Transcriptome association studies of neuropsychiatric traits in African Americans implicate PRMT7 in schizophrenia

Potential Candidate ◽

Candidate Locus ◽

Genome Wide ◽

Multiple Testing Adjustment

ABSTRACTIn the past fifteen years, genome-wide association studies (GWAS) have provided novel insight into the genetic architecture of various complex traits; however, this insight has been primarily focused on populations of European descent. This emphasis on European populations has led to individuals of recent African descent being grossly underrepresented in the study of genetics. With African Americans making up less than two percent of participants in neuropsychiatric GWAS, this discrepancy is magnified in diseases such as schizophrenia and bipolar disorder. In this study, we performed GWAS and the gene-based association method PrediXcan for schizophrenia (n=2,256) and bipolar disorder (n=1,019) in African American cohorts. In our PrediXcan analyses, we identified PRMT7 (P = 5.5 × 10−6, local false sign rate = 0.12) as significantly associated with schizophrenia following an adaptive shrinkage multiple testing adjustment. This association with schizophrenia was confirmed in the much larger, predominantly European, Psychiatric Genomics Consortium. In addition to the PRMT7 association with schizophrenia, we identified rs10168049 (P = 1.0 × 10−6) as a potential candidate locus for bipolar disorder with highly divergent allele frequencies across populations, highlighting the need for diversity in genetic studies.

networkGWAS: A network-based approach for genome-wide association studies in structured populations

10.1101/2021.11.11.468206 ◽

2021 ◽

Author(s):

Giulia Muzio ◽

Leslie O'Bray ◽

Laetitia Meng-Papaxanthos ◽

Juliane Klatt ◽

Karsten Borgwardt

Keyword(s):

Genetic Markers ◽

Complex Traits ◽

Multiple Testing ◽

Association Studies ◽

Search Space ◽

Structured Populations ◽

Genome Wide Association ◽

Multiple Testing Correction ◽

While the search for associations between genetic markers and complex traits has discovered tens of thousands of trait-related genetic variants, the vast majority of these only explain a tiny fraction of observed phenotypic variation. One possible strategy to detect stronger associations is to aggregate the effects of several genetic markers and to test entire genes, pathways or (sub)networks of genes for association to a phenotype. The latter, network-based genome-wide association studies, in particular suffers from a huge search space and an inherent multiple testing problem. As a consequence, current approaches are either based on greedy feature selection, thereby risking that they miss relevant associations, and/or neglect doing a multiple testing correction, which can lead to an abundance of false positive findings. To address the shortcomings of current approaches of network-based genome-wide association studies, we propose <tt>networkGWAS</tt>, a computationally efficient and statistically sound approach to gene-based genome-wide association studies based on mixed models and neighborhood aggregation. It allows for population structure correction and for well-calibrated p-values, which we obtain through a block permutation scheme. <tt>networkGWAS</tt> successfully detects known or plausible associations on simulated rare variants from H. sapiens data as well as semi-simulated and real data with common variants from A. thaliana and enables the systematic combination of gene-based genome-wide association studies with biological network information.

Detection of complex genetic architecture using two-locus population differentiation: modeling epistasis

10.1101/2020.07.01.182840 ◽

2020 ◽

Author(s):

Minjun Huang ◽

Britney Graham ◽

Ge Zhang ◽

Jacquelaine Bartlett ◽

Jason H. Moore ◽

...

Keyword(s):

Gene Pair ◽

Complex Traits ◽

Multiple Testing ◽

Genetic Architecture ◽

Association Studies ◽

Disease Prevalence ◽

Previous Method ◽

Complex Phenotypes ◽

AbstractRecent advances in genetics have increased our understanding of epistasis as important in the genetics of complex phenotypes. However, current analytical methods often cannot detect epistasis, given the multiple testing burden. To address this, we extended our previous method, Evolutionary Triangulation (ET), that uses differences among populations in both disease prevalence and allele frequencies to filter SNPs from association studies to generate novel interaction models. We show that two-locus ET identified several co-evolving gene pairs, where both genes associate with the same disease, and that the number of such pairs is significantly greater than expected by chance. Traits found by two-locus ET included those related to pigmentation and schizophrenia. We then applied two-locus ET to the analysis of preterm birth (PTB) genetics. Using ET to filter SNPs at loci identified by genome-wide association studies (GWAS), we showed that ET derived PTB two-locus models are novel and were not seen when only the index SNPs were used to generate epistatic models. One gene pair, ADCY5 and KCNAB1 5’, was identified as significantly interacting in a model of gestational age (p as low as 3 × 10−3). Notably, the same ET SNPs in these genes showed significant interactions in three of four cohorts analyzed. The robustness of this gene pair and others, demonstrated that the ET method can be used without prior biological hypotheses based on SNP function to select variants for epistasis testing that could not be identified otherwise. Two-locus ET clearly increased the ability to identify epistasis in complex traits.

Genome-wide detection of CNV regions and their potential association with growth and fatness traits in Duroc pigs

BMC Genomics ◽

10.1186/s12864-021-07654-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yibin Qiu ◽

Rongrong Ding ◽

Zhanwei Zhuang ◽

Jie Wu ◽

Ming Yang ◽

...

