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Polygenic Traits

Q: What are Polygenic Traits?

Polygenic Traits are multifactorial characteristics determined by the combined effects of multiple genes, rather than a single gene. These complex traits, which include common diseases and quantitative phenotypes, are influenced by both genetic and environmental factors.

Polygenic Traits: Multifactorial characteristics determined by the combined effects of multiple genes, rather than a single gene.
These complex traits, which include common diseases and quantitatice phenotypes, are influenced by both genetic and environmental factors.
PubCompare.ai's cutting-edge AI-driven research protocol optimization can help you easily locate the best protocols and products acrosss literature, pre-prints, and patents to enhance reproducibility and drive your polygenic trait research forward.

Most cited protocols related to «Polygenic Traits»

Mixed Linear Models for Genetic Association

Mixed linear models are an emerging method of choice when conducting association mapping in the presence of sample structure, including geographic population structure, family relatedness and/or cryptic relatedness^{1 (link)–12 (link)}. The basic approach is to build a genetic relationship matrix (GRM) modeling genome-wide sample structure, estimate its contribution to phenotypic variance using a random-effects model (with or without additional fixed effects), and compute association statistics that account for this component of phenotypic variance (Online Methods). We note that mixed linear models can also be used to estimate components of heritability explained by genotyped markers^{13 (link)–14 (link)}, and to predict complex traits using genetic data^{15 (link)–16 (link)}.
Mixed linear model association (MLMA) methods are effective in preventing false-positive associations due to sample structure in studies of humans and model organisms^{1 (link)–6 (link)}. In particular, simulations show that the correction for confounding is nearly perfect for common variants even when geographic population structure, which is a fixed effect, is modeled as a random effect based on overall covariance^{6 (link),17 (link)–19 (link)} (however, rare variants pose a greater challenge for all methods, due to differential confounding of rare and common variants^{20 (link)}). MLMA methods also provide an increase in power, by applying a correction that is specific to sample structure^{1 (link)–6 (link)}. In the case of geographic population structure, markers with large allele frequency differences between populations will receive a larger correction. In the case of relatedness structure, the contribution of related individuals to test statistics will be reduced, preventing overweighting of redundant information due to correlation structure.
An underappreciated point is that MLMA can also increase power in studies without sample structure, by implicitly conditioning on associated loci other than the candidate locus that are not genome-wide significant in the data being analyzed^{8 (link)}. For example, a GRM computed from all markers can be used to approximate the set of causal markers (implicitly assuming that all markers are causal), but this approximation can be generalized. The increase in power scales with the ratio N/M of the number of samples (N) to the effective number of independent markers (M), since the information about unknown associated loci depends on the number of samples. In simulations of a quantitative trait with no sample structure and no LD between markers (Online Methods), application of MLMA instead of linear regression increased average −log₁₀P-values at causal markers from 2.89 to 2.94 (1.8% increase) when N=10,000 and M=100,000, and from 2.92 to 3.46 (18% increase) when N=10,000 and M=10,000. We note that this improvement is contingent on the exclusion of the candidate marker from the GRM (see below).

Yang J., Zaitlen N.A., Goddard M.E., Visscher P.M, & Price A.L. (2014). Advantages and pitfalls in the application of mixed model association methods. Nature genetics, 46(2), 100-106.

Publication 2014

CFC1 protein, human Genetic Structures Genome Homo sapiens Phenotype Polygenic Traits Population Group Reproduction

