scholarly journals Genetic architecture of gene regulation in Indonesian populations identifies QTLs associated with local ancestry and archaic introgression

2020 ◽  
Author(s):  
Heini M. Natri ◽  
Georgi Hudjashov ◽  
Guy Jacobs ◽  
Pradiptajati Kusuma ◽  
Lauri Saag ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Complex Traits ◽  
Association Studies ◽  
Genomic Diversity ◽  
Human Genomics ◽  
Island Populations ◽  
Local Ancestry ◽  
Indonesian Population ◽  
Regulatory Loci ◽  
Archaic Introgression

AbstractLack of diversity in human genomics limits our understanding of the genetic underpinnings of complex traits, hinders precision medicine, and contributes to health disparities. To map genetic effects on gene regulation in the underrepresented Indonesian population, we have integrated genotype, gene expression, and CpG methylation data from 115 participants across three island populations that capture the major sources of genomic diversity on the region. In a comparison with a European dataset, we identify 166 uniquely Indonesia-specific eQTLs, highlighting the benefits of performing association studies on non-European populations. By combining local ancestry and archaic introgression inference eQTLs and methylQTLs, we identify regulatory loci driven by modern Papuan ancestry as well as introgressed Denisovan and Neanderthal variation. GWAS colocalization connects QTLs detected here to hematological traits. Our findings illustrate how local ancestry and archaic introgression drive variation in gene regulation across genetically distinct and in admixed populations.

scholarly journals Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chao-Yu Guo ◽  
Reng-Hong Wang ◽  
Hsin-Chou Yang
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Association Test ◽  
Whole Genome Sequence ◽  
Environment Interaction ◽  
Genome Wide Association Studies ◽  
Whole Genome ◽  
Sequence Kernel Association Test ◽  
Gene Environment ◽  
Family Based

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

scholarly journals Sept8/SEPTIN8 involvement in cellular structure and kidney damage is identified by genetic mapping and a novel human tubule hypoxic model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gregory R. Keele ◽  
Jeremy W. Prokop ◽  
Hong He ◽  
Katie Holl ◽  
John Littrell ◽  
...  
Keyword(s):  
Complex Traits ◽  
Genetic Model ◽  
Association Studies ◽  
Model Systems ◽  
Linear Mixed Effect Model ◽  
Genome Wide Association Studies ◽  
Tubulointerstitial Injury ◽  
Heritable Variation ◽  
Mixed Effect

AbstractChronic kidney disease (CKD), which can ultimately progress to kidney failure, is influenced by genetics and the environment. Genes identified in human genome wide association studies (GWAS) explain only a small proportion of the heritable variation and lack functional validation, indicating the need for additional model systems. Outbred heterogeneous stock (HS) rats have been used for genetic fine-mapping of complex traits, but have not previously been used for CKD traits. We performed GWAS for urinary protein excretion (UPE) and CKD related serum biochemistries in 245 male HS rats. Quantitative trait loci (QTL) were identified using a linear mixed effect model that tested for association with imputed genotypes. Candidate genes were identified using bioinformatics tools and targeted RNAseq followed by testing in a novel in vitro model of human tubule, hypoxia-induced damage. We identified two QTL for UPE and five for serum biochemistries. Protein modeling identified a missense variant within Septin 8 (Sept8) as a candidate for UPE. Sept8/SEPTIN8 expression increased in HS rats with elevated UPE and tubulointerstitial injury and in the in vitro hypoxia model. SEPTIN8 is detected within proximal tubule cells in human kidney samples and localizes with acetyl-alpha tubulin in the culture system. After hypoxia, SEPTIN8 staining becomes diffuse and appears to relocalize with actin. These data suggest a role of SEPTIN8 in cellular organization and structure in response to environmental stress. This study demonstrates that integration of a rat genetic model with an environmentally induced tubule damage system identifies Sept8/SEPTIN8 and informs novel aspects of the complex gene by environmental interactions contributing to CKD risk.

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 Population Admixture May Appear to Mask, Change or Reverse Genetic Effects of Genes Underlying Complex Traits

Genetics ◽  
2001 ◽  
Vol 159 (3) ◽  
pp. 1319-1323
Author(s):  
Hong-Wen Deng
Keyword(s):  
Population Genetics ◽  
False Positive ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Effects ◽  
Population Admixture ◽  
Reverse Genetic ◽  
Positive Identification ◽  
False Positive Identification

Abstract Association studies using random population samples are increasingly being applied in the identification and inference of genetic effects of genes underlying complex traits. It is well recognized that population admixture may yield false-positive identification of genetic effects for complex traits. However, it is less well appreciated that population admixture can appear to mask, change, or reverse true genetic effects for genes underlying complex traits. By employing a simple population genetics model, we explore the effects and the conditions of population admixture in masking, changing, or even reversing true genetic effects of genes underlying complex traits.

