The Medaka Inbred Kiyosu-Karlsruhe (MIKK) Panel

Unraveling the relationship between genetic variation and phenotypic traits remains a fundamental challenge in biology. Mapping variants underlying complex traits while controlling for confounding environmental factors is often problematic. To address this, we have established a vertebrate genetic resource specifically to allow for robust genotype-to-phenotype investigations. The teleost medaka (Oryzias latipes) is an established genetic model system with a long history of genetic research and a high tolerance to inbreeding from the wild. Here we present the Medaka Inbred Kiyosu-Karlsruhe (MIKK) panel: the first near-isogenic panel of 80 inbred lines in a vertebrate model derived from a wild founder population. Inbred lines provide fixed genomes that are a prerequisite for the replication of studies, studies which vary both the genetics and environment in a controlled manner and functional testing. The MIKK panel will therefore enable phenotype-to-genotype association studies of complex genetic traits while allowing for careful control of interacting factors, with numerous applications in genetic research, human health, and drug development and fundamental biology. Here we present a detailed characterisation of the genetic variation across the MIKK panel, which provides a rich and unique genetic resource to the community by enabling large-scale experiments for mapping complex traits.

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Prospects and pitfalls in whole genome association studies

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2005.1689 ◽

2005 ◽

Vol 360 (1460) ◽

pp. 1589-1595 ◽

Cited By ~ 28

Author(s):

Robert W Lawrence ◽

David M Evans ◽

Lon R Cardon

Keyword(s):

Genetic Variation ◽

Genetic Markers ◽

Complex Traits ◽

Large Scale ◽

Association Studies ◽

Whole Genome ◽

Common Genetic Variation ◽

Close Attention ◽

Genome Association ◽

Whole Genome Association

Recent large-scale studies of common genetic variation throughout the human genome are making it feasible to conduct whole genome studies of genotype–phenotype associations. Such studies have the potential to uncover novel contributors to common complex traits and thus lead to insights into the aetiology of multifactorial phenotypes. Despite this promise, it is important to recognize that the availability of genetic markers and the ability to assay them at realistic cost does not guarantee success of this approach. There are a number of practical issues that require close attention, some forms of allelic architecture are not readily amenable to the association approach with even the most rigorous design, and doubtless new hurdles will emerge as the studies begin. Here we discuss the promise and current challenges of the whole genome approach, and raise some issues to consider in interpreting the results of the first whole genome studies.

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

Scientific Reports ◽

10.1038/s41598-021-81550-8 ◽

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.

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Within and across populations complex traits and diseases prediction using summary statistics from large-scale genomewide association studies

10.14264/11574da ◽

2021 ◽

Author(s):

◽

Ying Wang

Keyword(s):

Complex Traits ◽

Large Scale ◽

Association Studies ◽

Summary Statistics ◽

Genomewide Association

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Better estimation of SNP heritability from summary statistics provides a new understanding of the genetic architecture of complex traits

10.1101/284976 ◽

2018 ◽

Cited By ~ 6

Author(s):

Doug Speed ◽

David J Balding

Keyword(s):

Complex Traits ◽

Genetic Architecture ◽

Large Scale ◽

Association Studies ◽

Genome Wide Association Studies ◽

Summary Statistics ◽

Confounding Bias ◽

Conserved Regions ◽

Genome Wide ◽

Variation Explained

LD Score Regression (LDSC) has been widely applied to the results of genome-wide association studies. However, its estimates of SNP heritability are derived from an unrealistic model in which each SNP is expected to contribute equal heritability. As a consequence, LDSC tends to over-estimate confounding bias, under-estimate the total phenotypic variation explained by SNPs, and provide misleading estimates of the heritability enrichment of SNP categories. Therefore, we present SumHer, software for estimating SNP heritability from summary statistics using more realistic heritability models. After demonstrating its superiority over LDSC, we apply SumHer to the results of 24 large-scale association studies (average sample size 121 000). First we show that these studies have tended to substantially over-correct for confounding, and as a result the number of genome-wide significant loci has under-reported by about 20%. Next we estimate enrichment for 24 categories of SNPs defined by functional annotations. A previous study using LDSC reported that conserved regions were 13-fold enriched, and found a further twelve categories with above 2-fold enrichment. By contrast, our analysis using SumHer finds that conserved regions are only 1.6-fold (SD 0.06) enriched, and that no category has enrichment above 1.7-fold. SumHer provides an improved understanding of the genetic architecture of complex traits, which enables more efficient analysis of future genetic data.

