Haplotype Block Partitioning and Tag SNP Selection Using Genotype Data and Their Applications to Association Studies

Recent studies have revealed that linkage disequilibrium (LD) patterns vary across the human genome with some regions of high LD interspersed by regions of low LD. A small fraction of SNPs (tag SNPs) is sufficient to capture most of the haplotype structure of the human genome. In this paper, we develop a method to partition haplotypes into blocks and to identify tag SNPs based on genotype data by combining a dynamic programming algorithm for haplotype block partitioning and tag SNP selection based on haplotype data with a variation of the expectation maximization (EM) algorithm for haplotype inference. We assess the effects of using either haplotype or genotype data in haplotype block identification and tag SNP selection as a function of several factors, including sample size, density or number of SNPs studied, allele frequencies, fraction of missing data, and genotyping error rate, using extensive simulations. We find that a modest number of haplotype or genotype samples will result in consistent block partitions and tag SNP selection. The power of association studies based on tag SNPs using genotype data is similar to that using haplotype data

Haplotype Block Partitioning and Tag SNP Selection Using Genotype Data and Their Applications to Association Studies

Recent studies have revealed that linkage disequilibrium (LD) patterns vary across the human genome with some regions of high LD interspersed by regions of low LD. A small fraction of SNPs (tag SNPs) is sufficient to capture most of the haplotype structure of the human genome. In this paper, we develop a method to partition haplotypes into blocks and to identify tag SNPs based on genotype data by combining a dynamic programming algorithm for haplotype block partitioning and tag SNP selection based on haplotype data with a variation of the expectation maximization (EM) algorithm for haplotype inference. We assess the effects of using either haplotype or genotype data in haplotype block identification and tag SNP selection as a function of several factors, including sample size, density or number of SNPs studied, allele frequencies, fraction of missing data, and genotyping error rate, using extensive simulations. We find that a modest number of haplotype or genotype samples will result in consistent block partitions and tag SNP selection. The power of association studies based on tag SNPs using genotype data is similar to that using haplotype data

pH-Responsive Polymer-Stabilized ZIF-8 Nanocomposites for Fluorescence and Magnetic Resonance Dual-Modal Imaging-Guided Chemo-/Photodynamic Combinational Cancer Therapy

A multifunctional diagnosis and treatment integration platform is crucial in cancer treatments. Here, we show that by integrating Gd-doped silicon nanoparticles (Si-Gd NPs), chlorine e6 (Ce6), doxorubicin (DOX), zeolitic imidazolate framework-8 (ZIF-8), poly(2-(diethylamino)ethyl methacrylate) polymers (HOOC-PDMAEMA-SH), and folic acid-polyethylene glycol)-maleimide (MaL-PEG-FA) into one single nanoplatform by a self-assembly method, novel multifunctional MOFs (named FZIF-8/DOX-PD-FA) are synthesized with great biocompatibility and tumor targeting as well as pH responsiveness and no drug leakage for drug delivery. In the design, Si-Gd NPs and Ce6 embedded in the nanocomposites are used for magnetic resonance and fluorescence dual-modal imaging, respectively. DOX loaded by the FZIF-8/DOX-PD-FA porous structure is used for chemotherapy, while Ce6 is excited by near-infrared radiation (NIR) for photodynamic therapy. In addition, the pH-responsive ability of HOOC-PDMAEMA-SH to effectively prevent drug leakage is demonstrated by drug release studies in vitro. From the results of confocal microscopy imaging in vitro and fluorescence/magnetic resonance imaging in vivo, FZIF-8/DOX-PD-FA showed a targeting effect on MCF-7 cancer cells. More importantly, the results of treatment experiments on tumor-bearing mice showed that the tumor volume of the FZIF-8/DOX-PD-FA + NIR group is decreased the most compared to the original volume. Owing to the unique dual-modal imaging capability and excellent chemo-/photodynamic combinational cancer therapy effect, the present hybrid nanocarrier provides a new research platform for a new generation of theranostic nanoparticles

Synaptic plasticity and filtering emulated in metal-organic frameworks nanosheets based transistors

Two‐dimensional (2D) metal–organic frameworks (MOFs) are widely used in a variety of mature applications, including catalysis, drug delivery, and sensors. Based on their highly accessible active sites, 2D MOFs are expected to be good charge trapping elements. Using 2D MOF, Zn‐TCPP (TCPP: tetrakis(4‐carboxyphenyl)porphyrin), as charge trapping materials by a simple solution process, a three‐terminal synaptic device which can realize the learning functions and signal transmission simultaneously is firstly fabricated. The as‐fabricated synaptic device exhibits ambipolar charge carrier trapping performance, large currenton/currentoff ratio (>103) and excellent endurance (500 cycle times). Moreover, the common biological synaptic behaviors, including postsynaptic current under different temperature, pulse duration time and pulse voltage, paired‐pulse facilitation, paired‐pulse depression, spiking rate dependent plasticity, dynamic filtering, transition from short‐term potentiation to long‐term potentiation, learning–forgetting–relearning process, are successfully simulated using our synaptic transistor. This research is highly relevant for broadening the application range of 2D MOFs and has important enlightenment for future neuromorphic computing