scholarly journals Evaluating the estimation of genetic correlation and heritability using summary statistics

2021 ◽  
Author(s):  
Ju Zhang ◽  
Fredrick R. Schumacher
Keyword(s):  
Sample Size ◽  
Genetic Correlation ◽  
Reference Panel ◽  
Summary Statistics ◽  
External Reference ◽  
Size Number ◽  
Admixed Population ◽  
Statistical Approaches ◽  
Panel Summary ◽  
Number Of Individuals

AbstractWhile novel statistical methods quantifying the shared heritability of traits and diseases between ancestral distinct populations have been recently proposed, a thorough evaluation of these approaches under differing circumstances remain elusive. Brown et al.2016 proposed the method Popcorn to estimate the shared heritability, i.e. genetic correlation, using only summary statistics. Here, we evaluate Popcorn under several parameters and circumstances: sample size, number of SNPs, sample size of external reference panel, various population pairs, inappropriate external reference panel, and admixed population involved. Our results determined the minimum sample size of the external reference panel, summary statistics, and number of SNPs required to accurately estimate both the genetic correlation and heritability. Moreover, the number of individuals and SNPs required to produce accurate and stable estimates was directly proportional with heritability in Popcorn. Misrepresentation of the reference panel overestimated the genetic correlation by 20% and heritability by 60%. Lastly, applying Popcorn to homogeneous (EUR) and admixed (ASW) populations underestimated the genetic correlation by 15%. Although statistical approaches estimating the shared heritability between ancestral populations will provide novel etiologic insight, caution is required ensuring results are based on the appropriate sample size, number of SNPs, and the generalizability of the reference panel to the discovery populations.

STATISTICAL METHODS IN MEDICAL RESEARCH II. SAMPLE SIZES REQUIRED IN EXPERIMENTS INVOLVING ALL-OR-NONE RESPONSES

1953 ◽  
Vol 31 (5) ◽  
pp. 406-416 ◽  
Author(s):  
Donald Mainland ◽  
Marion I. Sutcliffe
Keyword(s):  
Medical Research ◽  
Sample Size ◽  
Statistical Methods ◽  
Treatment Effects ◽  
Reliable Estimate ◽  
Sample Sizes ◽  
Size Number ◽  
Different Types ◽  
Experimental Treatments ◽  
Number Of Individuals

In order to obtain a reliable estimate of the sample size (number of individuals) required in an experiment, an investigator must first specify: (1) what risk he is willing to run of mistakenly concluding that his different experimental treatments have produced different effects; (2) what the magnitude of the effect produced by one of the treatments is likely to be; (3) what magnitude of difference between treatment effects would be important to him; (4) what risk he is willing to run of failing to detect a difference when it is of that magnitude—the risk of an unsuccessful experiment. Difficulties in answering these questions are discussed with reference to five examples from different types of medical research. For experiments involving the comparison of two treatments that produce an all-or-none effect such as death or survival, a table derived from binomial expansions is presented, which, for samples containing up to 100 individuals, shows the probabilities of successful experiments for different magnitudes of treatment effects.

Sample size in studies of geographic variation

1981 ◽  
Vol 59 (11) ◽  
pp. 2158-2159
Author(s):  
K. W. Newman ◽  
R. C. Jancey
Keyword(s):  
Sample Size ◽  
Geographic Variation ◽  
Pinus Contorta ◽  
Objective Basis ◽  
Number Of Individuals

A procedure is described which provides an objective basis for selecting the number of individuals to sample at each location in studies of geographic variation within species. An example is given using cone data for Pinus contorta.

Influences on the Mantel-Haenszel Chi-Square in Detection of Differential Item Functioning under Rasch Conditions

1995 ◽  
Vol 80 (3_suppl) ◽  
pp. 1071-1074 ◽  
Author(s):  
Thomas Uttaro
Keyword(s):  
Sample Size ◽  
Differential Item Functioning ◽  
Item Bias ◽  
Reference Groups ◽  
Chi Square ◽  
Item Functioning ◽  
Size Number ◽  
Gender Based ◽  
And Gender ◽  
Present Simulation

The Mantel-Haenszel chi-square (χ2MH) is widely used to detect differential item functioning (item bias) between ethnic and gender-based subgroups on educational and psychological tests. The empirical behavior of χ2MH has been incompletely understood; previous research is inconclusive. The present simulation study explored the effects of sample size, number of items, and trait distributions on the power of χ2MH to detect modeled differential item functioning. A significant effect was obtained for sample size with unacceptably low power for 250 subjects each in the focal and reference groups. The discussion supports the 1990 recommendations of Swaminathan and Rogers, opposes the 1993 view of Zieky that a sample size of 250 for each group is adequate.

