A strategy to discover genes that carry multi-allelic or mono-allelic risk for common diseases: A cohort allelic sums test (CAST)
Open accessAbstract
A method is described to discover if a gene carries one or more allelic mutations that confer risk for any specified common disease. The method does not depend upon genetic linkage of risk-conferring mutations to high frequency genetic markers such as single nucleotide polymorphisms. Instead, the sums of allelic mutation frequencies in case and control cohorts are determined and a statistical test is applied to discover if the difference in these sums is greater than would be expected by chance. A statistical model is presented that defines the ability of such tests to detect significant gene-disease relationships as a function of case and control cohort sizes and key confounding variables: zygosity and genicity, environmental risk factors, errors in diagnosis, limits to mutant detection, linkage of neutral and risk-conferring mutations, ethnic diversity in the general population and the expectation that among all exonic mutants in the human genome greater than 90% will be neutral with regard to any effect on disease risk. Means to test the null hypothesis for, and determine the statistical power of, each test are provided. For this "cohort allelic sums test" or "CAST", the statistical model and test are provided as an Excel program, CASTAT(c) at . Based on genetics, technology and statistics, a strategy of enumerating the mutant alleles carried in the exons and splice sites of the estimated approximately 25,000 human genes in case cohort samples of 10,000 persons for each of 100 common diseases is proposed and evaluated: A wide range of possible conditions of multi-allelic or mono-allelic and monogenic, multigenic or polygenic (including epistatic) risk are found to be detectable using the statistical criteria of 1 or 10 "false positive" gene associations approximately 25,000 gene-disease pair-wise trials and a statistical power of >0.8. Using estimates of the distribution of both neutral and gene-inactivating nondeleterious mutations in humans and the sensitivity of the test to multigenic or multicausal risk, it is estimated that about 80% of nullizygous, heterozygous and functionally dominant gene-common disease associations may be discovered. Limitations include relative insensitivity of CAST to about 60% of possible associations given homozygous (wild type) risk and, more rarely, other stochastic limits when the frequency of mutations in the case cohort approaches that of the control cohort and biases such as absence of genetic risk masked by risk derived from a shared cultural environment.
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Collapsing sets of rare variants into a single group and testing their collective frequency differences can overcome the power issues associated with testing rare variants individually.
“In order to overcome power issues associated with testing rare variants individually, one could ‘collapse’ sets of rare variants into a single group and test their collective frequency differences between cases and controls.28, 30”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [30]
The CAST method as first proposed does not easily accommodate covariates, cannot be used with quantitative phenotypes, and does not consider weighting of variants using variant frequency or functional annotations.
“The method as first proposed does not easily accommodate covariates, cannot be used with quantitative phenotypes, and does not consider weighting of the variants using, for example, variant frequency or functional annotations.”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [30]
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MethodInfluentialA Unified Mixed‐Effects Model for Rare‐Variant Association in Sequencing Studies
Sun, J., Zheng, Y., & Hsu, L. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21717 · Cited by 140 · see citing works
“These tests, some-times called burden tests [Li and Leal, 2008; Morgenthaler and Thilly, 2007], are motivated by population genetics evolutionary theory that most rare missense alleles are deleterious, and the effect is therefore generally considered one-sided [Kryukov et al., 2007].”
