Robust ranks of true associations in genome-wide case-control association studies
- Gang Zheng^{1}Email author,
- Jungnam Joo^{1},
- Jing-Ping Lin^{1},
- Mario Stylianou^{1},
- Myron A Waclawiw^{1} and
- Nancy L Geller^{1}
https://doi.org/10.1186/1753-6561-1-S1-S165
© Zheng et al; licensee BioMed Central Ltd. 2007
Published: 18 December 2007
Abstract
In whole-genome association studies, at the first stage, all markers are tested for association and their test statistics or p-values are ranked. At the second stage, some most significant markers are further analyzed by more powerful statistical methods. This helps reduce the number of hypotheses to be corrected for in multiple testing. Ranks of true associations in genome-wide scans using a single test statistic have been studied. In a case-control design for association, the trend test has been proposed. However, three different trend tests, optimal for the recessive, additive, and dominant models, respectively, are available for each marker. Because the true genetic model is unknown, we rank markers based on multiple test statistics or test statistics robust to model mis-specification. We studied this problem with application to Problem 3 of Genetic Analysis Workshop 15. An independent simulation study was also conducted to further evaluate the proposed procedure.
Keywords
Background
For a large genetic study, a two-stage analysis is often employed. At the first stage, each marker is tested for association with a disease. The p-values of all markers are ranked. Then some of the most significant markers are analyzed in the second stage. This two-stage analysis reduces the number of hypotheses to be tested in the second stage. Hence, it enhances the power to identify true marker susceptibility to the disease. However, it is important to know how many of the most significant markers one should study in the second stage so that the probability that one or several true markers will be studied in the second stage is greater than a given value. On the other hand, when a given number of the most significant markers is selected, it is important to know the probability that this list of markers would contain one or more true markers. A small list of the most significant markers may not contain any true markers at all, which leads to spurious associations or negative findings in the second stage.
Zaykin and Zhivotovsky [1] used p-values of a single test statistic to rank markers. In a case-control study for complex diseases, three trend tests can be applied under the recessive, additive, and dominant models. Because the genetic model of the marker is uncertain, ranking the markers with a single test statistic may not be robust when another genetic model is correct. Using the first simulated data set of Problem 3 from Genetic Analysis Workshop (GAW) 15, we study robust ranking when the underlying genetic model is unknown and examine whether robust test statistics would lead to robust rankings of about 10 K single-nucleotide polymorphisms (SNPs). The properties of the proposed robust ranking procedures are then further examined by an independent simulation study.
Methods
Notation and model
Consider a SNP with alleles D and d and frequencies p and q = 1 - p, respectively. In a case-control design, r cases and s controls are independently sampled from a population. The genotype counts of three genotypes G_{0} = dd, G_{1} = Dd, and G_{2} = DD are denoted as (r_{0}, r_{1}, r_{2}) in cases and (s_{0}, s_{1}, s_{2}) in controls, which follow multinomial distributions mul(r: p_{0}, p_{1}, p_{2}) and mul(s: q_{0}, q_{1}, q_{2}), respectively. Denote the disease prevalence as k and penetrances as f_{ i }= P(case|G_{ i }) for i = 0, 1, 2. By the Bayes Theorem, p_{ i }= g_{ i }f_{ i }/k and q_{ i }= g_{ i }(1 - f_{ i })/(1 - k), where g_{ i }= P(G_{ i }). Without loss of generality, assume that D has high risk. Then the null hypothesis of no association can be stated as H_{0}: f_{0} = f_{1} = f_{2} = k. The alternative hypothesis is H_{1}: f_{0} ≤ f_{1} ≤ f_{2} with at least one inequality. The genotype relative risks (GRRs) are defined as λ_{1} = f_{1}/f_{0} and λ_{2} = f_{2}/f_{0}. The recessive, additive, and dominant models are referred to as λ_{1} = 1, λ_{1} = (1 + λ_{2})/2, and λ_{1} = λ_{2}, respectively [2–4].
