RESULTS: A significant nonparametric linkage (NPL) score was detected in family 100. Other suggestive NPL and logarithm of the odds (LOD) scores were attained from families 50, 58, 99 and 100 under autosomal recessive mode. Heterogeneity LOD (HLOD) score ≥ 1 was determined for all families, confirming genetic heterogeneity of the population and indicating that a proportion of families might be linked to each other. Several candidate genes in linkage intervals were determined; LPHN2 at 1p31, SATB2 at 2q33.1-q35, PVRL3 at 3q13.3, COL21A1 at 6p12.1, FOXP2 at 7q22.3-q33, FOXG1 and HECTD1 at 14q12 and TOX3 at 16q12.1.
CONCLUSIONS: We have identified several novel and known candidate genes for nonsyndromic cleft lip and/or palate through genome-wide linkage analysis. Further analysis of the involvement of these genes in the condition will shed light on the disease mechanism. Comprehensive genetic testing of the candidate genes is warranted.
RESULTS: In this study, we propose the Context Based Dependency Network (CBDN), a method that is able to infer gene regulatory networks with the regulatory directions from gene expression data only. To determine the regulatory direction, CBDN computes the influence of source to target by evaluating the magnitude changes of expression dependencies between the target gene and the others with conditioning on the source gene. CBDN extends the data processing inequality by involving the dependency direction to distinguish between direct and transitive relationship between genes. We also define two types of important regulators which can influence a majority of the genes in the network directly or indirectly. CBDN can detect both of these two types of important regulators by averaging the influence functions of candidate regulator to the other genes. In our experiments with simulated and real data, even with the regulatory direction taken into account, CBDN outperforms the state-of-the-art approaches for inferring gene regulatory network. CBDN identifies the important regulators in the predicted network: 1. TYROBP influences a batch of genes that are related to Alzheimer's disease; 2. ZNF329 and RB1 significantly regulate those 'mesenchymal' gene expression signature genes for brain tumors.
CONCLUSION: By merely leveraging gene expression data, CBDN can efficiently infer the existence of gene-gene interactions as well as their regulatory directions. The constructed networks are helpful in the identification of important regulators for complex diseases.
Methods: We performed whole-genome sequencing on 121 H. pylori clinical strains, among which 73 were metronidazole-resistant. Sequence-alignment analysis of core protein clusters derived from clinical strains containing full-length RdxA was performed. Variable sites in each alignment were statistically compared between the resistant and susceptible groups to determine candidate genes along with their respective amino-acid changes that may account for the development of metronidazole resistance in H. pylori.
Results: Resistance due to RdxA truncation was identified in 34% of metronidazole-resistant strains. Analysis of core protein clusters derived from the remaining 48 metronidazole-resistant strains and 48 metronidazole-susceptible identified four variable sites significantly associated with metronidazole resistance. These sites included R16H/C in RdxA, D85N in the inner-membrane protein RclC (HP0565), V265I in a biotin carboxylase protein (HP0370) and A51V/T in a putative threonylcarbamoyl-AMP synthase (HP0918).
Conclusions: Our approach identified new potential mechanisms for metronidazole resistance in H. pylori that merit further investigation.