BackgroundComputational and statistical approaches to analyzing variants identified by exome sequencing
Stitziel, N. O., Kieżun, A., & Sunyaev, S. R. (2011) · Genome biology · doi:10.1186/gb-2011-12-9-227 · Cited by 137 · see citing works
Detecting Rare Variant Effects Using Extreme Phenotype Sampling in Sequencing Association Studies
Barnett, I., Lee, S., & Lin, X. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21699 · Cited by 136 · see citing works
Liu, L., Sabo, A., Neale, B. M., Nagaswamy, U., Stevens, C., Lim, E., Bodea, C. A., Muzny, D., Reid, J. G., Banks, E., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003443 · Cited by 135 · see citing works
Scouarnec, S. L., Karakachoff, M., Gourraud, J., Lindenbaum, P., Bonnaud, S., Portero, V., Duboscq-Bidot, L., Daumy, X., Simonet, F., Teusan, R., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv036 · Cited by 136 · see citing works
Comprehensive Approach to Analyzing Rare Genetic Variants
Hoffmann, T. J., Marini, N. J., & Witte, J. S. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013584 · Cited by 132 · see citing works
“An alternative is to combine rare variants together into groups in a reasonable manner so they can be efficiently analyzed. Note that when we use “efficient” in this manuscript, we will always be referring to statistical power; computational time will be referred to as runtime. One might simply tabulate in cases and c…”
BackgroundMapping Rare and Common Causal Alleles for Complex Human Diseases
Raychaudhuri, S. (2011) · Cell · doi:10.1016/j.cell.2011.09.011 · Cited by 132 · see citing works
“The idea of comparing the proportion of case individuals with rare alleles to control individuals with rare alleles was formalized into a statistical test, the ‘‘Cohort Allelic Sums Test’’ (CAST) (Morgenthaler and Thilly, 2007).”
MethodRare pathogenic variants predispose to hepatocellular carcinoma in nonalcoholic fatty liver disease
Pelusi, S., Baselli, G., Pietrelli, A., Dongiovanni, P., Donati, B., McCain, M. V., Meroni, M., Fracanzani, A. L., Romagnoli, R., Petta, S., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-39998-2 · Cited by 125 · see citing works
Chen, H., Huffman, J. E., Brody, J. A., Wang, C., Lee, S., Li, Z., Gogarten, S. M., Sofer, T., Bielak, L. F., Bis, J. C., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.12.012 · Cited by 112 · see citing works
Guey, L. T., Kravić, J., Melander, O., Burtt, N. P., Laramie, J. M., Lyssenko, V., Jonsson, A. E., Lindholm, E., Tuomilehto, J., Isomaa, B., et al. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20572 · Cited by 113 · see citing works
Human gene copy number spectra analysis in congenital heart malformations
Tomita‐Mitchell, A., Mahnke, D. K., Struble, C. A., Tuffnell, M. E., Stamm, K., Hidestrand, M., Harris, S. E., Goetsch, M. A., Simpson, P. M., Bick, D. P., et al. (2012) · Physiological Genomics · doi:10.1152/physiolgenomics.00013.2012 · Cited by 105 · see citing works
Pathogenic Anti-Müllerian Hormone Variants in Polycystic Ovary Syndrome
Gorsic, L. K., Kosova, G., Werstein, B., Sisk, R. K., Legro, R. S., Hayes, M. G., Teixeira, J. M., Dunaif, A. E., & Urbanek, M. (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-00612 · Cited by 103 · see citing works
Derkach, A., Lawless, J. F., & Sun, L. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21689 · Cited by 100 · see citing works
Rare Variant Analysis for Family-Based Design
De, G., Yip, W., Ionita‐Laza, I., & Laird, N. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0048495 · Cited by 103 · see citing works
Identification of deleterious synonymous variants in human genomes
Buske, O. J., Manickaraj, A. K., Mital, S. R., Ray, P. N., & Brudno, M. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt308 · Cited by 94 · see citing works
“Approaches like the Cohort Allelic Sums Test (CAST) (Morgenthaler and Thilly, 2007) and Combined Multivariate and Collapsing (CMC) method (Li and Leal, 2008) aggregate the rare variants seen within a gene or a pathway to mitigate this, but the applicability of association-based methods remains extremely limited for sm…”
MethodSchaid, D. J., McDonnell, S. K., Sinnwell, J. P., & Thibodeau, S. N. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21727 · Cited by 92 · see citing works
Gorsic, L. K., Dapas, M., Legro, R. S., Hayes, M. G., & Urbanek, M. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-02178 · Cited by 89 · see citing works
A Burden of Rare Variants Associated with Extremes of Gene Expression in Human Peripheral Blood
Zhao, J., Akinsheye, I., Arafat, D., Cradick, T. J., Lee, C. M., Banskota, S., Marigorta, U. M., Bao, G., & Gibson, G. C. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.12.023 · Cited by 81 · see citing works
In search of low-frequency and rare variants affecting complex traits
Panoutsopoulou, K., Tachmazidou, I., & Zeggini, E. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt376 · Cited by 87 · see citing works
“A plethora of such locus-specific statistical approaches have been developed and fall broadly into a few categories: collapsing methods based on summary statistics (Cohort Allelic Sum Test (41); Combined Multivariate and Collapsing Test (42); Weighted Sum Test (43); Variable-Threshold Approach (44)); methods based on…”
MethodAlmlöf, J. C., Nystedt, S., Leonard, D., Eloranta, M., Grosso, G., Sjöwall, C., Bengtsson, A. A., Jönsen, A., Gunnarsson, I., Svenungsson, E., et al. (2019) · Human Genetics · doi:10.1007/s00439-018-01966-7 · Cited by 87 · see citing works
Pooled Association Tests for Rare Genetic Variants: A Review and Some New Results
Derkach, A., Lawless, J. F., & Sun, L. (2014) · Statistical Science · doi:10.1214/13-sts456 · Cited by 78 · see citing works
“Basu and Pan (2011) provided a review, and we note two important cases: Morgenthaler and Thilly (2007) considered the “cohort allelic sums test” (CAST) where each wj = 1, and Madsen and Browning (2009) based wj on the (estimated) MAF, with larger weights for SNPs with smaller MAF.”