Genetic Correlations of Depression, Neuroticism, and Subjective Well-being

Details on the cohorts, phenotype measures, genotyping, quality-control filters, and association models are provided in Supplementary Note and Supplementary Table 2 to 5. As shown in Figure 3, there is substantial overlap in samples across the three GWAS meta-analyses.
All analyses were based on autosomal SNPs from cohorts with genotypes imputed against the 1000 Genomes reference panel. The input files in each meta-analysis were subject to a uniform set of quality-control and diagnostic procedures. These are described in the previous SSGAC study^{11 (link)} and Supplementary Note.
As expected under polygenicity^{23 (link)}, we observe inflation of the median test statistic in each GWAS (λ_GC,DEP = 1.36, λ_GC,NEUR = 1.24, λ_GC,SWB = 1.28; Supplementary Figure 4, Supplementary Table 6). The intercept estimates from LD score regression are all below 1.02, however, suggesting that nearly all of the observed inflation is due to polygenic signal^{14 (link)} (Supplementary Figure 5). When we report GWAS results, as in the SSGAC study^{11 (link)} we account for the potential bias due to this small amount of stratification by inflating the standard errors of our GWAS estimates by the square root of the LD score regression intercept.
Manhattan plots from each of the GWAS meta-analyses are shown in Supplementary Figures 6a, b, and c. Our NEUR meta-analysis was based on the same cohort-level data as the SSGAC study^{11 (link)} and unsurprisingly yielded substantively identical results: 10 lead SNPs. Consistent with what studies have reported for other complex traits, the increased discovery samples for DEP and SWB relative to the SSGAC study increased the number of lead SNPs: from 2 to 32 for DEP (N_eff = 149,707 to 354,862) and from 3 to 13 for SWB (N = 298,420 to 388,538). Applying bivariate LD score regression^{6 (link)} to the GWAS results, we estimate the genetic correlations to be 0.72 (s.e. = 0.026) between DEP and NEUR, −0.67 (s.e. = 0.027) between NEUR and SWB, and −0.69 (s.e. = 0.024) between DEP and SWB (Supplementary Table 7). The intercepts from each of these regressions are found in Supplementary Table 8. Lead SNPs with a P value less than 10⁻⁵ from the GWAS for each trait are listed in Supplementary Table 9.

Turley P., Walters R.K., Maghzian O., Okbay A., Lee J.J., Fontana M.A., Nguyen-Viet T.A., Wedow R., Zacher M., Furlotte N.A., Magnusson P., Oskarsson S., Johannesson M., Visscher P.M., Laibson D., Cesarini D., Neale B.M, & Benjamin D.J. (2018). Multi-trait analysis of genome-wide association summary statistics using MTAG. Nature genetics, 50(2), 229-237.

Publication 2017

Genome Genome-Wide Association Study Genotype MLL protein, human Phenotype Plant Roots Polygenic Traits Reproduction Single Nucleotide Polymorphism Tests, Diagnostic

Curated GWAS Summary-Level Dataset

We cleaned and harmonized 963 publicly available GWAS summary-level datasets from 36 consortia, which included 82 diseases, 154 complex traits, 576 metabolites and 151 immune markers (Hemani et al, in preparation).
From this database pool, we chose datasets that fit the following selection criteria:

Non-sex-stratified

Meta-analyses of predominantly European populations. We include a few GWAS meta-analyses that contain a small proportion of non-European individuals in them in the LD Hub database. Whilst we believe the effect of these small numbers of non-European individuals on the LD Score regression analyses will be relatively minor, users should be aware that results from these meta-analyses may be less robust because of inconsistent patterns of linkage disequilibrium between individuals of different ancestry. In order to flag these studies to the user, we have included an additional field in the Test Center and the GWAShare Center (last column) that indicates the population ancestry of individuals in the corresponding meta-analysis, as well as a similar field in the LD Score regression results file (see also Table S1).

Meta-analyses using a GWAS backbone chip only (i.e. exclude meta-analyses involving immuno | metabo | psych | exome chip or GWAS + custom chip)

Number of SNPs is large (N > 450 000)

Number of individuals is large (N > 5000)

