scholarly journals Accounting for genetic architecture in single- and multipopulation genomic prediction using weights from genomewide association studies in pigs

2016 ◽  
Vol 133 (3) ◽  
pp. 187-196 ◽  
Author(s):  
R. Veroneze ◽  
P.S. Lopes ◽  
M.S. Lopes ◽  
A.M. Hidalgo ◽  
S.E.F. Guimarães ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Genetic Architecture ◽  
Association Studies ◽  
Genomewide Association

Contributions of Nicholas Martin to Gambling Disorder Research

2020 ◽  
Vol 23 (2) ◽  
pp. 127-128
Author(s):  
Wendy S. Slutske ◽  
Penelope A. Lind
Keyword(s):  
Medical Research ◽  
Twin Study ◽  
Gambling Disorder ◽  
Genetic Architecture ◽  
Association Studies ◽  
Disordered Gambling ◽  
Study Methods ◽  
Medical Research Institute ◽  
International Collaborations ◽  
Genomewide Association

AbstractProfessor Nicholas G. Martin, from QIMR Berghofer Medical Research Institute in Brisbane, Australia, is a world leader in the effort to understand the genetic architecture underlying disordered gambling. This article pays tribute to Nick and his almost two decades of gambling research, highlighting his many strengths, ranging from the use of ingenious recruitment approaches, twin study methods, genomewide association studies, to facilitating international collaborations.

scholarly journals Genetic architecture of schizophrenia: a review of major advancements

2021 ◽  
pp. 1-10
Author(s):  
Sophie E. Legge ◽  
Marcos L. Santoro ◽  
Sathish Periyasamy ◽  
Adeniran Okewole ◽  
Arsalan Arsalan ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Association Studies ◽  
Copy Number Variants ◽  
Risk Scores ◽  
Genome Wide Association Studies ◽  
Loss Of Function ◽  
Genetic Loci ◽  
Significant Enrichment ◽  
High Heritability ◽  
Coding Variants

Abstract Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

scholarly journals Structural brain imaging studies offer clues about the effects of the shared genetic etiology among neuropsychiatric disorders

2021 ◽  
Author(s):  
Nevena V. Radonjić ◽  
Jonathan L. Hess ◽  
Paula Rovira ◽  
Ole Andreassen ◽  
Jan K. Buitelaar ◽  
...  
Keyword(s):  
Association Studies ◽  
Genetic Correlations ◽  
Neuropsychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Genetic Etiology ◽  
Compulsive Disorder ◽  
Pairwise Correlations ◽  
Genomewide Association ◽  
Structural Brain

AbstractGenomewide association studies have found significant genetic correlations among many neuropsychiatric disorders. In contrast, we know much less about the degree to which structural brain alterations are similar among disorders and, if so, the degree to which such similarities have a genetic etiology. From the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium, we acquired standardized mean differences (SMDs) in regional brain volume and cortical thickness between cases and controls. We had data on 41 brain regions for: attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), epilepsy, major depressive disorder (MDD), obsessive compulsive disorder (OCD), and schizophrenia (SCZ). These data had been derived from 24,360 patients and 37,425 controls. The SMDs were significantly correlated between SCZ and BD, OCD, MDD, and ASD. MDD was positively correlated with BD and OCD. BD was positively correlated with OCD and negatively correlated with ADHD. These pairwise correlations among disorders were correlated with the corresponding pairwise correlations among disorders derived from genomewide association studies (r = 0.494). Our results show substantial similarities in sMRI phenotypes among neuropsychiatric disorders and suggest that these similarities are accounted for, in part, by corresponding similarities in common genetic variant architectures.

scholarly journals A meta-analysis of genome-wide association studies for average daily gain and lean meat percentage in two Duroc pig populations

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Shenping Zhou ◽  
Rongrong Ding ◽  
Fanming Meng ◽  
Xingwang Wang ◽  
Zhanwei Zhuang ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Growth And Development ◽  
Genetic Architecture ◽  
Association Studies ◽  
Meta Analysis ◽  
Average Daily Gain ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Daily Gain

Abstract Background Average daily gain (ADG) and lean meat percentage (LMP) are the main production performance indicators of pigs. Nevertheless, the genetic architecture of ADG and LMP is still elusive. Here, we conducted genome-wide association studies (GWAS) and meta-analysis for ADG and LMP in 3770 American and 2090 Canadian Duroc pigs. Results In the American Duroc pigs, one novel pleiotropic quantitative trait locus (QTL) on Sus scrofa chromosome 1 (SSC1) was identified to be associated with ADG and LMP, which spans 2.53 Mb (from 159.66 to 162.19 Mb). In the Canadian Duroc pigs, two novel QTLs on SSC1 were detected for LMP, which were situated in 3.86 Mb (from 157.99 to 161.85 Mb) and 555 kb (from 37.63 to 38.19 Mb) regions. The meta-analysis identified ten and 20 additional SNPs for ADG and LMP, respectively. Finally, four genes (PHLPP1, STC1, DYRK1B, and PIK3C2A) were detected to be associated with ADG and/or LMP. Further bioinformatics analysis showed that the candidate genes for ADG are mainly involved in bone growth and development, whereas the candidate genes for LMP mainly participated in adipose tissue and muscle tissue growth and development. Conclusions We performed GWAS and meta-analysis for ADG and LMP based on a large sample size consisting of two Duroc pig populations. One pleiotropic QTL that shared a 2.19 Mb haplotype block from 159.66 to 161.85 Mb on SSC1 was found to affect ADG and LMP in the two Duroc pig populations. Furthermore, the combination of single-population and meta-analysis of GWAS improved the efficiency of detecting additional SNPs for the analyzed traits. Our results provide new insights into the genetic architecture of ADG and LMP traits in pigs. Moreover, some significant SNPs associated with ADG and/or LMP in this study may be useful for marker-assisted selection in pig breeding.

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

2016 ◽  
Vol 283 (1835) ◽  
pp. 20160569 ◽  
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 ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
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.

scholarly journals Improving accuracy of genomic prediction by genetic architecture based priors in a Bayesian model

BMC Genetics ◽  
2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Ning Gao ◽  
Jiaqi Li ◽  
Jinlong He ◽  
Guang Xiao ◽  
Yuanyu Luo ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Bayesian Model ◽  
Genetic Architecture ◽  
Improving Accuracy

scholarly journals Correction: Accounting for Genetic Architecture Improves Sequence Based Genomic Prediction for a Drosophila Fitness Trait

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0132980
Author(s):  
Ulrike Ober ◽  
Wen Huang ◽  
Michael Magwire ◽  
Martin Schlather ◽  
Henner Simianer ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Genetic Architecture ◽  
Fitness Trait
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