Holger Schwender

A genome-wide association study of cleft lip with and without cleft palate identifies risk variants near MAFB and ABCA4

Case-parent trios were used in a genome-wide association study of cleft lip with and without cleft palate. SNPs near two genes not previously associated with cleft lip with and without cleft palate (MAFB, most significant SNP rs13041247, with odds ratio (OR) per minor allele = 0.704, 95% CI 0.635–0.778, P = 1.44 × 10−11; and ABCA4, most significant SNP rs560426, with OR = 1.432, 95% CI 1.292–1.587, P = 5.01 × 10−12) and two previously identified regions (at chromosome 8q24 and IRF6) attained genome-wide significance. Stratifying trios into European and Asian ancestry groups revealed differences in statistical significance, although estimated effect sizes remained similar. Replication studies from several populations showed confirming evidence, with families of European ancestry giving stronger evidence for markers in 8q24, whereas Asian families showed stronger evidence for association with MAFB and ABCA4. Expression studies support a role for MAFB in palatal development.

Evidence for gene-environment interaction in a genome wide study of nonsyndromic cleft palate.

Nonsyndromic cleft palate (CP) is a common birth defect with a complex and heterogeneous etiology involving both genetic and environmental risk factors. We conducted a genome‐wide association study (GWAS) using 550 case‐parent trios, ascertained through a CP case collected in an international consortium. Family‐based association tests of single nucleotide polymorphisms (SNP) and three common maternal exposures (maternal smoking, alcohol consumption, and multivitamin supplementation) were used in a combined 2 df test for gene (G) and gene‐environment (G × E) interaction simultaneously, plus a separate 1 df test for G × E interaction alone. Conditional logistic regression models were used to estimate effects on risk to exposed and unexposed children. While no SNP achieved genome‐wide significance when considered alone, markers in several genes attained or approached genome‐wide significance when G × E interaction was included. Among these, MLLT3 and SMC2 on chromosome 9 showed multiple SNPs resulting in an increased risk if the mother consumed alcohol during the peri‐conceptual period (3 months prior to conception through the first trimester). TBK1 on chr. 12 and ZNF236 on chr. 18 showed multiple SNPs associated with higher risk of CP in the presence of maternal smoking. Additional evidence of reduced risk due to G × E interaction in the presence of multivitamin supplementation was observed for SNPs in BAALC on chr. 8. These results emphasize the need to consider G × E interaction when searching for genes influencing risk to complex and heterogeneous disorders, such as nonsyndromic CP. Genet. Epidemiol. 2011.  © 2011 Wiley‐Liss, Inc. 35: 469‐478, 2011

Confirming genes influencing risk to cleft lip with/without cleft palate in a case–parent trio study

A collection of 1,108 case–parent trios ascertained through an isolated, nonsyndromic cleft lip with or without cleft palate (CL/P) was used to replicate the findings from a genome-wide association study (GWAS) conducted by Beaty et al. (Nat Genet 42:525–529, 2010), where four different genes/regions were identified as influencing risk to CL/P. Tagging SNPs for 33 different genes were genotyped (1,269 SNPs). All four of the genes originally identified as showing genome-wide significance (IRF6, ABCA4 and MAF, plus the 8q24 region) were confirmed in this independent sample of trios (who were primarily of European and Southeast Asian ancestry). In addition, eight genes classified as ‘second tier’ hits in the original study (PAX7, THADA, COL8A1/FILIP1L, DCAF4L2, GADD45G, NTN1, RBFOX3 and FOXE1) showed evidence of linkage and association in this replication sample. Meta-analysis between the original GWAS trios and these replication trios showed PAX7, COL8A1/FILIP1L and NTN1 achieved genome-wide significance. Tests for gene–environment interaction between these 33 genes and maternal smoking found evidence for interaction with two additional genes: GRID2 and ELAVL2 among European mothers (who had a higher rate of smoking than Asian mothers). Formal tests for gene–gene interaction (epistasis) failed to show evidence of statistical interaction in any simple fashion. This study confirms that many different genes influence risk to CL/P.

Genetic variants in urinary bladder cancer: collective power of the “wimp SNPs”