BackgroundInfluentialFunctional Linear Models for Association Analysis of Quantitative Traits
Fan, R., Wang, Y., Mills, J. L., Wilson, A. F., Wilson, J., & Xiong, M. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21757 · Cited by 76 · see citing works
Tzeng, J., Zhang, D., Pongpanich, M., Smith, C., McCarthy, M. I., Sale, M. M., Worrall, B. B., Hsu, F., Thomas, D. C., & Sullivan, P. F. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.07.007 · Cited by 79 · see citing works
UK Biobank Whole-Exome Sequence Binary Phenome Analysis with Robust Region-Based Rare-Variant Test
Zhao, Z., Bi, W., Zhou, W., VandeHaar, P., Fritsche, L. G., & Lee, S. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.11.012 · Cited by 77 · see citing works
Test for Rare Variants by Environment Interactions in Sequencing Association Studies
Lin, X., Lee, S., Wu, M. C., Wang, C., Chen, H., Li, Z., & Lin, X. (2015) · Biometrics · doi:10.1111/biom.12368 · Cited by 71 · see citing works
“Specifically, the fourth method (CAST) is an extension of the cohort allelic sum test (Morgenthaler and Thilly, 2007), where the summary genetic variable is an indicator function of whether or not there is any rare variant within the region.”
Goldfeder, R. L., Wall, D. P., Khoury, M. J., Ioannidis, J. P. A., & Ashley, E. A. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kww224 · Cited by 75 · see citing works
Sha, Q., Wang, X., Wang, X., & Zhang, S. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21649 · Cited by 71 · see citing works
“Recently, several statistical methods for detecting associations of rare variants have been developed, including the cohort allelic sums test (CAST) [Morgenthaler and Thilly, 2007], the combined multivariate and collapsing (CMC) method [Li and Leal, 2008], the weighted sum statistic (WSS) [Madsen…”
MethodTUBB1 mutations cause thyroid dysgenesis associated with abnormal platelet physiology
Stoupa, A., Adam, F., Kariyawasam, D., Strassel, C., Gawade, S., Szinnai, G., Kauskot, A., Lasne, D., Janke, C., Natarajan, K., et al. (2018) · EMBO Molecular Medicine · doi:10.15252/emmm.201809569 · Cited by 71 · see citing works
“Rare variant burden testing was performed for the TUBB1 gene using the CAST collapsing method (Morgenthaler & Thilly, 2007) in 270 patients with TD, including 193 with ectopia, hemithyroid or hypoplasia and 77 with athyreosis.”
