Statistical tests of genetic association in the presence of gene-gene and gene-environment interactions

Epidemiological and clinicopathological findings in 15 fatal outbreaks of salmonellosis in dairy calves and virulence genes in the causative Salmonella enterica Typhimurium and Dublin strains

Salmonella enterica is a major food-borne pathogen that affects cattle-rearing systems worldwide. Little information is available on the epidemiology and pathology of salmonellosis and the virulence genes (VGs) carried by Salmonella in spontaneous outbreaks in cattle. We describe epidemiological findings in 15 fatal outbreaks of salmonellosis in Uruguayan dairy farms and the age, clinical signs, and pathology in 20 affected calves. We also describe the serotypes and frequencies of 17 VGs in the causative Salmonella strains and explore their associations with epidemiological, clinical, and pathological findings. Salmonella Typhimurium and Dublin were identified in 11/15 and 4/15 outbreaks, respectively. The most frequent reason for consultation was digestive disease (8 outbreaks caused by S. Typhimurium), followed by sudden death (4 outbreaks, 3 caused by S. Dublin). Morbidity, mortality, and lethality ranged 4.8-100%, 3.8-78.9%, and 10-100%, without significant differences between serotypes. Diarrhea, the most common clinical sign (14 cases), was associated with the Typhimurium serotype (OR = 26.95), especially in ≤ 30-day-old calves with fibrinous enteritis as the main autopsy finding. The Dublin serotype affected ≥ 50-day-old calves and was associated with fibrinosuppurative splenitis (p = 0.01) and tubulointerstitial nephritis (OR = 48.95). The chances of the Dublin serotype increased significantly with age. There was low variability of VG across serotypes. The pefA gene was associated with the Typhimurium serotype (OR = 21.95), macroscopic enteritis (p = 0.03), and microscopic fibrinosuppurative splenitis (p = 0.04). Understanding the epidemiology, pathology, and virulence of S. enterica at the farm level is key to delineating prevention and control strategies to mitigate its impact on animal and human health.

Selecting Genetic Variants and Interactions Associated with Amyotrophic Lateral Sclerosis: A Group LASSO Approach

Amyotrophic lateral sclerosis (ALS) is a multi-system neurodegenerative disease that affects both upper and lower motor neurons, resulting from a combination of genetic, environmental, and lifestyle factors. Usually, the association between single-nucleotide polymorphisms (SNPs) and this disease is tested individually, which leads to the testing of multiple hypotheses. In addition, this classical approach does not support the detection of interaction-dependent SNPs. We applied a two-step procedure to select SNPs and pairwise interactions associated with ALS. SNP data from 276 ALS patients and 268 controls were analyzed by a two-step group LASSO in 2000 iterations. In the first step, we fitted a group LASSO model to a bootstrap sample and a random subset of predictors (25%) from the original data set aiming to screen for important SNPs and, in the second step, we fitted a hierarchical group LASSO model to evaluate pairwise interactions. An in silico analysis was performed on a set of variables, which were prioritized according to their bootstrap selection frequency. We identified seven SNPs (rs16984239, rs10459680, rs1436918, rs1037666, rs4552942, rs10773543, and rs2241493) and two pairwise interactions (rs16984239:rs2118657 and rs16984239:rs3172469) potentially involved in nervous system conservation and function. These results may contribute to the understanding of ALS pathogenesis, its diagnosis, and therapeutic strategy improvement.

Detecting a weak association by testing its multiple perturbations: a data mining approach

Many risk factors/interventions in epidemiologic/biomedical studies are of minuscule effects. To detect such weak associations, one needs a study with a very large sample size (the number of subjects, n). The n of a study can be increased but unfortunately only to an extent. Here, we propose a novel method which hinges on increasing sample size in a different direction–the total number of variables (p). We construct a p-based ‘multiple perturbation test', and conduct power calculations and computer simulations to show that it can achieve a very high power to detect weak associations when p can be made very large. As a demonstration, we apply the method to analyze a genome-wide association study on age-related macular degeneration and identify two novel genetic variants that are significantly associated with the disease. The p-based method may set a stage for a new paradigm of statistical tests.