Mean Chi-square of the test statistics is larger than 1

As shown in Figure 2, after filtering on the selection criteria, genome-wide results for 173 traits were included in LD Hub, of which 18 are GWAS of diseases (Boraska et al., 2014 (link); Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013 (link); Lambert et al., 2013 (link); Liu et al 2015 (link); Moffatt et al., 2007 (link); Morris et al., 2012 (link); Neale et al., 2010 (link); Nikpay et al., 2015 (link); Okada et al., 2013 (link); Paternoster et al., 2015 (link); Ripke et al., 2012 (link); Ripke et al., 2014 (link); Simon-Sanchez et al., 2009 (link); Sklar et al., 2011 (link)), 48 are medically relevant risk factors/complex traits (Benyamin et al., 2013 (link); Berndt et al., 2013 (link); Bradfield et al., 2012 (link); Dastani et al., 2012 (link); de Moor et al., 2010 (link); Dupuis et al., 2010 (link); Estrada et al., 2012 (link); Furberg et al., 2010 (link); Horikoshi et al., 2012 (link); Huffman et al., 2015 (link); Lango Allen et al., 2010 (link); Manning et al. 2012 (link); Moffatt et al., 2007 (link); Pattaro et al., 2016 ; Perry et al., 2014 (link); Rietveld et al., 2014 (link); Rietveld et al., 2013 (link); Saxena et al., 2010 (link); Shungin et al., 2015 (link); Soranzo et al., 2010 (link); Speliotes et al., 2010 (link); Taal et al., 2012 (link); Teslovich et al., 2010 (link); Teumer et al., 2016 (link); van den Berg et al., 2014 (link); van der Valk et al., 2014 (link)) and 107 are metabolites (Kettunen et al., 2016 ). Table S1, displays descriptive information for each of the GWAS in LD Hub, including, trait name, consortium name, ethnicity, gender, sample size, PubMed ID, year of publication and other relevant information.

Zheng J., Erzurumluoglu A.M., Elsworth B.L., Kemp J.P., Howe L., Haycock P.C., Hemani G., Tansey K., Laurin C., Pourcain B.S., Warrington N.M., Finucane H.K., Price A.L., Bulik-Sullivan B.K., Anttila V., Paternoster L., Gaunt T.R., Evans D.M, & Neale B.M. (2016). LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics, 33(2), 272-279.

Publication 2016

DNA Chips Ethnicity Europeans Exome Gender Genome Genome-Wide Association Study Mental Disorders Polygenic Traits Single Nucleotide Polymorphism Vertebral Column

Curated GWAS Summary Data for LD Hub

We cleaned and harmonized 963 publicly available GWAS summary-level data sets from 36 consortia, which included 82 diseases, 154 complex traits, 576 metabolites and 151 immune markers (Hemani et al, in preparation).
From this database pool, we chose datasets that fit the following selection criteria:

Non-sex-stratified

Meta-analyses using a GWAS backbone chip only (i.e. exclude meta-analyses involving immuno | metabo | psych | exome chip or GWAS + custom chip)

Number of SNPs is large (N>450,000)

Number of individuals is large (N>5,000)

Mean Chi-square of the test statistics is larger than 1

As shown in Figure 2, after filtering on the selection criteria, genome-wide results for 173 traits were included in LD Hub, of which 18 are GWAS of diseases, 48 are medically relevant risk factors/complex traits and 107 are metabolites. Table S1, displays descriptive information for each of the GWAS in LD Hub, including, trait name, consortium name, ethnicity, gender, sample size, PubMed ID, year of publication, and other relevant information.

Publication 2016

DNA Chips Ethnicity Europeans Exome Gender Genome Genome-Wide Association Study Polygenic Traits Single Nucleotide Polymorphism Vertebral Column

Genetic Covariance and Correlation Analysis

Our tests relate to previously defined estimators of genetic correlation and covariance between traits. We consider two definitions of genetic covariance at a locus: 1) the covariance between the genetic component of expression and the genetic component of trait; 2) the covariance between the causal effect sizes for expression and the causal effect-sizes for trait. Under assumptions of independent effect-sizes, these definitions yield asymptotically identical quantities^{37 (link)}. Assuming a substantially large training set where the genetic component of expression can be perfectly predicted, the direct TWAS tests for a significant association between the genetic component of expression and the trait; equivalent to testing definition #1 for a polygenic trait. Likewise, the summary-based TWAS tests for a significant sum of products of the causal expression effect sizes and the causal trait effect sizes; equivalent to definition #2 up to a scaling factor. The TWAS approach therefore fits naturally with the broader study of shared genetic etiology of multiple phenotypes. At the sample sizes evaluated in this study, the TWAS approach is substantially better powered than an LD-based estimate of local genetic correlation (Supplementary Note).