MethodThe contribution of rare and common variants in 30 genes to risk nicotine dependence
Yang, J., Wang, S., Yang, Z., Hodgkinson, C. A., Iarikova, P., Ma, J. Z., Payne, T. J., Goldman, D. A., & Li, M. D. (2014) · Molecular Psychiatry · doi:10.1038/mp.2014.156 · Cited by 68 · see citing works
Sequence Kernel Association Test for Survival Traits
Chen, H., Lumley, T., Brody, J. A., Heard‐Costa, N. L., Fox, C. S., Cupples, L. A., & Dupuis, J. (2014) · Genetic Epidemiology · doi:10.1002/gepi.21791 · Cited by 64 · see citing works
Rao, D. C., Sung, Y. J., Winkler, T. W., Schwander, K., Borecki, I. B., Cupples, L. A., Gauderman, W. J., Rice, K. M., Munroe, P. B., & Psaty, B. M. (2017) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.116.001649 · Cited by 61 · see citing works
Li, Z., Li, X., Liu, Y., Shen, J., Chen, H., Zhou, H., Morrison, A. C., Boerwinkle, E., & Lin, X. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.03.002 · Cited by 59 · see citing works
Feng, T., Elston, R. C., & Zhu, X. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20588 · Cited by 61 · see citing works
Epstein, M. P., Duncan, R., Jiang, Y., Conneely, K. N., Allen, A. S., & Satten, G. A. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.06.004 · Cited by 61 · see citing works
Wang, Q., Lu, Q., & Zhao, H. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00149 · Cited by 57 · see citing works
“For example, gene SH3TC2 was found to contain causative alleles for Charcot-Marie-Tooth disease using WGS on one patient (Lupski et al., 2010); WES of four patients identified a causal gene OHODH for Miller syndrome (Ng et al., 2010).”
BackgroundMethodLiu, H., Leslie, E. J., Jia, Z., Smith, T., Eshete, M. A., Butali, A., Dunnwald, M., Murray, J. C., & Cornell, R. A. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv614 · Cited by 58 · see citing works
“Next we employed two burden tests: the cohort allelic sums test (CAST) (43) and the variable threshold (VT) method (44).”
MethodInfluentialMomozawa, Y., Akiyama, M., Kamatani, Y., Arakawa, S., Yasuda, M., Yoshida, S., Oshima, Y., Mori, R., Tanaka, K., Mori, K., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw335 · Cited by 57 · see citing works
Robust Rare Variant Association Testing for Quantitative Traits in Samples With Related Individuals
Jiang, D., & McPeek, M. S. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21775 · Cited by 56 · see citing works
Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models
Wang, Y., Liu, A., Mills, J. L., Boehnke, M., Wilson, A. F., Wilson, J., Xiong, M., Wu, C. O., & Fan, R. (2015) · Genetic Epidemiology · doi:10.1002/gepi.21895 · Cited by 56 · see citing works
Next generation analytic tools for large scale genetic epidemiology studies of complex diseases
Mechanic, L. E., Chen, H., Amos, C. I., Chatterjee, N., Cox, N. J., Divi, R. L., Fan, R., Harris, E. L., Jacobs, K. B., Kraft, P. L., et al. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20652 · Cited by 56 · see citing works
Advances in the Genetics of Hypertension: The Effect of Rare Variants
RUSSO, A. E., Gaetano, C. D., Cugliari, G., & Matullo, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19030688 · Cited by 57 · see citing works
Adaptive tests for association analysis of rare variants
Pan, W., & Shen, X. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20586 · Cited by 55 · see citing works
“Our proposed tests are also general and flexible with the ability to incorporate weights on RVs and to adjust for covariates.”
Uricchio, L. H., Zaitlen, N., Ye, C., Witte, J. S., & Hernandez, R. D. (2016) · Genome Research · doi:10.1101/gr.202440.115 · Cited by 54 · see citing works
An Evolutionary Framework for Association Testing in Resequencing Studies
King, C. R., Rathouz, P. J., & Nicolae, D. L. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001202 · Cited by 53 · see citing works
“To compare power with existing methods, we included several proposed methods of analysis. First, we test the method of Bonferroni corrected minimum p-value of SNPs with minor allele frequency >1% or >5%. Other proposed methods using allele counts like CAST”
MethodDiscovery of rare variants for complex phenotypes
Kosmicki, J. A., Churchhouse, C. L., Rivas, M. A., & Neale, B. M. (2016) · Human Genetics · doi:10.1007/s00439-016-1679-1 · Cited by 52 · see citing works
“Burden tests function by comparing the number or burden of variants in cases and controls, and are the most straightforward of the gene-based tests (Li and Leal 2008; Madsen and Browning 2009; Asimit et al. 2012; Morgenthaler and Thilly 2007; Morris and Zeggini 2010).”