Minimum Numbers and Sample Size in Vertebrate Faunal Analysis

1978 ◽  
Vol 43 (1) ◽  
pp. 53-65 ◽  
Author(s):  
Donald K. Grayson
Keyword(s):  
Sample Size ◽  
Faunal Analysis ◽  
Minimum Number ◽  
Minimum Number Of Individuals ◽  
Minimum Numbers ◽  
Number Of Individuals ◽  
The Relationship

Of the measures currently available for quantifying the abundance of taxa within archaeological and paleontological vertebrate faunas, the minimum number of individuals per taxon is most frequently employed. This paper explores the relationship between the minimum number of individuals (MNI) calculated for a given taxon and the number of specimens (E) from which these values were calculated. Several approaches for controlling for the complex interrelationships between MNI and E are advanced and discussed.

scholarly journals Beyond SNP Heritability: Polygenicity and Discoverability of Phenotypes Estimated with a Univariate Gaussian Mixture Model

2017 ◽  
Author(s):  
Dominic Holland ◽  
Oleksandr Frei ◽  
Rahul Desikan ◽  
Chun-Chieh Fan ◽  
Alexey A. Shadrin ◽  
...  
Keyword(s):  
Association Studies ◽  
Causal Snps ◽  
Reference Panel ◽  
Causal Effects ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Common Variants ◽  
Genome Wide ◽  
Causal Variants

AbstractEstimating the polygenicity (proportion of causally associated single nucleotide polymorphisms (SNPs)) and discoverability (effect size variance) of causal SNPs for human traits is currently of considerable interest. SNP-heritability is proportional to the product of these quantities. We present a basic model, using detailed linkage disequilibrium structure from an extensive reference panel, to estimate these quantities from genome-wide association studies (GWAS) summary statistics. We apply the model to diverse phenotypes and validate the implementation with simulations. We find model polygenicities ranging from ≃ 2 × 10−5to ≃ 4 × 10−3, with discoverabilities similarly ranging over two orders of magnitude. A power analysis allows us to estimate the proportions of phenotypic variance explained additively by causal SNPs reaching genome-wide significance at current sample sizes, and map out sample sizes required to explain larger portions of additive SNP heritability. The model also allows for estimating residual inflation (or deflation from over-correcting of z-scores), and assessing compatibility of replication and discovery GWAS summary statistics.Author SummaryThere are ~10 million common variants in the genome of humans with European ancestry. For any particular phenotype a number of these variants will have some causal effect. It is of great interest to be able to quantify the number of these causal variants and the strength of their effect on the phenotype.Genome wide association studies (GWAS) produce very noisy summary statistics for the association between subsets of common variants and phenotypes. For any phenotype, these statistics collectively are difficult to interpret, but buried within them is the true landscape of causal effects. In this work, we posit a probability distribution for the causal effects, and assess its validity using simulations. Using a detailed reference panel of ~11 million common variants – among which only a small fraction are likely to be causal, but allowing for non-causal variants to show an association with the phenotype due to correlation with causal variants – we implement an exact procedure for estimating the number of causal variants and their mean strength of association with the phenotype. We find that, across different phenotypes, both these quantities – whose product allows for lower bound estimates of heritability – vary by orders of magnitude.

scholarly journals Comparison of methods for estimating genetic correlation between complex traits using GWAS summary statistics

2020 ◽  
Author(s):  
Yiliang Zhang ◽  
Youshu Cheng ◽  
Wei Jiang ◽  
Yixuan Ye ◽  
Qiongshi Lu ◽  
...  
Keyword(s):  
Genetic Correlation ◽  
Complex Traits ◽  
Association Studies ◽  
Genetic Correlations ◽  
Real Data ◽  
Estimation Methods ◽  
Easy Access ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Correlation Estimation

AbstractGenetic correlation is the correlation of additive genetic effects on two phenotypes. It is an informative metric to quantify the overall genetic similarity between complex traits, which provides insights into their polygenic genetic architecture. Several methods have been proposed to estimate genetic correlations based on data collected from genome-wide association studies (GWAS). Due to the easy access of GWAS summary statistics and computational efficiency, methods only requiring GWAS summary statistics as input have become more popular than methods utilizing individual-level genotype data. Here, we present a benchmark study for different summary-statistics-based genetic correlation estimation methods through simulation and real data applications. We focus on two major technical challenges in estimating genetic correlation: marker dependency caused by linkage disequilibrium (LD) and sample overlap between different studies. To assess the performance of different methods in the presence of these two challenges, we first conducted comprehensive simulations with diverse LD patterns and sample overlaps. Then we applied these methods to real GWAS summary statistics for a wide spectrum of complex traits. Based on these experiments, we conclude that methods relying on accurate LD estimation are less robust in real data applications compared to other methods due to the imprecision of LD obtained from reference panels. Our findings offer a guidance on how to appropriately choose the method for genetic correlation estimation in post-GWAS analysis in interpretation.

scholarly journals Assessing Causal Relationship Between Human Blood Metabolites and Five Neurodegenerative Diseases With GWAS Summary Statistics

2021 ◽  
Vol 15 ◽  
Author(s):  
Haimiao Chen ◽  
Jiahao Qiao ◽  
Ting Wang ◽  
Zhonghe Shao ◽  
Shuiping Huang ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Genetic Correlation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Metabolic Pathway Analysis ◽  
Genome Wide ◽  
Maximum Likelihood Methods ◽  
Weighted Median ◽  
The Relationship

