scholarly journals Direct-To-Consumer DNA testing of 6,000 dogs reveals 98.6-kb duplication causing blue eyes and heterochromia in Siberian Huskies

2018 ◽  
Author(s):  
P. E. Deane-Coe ◽  
E. T. Chu ◽  
A. R. Boyko ◽  
A. J. Sams
Keyword(s):  
Complex Traits ◽  
Association Studies ◽  
Genomic Data ◽  
Genomic Research ◽  
Human Model ◽  
Probe Intensity ◽  
Genome Wide Association Studies ◽  
Web Based ◽  
Personal Genomic ◽  
Blue Eyes

SummaryConsumer genomics enables genetic discovery on an unprecedented scale by linking very large databases of personal genomic data with phenotype information voluntarily submitted via web-based surveys1. These databases are having a transformative effect on human genomic research, yielding insights on increasingly complex traits, behaviors, and disease by including many thousands of individuals in genome-wide association studies (GWAS)2, 3. The promise of consumer genomic data is not limited to human research, however. Genomic tools for dogs are readily available, with hundreds of causal Mendelian variants already characterized4–6, because selection and breeding have led to dramatic phenotypic diversity underlain by a simple genetic structure7, 8. Here, we report the results of the first consumer genomics study ever conducted in a non-human model: a GWAS of blue eyes based on more than 3,000 customer dogs with a validation panel of nearly 3,000 more, the largest canine GWAS to date. We discovered a novel association with blue eyes on chromosome 18 (P = 1x10-65) and used both sequence coverage and microarray probe intensity data to identify the putative causal variant: a 98.6-kb duplication directly upstream of the hox gene ALX4, which plays an important role in mammalian eye development9, 10. This duplication was largely restricted to Siberian Huskies and is highly, but not completely, penetrant. These results underscore the power of consumer-data-driven discovery in nonhuman species, especially dogs, where there is intense owner interest in the personal genomic information of their pets, a high level of engagement with web-based surveys, and an underlying genetic architecture ideal for mapping studies.

scholarly journals Transcriptome Analysis in Domesticated Species: Challenges and Strategies

2015 ◽  
Vol 9S4 ◽  
pp. BBI.S29334 ◽  
Author(s):  
Jessica P. Hekman ◽  
Jennifer L Johnson ◽  
Anna V. Kukekova
Keyword(s):  
Complex Traits ◽  
Gene Networks ◽  
Association Studies ◽  
Cultural Value ◽  
Genomic Research ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Rna Seq ◽  
Genome Wide ◽  
Genome Assemblies

Domesticated species occupy a special place in the human world due to their economic and cultural value. In the era of genomic research, domesticated species provide unique advantages for investigation of diseases and complex phenotypes. RNA sequencing, or RNA-seq, has recently emerged as a new approach for studying transcriptional activity of the whole genome, changing the focus from individual genes to gene networks. RNA-seq analysis in domesticated species may complement genome-wide association studies of complex traits with economic importance or direct relevance to biomedical research. However, RNA-seq studies are more challenging in domesticated species than in model organisms. These challenges are at least in part associated with the lack of quality genome assemblies for some domesticated species and the absence of genome assemblies for others. In this review, we discuss strategies for analyzing RNA-seq data, focusing particularly on questions and examples relevant to domesticated species.

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.

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 Data safe havens to combine health and genomic data: benefits and challenges

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kerina H Jones ◽  
Arron S Lacey ◽  
Brian L Perkins ◽  
Mark I Rees
Keyword(s):  
Association Studies ◽  
Genomic Data ◽  
Population Level ◽  
Data Availability ◽  
Genome Wide Association Studies ◽  
Related Data ◽  
Research Areas ◽  
Individual Privacy ◽  
Access Controls ◽  
Health Related

ABSTRACTObjectivesData safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely. ApproachWe explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data. ResultsThe benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals. ConclusionGenomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.

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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