BackgroundSequence Kernel Association Test of Multiple Continuous Phenotypes
Wu, B., & Pankow, J. S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21945 · Cited by 53 · see citing works
Rare Coding Variants in ANGPTL6 Are Associated with Familial Forms of Intracranial Aneurysm
Bourcier, R., Scouarnec, S. L., Bonnaud, S., Karakachoff, M., Bourcereau, E., Heurtebise-Chrétien, S., Baron‐Menguy, C., Christian Dina, C., Simonet, F., Moles, A., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.12.006 · Cited by 52 · see citing works
“Burden test was performed using SKAT (22) and CAST (23), by comparing the proportion of individuals carrying at least one rare coding”
Association Tests for Rare Variants
Nicolae, D. L. (2016) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083115-022609 · Cited by 50 · see citing works
Gene- and pathway-based association tests for multiple traits with GWAS summary statistics
Kwak, I., & Pan, W. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw577 · Cited by 48 · see citing works
Meta-analysis for Discovering Rare-Variant Associations: Statistical Methods and Software Programs
Tang, Z.-Z., & Lin, D.-Y. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.05.001 · Cited by 43 · see citing works
“Under the dominant mode of inheritance, the burden score indicates the presence of any mutation at the m variant sites.(1,5) In this case, Equation 2 no longer holds, and we cannot directly construct u* from U.”
BackgroundLippert, C., Xiang, J., Horta, D., Widmer, C., Kadie, C. M., Heckerman, D. E., & Listgarten, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu504 · Cited by 47 · see citing works
“…been proposed (Bhatia et al., 2010; Han and Pan, 2010; Ionita-Laza et al., 2011; Li and Leal, 2008; Liu and Leal, 2010; Madsen and Browning, 2009; Morgenthaler and Thilly, 2007; Neale et al., 2011; Price et al., 2010; Schwender et al., 2011, Wu et al., 2011; Zawistowski et al., 2010), some of…”
Determinants of Power in Gene-Based Burden Testing for Monogenic Disorders
Guo, M. H., Dauber, A. N., Lippincott, M. F., Chan, Y.-M., Salem, R. M., & Hirschhorn, J. N. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.06.031 · Cited by 45 · see citing works
Power and sample size calculations for high-throughput sequencing-based experiments
Li, C.-I., Samuels, D. C., Zhao, Y., Shyr, Y., & Guo, Y. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx061 · Cited by 46 · see citing works
Lange, K. L., Papp, J. C., Sinsheimer, J. S., & Sobel, E. M. (2013) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-022513-115638 · Cited by 44 · see citing works
Sul, J. H., Han, B., He, D., & Eskin, E. (2011) · Genetics · doi:10.1534/genetics.110.125070 · Cited by 45 · see citing works
SBERIA: Set‐Based Gene‐Environment Interaction Test for Rare and Common Variants in Complex Diseases
Jiao, S., Hsu, L., Bezieau, S., Brenner, H., Chan, A. T., Chang-Claude, J. C., Marchand, L. L., Lemire, M., Newcomb, P. A., Slattery, M. L., et al. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21735 · Cited by 43 · see citing works
“First, assumptions that are reasonable for main effects may not be reasonable for G × E, i.e., the power of burden tests for rare variants depends on the assumption that most rare missense variants are deleterious but it is not reasonable to assume all G × E’s have the same direction.”
Recent advances and challenges of rare variant association analysis in the biobank sequencing era
Chen, W., Coombes, B. J., & Larson, N. B. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1014947 · Cited by 42 · see citing works
“One of the simplest burden testing procedures is the collapsing and sum test (CAST) (Morgenthaler and Thilly, 2007), which is a 2 × 2 Fisher’s Exact Test for a binary RV carrier status for case-control studies.”