Comparison of three summary statistics for ranking genes in genome-wide association studies

Problems associated with insufficient power have haunted the analysis of genome-wide association studies and are likely to be the main challenge for the analysis of next-generation sequencing data. Ranking genes according to their strength of association with the investigated phenotype is one solution. To obtain rankings for genes, researchers can draw from a wide range of statistics summarizing the relationships between variants mapped to a gene and the phenotype. Hence, it is of interest to explore the performance of these statistics in the context of rankings. To this end, we conducted a simulation study (limited to genes of equal sizes) of three different summary statistics examining the ability to rank genes in a meaningful order. The weighted sum of squared marginal score test (Pan, 2009), RareCover algorithm (Bahtia et al., 2010) and the elastic net regularization (Zou and Hastie, 2005) were chosen, because they can handle common as well as rare variants. The test based on the score statistic outperformed both other methods in almost all investigated scenarios. It was the only measure to consistently detect genes with interacting causal variants. However, the RareCover algorithm proved better at identifying genes including causal variants with small effect sizes and low minor allele frequency than the weighted sum of squared marginal score test. The performance of the elastic net regularization was unimpressive for all but the simplest scenarios.

Detecting rare haplotype-environment interaction with logistic Bayesian LASSO

Two important contributors to missing heritability are believed to be rare variants and gene-environment interaction (GXE). Thus, detecting GXE where G is a rare haplotype variant (rHTV) is a pressing problem. Haplotype analysis is usually the natural second step to follow up on a genomic region that is implicated to be associated through single nucleotide variants (SNV) analysis. Further, rHTV can tag associated rare SNV and provide greater power to detect them than popular collapsing methods. Recently we proposed Logistic Bayesian LASSO (LBL) for detecting rHTV association with case-control data. LBL shrinks the unassociated (especially common) haplotypes toward zero so that an associated rHTV can be identified with greater power. Here, we incorporate environmental factors and their interactions with haplotypes in LBL. As LBL is based on retrospective likelihood, this extension is not trivial. We model the joint distribution of haplotypes and covariates given the case-control status. We apply the approach (LBL-GXE) to the Michigan, Mayo, AREDS, Pennsylvania Cohort Study on Age-related Macular Degeneration (AMD). LBL-GXE detects interaction of a specific rHTV in CFH gene with smoking. To the best of our knowledge, this is the first time in the AMD literature that an interaction of smoking with a specific (rather than pooled) rHTV has been implicated. We also carry out simulations and find that LBL-GXE has reasonably good powers for detecting interactions with rHTV while keeping the type I error rates well controlled. Thus, we conclude that LBL-GXE is a useful tool for uncovering missing heritability.

A genetic association study of NLRP2 and NLRP7 genes in idiopathic recurrent miscarriage

STUDY QUESTION

Do gene polymorphisms of two members of the human innate immune sensor nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain-containing proteins (NLRP) family, NLRP2 and NLRP7, confer susceptibility to idiopathic recurrent miscarriage (RM)?

SUMMARY ANSWER

We found a significant association of a tag single-nucleotide polymorphism (SNP) of NLRP7 (rs26949) with idiopathic RM, while a tag SNP of NLRP2 (rs127868) showed a marginally significant association.

WHAT IS KNOWN ALREADY

Human NLRP2 and NLRP7 have been suggested to be maternal effect genes, regulating early embryonic development and establishment of maternal imprints. Anecdotal evidence showed women who had experienced at least three consecutive miscarriages without hydatidiform mole carried non-synonymous NLRP7 variants. Whether these two genes are associated with idiopathic RM remains obscure.

STUDY DESIGN, SIZE AND DURATION

In this case-controlled study, 143 women who had experienced at least two consecutive spontaneous miscarriages (n = 91 women with two miscarriages, n = 52 with three or more) and 149 controls were included between 2004 and 2010.

