scholarly journals Detecting Association of Rare Variants by Testing an Optimally Weighted Combination of Variants for Quantitative Traits in General Families

2013 ◽  
Vol 77 (6) ◽  
pp. 524-534 ◽  
Author(s):  
Shurong Fang ◽  
Shuanglin Zhang ◽  
Qiuying Sha
Keyword(s):  
Quantitative Traits ◽  
Rare Variants ◽  
Weighted Combination

scholarly journals Testing an optimally weighted combination of common and/or rare variants with multiple traits

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0201186 ◽  
Author(s):  
Zhenchuan Wang ◽  
Qiuying Sha ◽  
Shurong Fang ◽  
Kui Zhang ◽  
Shuanglin Zhang
Keyword(s):  
Rare Variants ◽  
Multiple Traits ◽  
Weighted Combination

A Powerful Approach to Test an Optimally Weighted Combination of Rare Variants in Admixed Populations

2015 ◽  
Vol 39 (4) ◽  
pp. 294-305 ◽  
Author(s):  
Xuexia Wang ◽  
Shuanglin Zhang ◽  
Yun Li ◽  
Mingyao Li ◽  
Qiuying Sha
Keyword(s):  
Rare Variants ◽  
Powerful Approach ◽  
Weighted Combination

scholarly journals Detecting Rare Variants for Quantitative Traits Using Nuclear Families

2012 ◽  
Vol 73 (3) ◽  
pp. 148-158 ◽  
Author(s):  
Wei Guo ◽  
Yin Yao Shugart
Keyword(s):  
Quantitative Traits ◽  
Rare Variants ◽  
Nuclear Families

scholarly journals A Flexible Approach for the Analysis of Rare Variants Allowing for a Mixture of Effects on Binary or Quantitative Traits

PLoS Genetics ◽  
2013 ◽  
Vol 9 (8) ◽  
pp. e1003694 ◽  
Author(s):  
Geraldine M. Clarke ◽  
Manuel A. Rivas ◽  
Andrew P. Morris
Keyword(s):  
Quantitative Traits ◽  
Rare Variants ◽  
Flexible Approach

scholarly journals Exome sequencing identifies high-impact trait-associated alleles enriched in Finns

2018 ◽  
Author(s):  
Adam E Locke ◽  
Karyn Meltz Steinberg ◽  
Charleston WK Chiang ◽  
Susan K Service ◽  
Aki S Havulinna ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Quantitative Traits ◽  
Rare Variants ◽  
Mendelian Disease ◽  
Finnish Population ◽  
Isolated Populations ◽  
Deleterious Alleles ◽  
Disease Mutations ◽  
Sequencing Studies ◽  
Novel Associations

ABSTRACTAs yet undiscovered rare variants are hypothesized to substantially influence an individual’s risk for common diseases and traits, but sequencing studies aiming to identify such variants have generally been underpowered. In isolated populations that have expanded rapidly after a population bottleneck, deleterious alleles that passed through the bottleneck may be maintained at much higher frequencies than in other populations. In an exome sequencing study of nearly 20,000 cohort participants from northern and eastern Finnish populations that exemplify this phenomenon, most novel trait-associated deleterious variants are seen only in Finland or display frequencies more than 20 times higher than in other European populations. These enriched alleles underlie 34 novel associations with 21 disease-related quantitative traits and demonstrate a geographical clustering equivalent to that of Mendelian disease mutations characteristic of the Finnish population. Sequencing studies in populations without this unique history would require hundreds of thousands to millions of participants for comparable power for these variants.

scholarly journals Testing an Optimally Weighted Combination of Common and/or Rare Variants with Multiple Traits

2018 ◽  
Author(s):  
Zhenchuan Wang ◽  
Qiuying Sha ◽  
Kui Zhang ◽  
Shuanglin Zhang
Keyword(s):  
Statistical Power ◽  
Rare Variants ◽  
Association Studies ◽  
Error Rates ◽  
Common Variant ◽  
Joint Analysis ◽  
Type I ◽  
Multiple Traits ◽  
Multiple Sequence ◽  
Weighted Combination