MethodIncorporating model uncertainty in detecting rare variants: the Bayesian risk index
Quintana, M. A., Jl, B., Thomas, D. C., & Conti, D. V. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20613 · Cited by 43 · see citing works
An Exponential Combination Procedure for Set-Based Association Tests in Sequencing Studies
Chen, L., Hsu, L., Gamazon, E. R., Cox, N. J., & Nicolae, D. L. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2012.09.017 · Cited by 42 · see citing works
Kou, I., Watanabe, K., Takahashi, Y., Momozawa, Y., Khanshour, A. M., Grauers, A., Zhou, H., Liu, G., Fan, Y., Takeda, K., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-29011-7 · Cited by 42 · see citing works
Walters, R. G., Coin, L., Ruokonen, A., de Smith, A. J., Moustafa, J. S. E.-S., Jacquemont, S., Elliott, P., Esko, T., Hartikainen, A., Laitinen, J. H., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058048 · Cited by 42 · see citing works
Rare-Variant Genome-Wide Association Studies: A New Frontier in Genetic Analysis of Complex Traits
Wagner, M. J. (2013) · Pharmacogenomics · doi:10.2217/pgs.13.36 · Cited by 41 · see citing works
A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases
Greene, D., Richardson, S. T., & Turro, E. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.05.015 · Cited by 41 · see citing works
Zhu, X., Chen, M., Tian, L., Li, D., Xu, P., Lin, M. C.-M., Xie, D., & Kung, H. (2009) · International Journal of Cancer · doi:10.1002/ijc.24487 · Cited by 39 · see citing works
“For a complex multigenic and multistage disease involving the interaction of many genetic and environmental factors it is unlikely that any single-nucleotide polymorphism would have a dramatic effect on cancer risk.(30,31) In fact, the reported significant HRs for individual variants in this study are typically less t…”
BackgroundMoore, C., Wallace, J. R., Frase, A., Pendergrass, S. A., & Ritchie, M. D. (2013) · BMC Medical Genomics · doi:10.1186/1755-8794-6-s2-s6 · Cited by 38 · see citing works
“In the past five years, many collapsing methods have been published [5,10-18].”
MethodZhang, Y., Guan, W., & Pan, W. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21691 · Cited by 38 · see citing works
Huang, G., Lin, C., Cai, Y., Chen, T., Hsu, S., Lu, N., Chen, H., & Wu, Y. (2020) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11480 · Cited by 38 · see citing works
“Therefore, as is common in extreme value analysis 44 or in genetic studies for testing the contribution of rare variants to the disease, 45,46 new approaches are required that are based on collapsing extreme values in different features into a single variable (eg, block maxima), followed by…”
BackgroundIncorporating Gene-Environment Interaction in Testing for Association with Rare Genetic Variants
Chen, H., Meigs, J. B., & Dupuis, J. (2014) · Human Heredity · doi:10.1159/000363347 · Cited by 36 · see citing works
- Authors
- Morgenthaler, S., & Thilly, W. G.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Thilly, William G.
- Venue
- Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis
- Publisher
- Elsevier BV
- Published
- 2007-02
- Volume
- 615
- Issue
- 1-2
- Pages
- 28-56
- ISSN
- 0027-5107 (ISSN-L)
- 1386-1964
- OpenAlex
- W1971102564
- Cited by
- 507
- References
- 50
- Citations by year
- 2026: 6 · 2025: 14 · 2024: 17 · 2023: 27 · 2022: 15 · 2021: 18 · 2020: 23 · 2019: 40 · 2018: 35 · 2017: 28 · 2016: 46 · 2015: 43 · 2014: 52 · 2013: 49 · 2012: 41
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- green
- infoscience.epfl.ch
- Licence
- Locations
- Mutation research. Fundamental and molecular mechanisms of mutagenesis (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Forensic and Genetic Research
- Keywords
- CAST, gene-disease association, common diseases, statistical power, false positive rate, zygosity, polygenic risk, epistasis, heterozygous mutation, statistical models
- MeSH terms
- Alleles, Analysis of Variance, Biometry, Ethnicity, Genes, Dominant, Genetic Techniques, Genetics, Population, Heterozygote, Homozygote, Humans, Models, Genetic, Mutation, Risk Factors, United States, Models, Statistical, Cohort Studies, Case-Control Studies, Gene Deletion, Genetic Predisposition to Disease
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