Comprehensive evaluation of mapping complex traits in wheat using genome-wide association studies

2021 ◽  
Vol 42 (1) ◽  
Author(s):  
Dinesh K. Saini ◽  
Yuvraj Chopra ◽  
Jagmohan Singh ◽  
Karansher S. Sandhu ◽  
Anand Kumar ◽  
...  
Keyword(s):  
Complex Traits ◽  
Comprehensive Evaluation ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Mapping Complex Traits

Abstract 1443: Systems-Based Analysis of HDL Metabolism in a Mouse Intercross between Strains CAST and C57BL6/J (CxB)

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Margarete Mehrabian ◽  
Charles Farber ◽  
Peter Langfelder ◽  
Anatole Ghazalpour ◽  
Zhiqiang Zhou ◽  
...  
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Plasma Lipid ◽  
Heart Association ◽  
Hdl Cholesterol ◽  
Chow Diet ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Genomic Study ◽  
Hdl Metabolism

A recent meta-analysis of three large genome-wide association studies for HDL cholesterol levels revealed several highly significant associations, but altogether these explained less than 10% of the population variance of HDL. Since HDL levels are highly heritable, with heritability estimated at 50–70% in many studies, there are clearly many additional genes, and probably complex genetic and environmental interactions, involved in HDL metabolism. Thus, if “personalized medicine” is to become a reality, these complex factors must be addressed. Combined genetic-genomic approaches have rejuvenated the analysis of complex traits using mouse models, and here report an integrative genomic study of HDL in a large mouse cross. We previously reported the identification of loci associated with HDL cholesterol concentrations using a CXB F2 intercross. We have now generated a much larger CXB cross, consisting of 438 mice, and have integrated genome wide gene expression analysis of liver and adipose with quantitative trait locus (QTL) mapping and causality modeling. These studies were carried out on mice fed a low fat, chow diet and then switched to a high fat, ’Western’ diet. QTL analysis on the clinical traits using R/QTL (http://cran.r-project.org/) revealed a complex inheritance pattern with significant LOD scores at 9 loci, on chromosomes 1,2,4,5,8,9,10,16,18. Of these loci, 6 (chr: 1,4,5,10,16,18) were seen to be involved in genetic-dietary regulation of HDL cholesterol. Expression QTLs (eQTL) were determined using Agilent microarrays for 23,624 transcripts. Genes expressed within a 1-LOD support interval or correlated with HDL (p<2.7E-11) in both adipose and liver were identified. Using Network Edge Orienting (NEO) methods, causal relationships between the identified genes, related QTL peak markers and HDL levels were accessed. The genes were then ranked based on the NEO scores. In liver the highest ranked genes were associated with mitochondrial, ER and golgi trafficking. In adipose, on the other hand, pathways associated with cell signaling, transcription regulation and protein ubiquitation were predicted to be causal for HDL levels. In conclusion, our results reveal a large number of novel pathways and candidate genes for plasma lipid metabolism. This research has received full or partial funding support from the American Heart Association, AHA Western States Affiliate (California, Nevada & Utah).

scholarly journals GWAS of three molecular traits highlights core genes and pathways alongside a highly polygenic background

2020 ◽  
Author(s):  
Nasa Sinnott-Armstrong ◽  
Sahin Naqvi ◽  
Manuel Rivas ◽  
Jonathan K Pritchard
Keyword(s):  
Complex Traits ◽  
Genetic Basis ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Uk Biobank ◽  
The Core ◽  
Genome Wide ◽  
Core Genes

SummaryGenome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. However, for most traits it remains difficult to interpret what genes and biological processes are impacted by the top hits. Here, as a contrast, we describe UK Biobank GWAS results for three molecular traits—urate, IGF-1, and testosterone—that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. Unlike most GWAS of complex traits, for all three traits we find that most top hits are readily interpretable. We observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. We show how GWAS data illuminate the biology of variation in each trait, including insights into differences in testosterone regulation between females and males. Meanwhile, in other respects the results are reminiscent of GWAS for more-complex traits. In particular, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. Given that diseases are often impacted by many distinct biological processes, including these three, our results help to illustrate why so many variants can affect risk for any given disease.

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