scholarly journals Detecting polygenic adaptation in admixture graphs

2017 ◽  
Author(s):  
Fernando Racimo ◽  
Jeremy J. Berg ◽  
Joseph K. Pickrell
Keyword(s):  
Human Evolution ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Mcmc Algorithm ◽  
Genome Wide ◽  
Polygenic Adaptation ◽  
History Of ◽  
Different Populations ◽  
The Mean ◽  
Open Question

AbstractAn open question in human evolution is the importance of polygenic adaptation: adaptive changes in the mean of a multifactorial trait due to shifts in allele frequencies across many loci. In recent years, several methods have been developed to detect polygenic adaptation using loci identified in genome-wide association studies (GWAS). Though powerful, these methods suffer from limited interpretability: they can detect which sets of populations have evidence for polygenic adaptation, but are unable to reveal where in the history of multiple populations these processes occurred. To address this, we created a method to detect polygenic adaptation in an admixture graph, which is a representation of the historical divergences and admixture events relating different populations through time. We developed a Markov chain Monte Carlo (MCMC) algorithm to infer branch-specific parameters reflecting the strength of selection in each branch of a graph. Additionally, we developed a set of summary statistics that are fast to compute and can indicate which branches are most likely to have experienced polygenic adaptation. We show via simulations that this method - which we call PolyGraph - has good power to detect polygenic adaptation, and applied it to human population genomic data from around the world. We also provide evidence that variants associated with several traits, including height, educational attainment, and self-reported unibrow, have been influenced by polygenic adaptation in different populations during human evolution.

scholarly journals A publicly available repository of ROH islands reveals signatures of selection in different livestock and pet species

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Wim Gorssen ◽  
Roel Meyermans ◽  
Steven Janssens ◽  
Nadine Buys
Keyword(s):  
Association Studies ◽  
Genome Wide Association Studies ◽  
Single Nucleotide ◽  
Future Studies ◽  
Snp Data ◽  
Animal Populations ◽  
Genome Wide ◽  
Starting Point ◽  
Different Populations ◽  
Signatures Of Selection

Abstract Background Runs of homozygosity (ROH) have become the state-of-the-art method for analysis of inbreeding in animal populations. Moreover, ROH are suited to detect signatures of selection via ROH islands and are used in other applications, such as genomic prediction and genome-wide association studies (GWAS). Currently, a vast amount of single nucleotide polymorphism (SNP) data is available online, but most of these data have never been used for ROH analysis. Therefore, we performed a ROH analysis on large medium-density SNP datasets in eight animal species (cat, cattle, dog, goat, horse, pig, sheep and water buffalo; 442 different populations) and make these results publicly available. Results The results include an overview of ROH islands per population and a comparison of the incidence of these ROH islands among populations from the same species, which can assist researchers when studying other (livestock) populations or when looking for similar signatures of selection. We were able to confirm many known ROH islands, for example signatures of selection for the myostatin (MSTN) gene in sheep and horses. However, our results also included multiple other ROH islands, which are common to many populations and not identified to date (e.g. on chromosomes D4 and E2 in cats and on chromosome 6 in sheep). Conclusions We are confident that our repository of ROH islands is a valuable reference for future studies. The discovered ROH island regions represent a unique starting point for new studies or can be used as a reference for future studies. Furthermore, we encourage authors to add their population-specific ROH findings to our repository.

scholarly journals Reproducibility in the UK Biobank of Genome-Wide Significant Signals Discovered in Earlier Genome-wide Association Studies

2020 ◽  
Author(s):  
Jack W. O’Sullivan ◽  
John P. A. Ioannidis
Keyword(s):  
Effect Size ◽  
Association Studies ◽  
Genome Wide Association ◽  
P Value ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
Single Nucleotide ◽  
Genome Wide ◽  
The Uk ◽  
Open Question

AbstractWith the establishment of large biobanks, discovery of single nucleotide polymorphism (SNPs) that are associated with various phenotypes has been accelerated. An open question is whether SNPs identified with genome-wide significance in earlier genome-wide association studies (GWAS) are replicated also in later GWAS conducted in biobanks. To address this question, the authors examined a publicly available GWAS database and identified two, independent GWAS on the same phenotype (an earlier, “discovery” GWAS and a later, replication GWAS done in the UK biobank). The analysis evaluated 136,318,924 SNPs (of which 6,289 had reached p<5e-8 in the discovery GWAS) from 4,397,962 participants across nine phenotypes. The overall replication rate was 85.0% and it was lower for binary than for quantitative phenotypes (58.1% versus 94.8% respectively). There was a18.0% decrease in SNP effect size for binary phenotypes, but a 12.0% increase for quantitative phenotypes. Using the discovery SNP effect size, phenotype trait (binary or quantitative), and discovery p-value, we built and validated a model that predicted SNP replication with area under the Receiver Operator Curve = 0.90. While non-replication may often reflect lack of power rather than genuine false-positive findings, these results provide insights about which discovered associations are likely to be seen again across subsequent GWAS.