Keyword(s):

Copy Number Variation ◽

Complex Traits ◽

Copy Number ◽

Association Studies ◽

Average Daily Gain ◽

Potential Candidate ◽

Genome Wide ◽

Number Variation ◽

Genetic Mechanisms

Abstract Background In the process of pig breeding, the average daily gain (ADG), days to 100 kg (AGE), and backfat thickness (BFT) are directly related to growth rate and fatness. However, the genetic mechanisms involved are not well understood. Copy number variation (CNV), an important source of genetic diversity, can affect a variety of complex traits and diseases and has gradually been thrust into the limelight. In this study, we reported the genome-wide CNVs of Duroc pigs using SNP genotyping data from 6627 animals. We also performed a copy number variation region (CNVR)-based genome-wide association studies (GWAS) for growth and fatness traits in two Duroc populations. Results Our study identified 953 nonredundant CNVRs in U.S. and Canadian Duroc pigs, covering 246.89 Mb (~ 10.90%) of the pig autosomal genome. Of these, 802 CNVRs were in U.S. Duroc pigs with 499 CNVRs were in Canadian Duroc pigs, indicating 348 CNVRs were shared by the two populations. Experimentally, 77.8% of nine randomly selected CNVRs were validated through quantitative PCR (qPCR). We also identified 35 CNVRs with significant association with growth and fatness traits using CNVR-based GWAS. Ten of these CNVRs were associated with both ADG and AGE traits in U.S. Duroc pigs. Notably, four CNVRs showed significant associations with ADG, AGE, and BFT, indicating that these CNVRs may play a pleiotropic role in regulating pig growth and fat deposition. In Canadian Duroc pigs, nine CNVRs were significantly associated with both ADG and AGE traits. Further bioinformatic analysis identified a subset of potential candidate genes, including PDGFA, GPER1, PNPLA2 and BSCL2. Conclusions The present study provides a necessary supplement to the CNV map of the Duroc genome through large-scale population genotyping. In addition, the CNVR-based GWAS results provide a meaningful way to elucidate the genetic mechanisms underlying complex traits. The identified CNVRs can be used as molecular markers for genetic improvement in the molecular-guided breeding of modern commercial pigs.

Ancestry-informative markers for African Americans based on the Affymetrix Pan-African genotyping array

10.7287/peerj.preprints.545 ◽

2014 ◽

Author(s):

Xu Zhang ◽

Wenbo Mu ◽

Cong Liu ◽

Wei Zhang

Keyword(s):

African Americans ◽

Complex Traits ◽

Association Studies ◽

African Ancestry ◽

Genetic Admixture ◽

Admixture Mapping ◽

Ancestry Informative Markers ◽

Pan African ◽

Genetic admixture has been utilized as a tool for identifying loci associated with complex traits and diseases in recently admixed populations such as African Americans. In particular, admixture mapping is an efficient approach to identifying genetic basis for those complex diseases with substantial racial or ethnic disparities. Though current advances in admixture mapping algorithms may utilize the entire panel of SNPs, providing ancestry-informative markers (AIMs) that can differentiate parental populations and estimate ancestry proportions in an admixed population may particularly benefit admixture mapping in studies of limited samples, help identify unsuitable individuals (e.g., through genotyping the most informative ancestry markers) before starting large genome-wide association studies (GWAS), or guide larger scale targeted deep re-sequencing for determining specific disease-causing variants. Defining panels of AIMs based on commercial, high-throughput genotyping platforms will facilitate the utilization of these platforms for simultaneous admixture mapping of complex traits and diseases, in addition to conventional GWAS. Here, we describe AIMs detected based on the Shannon Information Content (SIC) or Fst for African Americans with genome-wide coverage that were selected from ~2.3 million single nucleotide polymorphisms (SNPs) covered by the Affymetrix Axiom Pan-African array, a newly developed genotyping platform optimized for individuals of African ancestry.

Ancestry-informative markers for African Americans based on the Affymetrix Pan-African genotyping array

10.7287/peerj.preprints.545v1 ◽

2014 ◽

Author(s):

Xu Zhang ◽

Wenbo Mu ◽

Cong Liu ◽

Wei Zhang

Keyword(s):

African Americans ◽

Complex Traits ◽

Association Studies ◽

African Ancestry ◽

Genetic Admixture ◽

Admixture Mapping ◽

Ancestry Informative Markers ◽

Pan African ◽

Genetic admixture has been utilized as a tool for identifying loci associated with complex traits and diseases in recently admixed populations such as African Americans. In particular, admixture mapping is an efficient approach to identifying genetic basis for those complex diseases with substantial racial or ethnic disparities. Though current advances in admixture mapping algorithms may utilize the entire panel of SNPs, providing ancestry-informative markers (AIMs) that can differentiate parental populations and estimate ancestry proportions in an admixed population may particularly benefit admixture mapping in studies of limited samples, help identify unsuitable individuals (e.g., through genotyping the most informative ancestry markers) before starting large genome-wide association studies (GWAS), or guide larger scale targeted deep re-sequencing for determining specific disease-causing variants. Defining panels of AIMs based on commercial, high-throughput genotyping platforms will facilitate the utilization of these platforms for simultaneous admixture mapping of complex traits and diseases, in addition to conventional GWAS. Here, we describe AIMs detected based on the Shannon Information Content (SIC) or Fst for African Americans with genome-wide coverage that were selected from ~2.3 million single nucleotide polymorphisms (SNPs) covered by the Affymetrix Axiom Pan-African array, a newly developed genotyping platform optimized for individuals of African ancestry.

Faculty Opinions recommendation of Estimation of complex effect-size distributions using summary-level statistics from genome-wide association studies across 32 complex traits.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.733803377.793550136 ◽

2018 ◽

Author(s):

Mohan Liu

Keyword(s):

Effect Size ◽

Complex Traits ◽

Association Studies ◽

Genome Wide Association ◽

Size Distributions ◽

Complex Effect ◽

Genome Wide ◽

Level Statistics

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 ◽

Nucleotide Polymorphisms ◽

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.

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 ◽

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.