Gusev A., Ko A., Shi H., Bhatia G., Chung W., Penninx B.W., Jansen R., de Geus E.J., Boomsma D.I., Wright F.A., Sullivan P.F., Nikkola E., Alvarez M., Civelek M., Lusis A.J., Lehtimäki T., Raitoharju E., Kähönen M., Seppälä I., Raitakari O.T., Kuusisto J., Laakso M., Price A.L., Pajukanta P, & Pasaniuc B. (2016). Integrative approaches for large-scale transcriptome-wide association studies. Nature genetics, 48(3), 245-252.

Publication 2016

Gene Components Multiple Birth Offspring Phenotype Polygenic Traits Reproduction Seizures

Most recents protocols related to «Polygenic Traits»

Genome-Wide Complex Trait Analysis

The intracellular MAP load at 2 h and 7 d p. i. and the expression of NO^-, EREG, Gal9, and C3 were the quantitative phenotypes analyzed. The variance components and h² explained by all the SNPs were calculated using the genome-wide complex trait analysis (GCTA) software 1.93.2, according to the following formula
where

σ_{G}^{2}

Is the variance explained by all the SNPs and

σ_{e}^{2}

is the residual variance (28 (link)).

Badia-Bringué G., Canive M, & Alonso-Hearn M. (2023). Control of Mycobacterium avium subsp. paratuberculosis load within infected bovine monocyte-derived macrophages is associated with host genetics. Frontiers in Immunology, 14, 1042638.

Publication 2023

EREG protein, human Genome LGALS9 protein, human MAP2 protein, human Phenotype Polygenic Traits Protoplasm Single Nucleotide Polymorphism

Genetic Profiling of Lipid Metabolism in ALS

As an exploratory analysis, we estimated PPS for biomarkers of lipid metabolism.^{29 (link)} The PPS estimates the sum of additive genetic effects across all alleles that affect the biomarkers of lipid metabolism at the patient level. We used the PPS to explore a potential genetic link between lipid metabolism, ALS, and survival time by assessing (1) how much of the variance in biomarker levels at diagnosis can be explained by genetic profile scores and (2) whether the genetic profile score itself is associated with overall survival time. Because PPS does not change over time,^{30 (link)} a statistical association between the genetic profile score and survival may be evidence of abnormal lipid levels caused by genetic variation or hold potential for therapeutic interventions.^{30 (link)} Moreover, their time invariance allowed us to estimate the link between the genetic profile score and overall survival time, defined as time between symptom onset and death.
For all individuals who were enrolled in both our population-based registry and our latest genome-wide association study (GWAS),^{5 (link)} we calculated the PPS. PPS was based on summary statistics from a GWAS on biomarker levels of lipid metabolism in the UK Biobank.³¹ For each single-nucleotide polymorphism, we calculated a weight for each biomarker using the summary-BavesR module in the Genome-Wide Complex Trait Bayesian analysis toolkit (default parameters)^{29 (link)} and a linkage-disequilibrium matrix originating from 50,000 unrelated individuals of inferred European ancestries included in the UK Biobank. Because the genotype data originated from several different cohorts in the ALS GWAS, we scaled the PPS per GWAS cohort to a mean of zero and a standard deviation of 1. Linear regression models were used to calculate how much of the variance in the biomarker level was explained by their PPS (expressed as adjusted R²); 95% confidence intervals were obtained by means of bootstrapping. Simple univariable Cox models for overall survival time (i.e., from onset to death) were used to estimate HRs.

Janse van Mantgem M.R., van Rheenen W., Hackeng A.V., van Es M.A., Veldink J.H., van den Berg L.H, & van Eijk R.P. (2023). Association Between Serum Lipids and Survival in Patients With Amyotrophic Lateral Sclerosis: A Meta-analysis and Population-Based Study. Neurology, 100(10), e1062-e1071.