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EVOLUTION OF DNA SEQUENCING METHODS AND PROSPECTS FOR THEIR USE IN FORENSIC DNA EXAMINATION

Young Scientist ◽

10.32839/2304-5809/2020-11-87-27 ◽

2020 ◽

Vol 11 (87) ◽

Author(s):

Zhanna Bazyliuk ◽

Keyword(s):

Dna Sequencing ◽

Molecular Genetic ◽

Genetic Research ◽

Snp Markers ◽

Degraded Dna ◽

Phenotypic Traits ◽

Nucleotide Polymorphisms ◽

Biological Origin ◽

Genetic Traits ◽

Str Loci

The study of the human genome makes it possible to use genetic information to identify individual traits, diagnosis of diseases and forecasting and prevention of their development, promotes a personal approach when choosing treatment methods; population research, ethnogenesis and evolutionary processes. Introduction of DNA sequencing methods in domestic genetic fingerprinting will contribute to a more informative establishment of human genetic traits. The main purpose of molecular genetic research is to establish the genetic features of missing people, their relatives, to conduct paternity, to identify traces of biological origin and their identification. This article talks about the gradual development of DNA sequencing technology, which is conventionally divided into three types. The first type includes sequencing using capillary electrophoresis and pyrosequencing. The second type is high-throughput pyrosequencing, semiconductor, cyclic ligase, and the use of fluorescently labeled precursors, based on the sequencing of millions of DNA fragments simultaneously. The third stage includes methods that do not require prior sample preparation. These are methods of nanoporous sequencing, sequencing of one molecule, one-molecular sequencing. Today, each of the sequencing methods is aimed at performing different tasks. A number of methods are promising in the field of molecular-genetic examination. In world jurisprudence, sequencing is implemented mainly with the help of devices - Illumina’s, MiSeq FGx, Ion Torrent PGM from ThermoFisher and Ion S5. Research in forensic expertise of single nucleotide polymorphisms (SNP), sequencing of STR-loci and mitochondrial DNA, STR-loci and SNP-markers of the Y chromosome, will provide a high level of information, determination of human phenotypic traits, the possibility of establishing genetic traits from significantly degraded DNA. This article deals with modern problems of identification of human genetic traits and the prospect of introduction of the newest methods of sequencing for their qualitative and complete establishment.

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The Y Chromosome: A Complex Locus for Genetic Analyses of Complex Human Traits

Genes ◽

10.3390/genes11111273 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1273

Author(s):

Katherine Parker ◽

A. Mesut Erzurumluoglu ◽

Santiago Rodriguez

Keyword(s):

Population Genetics ◽

Genetic Variation ◽

Y Chromosome ◽

Complex Traits ◽

Association Studies ◽

Epidemiological Studies ◽

Complex Locus ◽

Complex Human Traits ◽

Human Complex ◽

Human Y Chromosome

The Human Y chromosome (ChrY) has been demonstrated to be a powerful tool for phylogenetics, population genetics, genetic genealogy and forensics. However, the importance of ChrY genetic variation in relation to human complex traits is less clear. In this review, we summarise existing evidence about the inherent complexities of ChrY variation and their use in association studies of human complex traits. We present and discuss the specific particularities of ChrY genetic variation, including Y chromosomal haplogroups, that need to be considered in the design and interpretation of genetic epidemiological studies involving ChrY.

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Investigating tissue-relevant causal molecular mechanisms of complex traits using probabilistic TWAS analysis

10.1101/808295 ◽

2019 ◽

Cited By ~ 2

Author(s):

Yuhua Zhang ◽

Corbin Quick ◽

Ketian Yu ◽

Alvaro Barbeira ◽

Francesca Luca ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Large Scale ◽

Molecular Mechanisms ◽

Association Studies ◽

Complex Trait ◽

Causal Effects ◽

Biological Mechanisms ◽

Integrative Framework ◽

Eqtl Data

AbstractTranscriptome-wide association studies (TWAS), an integrative framework using expression quantitative trait loci (eQTLs) to construct proxies for gene expression, have emerged as a promising method to investigate the biological mechanisms underlying associations between genotypes and complex traits. However, challenges remain in interpreting TWAS results, especially regarding their causality implications. In this paper, we describe a new computational framework, probabilistic TWAS (PTWAS), to detect associations and investigate causal relationships between gene expression and complex traits. We use established concepts and principles from instrumental variables (IV) analysis to delineate and address the unique challenges that arise in TWAS. PTWAS utilizes probabilistic eQTL annotations derived from multi-variant Bayesian fine-mapping analysis conferring higher power to detect TWAS associations than existing methods. Additionally, PTWAS provides novel functionalities to evaluate the causal assumptions and estimate tissue- or cell-type specific causal effects of gene expression on complex traits. These features make PTWAS uniquely suited for in-depth investigations of the biological mechanisms that contribute to complex trait variation. Using eQTL data across 49 tissues from GTEx v8, we apply PTWAS to analyze 114 complex traits using GWAS summary statistics from several large-scale projects, including the UK Biobank. Our analysis reveals an abundance of genes with strong evidence of eQTL-mediated causal effects on complex traits and highlights the heterogeneity and tissue-relevance of these effects across complex traits. We distribute software and eQTL annotations to enable users performing rigorous TWAS analysis by leveraging the full potentials of the latest GTEx multi-tissue eQTL data.