In recent years, genome-wide association studies (GWAS) have identified more than 300 validated associations between genetic variants and risk of approximately 70 common diseases. A small number of rare variants with a frequency of usually less than 1% are associated with a strongly enhanced risk, such as genetic variants of TP53, RB1, BRCA1, and BRCA2. Only a very small number of SNPs (with a frequency of more that 1% of the rare allele) have effects of a factor of two or higher. Examples include APOE4 in Alzheimer’s disease, LOXL1 in exfoliative glaucoma, and CFH in age-related macular degeneration. However, the majority of all identified SNPs have odds ratios between 1.1 and 1.5. In the case of urinary bladder cancer, all known SNPs that have been validated in sufficiently large populations are associated with odds ratios smaller than 1.5. These SNPs are located next to the following genes: MYC, TP63, PSCA, the TERT-CLPTM1L locus, FGFR3, TACC3, NAT2, CBX6, APOBEC3A, CCNE1, and UGT1A. It is likely that these moderate risk or “wimp SNPs” interact, and because of their high number, collectively have a strong influence on whether an individual will develop cancer or not. It should be considered that variants identified so far explain only approximately 5–10% of the overall inherited risk. Possibly, the remaining variance is due to an even higher number of SNPs with odds ratios smaller than 1.1. Recent studies have provided the following information: (1) The functions of genes identified as relevant for bladder cancer focus on detoxification of carcinogens, control of the cell cycle and apoptosis, as well as maintenance of DNA integrity. (2) Many novel SNPs are far away from the protein coding regions, suggesting that these SNPs are located on distant-acting transcriptional enhancers. (3) The low odds ratio of each individual bladder cancer-associated SNP is too low to justify reasonable preventive measures. However, if the recently identified SNPs interact, they may collectively result in a substantial risk that is of preventive relevance. In addition to the “novel SNPs” identified by the recent GWAS, at least 163 further variants have been reported in relation to bladder cancer, although they have not been consistently validated in independent case–control series. Moreover, given that only 60 of these 163 “old SNPs” are covered by the SNP chips used in the recent GWAS, there are in principle 103 published variants still awaiting validation or disproval. In future, besides identifying novel disease-associated rare variants by deep sequencing, it will also be important to understand how the already identified variants interact.

Detecting high-order interactions of single nucleotide polymorphisms using genetic programming

MOTIVATION Not individual single nucleotide polymorphisms (SNPs), but high-order interactions of SNPs are assumed to be responsible for complex diseases such as cancer. Therefore, one of the major goals of genetic association studies concerned with such genotype data is the identification of these high-order interactions. This search is additionally impeded by the fact that these interactions often are only explanatory for a relatively small subgroup of patients. Most of the feature selection methods proposed in the literature, unfortunately, fail at this task, since they can either only identify individual variables or interactions of a low order, or try to find rules that are explanatory for a high percentage of the observations. In this article, we present a procedure based on genetic programming and multi-valued logic that enables the identification of high-order interactions of categorical variables such as SNPs. This method called GPAS cannot only be used for feature selection, but can also be employed for discrimination. RESULTS In an application to the genotype data from the GENICA study, an association study concerned with sporadic breast cancer, GPAS is able to identify high-order interactions of SNPs leading to a considerably increased breast cancer risk for different subsets of patients that are not found by other feature selection methods. As an application to a subset of the HapMap data shows, GPAS is not restricted to association studies comprising several 10 SNPs, but can also be employed to analyze whole-genome data. AVAILABILITY Software can be downloaded from http://ls2-www.cs.uni-dortmund.de/~nunkesser/#Software

Integrated analysis of copy number alterations and gene expression

MOTIVATION The analysis of a number of different genetic features like copy number (CN) variation, gene expression (GE) or loss of heterocygosity has considerably increased in recent years, as well as the number of available datasets. This is particularly due to the success of microarray technology. Thus, to understand mechanisms of disease pathogenesis on a molecular basis, e.g. in cancer research, the challenge of analyzing such different data types in an integrated way has become increasingly important. In order to tackle this problem, we propose a new procedure for an integrated analysis of two different data types that searches for genes and genetic regions which for both inputs display strong equally directed deviations from the reference median. We employ this approach, based on a modified correlation coefficient and an explorative Wilcoxon test, to find DNA regions of such abnormalities in GE and CN (e.g. underexpressed genes accompanied by a loss of DNA material). RESULTS In an application to acute myeloid leukemia, our procedure is able to identify various regions on different chromosomes with characteristic abnormalities in GE and CN data and shows a higher sensitivity to differences in abnormalities than standard approaches. While the results support various findings of previous studies, some new interesting DNA regions can be identified. In a simulation study, our procedure also shows more reliable results than standard approaches. AVAILABILITY Code and data available as R packages edira and ediraAMLdata from http://www.statistik.tu-dortmund.de/~schaefer/ CONTACT martin.schaefer@udo.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Genotyping NAT2 with only two SNPs (rs1041983 and rs1801280) outperforms the tagging SNP rs1495741 and is equivalent to the conventional 7-SNP NAT2 genotype.