MATERIALS, SETTING, METHODS

A total of five tag SNPs of NLRP2 and eight tag SNPs of NLRP7 were genotyped using the primer extension analysis. The deviation from the Hardy-Weinberg equilibrium was checked using χ(2) analysis. The logistic odds ratios (ORs) of RM were estimated with a 95% confidence interval (CI) in multivariate analysis after maternal age adjustment. The false discovery rate (FDR) was used to adjust for multiple testing. Tests for haplotype association with RM were performed. Gene-gene interactions among loci of the two genes were evaluated by using the multifactor dimensionality reduction (MDR) method.

MAIN RESULTS AND THE ROLE OF CHANCE

One tag SNP rs269949 of NLRP7 showed significant difference between patients and controls in a recessive model (FDR P = 0.0456, age-adjusted OR (AOR) = 16.49, 95% CI = 2.00-136.11 for the GG genotype). The difference was significant in patients with two consecutive miscarriages and also in those with three or more consecutive miscarriages. Meanwhile, one tag SNP of NLRP2 (rs12768) showed marginal significance between patients and controls in a co-dominant model (FDR P = 0.0505, AOR = 2.15, 95% CI = 1.29-3.58 for the AC genotype). In the haplotype analysis, NLRP2 and NLRP7 did not show any significant difference between the patients and controls. MDR test revealed that there is no significant gene-gene interaction among loci of NLRP2 and NLRP7.

LIMITATIONS, REASONS FOR CAUTION

The results may be biased by heterogeneous ethnicities of the Taiwanese Han and a small sample size. The genetic loci responsible for the disease as well as their functional significance also await further investigation.

WIDER IMPLICATIONS OF THE FINDINGS

Our study suggests the role of the NLRP family proteins in RM.

STUDY FUNDING/COMPETING INTEREST(S)

This study was supported by grants from the National Science Council of the Republic of China (NSC-100-2314-B-006-011-MY3). None of the authors have any conflicts of interest.

Adaptive tests for detecting gene-gene and gene-environment interactions

There has been an increasing interest in detecting gene-gene and gene-environment interactions in genetic association studies. A major statistical challenge is how to deal with a large number of parameters measuring possible interaction effects, which leads to reduced power of any statistical test due to a large number of degrees of freedom or high cost of adjustment for multiple testing. Hence, a popular idea is to first apply some dimension reduction techniques before testing, while another is to apply only statistical tests that are developed for and robust to high-dimensional data. To combine both ideas, we propose applying an adaptive sum of squared score (SSU) test and several other adaptive tests. These adaptive tests are extensions of the adaptive Neyman test [Fan, 1996], which was originally proposed for high-dimensional data, providing a simple and effective way for dimension reduction. On the other hand, the original SSU test coincides with a version of a test specifically developed for high-dimensional data. We apply these adaptive tests and their original nonadaptive versions to simulated data to detect interactions between two groups of SNPs (e.g. multiple SNPs in two candidate regions). We found that for sparse models (i.e. with only few non-zero interaction parameters), the adaptive SSU test and its close variant, an adaptive version of the weighted sum of squared score (SSUw) test, improved the power over their non-adaptive versions, and performed consistently well across various scenarios. The proposed adaptive tests are built in the general framework of regression analysis, and can thus be applied to various types of traits in the presence of covariates.

Powerful multi-marker association tests: unifying genomic distance-based regression and logistic regression

To detect genetic association with common and complex diseases, many statistical tests have been proposed for candidate gene or genome-wide association studies with the case-control design. Due to linkage disequilibrium (LD), multi-marker association tests can gain power over single-marker tests with a Bonferroni multiple testing adjustment. Among many existing multi-marker association tests, most target to detect only one of many possible aspects in distributional differences between the genotypes of cases and controls, such as allele frequency differences, while a few new ones aim to target two or three aspects, all of which can be implemented in logistic regression. In contrast to logistic regression, a genomic distance-based regression (GDBR) approach aims to detect some high-order genotypic differences between cases and controls. A recent study has confirmed the high power of GDBR tests. At this moment, the popular logistic regression and the emerging GDBR approaches are completely unrelated; for example, one has to choose between the two. In this article, we reformulate GDBR as logistic regression, opening a venue to constructing other powerful tests while overcoming some limitations of GDBR. For example, asymptotic distributions can replace time-consuming permutations for deriving P-values and covariates, including gene-gene interactions, can be easily incorporated. Importantly, this reformulation facilitates combining GDBR with other existing methods in a unified framework of logistic regression. In particular, we show that Fisher's P-value combining method can boost statistical power by incorporating information from allele frequencies, Hardy-Weinberg disequilibrium, LD patterns, and other higher-order interactions among multi-markers as captured by GDBR.

