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The UK Biobank resource with deep phenotyping and genomic data | Nature
Should be useful though bizarrely do not offer male infertility as an analysis!!

The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
13 days ago by Segalllab
Genetic Nature or Genetic Nurture? Quantifying Bias in Analyses Using Polygenic Scores | bioRxiv
> For complex, behavioral traits, the correlation between individual PGS and phenotype may contain bias alongside the causal effect of the individual's genes (due to geographic, ancestral, and/or socioeconomic confounding). We formalize the recent introduction of a different source of bias in regression models using PGSs: the effects of parental genes on offspring outcomes (i.e. genetic nurture). GWAS do not discriminate between the various pathways through which genes influence outcomes, meaning existing PGSs capture both direct genetic effects and genetic nurture effects
genetics  GWAS  polygenic  nurture 
9 weeks ago by porejide
Polygenic scores and tea drinking | gcbias
Some of these complications are perhaps best illustrated with a toy example. Say we perform a GWAS of the amount of tea that individuals in the UK drink (e.g. in the UK Biobank). On the basis of this tea GWAS, someone (let’s call him Bob) could claim that we could learn about France-UK differences in tea consumption by just counting up the average number of alleles for tea preference that individuals in the UK and France carry. If the British, overall, are more likely to have alleles that increase tea consumption than French people, then Bob might say that we have demonstrated that the difference between French and UK people’s preference for tea is in part genetic. Bob would assure us that these alleles are polymorphic in both countries, and that both environment and culture plays a role. He would further reassure us that there’ll be an overlapping distribution of tea drinking preferences in both countries, so he’s not saying that all British people drink more tea for genetic reasons. He’ll tell us he’s simply interested in showing that the average difference in tea consumption is partly genetic.
genetics  bioinformatics  GWAS  good-example  nature-and-nurture-sittin-in-a-tree  population-biology  cultural-norms 
12 weeks ago by Vaguery
Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder | Nature Genetics
> Strong concordance with GWAS of quantitative population measures of ADHD symptoms supports that clinical diagnosis of ADHD is an extreme expression of continuous heritable traits.
ADHD  spectrum  GWAS 
december 2018 by porejide
Navigome: GWAS-based phenome visualization
The website is based on a collection of 465 phenotypes from different GWAS studies. Here, you will be able to:

Visualize an interactive 2D map of phenotypes based on genetic correlations
Browse pathways, gene and tissue analyses, using interactive visualizations
Generate gene profiles across phenotypes and tissues
genomics  gwas  visualization 
november 2018 by mark.larios
Genetic disease risks can be misestimated across global populations | Genome Biology | Full Text
> Strikingly, ancestral risk alleles are found at 9.51% higher frequency in Africa, and derived risk alleles are found at 5.40% lower frequency in Africa. By simulating GWAS with different study populations, we find that non-African cohorts yield disease associations that have biased allele frequencies and that African cohorts yield disease associations that are relatively free of bias. We also find empirical evidence that genotyping arrays and SNP ascertainment bias contribute to continental differences in risk allele frequencies. Because of these causes, polygenic risk scores can be grossly misestimated for individuals of African descent. Importantly, continental differences in risk allele frequencies are only moderately reduced if GWAS use whole genome sequences and hundreds of thousands of cases and controls. Finally, comparisons between uncorrected and corrected genetic risk scores reveal the benefits of considering whether risk alleles are ancestral or derived.
GWAS  ancestry  polygenic 
november 2018 by porejide
Sociogenomics is opening a new door to eugenics
New ways of using your genetic data could bolster scientific racism and encourage discrimination.
genomics  polygenic  GWAS  counterpoint  sociogenomics 
october 2018 by PieroRivizzigno
About Monarch
Monarch site for integrate genotype and phenotype

Integrate, align, and re-distribute cross-species gene, genotype, variant, disease, and phenotype data
Provide a portal for exploration of phenotype-based similarity
Facilitate identification of animal models of human disease through phenotypic similarity
Enable quantitative comparison of cross-species phenotypes
Develop embeddable widgets for data exploration
Influence genotype and phenotype reporting standards
Improve ontologies to better curate genotype-phenotype data

Our philosophy is based on the premise that we want to make all the data count. Monarch isn’t just another database that slurps data from the typical places and renders it in a different format. We are driven to truly integrate biological information using semantics, and present it in a novel way, leveraging phenotypes to bridge the knowledge gap. Our niche is the use of computational reasoning to enable phenotype comparison both within and across species, with the ultimate goal of improving biomedical research.
september 2018 by Segalllab
Association analysis identifies 65 new breast cancer risk loci | Nature
rs9867461 is in the intron of KIF9 and Monarch says has an association with breast cancer....just in Table8
kif9  breast_cancer  gwas 
september 2018 by Segalllab
GitHub - weizhouUMICH/SAIGE
Scalable and Accurate Implementation of Generalized mixed model that uses the saddlepoint approximation for running GWAS on biobank-sized binary traits.
gwas  biobank  GLMM 
august 2018 by mark.larios
Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations | Nature Genetics
> The approach identifies 8.0, 6.1, 3.5, 3.2, and 1.5% of the population at greater than threefold increased risk for coronary artery disease, atrial fibrillation, type 2 diabetes, inflammatory bowel disease, and breast cancer, respectively. For coronary artery disease, this prevalence is 20-fold higher than the carrier frequency of rare monogenic mutations conferring comparable risk
polygenic_risk  GWAS 
august 2018 by porejide
SBAT – Marchini Research Group
SBAT (Sparse Bayesian Association Testing)

A program for performing Genome-wide association testing with multiple, continuous traits in related samples, using a multi-trait mixed model framework.

SBAT implements 2 different models

A full model – where a SNP has a non-zero effect on all the traits.
A sparse model – where a SNP has a non-zero effect on a subset of the traits.
genetics  genomics  gwas  bayesian 
june 2018 by mark.larios
TWAS hub
transcriptome-wide association study database. candidate susceptibility genes for hundreds of complex traits
gwas  genomics 
june 2018 by mark.larios

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