AbstractJoint analysis of multiple traits has recently become popular since it can increase statistical power to detect genetic variants and there is increasing evidence showing that pleiotropy is a widespread phenomenon in complex diseases. Currently, most of existing methods test the association between multiple traits and a single common variant. However, the variant-by-variant methods for common variant association studies may not be optimal for rare variant association studies due to the allelic heterogeneity as well as the extreme rarity of individual variants. In this article, we developed a statistical method by testing an optimally weighted combination of variants with multiple traits (TOWmuT) to test the association between multiple traits and a weighted combination of variants (rare and/or common) in a genomic region. TOWmuT is robust to the directions of effects of causal variants and is applicable to different types of traits. Using extensive simulation studies, we compared the performance of TOWmuT with the following five existing methods: gene association with multiple traits (GAMuT), multiple sequence kernel association test (MSKAT), adaptive weighting reverse regression (AWRR), single-TOW, and MANOVA. Our results showed that, in all of the simulation scenarios, TOWmuT has correct type I error rates and is consistently more powerful than the other five tests. We also illustrated the usefulness of TOWmuT by analyzing a whole-genome genotyping data from a lung function study.

scholarly journals A General Statistic to Test an Optimally Weighted Combination of Common and/or Rare Variants

2019 ◽  
Author(s):  
Jianjun Zhang ◽  
Baolin Wu ◽  
Qiuying Sha ◽  
Shuanglin Zhang ◽  
Xuexia Wang
Keyword(s):  
Rare Variants ◽  
Association Studies ◽  
Genome Wide Association ◽  
P Value ◽  
Type I ◽  
Genome Wide Association Studies ◽  
Simulation Studies ◽  
Genome Wide ◽  
Combination Scheme ◽  
Weighted Combination

AbstractBoth genome-wide association study and next generation sequencing data analyses are widely employed in order to identify disease susceptible common and/or rare genetic variants in many large scale genetic studies. Rare variants generally have large effects though they are hard to detect due to their low frequency. Currently, many existing statistical methods for rare variants association studies employ a weighted combination scheme, which usually puts subjective weights or suboptimal weights based on some ad hoc assumptions (e.g. ignoring dependence between rare variants). In this study, we analytically derive optimal weights for both common and rare variants and propose a General and novel approach to Test association between an Optimally Weighted combination of variants (G-TOW) in a gene or pathway for a continuous or dichotomous trait while easily adjusting for covariates. We conduct extensive simulation studies to evaluate the performance of G-TOW. Results of the simulation studies show that G-TOW has properly controlled type I error rates and it is the most powerful test among the methods we compared, when testing effects of either both rare and common variants or rare variants only. We also illustrate the effectiveness of G-TOW using the Genetic Analysis Workshop 17 (GAW17) data. In addition, we applied G-TOW and other competitive methods to test association for schizophrenia. The G-TOW have successfully verified genes FYN and VPS39 which are associated with schizophrenia reported in existing publications. Both of these genes are missed by the weighted sum statistic (WSS) and the sequence kernel association test (SKAT). G-TOW also showed much stronger significance (p-value=0.0037) than our previously developed method named Testing the effect of an Optimally Weighted combination of variants (TOW) (p-value=0.0143) on gene FYN. FYN is a member of the protein-tyrosine kinase oncogene family that phosphorylates glutamate metabotropic receptors and ionotropic N-methyl-d-aspartate (NMDA) receptors. NMDA modulates trafficking, subcellular distribution and function. It is involved in neuronal apoptosis, brain development and synaptic transmission and lower expression, which has been observed in the platelets of schizophrenic patients compared with controls. The application for schizophrenia indicates that G-TOW is a powerful tool in genome-wide association studies.

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