Alzheimer's disease genetics: lessons to improve disease modelling

2011 ◽  
Vol 39 (4) ◽  
pp. 910-916 ◽  
Author(s):  
Rita J. Guerreiro ◽  
John Hardy
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Current Knowledge ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Biological Processes ◽  
Complete Understanding ◽  
Genome Wide ◽  
History Of ◽  
Ad Alzheimer’S Disease

In the present review, we look back at the recent history of GWAS (genome-wide association studies) in AD (Alzheimer's disease) and integrate the major findings with current knowledge of biological processes and pathways. These topics are essential for the development of animal models, which will be fundamental to our complete understanding of AD.

scholarly journals Signals of polygenic adaptation on height have been overestimated due to uncorrected population structure in genome-wide association studies

2018 ◽  
Author(s):  
Mashaal Sohail ◽  
Robert M. Maier ◽  
Andrea Ganna ◽  
Alex Bloemendal ◽  
Alicia R. Martin ◽  
...  
Keyword(s):  
Population Structure ◽  
Association Studies ◽  
Meta Analysis ◽  
Human Populations ◽  
Genome Wide Association Studies ◽  
Multiple Traits ◽  
Large Numbers ◽  
Genome Wide ◽  
Polygenic Adaptation ◽  
The Uk

AbstractGenetic predictions of height differ among human populations and these differences are too large to be explained by genetic drift. This observation has been interpreted as evidence of polygenic adaptation. Differences across populations were detected using SNPs genome-wide significantly associated with height, and many studies also found that the signals grew stronger when large numbers of subsignificant SNPs were analyzed. This has led to excitement about the prospect of analyzing large fractions of the genome to detect subtle signals of selection and claims of polygenic adaptation for multiple traits. Polygenic adaptation studies of height have been based on SNP effect size measurements in the GIANT Consortium meta-analysis. Here we repeat the height analyses in the UK Biobank, a much more homogeneously designed study. Our results show that polygenic adaptation signals based on large numbers of SNPs below genome-wide significance are extremely sensitive to biases due to uncorrected population structure.

scholarly journals Host genetics and microbiome associations from the lens of genome wide association studies

2018 ◽  
Author(s):  
Omer Weissbrod ◽  
Daphna Rothschild ◽  
Elad Barkan ◽  
Eran Segal
Keyword(s):  
Gut Microbiome ◽  
Complex Traits ◽  
Association Studies ◽  
Meta Analysis ◽  
Lessons Learned ◽  
Host Genome ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
History Of

Recent studies indicate that the gut microbiome is partially heritable, motivating the need to investigate microbiome-host genome associations via microbial genome-wide association studies (mGWAS). Existing mGWAS demonstrate that microbiome-host genotypes associations are typically weak and are spread across multiple variants, similar to associations often observed in genome-wide association studies (GWAS) of complex traits. Here we reconsider mGWAS by viewing them through the lens of GWAS, and demonstrate that there are striking similarities between the challenges and pitfalls faced by the two study designs. We further advocate the mGWAS community to adopt three key lessons learned over the history of GWAS: (a) Adopting uniform data and reporting formats to facilitate replication and meta-analysis efforts; (b) enforcing stringent statistical criteria to reduce the number of false positive findings; and (c) considering the microbiome and the host genome as distinct entities, rather than studying different taxa and single nucleotide polymorphism (SNPs) separately. Finally, we anticipate that mGWAS sample sizes will have to increase by orders of magnitude to reproducibly associate the host genome with the gut microbiome.

scholarly journals Predicting Parkinson’s: Using Neural Networks to Evaluate the Genetic Risk Factors Behind the Disease

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Andrew Yuan ◽  
Isha Jagadish ◽  
Trisha Gongalore ◽  
Joseph Alzagatiti
Keyword(s):  
Risk Factors ◽  
Dopaminergic Neurons ◽  
Association Studies ◽  
Substantia Nigra Pars Compacta ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Predictive Diagnosis ◽  
Genome Wide ◽  
Significant Research ◽  
History Of

To date, researchers do not know the exact reasons for the loss of dopaminergic neurons in the substantia nigra pars compacta that leads to Parkinson’s Disease (PD). Thus, it is extremely difficult to predict whether or not a patient is likely to develop the disease later on, as their risk increases with age. However, once patients present with the common symptoms indicative of the illness, a substantial amount of dopaminergic neurons are already lost. Seeing as there are no current avenues of replacing those neurons, predictive diagnosis and preventive measures could be of extraordinary help in devising treatments. Our aim was to use the significant research into possible high-risk genetic factors from genome-wide association studies (GWAS) to formulate a predictive neural network model for Parkinson’s. We analyzed patient genomes for mutations in the top 20 genes associated with PD, as well as 21 genes implicated in axon guidance pathways, to determine whether the patients were at high or low risk for Parkinson’s. Our model produced an accuracy and AUROC of 94%. We found this significant because it showed a strong correlation between the single nucleotide polymorphisms (SNPs) we analyzed and PD. We believe our model can be further improved upon by adding considerations for other investigated risk factors, such as patient age, familial history of disease, or gut microbiota inconsistencies among others.