Trend tests and robust tests
where (x_{0}, x_{1}, x_{2}) = (0, x, 1) and 0 ≤ x ≤ 1. Given x, Z_{ x }follows asymptotically N(0,1). The choice of x is 0, 1/2, and 1 for the recessive, additive/multiplicative, and dominant models, respectively [5]. In practice, however, the true genetic model is unknown. Hence the robust tests, maximin efficiency robust test (MERT) and maximum test (MAX), can be applied, which are given by MERT = (Z_{0} + Z_{1})/{2(1 + ρ)}^{1/2} and MAX = max(|Z_{0}|, |Z_{1/2}|, |Z_{1}|), where ρ = [n_{0}n_{2}/{(n_{0} + n_{1})(n_{1} + n_{2})}]^{1/2} [4]. Note that Pearson's association test can also be used. However, Zheng et al. [6] showed that the MAX is often more powerful than the Pearson chi-squared test for a case-control design. Comparison of MERT and MAX can be found in Freidlin et al. [7]. The MAX and MERT have also been applied to other designs for GAW14 [8, 9].
Ranking markers with multiple statistics
When the genetic model is unknown, the three CATTs (Z_{0}, Z_{1/2}, Z_{2}) are calculated for each of M SNPs. Then the p-values of MERT and MAX can be obtained for ranking. However, computing the p-value of MAX needs extensive simulation. Thus, alternatively, the minimum of the p-values (min p) of the three CATTs can be used for ranking. Rather than ranking M SNPs based on any single CATT, we propose ranking the SNPs by the MERT and the minimum of the p-values. We expect that ranking SNPs based on this approach would be more robust compared to ranking by a single CATT when the ranks by the three CATTs are quite different.
Results
Application to GAW15
As an application, we consider the first simulated data set of Problem 3 from GAW15. A simulated data set was considered, as we knew that there were eight candidate genes. One of them at chromosome 6 with physical location 32,484,648 bp was simulated based on the DRB1 locus of the HLA gene. We selected four SNPs closest in physical distance to the eight known candidate genes as candidate SNPs. We examined the ranks of the 32 candidate SNPs among all 9187 SNPs. All 2000 unrelated controls were used. For the affected sib-pair (ASP) data, we selected an affected sib (case) with the first individual ID from each family. A total of 1500 unrelated cases were used. In the simulated data set, genotypes of all 9187 SNPs from 22 chromosomes were generated (no missing genotypes and no genotyping errors). All SNPs had minor allele frequency (MAF) greater than 1% and there were no monomorphisms. Because we considered the CATTs, Hardy-Weinberg equilibrium in the population was not required [2]. If any genotype count in cases or controls was 0, 0.5 was added to all genotype counts in cases and controls.
Ranks of candidate genes among 9187 SNPs across 22 chromosomes based on five ranking methods, sorted by chromosome and location
Rank | |||||||
---|---|---|---|---|---|---|---|
Chr | Location (bp) | Diff^{a} | Z _{0} | Z _{1/2} | Z _{1} | min p | MERT |
6 | 32447149 | 37 kb | 4 | 4 | 4 | 3 | 4 |
6 | 32499465 | 14 kb | 2 | 2 | 2 | 1 | 2 |
6 | 32521277 | 36 kb | 3 | 3 | 3 | 2 | 3 |
6 | 32772203 | 387 kb | 5 | 5 | 5 | 4 | 5 |
6 | 36900959 | 330 kb | 966 | 1190 | 2028 | 1881 | 647 |
6 | 37363880 | 130 kb | 8172 | 6 | 6 | 6 | 10 |
6 | 37539191 | 300 kb | 6359 | 1430 | 464 | 931 | 2897 |