Background: Neurodegenerative diseases (NDDs) are the leading cause of disability worldwide while their metabolic pathogenesis is unclear. Genome-wide association studies (GWASs) offer an unprecedented opportunity to untangle the relationship between metabolites and NDDs.Methods: By leveraging two-sample Mendelian randomization (MR) approaches and relying on GWASs summary statistics, we here explore the causal association between 486 metabolites and five NDDs including Alzheimer’s Disease (AD), amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), Parkinson’s disease (PD), and multiple sclerosis (MS). We validated our MR results with extensive sensitive analyses including MR-PRESSO and MR-Egger regression. We also performed linkage disequilibrium score regression (LDSC) and colocalization analyses to distinguish causal metabolite-NDD associations from genetic correlation and LD confounding of shared causal genetic variants. Finally, a metabolic pathway analysis was further conducted to identify potential metabolite pathways.Results: We detected 164 metabolites which were suggestively associated with the risk of NDDs. Particularly, 2-methoxyacetaminophen sulfate substantially affected ALS (OR = 0.971, 95%CIs: 0.961 ∼ 0.982, FDR = 1.04E-4) and FTD (OR = 0.924, 95%CIs: 0.885 ∼ 0.964, FDR = 0.048), and X-11529 (OR = 1.604, 95%CIs: 1.250 ∼ 2.059, FDR = 0.048) and X-13429 (OR = 2.284, 95%CIs: 1.457 ∼ 3.581, FDR = 0.048) significantly impacted FTD. These associations were further confirmed by the weighted median and maximum likelihood methods, with MR-PRESSO and the MR-Egger regression removing the possibility of pleiotropy. We also observed that ALS or FTD can alter the metabolite levels, including ALS and FTD on 2-methoxyacetaminophen sulfate. The LDSC and colocalization analyses showed that none of the identified associations could be driven by genetic correlation or confounding by LD with common causal loci. Multiple metabolic pathways were found to be involved in NDDs, such as “urea cycle” (P = 0.036), “arginine biosynthesis” (P = 0.004) on AD and “phenylalanine, tyrosine and tryptophan biosynthesis” (P = 0.046) on ALS.Conclusion: our study reveals robust bidirectional causal associations between servaral metabolites and neurodegenerative diseases, and provides a novel insight into metabolic mechanism for pathogenesis and therapeutic strategies of these diseases.

scholarly journals Estimating Heritability and Genetic Correlation in Case Control Studies Directly and with Summary Statistics

2018 ◽  
Author(s):  
Omer Weissbrod ◽  
Jonathan Flint ◽  
Saharon Rosset
Keyword(s):  
Genetic Correlation ◽  
Association Studies ◽  
Genetic Correlations ◽  
Large Data ◽  
Case Control ◽  
Data Sets ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Case Control Studies ◽  
Individual Level

AbstractMethods that estimate heritability and genetic correlations from genome-wide association studies have proven to be powerful tools for investigating the genetic architecture of common diseases and exposing unexpected relationships between disorders. Many relevant studies employ a case-control design, yet most methods are primarily geared towards analyzing quantitative traits. Here we investigate the validity of three common methods for estimating genetic heritability and genetic correlation. We find that the Phenotype-Correlation-Genotype-Correlation (PCGC) approach is the only method that can estimate both quantities accurately in the presence of important non-genetic risk factors, such as age and sex. We extend PCGC to work with summary statistics that take the case-control sampling into account, and demonstrate that our new method, PCGC-s, accurately estimates both heritability and genetic correlations and can be applied to large data sets without requiring individual-level genotypic or phenotypic information. Finally, we use PCGC-S to estimate the genetic correlation between schizophrenia and bipolar disorder, and demonstrate that previous estimates are biased due to incorrect handling of sex as a strong risk factor. PCGC-s is available at https://github.com/omerwe/PCGCs.

scholarly journals Improved imputation of summary statistics for admixed populations

2017 ◽  
Author(s):  
Sina Rüeger ◽  
Aaron McDaid ◽  
Zoltán Kutalik
Keyword(s):  
Meta Analysis ◽  
Country Of Origin ◽  
Real Data ◽  
Genotype Imputation ◽  
Reference Panel ◽  
Summary Statistics ◽  
Panel Size ◽  
Noticeable Improvement

AbstractMotivationSummary statistics imputation can be used to infer association summary statistics of an already conducted, genotype-based meta-analysis to higher ge-nomic resolution. This is typically needed when genotype imputation is not feasible for some cohorts. Oftentimes, cohorts of such a meta-analysis are variable in terms of (country of) origin or ancestry. This violates the assumption of current methods that an external LD matrix and the covariance of the Z-statistics are identical.ResultsTo address this issue, we present variance matching, an extention to the existing summary statistics imputation method, which manipulates the LD matrix needed for summary statistics imputation. Based on simulations using real data we find that accounting for ancestry admixture yields noticeable improvement only when the total reference panel size is > 1000. We show that for population specific variants this effect is more pronounced with increasing FST.

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