Genotyping N-acetyltransferase 2 (NAT2) is of high relevance for individualized dosing of antituberculosis drugs and bladder cancer epidemiology. In this study we compared a recently published tagging single nucleotide polymorphism (SNP) (rs1495741) to the conventional 7-SNP genotype (G191A, C282T, T341C, C481T, G590A, A803G and G857A haplotype pairs) and systematically analysed if novel SNP combinations outperform the latter. For this purpose, we studied 3177 individuals by PCR and phenotyped 344 individuals by the caffeine test. Although the tagSNP and the 7-SNP genotype showed a high degree of correlation (R=0.933, P<0.0001) the 7-SNP genotype nevertheless outperformed the tagging SNP with respect to specificity (1.0 vs. 0.9444, P=0.0065). Considering all possible SNP combinations in a receiver operating characteristic analysis we identified a 2-SNP genotype (C282T, T341C) that outperformed the tagging SNP and was equivalent to the 7-SNP genotype. The 2-SNP genotype predicted the correct phenotype with a sensitivity of 0.8643 and a specificity of 1.0. In addition, it predicted the 7-SNP genotype with sensitivity and specificity of 0.9993 and 0.9880, respectively. The prediction of the NAT2 genotype by the 2-SNP genotype performed similar in populations of Caucasian, Venezuelan and Pakistani background. A 2-SNP genotype predicts NAT2 phenotypes with similar sensitivity and specificity as the conventional 7-SNP genotype. This procedure represents a facilitation in individualized dosing of NAT2 substrates without losing sensitivity or specificity.

Methods for Identifying SNP Interactions: A Review on Variations of Logic Regression, Random Forest and Bayesian Logistic Regression

Due to advancements in computational ability, enhanced technology and a reduction in the price of genotyping, more data are being generated for understanding genetic associations with diseases and disorders. However, with the availability of large data sets comes the inherent challenges of new methods of statistical analysis and modeling. Considering a complex phenotype may be the effect of a combination of multiple loci, various statistical methods have been developed for identifying genetic epistasis effects. Among these methods, logic regression (LR) is an intriguing approach incorporating tree-like structures. Various methods have built on the original LR to improve different aspects of the model. In this study, we review four variations of LR, namely Logic Feature Selection, Monte Carlo Logic Regression, Genetic Programming for Association Studies, and Modified Logic Regression-Gene Expression Programming, and investigate the performance of each method using simulated and real genotype data. We contrast these with another tree-like approach, namely Random Forests, and a Bayesian logistic regression with stochastic search variable selection.

ERBB2 induces an antiapoptotic expression pattern of Bcl-2 family members in node-negative breast cancer.

Purpose: Members of the Bcl-2 family act as master regulators of mitochondrial homeostasis and apoptosis. We analyzed whether ERBB2 influences the prognosis of breast cancer by influencing the proapoptotic versus antiapoptotic balance of Bcl-2 family members. Experimental Design: ERBB2-regulated Bcl-2 family members were identified by inducible expression of ERBB2 in MCF-7 breast cancer cells and by correlation analysis with ERBB2 expression in breast carcinomas. The prognostic relevance of ERBB2-regulated and all additional Bcl-2 family members was determined in 782 patients with untreated node-negative breast cancer. The biological relevance of ERBB2-induced inhibition of apoptosis was validated in a murine tumor model allowing conditional ERBB2 expression. Results: ERBB2 caused an antiapoptotic phenotype by upregulation of MCL-1, TEGT, BAG1, BNIP1, and BECN1 as well as downregulation of BAX, BMF, BNIPL, CLU, and BCL2L13. Upregulation of the antiapoptotic MCL-1 [P = 0.001, hazard ratio (HR) 1.5] and BNIP3 (P = 0.024; HR, 1.4) was associated with worse prognosis considering metastasis-free interval, whereas clusterin (P = 0.008; HR, 0.88) and the proapoptotic BCL2L13 (P = 0.019; HR, 0.45) were associated with better prognosis. This indicates that ERBB2 alters the expression of Bcl-2 family members in a way that leads to adverse prognosis. Analysis of apoptosis and tumor remission in a murine tumor model confirmed that the prototypic Bcl-2 family member Bcl-xL could partially substitute for ERBB2 to antagonize tumor remission. Conclusions: Our results support the concept that ERBB2 influences the expression of Bcl-2 family members to induce an antiapoptotic phenotype. Antagonization of antiapoptotic Bcl-2 family members might improve breast cancer therapy, whereby MCL-1 and BNIP3 represent promising targets. Clin Cancer Res; 16(2); 451–60

Testing SNPs and sets of SNPs for importance in association studies

A major goal of genetic association studies concerned with single nucleotide polymorphisms (SNPs) is the detection of SNPs exhibiting an impact on the risk of developing a disease. Typically, this problem is approached by testing each of the SNPs individually. This, however, can lead to an inaccurate measurement of the influence of the SNPs on the disease risk, in particular, if SNPs only show an effect when interacting with other SNPs, as the multivariate structure of the data is ignored. In this article, we propose a testing procedure based on logic regression that takes this structure into account and therefore enables a more appropriate quantification of importance and ranking of the SNPs than marginal testing. Since even SNP interactions often exhibit only a moderate effect on the disease risk, it can be helpful to also consider sets of SNPs (e.g. SNPs belonging to the same gene or pathway) to borrow strength across these SNP sets and to identify those genes or pathways comprising SNPs that are most consistently associated with the response. We show how the proposed procedure can be adapted for testing SNP sets, and how it can be applied to blocks of SNPs in linkage disequilibrium (LD) to overcome problems caused by LD.