the genetic mechanism behind albumin concentration and for molecular breeding of disease-resistance traits in dairy cattle.

Animal-ImputeDB: a comprehensive database with multiple animal reference panels for genotype imputation

Nucleic Acids Research ◽

10.1093/nar/gkz854 ◽

2019 ◽

Vol 48 (D1) ◽

pp. D659-D667 ◽

Cited By ~ 2

Author(s):

Wenqian Yang ◽

Yanbo Yang ◽

Cecheng Zhao ◽

Kun Yang ◽

Dongyang Wang ◽

...

Keyword(s):

Large Scale ◽

Association Studies ◽

Genotype Imputation ◽

High Quality ◽

Single Nucleotide ◽

Genome Wide ◽

Whole Genome Resequencing ◽

Missing Genotypes

Abstract Animal-ImputeDB (http://gong_lab.hzau.edu.cn/Animal_ImputeDB/) is a public database with genomic reference panels of 13 animal species for online genotype imputation, genetic variant search, and free download. Genotype imputation is a process of estimating missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs) and thus can be widely used in large-scale genome-wide association studies (GWASs) using relatively inexpensive and low-density SNP arrays. However, most animals except humans lack high-quality reference panels, which greatly limits the application of genotype imputation in animals. To overcome this limitation, we developed Animal-ImputeDB, which is dedicated to collecting genotype data and whole-genome resequencing data of nonhuman animals from various studies and databases. A computational pipeline was developed to process different types of raw data to construct reference panels. Finally, 13 high-quality reference panels including ∼400 million SNPs from 2265 samples were constructed. In Animal-ImputeDB, an easy-to-use online tool consisting of two popular imputation tools was designed for the purpose of genotype imputation. Collectively, Animal-ImputeDB serves as an important resource for animal genotype imputation and will greatly facilitate research on animal genomic selection and genetic improvement.