Publication 2023

Alleles Biological Markers Diagnosis Europeans Genes, vif Genetic Diversity Genetic Profile Genome Genome-Wide Association Study Genotype Lipid A Lipid Metabolism Lipids Metabolism Patients Polygenic Traits Reproduction Single Nucleotide Polymorphism Therapeutics

Polygenic Risk Scores and C4 Expression

Polygenic risk scores (PRS) were calculated for EUR subjects. Genotype imputation was performed on the Michigan Imputation Server [69 (link)] using the TOPMed reference panel. Quality control included filtering for genotyping rate > 0.99, sample missingness < 0.01, Hardy-Weinberg Equilibrium P > 1 × 10⁻⁶, minor allele frequency > 1%, and imputation score > 0.8. GCTA v1.93.31 [70 (link)] was used to compute the genetic relationship matrix; a relatedness cutoff of 0.05 was applied. Genetic variants in the major histocompatibility complex (MHC) locus were excluded prior to PRS calculation. PRS were generated using summary statistics from the largest available genome-wide association study of schizophrenia [10 (link)] (40,675 cases, 64,643 controls) and adult brain surface area [71 ] (33,992 subjects) in PRS-CS [72 ], applying the LD reference panel from the 1000 Genomes Project phase 3 samples and the recommended global shrinkage parameter for highly polygenic traits (phi = 1e−2). Analyses assessing the interaction between C4 expression and PRS were restricted to youth with common C4 structural haplotypes (AL, AL-AL, AL-BL, AL-BS, BS), resulting in a final sample of 3730 EUR youth (female N = 1737; male N = 1993) and 7,715,663 genetic variants.

Hernandez L.M., Kim M., Zhang P., Bethlehem R.A., Hoftman G., Loughnan R., Smith D., Bookheimer S.Y., Fan C.C., Bearden C.E., Thompson W.K, & Gandal M.J. (2023). Multi-ancestry phenome-wide association of complement component 4 variation with psychiatric and brain phenotypes in youth. Genome Biology, 24, 42.

Publication 2023

Adult Brain Females Genetic Diversity Genome Genome-Wide Association Study Haplotypes Major Histocompatibility Complex Males Polygenic Traits Reproduction Schizophrenia Strains Youth

Pleiotropy of BCAAs-Associated SNPs

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Open the protocol to access the free full text link

Publication 2023

Genes Genome Genome-Wide Association Study Phenotype Polygenic Traits Single Nucleotide Polymorphism

Developing a Parkinson's Disease Genetics Browser

To facilitate investigations of nominated risk variants, members of IPDGC have created a PD GWAS locus browser (https://pdgenetics.shinyapps.io/GWASBrowser/) that makes relevant statistics and datasets available to the public^{30 (link)}. Throughout the hackathon, our team continued the development of this browser through the addition of new datasets and features. To identify secondary association signals at each locus from the Nalls et al. 2019 study, we performed conditional analysis using the Genome-wide Complex Trait Analysis (GCTA) tool^{31 (link),32 (link)}. Locus zoom plots were added to display the results of this conditional analysis (Fig. 4b)³³. Power calculations were done for each risk variant by Nalls et al. 2019 to determine if the findings were sufficiently powered. To do so, we followed methods used by the Genetic Association Study Power Calculator tool (https://csg.sph.umich.edu/abecasis/gas_power_calculator/), using summary statistics from Nalls et al. 2019, a disease prevalence of 0.01, and a significance level of 0.05 as input. We queried blood gene expression data included in the AMP PD version 2.5 release to measure expression levels in PD cases and controls. We obtained TPM expression at baseline for samples that had case or control status and no PD mutations in whole-genome sequencing data, leaving a total of 1710 samples. Expression data for each gene was displayed in a violin plot and added to the expression section of the browser (Fig. 4b). The literature section of the browser was updated to display a description, PubMed hit count, and word cloud plot for each gene within 1 MB of a PD risk variant. Our last addition to the browser was a display of user statistics. We used the googleAnalyticsR package³⁴ to record and visualize the number of visits for the browser and each risk variant within a period specified by the user (Fig. 4b).

