minPtest: a resampling based gene region-level testing procedure for genetic case-control studies

Association between AMPKα1 gene polymorphisms and gestational diabetes in the Chinese population: A case-control study

AIM

The aim is to examine the association between seven candidate single nucleotide polymorphisms in AMPKα1 and gestational diabetes in Chinese people.

METHOD

We used a matched nested case-control study design, individuals including 334 participants with gestational diabetes and 334 healthy pregnant women. Confirmed 334 gestational diabetes cases and maternal age and district of residence matched controls (1:1) were enrolled. We examined seven candidate single nucleotide polymorphisms in AMPKα1 gene and the risk of gestational diabetes. The associations were estimated in Co-dominant, Dominant, Recessive, and Alleles models. The odds ratios (ORs) and their 95% confidence intervals (95% CI) were estimated by unconditional logistical regression as a measure of the associations between genotypes and gestational diabetes adjusting for maternal age, prepregnancy body mass index (BMI), fetal sex and parity.

RESULT

At the gene level, we found that AMPKα1 was associated with gestational diabetes (p = 0.008). After adjusting the covariates and multiple comparison correction, AMPKα1 (rsc1002424, rs10053664, rs13361707) polymorphisms were associated with the risk of gestational diabetes. In addition, gestational diabetes was related to the AAGGA haplotype comprising rs1002424, rs2570091, rs10053664, rs13361707 and rs3805486 in the haplotype models (p = 0.011).

CONCLUSIONS

This study provides evidence that the AMPKα1 genotypes (rs1002424 G/A, rs10053664 A/G, rs13361707 A/G) and the haplotype (AAGGA) are relevant genetic factors in a Chinese population with gestational diabetes.

A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia

BACKGROUND

Technological advances are enabling us to collect multimodal datasets at an increasing depth and resolution while with decreasing labors. Understanding complex interactions among multimodal datasets, however, is challenging.

NEW METHOD

In this study, we tested the interaction effect of multimodal datasets using a novel method called the kernel machine for detecting higher order interactions among biologically relevant multimodal data. Using a semiparametric method on a reproducing kernel Hilbert space, we formulated the proposed method as a standard mixed-effects linear model and derived a score-based variance component statistic to test higher order interactions between multimodal datasets.

RESULTS

The method was evaluated using extensive numerical simulation and real data from the Mind Clinical Imaging Consortium with both schizophrenia patients and healthy controls. Our method identified 13-triplets that included 6 gene-derived SNPs, 10 ROIs, and 6 gene-specific DNA methylations that are correlated with the changes in hippocampal volume, suggesting that these triplets may be important for explaining schizophrenia-related neurodegeneration.

COMPARISON WITH EXISTING METHOD(S)

The performance of the proposed method is compared with the following methods: test based on only first and first few principal components followed by multiple regression, and full principal component analysis regression, and the sequence kernel association test.

CONCLUSIONS

With strong evidence (p-value ≤0.000001), the triplet (MAGI2, CRBLCrus1.L, FBXO28) is a significant biomarker for schizophrenia patients. This novel method can be applicable to the study of other disease processes, where multimodal data analysis is a common task.

Improving power and accuracy of genome-wide association studies via a multi-locus mixed linear model methodology

Genome-wide association studies (GWAS) have been widely used in genetic dissection of complex traits. However, common methods are all based on a fixed-SNP-effect mixed linear model (MLM) and single marker analysis, such as efficient mixed model analysis (EMMA). These methods require Bonferroni correction for multiple tests, which often is too conservative when the number of markers is extremely large. To address this concern, we proposed a random-SNP-effect MLM (RMLM) and a multi-locus RMLM (MRMLM) for GWAS. The RMLM simply treats the SNP-effect as random, but it allows a modified Bonferroni correction to be used to calculate the threshold p value for significance tests. The MRMLM is a multi-locus model including markers selected from the RMLM method with a less stringent selection criterion. Due to the multi-locus nature, no multiple test correction is needed. Simulation studies show that the MRMLM is more powerful in QTN detection and more accurate in QTN effect estimation than the RMLM, which in turn is more powerful and accurate than the EMMA. To demonstrate the new methods, we analyzed six flowering time related traits in Arabidopsis thaliana and detected more genes than previous reported using the EMMA. Therefore, the MRMLM provides an alternative for multi-locus GWAS.