A graphical weighted power improving multiplicity correction approach for SNP selections

Mapping morphological shape as a high-dimensional functional curve

Detecting how genes regulate biological shape has become a multidisciplinary research interest because of its wide application in many disciplines. Despite its fundamental importance, the challenges of accurately extracting information from an image, statistically modeling the high-dimensional shape and meticulously locating shape quantitative trait loci (QTL) affect the progress of this research. In this article, we propose a novel integrated framework that incorporates shape analysis, statistical curve modeling and genetic mapping to detect significant QTLs regulating variation of biological shape traits. After quantifying morphological shape via a radius centroid contour approach, each shape, as a phenotype, was characterized as a high-dimensional curve, varying as angle θ runs clockwise with the first point starting from angle zero. We then modeled the dynamic trajectories of three mean curves and variation patterns as functions of θ. Our framework led to the detection of a few significant QTLs regulating the variation of leaf shape collected from a natural population of poplar, Populus szechuanica var tibetica. This population, distributed at altitudes 2000-4500 m above sea level, is an evolutionarily important plant species. This is the first work in the quantitative genetic shape mapping area that emphasizes a sense of 'function' instead of decomposing the shape into a few discrete principal components, as the majority of shape studies do.

Case-control pharmacogenetic study of HCN1/HCN2 variants and genetic generalized epilepsies

Epilepsy is a common complex neurological disorder, and some forms are resistant to drug treatment. The HCN1/HCN2 genes encode hyperpolarization-activated cyclic nucleotide-gated channels, which play important roles in the electrophysiology of neurons. We investigated the association between HCN1/HCN2 variants and drug resistance or the risk of genetic generalized epilepsies (GGEs). We used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry to assess nine variants of HCN1/HCN2 in 284 healthy participants and 483 GGEs (279 drug-responsive, 204 drug-resistant). Frequencies of HCN2 rs7255568 and rs3752158 G alleles differed in GGEs and in controls (P = .039, P = .027, respectively). The frequency of HCN2 haplotype (CAC) was higher in patients than controls (P = .046). The frequency of the HCN1 rs10462087 CC+CT genotype was lower in patients with childhood absence epilepsy (CAE) than controls (P = .047). Rs7255568 was associated with the risk of CAE (P = .028) and juvenile myoclonic epilepsy (JME) (P = .02). Rs3752158 was associated with the risk of generalized tonic-clonic seizures, JME, and febrile seizures (all P < .05). The frequency of the HCN2 haplotype (CAC) was higher in patients with JME (P = .015) and in those with febrile seizures (P = .024) than in controls. No significant association was found between HCN1/HCN2 alleles, genotypes or haplotypes, and drug resistance in patients. After Bonferroni's multiple comparisons correction, only the HCN2 rs3752158 C allele and GC+CC genotype frequencies in patients with JME were higher than those in controls (19.2% vs 11.6%, odds ratio (OR) = 1.71, 95% CI = 1.18-2.32), P = .004 < 0.05/9; 36% vs 22.2%, OR = 1.62(1.18-2.23), P = .003 < 0.05/9). Our study suggests that HCN2 rs3752158 is involved in the susceptibility to JME.

A Bivariate Hypothesis Testing Approach for Mapping the Trait-Influential Gene

The linkage disequilibrium (LD) based quantitative trait loci (QTL) model involves two indispensable hypothesis tests: the test of whether or not a QTL exists, and the test of the LD strength between the QTaL and the observed marker. The advantage of this two-test framework is to test whether there is an influential QTL around the observed marker instead of just having a QTL by random chance. There exist unsolved, open statistical questions about the inaccurate asymptotic distributions of the test statistics. We propose a bivariate null kernel (BNK) hypothesis testing method, which characterizes the joint distribution of the two test statistics in two-dimensional space. The power of this BNK approach is verified by three different simulation designs and one whole genome dataset. It solves a few challenging open statistical questions, closely separates the confounding between ‘linkage’ and ‘QTL effect’, makes a fine genome division, provides a comprehensive understanding of the entire genome, overcomes limitations of traditional QTL approaches, and connects traditional QTL mapping with the newest genotyping technologies. The proposed approach contributes to both the genetics literature and the statistics literature, and has a potential to be extended to broader fields where a bivariate test is needed.