Leonard H.L., Murtadha R., Martinez-Carrasco A., Jama A., Müller-Nedebock A.C., Gil-Martinez A.L., Illarionova A., Moore A., Bustos B.I., Jadhav B., Huxford B., Storm C., Towns C., Vitale D., Chetty D., Yu E., Grenn F.P., Salazar G., Rateau G., Iwaki H., Elsayed I., Foote I.F., Jansen van Rensburg Z., Kim J.J., Yuan J., Lake J., Brolin K., Senkevich K., Wu L., Tan M.M., Periñán M.T., Makarious M.B., Ta M., Pillay N.S., Betancor O.L., Reyes-Pérez P.R., Alvarez Jerez P., Saini P., al-Ouran R., Sivakumar R., Real R., Reynolds R.H., Hu R., Abrahams S., Rao S.C., Antar T., Leal T.P., Iankova V., Scotton W.J., Song Y., Singleton A., Nalls M.A., Dey S., Bandres-Ciga S., Blauwendraat C, & Noyce A.J. (2023). The IPDGC/GP2 Hackathon - an open science event for training in data science, genomics, and collaboration using Parkinson’s disease data. NPJ Parkinson's Disease, 9, 33.

Publication 2023

BLOOD Gene Expression Genes Genetic Association Studies Genome Genome-Wide Association Study Mutation Polygenic Traits

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What are Polygenic Traits?

How can PubCompare.ai help with Polygenic Trait research?

PubCompare.ai allows you to: 1. Screen protocol literature more efficiently. 2. Leverage AI to pinpoint Critical Insights. The platform's AI-driven analysis can help researchers identify the most effective protocols related to Polygenic Traits for their specific research goals, and highlight key differences in protocol effectiveness, enabling them to choose the best option for reproducibility and accuracy.

What are some common challenges in working with Polygenic Traits?

One of the main challenges in working with Polygenic Traits is the complex interplay between multiple genes and environmental factors. Accurately modeling these interactions and quantifying the contributions of different factors can be difficult. Additionally, Polygenic Traits often have a wide range of phenotypic variability, making it challenging to establish clear genotype-phenotype relationships.

Are there different types or variations of Polygenic Traits?

Yes, Polygenic Traits can vary in the number of genes involved, the strength of their individual effects, and the specific pathways and mechanisms through which they influence the phenotype. Some Polygenic Traits may be dominated by a few major-effect genes, while others may involve the cumulative contribution of many small-effect genes. The relative importance of genetic and environmental factors can also differ among Polygenic Traits.

What are some common applications of Polygenic Trait research?

Polygenic Trait research has a wide range of applications, including: 1) Understanding the genetic architecture of complex diseases and traits, 2) Developing more accurate risk prediction models for personalized medicine, 3) Identifying genetic targets for therapeutic intervention, and 4) Exploring the role of gene-environment interactions in shaping human phenotypes and health outcomes. Researchers can leverge PubCompare.ai to optimise their Polygenic Trait research protocls and enhance the reproducibility and accuracy of their findings.

More about "Polygenic Traits"

Polygenic traits are complex, multifactorial characteristics that are influenced by the combined effects of multiple genes, rather than a single gene.
These traits, which include common diseases and quantitative phenotypes, are affected by both genetic and environmental factors.
Researchers can leverage cutting-edge AI-driven research protocol optimization tools like PubCompare.ai to easily locate the best protocols and products across literature, preprints, and patents, enhancing reproducibility and driving their polygenic trait research forward.
Techniques like the BovineSNP50 BeadChip, Human610-Quad BeadChip, and Infinium HD ultra protocol can be used to genotype and analyze genetic variants associated with polygenic traits.
Statistical software such as SAS 9.4 and MATLAB can be employed to perform complex data analysis and modelling.
The Human1M and HumanExome BeadChip-12v1_A platforms provide high-density genome-wide coverage, while the GenomeStudio software and Infinium Global Screening Array enable comprehensive data visualization and interpretation.
Genome-Wide Human SNP Array 6.0 is another powerful tool that can be utilized to investigate the genetic architecture of polygenic traits.
By combining these cutting-edge technologies with PubCompare.ai's AI-driven research optimization, scientists can enhance the reproducibility and efficiency of their polygenic trait studies, ultimately accelerating our understanding of these complex, multifactorial characteristics.