Dissecting the phenotype in genome-wide association studies of
                        psychiatric illness

SummaryOver the past 2 years genome-wide association studies have made major contributions to understanding the genetic architecture of many common human diseases. This editorial outlines the development of such studies in psychiatry and highlights the opportunities for advancing understanding of the biological underpinnings and nosological structure of psychiatric disorders.

Download Full-text

Genome-wide association studies and genetic architecture of common human diseases

BMC Proceedings ◽

10.1186/1753-6561-5-s4-s16 ◽

2011 ◽

Vol 5 (S4) ◽

Cited By ~ 1

Author(s):

Grant W Montgomery

Keyword(s):

Genetic Architecture ◽

Association Studies ◽

Human Diseases ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

Download Full-text

Genome-wide association studies in psychiatry: what have we learned?

The British Journal of Psychiatry ◽

10.1192/bjp.bp.112.117002 ◽

2013 ◽

Vol 202 (1) ◽

pp. 1-4 ◽

Cited By ~ 33

Author(s):

Ann L. Collins ◽

Patrick F. Sullivan

Keyword(s):

Psychiatric Disorders ◽

Empirical Data ◽

Genetic Architecture ◽

Genetic Basis ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Considerable Effort ◽

Genome Wide

SummaryGenome-wide association studies (GWAS) have been the focus of considerable effort in psychiatry. These efforts have markedly increased knowledge of the genetic basis of psychiatric disorders, and yielded empirical data on genetic architecture critical to addressing long-standing debates in the field. There is a now a clear path to increased knowledge of the ‘parts lists’ for these disorders.

Download Full-text

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

BMC Genomics ◽

10.1186/s12864-020-07288-1 ◽

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.

Download Full-text

Genetic architecture of grain yield in bread wheat based on genome-wide association studies

BMC Plant Biology ◽

10.1186/s12870-019-1781-3 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 29

Author(s):

Faji Li ◽

Weie Wen ◽

Jindong Liu ◽

Yong Zhang ◽

Shuanghe Cao ◽

...

Keyword(s):

Grain Yield ◽

Bread Wheat ◽

Genetic Architecture ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

Download Full-text

Dissecting the Genetic Architecture of Seed Protein and Oil Content in Soybean from the Yangtze and Huaihe River Valleys Using Multi-Locus Genome-Wide Association Studies

International Journal of Molecular Sciences ◽

10.3390/ijms20123041 ◽

2019 ◽

Vol 20 (12) ◽

pp. 3041 ◽

Cited By ~ 4

Author(s):

Li ◽

Xu ◽

Yang ◽

Zhao

Keyword(s):

Candidate Genes ◽

Genetic Architecture ◽

Oil Content ◽

Seed Protein ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Huaihe River ◽

Genome Wide ◽

River Valleys

Soybean is a globally important legume crop that provides a primary source of high-quality vegetable protein and oil. Seed protein and oil content are two valuable quality traits controlled by multiple genes in soybean. In this study, the restricted two-stage multi-locus genome-wide association analysis (RTM-GWAS) procedure was performed to dissect the genetic architecture of seed protein and oil content in a diverse panel of 279 soybean accessions from the Yangtze and Huaihe River Valleys in China. We identified 26 quantitative trait loci (QTLs) for seed protein content and 23 for seed oil content, including five associated with both traits. Among these, 39 QTLs corresponded to previously reported QTLs, whereas 10 loci were novel. As reported previously, the QTL on chromosome 20 was associated with both seed protein and oil content. This QTL exhibited opposing effects on these traits and contributed the most to phenotype variation. From the detected QTLs, 55 and 51 candidate genes were identified for seed protein and oil content, respectively. Among these genes, eight may be promising candidate genes for improving soybean nutritional quality. These results will facilitate marker-assisted selective breeding for soybean protein and oil content traits.

Download Full-text

Genome-wide association studies to dissect the genetic architecture of yield-related traits in maize and the genetic basis of heterosis

10.31274/etd-180810-3818 ◽

2014 ◽

Author(s):

Jinliang Yang

Keyword(s):

Genetic Architecture ◽

Genetic Basis ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

Download Full-text

Genome-Wide Association Studies and Whole-Genome Prediction Reveal the Genetic Architecture of KRN in Maize

10.21203/rs.3.rs-32231/v1 ◽

2020 ◽

Author(s):

Yixin An ◽

Lin Chen ◽

Yongxiang Li ◽

Chunhui Li ◽

Yunsu Shi ◽

...