6 | 37657759 | 423 kb | 968 | 1341 | 4671 | 1884 | 1414 |
8 | 140606402 | 3.2 mb | 3012 | 4237 | 5775 | 5167 | 3328 |
8 | 140676097 | 3.1 mb | 8391 | 7443 | 7097 | 8726 | 7382 |
8 | 140679773 | 3.1 mb | 7936 | 7288 | 7096 | 8727 | 7225 |
8 | 142073109 | 1.7 mb | 8918 | 6991 | 6588 | 8459 | 7407 |
9 | 25996861 | 262 kb | 2921 | 4074 | 6290 | 5039 | 3556 |
9 | 26089466 | 169 kb | 2179 | 9009 | 4702 | 3930 | 6948 |
9 | 26484252 | 225 kb | 2374 | 2254 | 4205 | 3889 | 2291 |
9 | 26521692 | 262 kb | 2909 | 2113 | 2819 | 3677 | 1947 |
9 | 27418665 | 118 kb | 3667 | 3963 | 6458 | 5915 | 4070 |
9 | 27505967 | 31 kb | 6228 | 7286 | 8222 | 8279 | 7989 |
9 | 27697461 | 160 kb | 5582 | 7177 | 5317 | 7490 | 8915 |
9 | 27697600 | 160 kb | 5195 | 4841 | 3323 | 5329 | 7532 |
11 | 110204257 | 30 kb | 1 | 1 | 1 | 5 | 1 |
11 | 110259778 | 24 kb | 3492 | 3162 | 4276 | 5125 | 2930 |
11 | 110264385 | 29 kb | 271 | 222 | 857 | 419 | 186 |
11 | 110322303 | 87 kb | 6840 | 3492 | 1930 | 3411 | 3030 |
16 | 12527182 | 9 kb | 7729 | 4194 | 4148 | 6328 | 4884 |
16 | 12577812 | 60 kb | 4288 | 5913 | 4696 | 6589 | 8924 |
16 | 12618035 | 100 kb | 6212 | 7783 | 8356 | 8266 | 6771 |
16 | 12783679 | 266 kb | 5824 | 4802 | 5334 | 7101 | 4733 |
18 | 65844474 | 225 kb | 6522 | 4959 | 5282 | 7254 | 4864 |
18 | 66045171 | 24 kb | 7063 | 8720 | 9182 | 8750 | 7913 |
18 | 66048927 | 20 kb | 15 | 15 | 15 | 15 | 13 |
18 | 66230498 | 160 kb | 5441 | 6135 | 6872 | 7732 | 5409 |
An independent simulation study
Average ranks of nine SNPs with true association in ten replicates in a genome-wide association study with 100 K SNPs
Rank | |||||||
---|---|---|---|---|---|---|---|
Model | SNPs | λ _{2} | Z _{0} | Z _{1/2} | Z _{1} | min p | MERT |
Recessive | 1 | 1.5 | 17582.8 | 15273.2 | 33593.4 | 16675.5 | 14511.4 |
Recessive | 2 | 2.0 | 645.5 | 2591.3 | 21714.1 | 1331.2 | 1476.4 |
Recessive | 3 | 2.5 | 1.5 | 385.9 | 19531.0 | 4.2 | 82.6 |
Additive | 4 | 1.5 | 10106.3 | 5501.4 | 12420.2 | 6265.1 | 4808.4 |
Additive | 5 | 2.0 | 5054.7 | 49.9 | 65.7 | 91.0 | 78.9 |
Additive | 6 | 2.5 | 440.6 | 2.6 | 3.5 | 3.3 | 2.5 |
Dominant | 7 | 1.5 | 30772.7 | 3510.1 | 3118.1 | 4245.9 | 4980.7 |
Dominant | 8 | 2.0 | 11644.0 | 6.8 | 3.1 | 4.1 | 19.0 |
Dominant | 9 | 2.5 | 6364.8 | 1.3 | 1.0 | 1.2 | 1.8 |
Conclusion
In this article, we studied the robust properties of ranks of true associations in genome-wide scans. In some situations, ranking markers by a single trend test may not be robust, in particular, when the true genetic model is unknown. Using robust methods, such as min p and MERT, to rank markers may lead to higher power when the ranks by three CATTs are quite different. The results showed that they are particularly useful in ensuring that recessive effects are not missed. While min p and MERT improve the univariate approach to the first stage of gene discovery, simulated data shows that some SNPs are not found via these univariate methods.
Declarations
Acknowledgements
This article has been published as part of BMC Proceedings Volume 1 Supplement 1, 2007: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci. The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/1?issue=S1.
Authors’ Affiliations
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