Prostate Cancer

2017 ◽  
Author(s):  
Catherine M. Tangen ◽  
Marian L. Neuhouser ◽  
Janet L. Stanford
Keyword(s):  
Prostate Cancer ◽  
Cancer Susceptibility ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Rare Mutations ◽  
Genome Wide ◽  
Sequencing Studies ◽  
Distinct Disease ◽  
History Of ◽  
Family Based

Prostate cancer is the most common solid tumor and the second leading cause of cancer-related mortality in American men. Worldwide, prostate cancer ranks second and fifth as a cause of cancer and cancer deaths, respectively. Despite the international burden of disease due to prostate cancer, its etiology is unclear in most cases. Established risk factors include age, race/ancestry, and family history of the disease. Prostate cancer has a strong heritable component, and genome-wide association studies have identified over 110 common risk-associated genetic variants. Family-based sequencing studies have also found rare mutations (e.g., HOXB13) that contribute to prostate cancer susceptibility. Numerous environmental and lifestyle factors (e.g., obesity, diet) have been examined in relation to prostate cancer incidence, but few modifiable exposures have been consistently associated with risk. Some of the variability in results may be related to etiological heterogeneity, with different causes underlying the development of distinct disease subgroups.

scholarly journals Ultralow amounts of DNA from long-term archived serum samples produce quality genotypes

2019 ◽  
Vol 28 (4) ◽  
pp. 521-524
Author(s):  
Trine B. Rounge ◽  
Marianne Lauritzen ◽  
Sten Even Erlandsen ◽  
Hilde Langseth ◽  
Oddgeir Lingaas Holmen ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association Studies ◽  
Serum Samples ◽  
Dna Yield ◽  
Genome Wide ◽  
Thermo Fisher Scientific ◽  
Storage Effects ◽  
The Mean ◽  
And Storage

Abstract While genotyping studies are scavenging for suitable samples to analyze, large serum collections are currently left unused as they are assumed to provide insufficient amounts of DNA for array-based genotyping. Long-term stored serum is considered to be difficult to genotype since preanalytical treatments and storage effects on DNA yields are not well understood. Successful genotyping of such samples has the potential to activate large biobanks for future genome-wide association studies (GWAS). We aimed to evaluate genotyping of ultralow amounts of DNA from samples stored up to 45 years in the Janus Serum Bank with two commercially available platforms. 64 samples, with various preanalytical treatments, were genotyped on the Axiom Array from Thermo Fisher Scientific and a subset of 24 samples with slightly higher yield were genotyped on the HumanCoreExome array from Illumina. Our results showed that about 80% of the serum samples produced call rates with the Axiom arrays that would be satisfactory in GWAS. The mean DNA yield was 5.8 ng as measured with PicoGreen, 3–6% of recommended yield. The failed samples had on average lower input amounts of DNA. All serum samples genotyped on the HumanCoreExome with a standard and FFPE protocol produced GWAS satisfactory call rates, with mean 97.57% and 98.35% call rates, respectively. The mean yield was 10.65 ng, 6% of the recommendations. Successful array-based genotyping of ultralow DNA yields from serum samples stored up to 45 years is possible. These results demonstrate the potential to activate large serum biobank collections for future studies.

scholarly journals Multivariate Modeling of Direct and Proxy GWAS Indicates Substantial Common Variant Heritability of Alzheimer's Disease

2021 ◽  
Author(s):  
Javier de la Fuente ◽  
Andrew D. Grotzinger ◽  
Riccardo E. Marioni ◽  
Michel G. Nivard ◽  
Elliot M. Tucker-Drob
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Association Studies ◽  
Common Variant ◽  
Genome Wide Association Studies ◽  
Imaging Data ◽  
Genome Wide ◽  
History Of ◽  
Unbiased Estimates ◽  
Summary Data

Genome-wide association studies (GWAS) of proxy-phenotypes using family history of disease (GWAX) substantially boost power for genetic discovery when combined with direct case-control GWAS, most prominently in the context of Alzheimer's Disease (AD). However, despite twin study heritability estimates of approximately 60%, recent SNP-based estimates of common variant heritability of AD from meta-analyzed GWAS-GWAX data have been particularly low (2.5%), calling into question the prospects of continued progress in AD genetics. We demonstrate that commonly used approaches for combining GWAX and GWAS data produce dramatic underestimates of heritability, and we introduce a multivariate method for estimating individual SNP effects and recovering unbiased estimates of SNP heritability when combining GWAS and GWAX summary data. We estimate the SNP heritability of Clinical AD diagnoses excluding the APOE region at ~6-10%, with the corresponding estimate for biological AD (based on prevalence rate estimates from recently published molecular imaging data) as high as ~20%. Common variant risk for AD appears to represent a very strong effect of APOE superimposed upon a relatively diffuse polygenic signal that is distributed across the genome.

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