Keyword(s):

Genetic Architecture ◽

Association Studies ◽

Genome Wide Association ◽

Cytokinin Oxidase ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Maize Breeding ◽

Candidate Gene Association ◽

Genome Wide ◽

Genome Prediction

Abstract Background: Kernel row number (KRN) is an important trait for the domestication and improvement of maize. To explore the genetic basis of KRN has great research significance and can provide the valuable information for molecular assisted selection.Results: In this study, one single-locus method (MLM) and six multi-locus methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, ARF29 (Zm00001d026540, encoding auxin response factor 29) and CKO4 (Zm00001d043293, encoding cytokinin oxidase protein) were significantly related to KRN based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) for KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate the total KRN-associated tagSNPs identified by all GWAS models.Conclusions: These results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding.

Download Full-text

Bench Research Informed by GWAS Results

Cells ◽

10.3390/cells10113184 ◽

2021 ◽

Vol 10 (11) ◽

pp. 3184

Author(s):

Nikolay V. Kondratyev ◽

Margarita V. Alfimova ◽

Arkadiy K. Golov ◽

Vera E. Golimbet

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Reverse Genetics ◽

Genetic Factors ◽

Association Studies ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genome Wide

Scientifically interesting as well as practically important phenotypes often belong to the realm of complex traits. To the extent that these traits are hereditary, they are usually ‘highly polygenic’. The study of such traits presents a challenge for researchers, as the complex genetic architecture of such traits makes it nearly impossible to utilise many of the usual methods of reverse genetics, which often focus on specific genes. In recent years, thousands of genome-wide association studies (GWAS) were undertaken to explore the relationships between complex traits and a large number of genetic factors, most of which are characterised by tiny effects. In this review, we aim to familiarise ‘wet biologists’ with approaches for the interpretation of GWAS results, to clarify some issues that may seem counterintuitive and to assess the possibility of using GWAS results in experiments on various complex traits.

Download Full-text

Genome-wide association analysis reveals extensive genetic overlap between mood instability and psychiatric disorders but divergent patterns of genetic effects

10.1101/2021.07.16.21260608 ◽

2021 ◽

Author(s):

Guy Hindley ◽

Kevin S O'Connell ◽

Zillur Rahman ◽

Oleksandr Frei ◽

Shahram Bahrami ◽

...

Keyword(s):

Psychiatric Disorders ◽

Genetic Correlation ◽

Association Studies ◽

Single Gene ◽

Genetic Effects ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Genetic Determinants ◽

Mood Instability ◽

Genome Wide

Mood instability (MOOD) is a transdiagnostic phenomenon with a prominent neurobiological basis. Recent genome-wide association studies found significant positive genetic correlation between MOOD and major depression (DEP) and weak correlations with other psychiatric disorders. We investigated the polygenic overlap between MOOD and psychiatric disorders beyond genetic correlation to better characterize putative shared genetic determinants. Summary statistics for schizophrenia (SCZ, n=105,318), bipolar disorder (BIP, n=413,466), DEP (n=450,619), attention-deficit hyperactivity disorder (ADHD, n=53,293) and MOOD (n=363,705), were analysed using the bivariate causal mixture model and conjunctional false discovery rate methods to estimate the proportion of shared variants influencing MOOD and each disorder, and identify jointly associated genomic loci. MOOD correlated positively with all psychiatric disorders, but with wide variation in strength (rg=0.10-0.62). Of 10.4K genomic variants influencing MOOD, 4K-9.4K were estimated to influence psychiatric disorders. MOOD was jointly associated with DEP at 163 loci, SCZ at 110, BIP at 60 and ADHD at 25, with consistent genetic effects in independent samples. Fifty-three jointly associated loci were overlapping across two or more disorders (transdiagnostic), seven of which had discordant effect directions on psychiatric disorders. Genes mapped to loci associated with MOOD and all four disorders were enriched in a single gene-set, synapse organization. The extensive polygenic overlap indicates shared molecular underpinnings across MOOD and psychiatric disorders. However, distinct patterns of genetic correlation and effect directions of shared loci suggest divergent effects on corresponding neurobiological mechanisms which may relate to differences in the core clinical features of each disorder.

Download Full-text