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Barcoded Bulk QTL mapping reveals highly polygenic and epistatic architecture of complex traits in yeast

10.1101/2021.09.08.459513 ◽

2021 ◽

Author(s):

Alex N. Nguyen Ba ◽

Katherine R. Lawrence ◽

Artur Rego-Costa ◽

Shreyas Gopalakrishnan ◽

Daniel Temko ◽

...

Keyword(s):

Quantitative Trait Locus ◽

Qtl Mapping ◽

Quantitative Trait ◽

Complex Traits ◽

Large Scale ◽

Genetic Basis ◽

Association Studies ◽

Model Organisms ◽

Genome Wide Association Studies ◽

Trait Locus

Mapping the genetic basis of complex traits is critical to uncovering the biological mechanisms that underlie disease and other phenotypes. Genome-wide association studies (GWAS) in humans and quantitative trait locus (QTL) mapping in model organisms can now explain much of the observed heritability in many traits, allowing us to predict phenotype from genotype. However, constraints on power due to statistical confounders in large GWAS and smaller sample sizes in QTL studies still limit our ability to resolve numerous small-effect variants, map them to causal genes, identify pleiotropic effects across multiple traits, and infer non-additive interactions between loci (epistasis). Here, we introduce barcoded bulk quantitative trait locus (BB-QTL) mapping, which allows us to construct, genotype, and phenotype 100,000 offspring of a budding yeast cross, two orders of magnitude larger than the previous state of the art. We use this panel to map the genetic basis of eighteen complex traits, finding that the genetic architecture of these traits involves hundreds of small-effect loci densely spaced throughout the genome, many with widespread pleiotropic effects across multiple traits. Epistasis plays a central role, with thousands of interactions that provide insight into genetic networks. By dramatically increasing sample size, BB-QTL mapping demonstrates the potential of natural variants in high-powered QTL studies to reveal the highly polygenic, pleiotropic, and epistatic architecture of complex traits.Significance statementUnderstanding the genetic basis of important phenotypes is a central goal of genetics. However, the highly polygenic architectures of complex traits inferred by large-scale genome-wide association studies (GWAS) in humans stand in contrast to the results of quantitative trait locus (QTL) mapping studies in model organisms. Here, we use a barcoding approach to conduct QTL mapping in budding yeast at a scale two orders of magnitude larger than the previous state of the art. The resulting increase in power reveals the polygenic nature of complex traits in yeast, and offers insight into widespread patterns of pleiotropy and epistasis. Our data and analysis methods offer opportunities for future work in systems biology, and have implications for large-scale GWAS in human populations.

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Capturing SNP Association across the NK Receptor and HLA Gene Regions in Multiple Sclerosis by Targeted Penalised Regression Models

Genes ◽

10.3390/genes13010087 ◽

2021 ◽

Vol 13 (1) ◽

pp. 87

Author(s):

Sean M. Burnard ◽

Rodney A. Lea ◽

Miles Benton ◽

David Eccles ◽

Daniel W. Kennedy ◽

...

Keyword(s):

Multiple Sclerosis ◽

Complex Traits ◽

Multiple Testing ◽

Large Scale ◽

Disease Risk ◽

Association Studies ◽

Meta Analysis ◽

Elastic Net ◽

Genome Wide Association Studies ◽

Multiple Testing Correction

Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.

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Integrative transcriptome imputation reveals tissue-specific and shared biological mechanisms mediating susceptibility to complex traits

10.1101/532929 ◽

2019 ◽

Cited By ~ 1

Author(s):

Wen Zhang ◽

Georgios Voloudakis ◽

Veera M. Rajagopal ◽

Ben Reahead ◽

Joel T. Dudley ◽

...

Keyword(s):

Gene Expression ◽

Complex Traits ◽

Large Scale ◽

Association Studies ◽

Drug Repurposing ◽

Joint Analysis ◽

Expression Trait ◽

Significant Expression ◽

Insight Into ◽

Trait Associations

AbstractTranscriptome-wide association studies integrate gene expression data with common risk variation to identify gene-trait associations. By incorporating epigenome data to estimate the functional importance of genetic variation on gene expression, we improve the accuracy of transcriptome prediction and the power to detect significant expression-trait associations. Joint analysis of 14 large-scale transcriptome datasets and 58 traits identify 13,724 significant expression-trait associations that converge to biological processes and relevant phenotypes in human and mouse phenotype databases. We perform drug repurposing analysis and identify known and novel compounds that mimic or reverse trait-specific changes. We identify genes that exhibit agonistic pleiotropy for genetically correlated traits that converge on shared biological pathways and elucidate distinct processes in disease etiopathogenesis. Overall, this comprehensive analysis provides insight into the specificity and convergence of gene expression on susceptibility to complex traits.

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