Association of polymorphisms/haplotypes of the genes encoding vascular endothelial growth factor and its KDR receptor with recurrent pregnancy loss

BACKGROUND Vascular endothelial growth factor (VEGF) and its kinase insert domain receptor (KDR) play an important role in angiogenesis, and their gene expression patterns also suggest a close relationship with early pregnancy. However, limited information is available regarding the role of the VEGF system, especially its KDR receptor, in recurrent pregnancy loss (RPL). This study was conducted to investigate a genetic association between VEGF and its receptor gene (KDR) with idiopathic RPL. METHODS In this case-control study, 115 women who had experienced at least two consecutive spontaneous miscarriages (n= 62 women with two miscarriages, n= 53 with three or more) and 170 controls were included. A total of 14 tag single-nucleotide polymorphisms (SNPs) of VEGF and KDR were selected from the HapMap Web site and three functional SNPs [rs1570360 (-1154G/A) of VEGF; rs2305948 (V297I) and rs1870377 (Q472H) of the KDR gene] were genotyped using primer extension analysis. We further used multifactor dimensionality reduction analysis to evaluate gene-gene interactions. RESULTS One tag SNP (rs6838752) and the functional SNP (Q472H) of the KDR gene were in complete linkage and showed significant differences between patients and controls (P< 0.05). The frequencies of haplotypes of VEGF (A-T-G haplotype) and KDR (A-C-A-T-G haplotype) showed significant differences in patients versus controls (P< 0.05). All comparisons with controls remained significant when the subgroup of women with three or more miscarriages was analyzed. CONCLUSIONS VEGF and its receptor gene (KDR) are associated with idiopathic RPL. The VEGF/KDR system jointly contributes to recurrent miscarriage in Taiwanese Han women.

Polymorphisms of endocrine gland-derived vascular endothelial growth factor gene and its receptor genes are associated with recurrent pregnancy loss

BACKGROUND

Endocrine gland-derived vascular endothelial growth factor (EG-VEGF) and its receptor genes [prokineticin receptor 1 (PKR1) and prokineticin receptor 2 (PKR2)] have been identified in the last decade and their expression is restricted to the steroidogenic glands (ovary, testis, adrenal gland and placenta). Their expression patterns also suggest a close relationship to early pregnancy. However, little information is available regarding the role of EG-VEGF and its receptors (PKR1 and PKR2) in recurrent pregnancy loss (RPL). This study was conducted to investigate the association between polymorphisms of EG-VEGF and its receptor genes (PKR1 and PKR2) and idiopathic RPL.

METHODS

In this case-control study, 115 women with a history of idiopathic RPL and 170 controls were included. A total of 11 tag single nucleotide polymorphisms (SNPs) selected from EG-VEGF, PKR1 and PKR2 were genotyped. We further used multifactor dimensionality reduction (MDR) analysis to choose a best model and evaluate gene-gene interactions.

RESULTS

Two tag SNPs of PKR1 (rs4627609, rs6731838) and one tag SNP of PKR2 (rs6053283) were significantly associated with idiopathic RPL (P < 0.05). The frequencies of haplotypes C-G and T-A of PKR1 and haplotype A-G-C-G-G of PKR2 were significantly increased in women with idiopathic RPL (P < 0.05); MDR tests revealed gene-gene interactions between three loci [EG-VEGF (rs7513898), PKR1(rs6731838), PKR2(rs6053283)] based on the association model (P = 0.008). The adjusted odds ratio of high- and low-risk genotype combinations in the three-locus model was 3.94 (95% confidence interval: 2.38-6.52).

CONCLUSIONS

EG-VEGF receptor (PKR1, PKR2) gene polymorphisms and haplotypes were associated with idiopathic RPL. These three genes (EG-VEGF, PKR1 and PRK2) jointly contribute to RPL in the Taiwanese Han population.