Biology subjects

Pandit, A.

Publications and source records attributed to Pandit, A..

4 recordsLinked to original sources

bioRxiv2018

Conformational dynamics of a photosynthetic light-harvesting complex in native thylakoid membranes

Photosynthetic light-harvesting complexes of higher plants, moss and green algae can undergo dynamic conformational transitions, which have been correlated to their ability to adapt to fluctuations in the light environment. Herein, we demonstrate the application of solid-state NMR spectroscopy on native, heterogeneous thylakoid membranes of Chlamydomonas reinhardtii (Cr) and on Cr Light-Harvesting Complex II (LHCII) in thylakoid lipid bilayers to detect LHCII conformational dynamics in its native membrane environment. We show that membrane-reconstituted LHCII contains selective sites that undergo fast, large-amplitude motions, including the phytol tails of two chlorophylls. Protein plasticity is also observed in the N-terminal stromal loop and in protein fragments facing the lumen, involving sites that stabilize the xanthophyll-cycle carotenoid violaxanthin and the two luteins. The results report on the intrinsic flexibility of LHCII pigment-protein complexes in a membrane environment, revealing putative sites for conformational switching. In thylakoid membranes, fast dynamics of protein and pigment sites is significantly reduced, which suggests that in their native organelle membranes, LHCII complexes are locked in specific conformational states. STATEMENT OF SIGNIFICANCEPhotosynthetic Light-Harvesting Complexes undergo dynamic conformational transitions that regulate the capacity of the light-harvesting antenna. We demonstrate the application of solid-state (ss)NMR spectroscopy to investigate the structural dynamics of LHCII, the most abundant LHC complex of plants and algae, in native membranes. Selective dynamic protein and pigment residues are identified that are putative sites for a conformational switch.

biophysics↗

bioRxiv2017

Proper Conditional Analysis in the Presence of Missing Data Identified Novel Independently Associated Low Frequency Variants in Nicotine Dependence Genes

Meta-analysis of genetic association studies increases sample size and the power for mapping complex traits. Existing methods are mostly developed for datasets without missing values. In practice, genotype imputation is not always effective, e.g. when targeted genotyping/sequencing assays are used or when the un-typed genetic variant is rare. Therefore, contributed summary statistics often contain missing values. Naive extensions of existing methods either replace missing summary statistics with 0 or discard studies with missing data. These approaches can bias genetic effect estimates and lead to seriously inflated type-I or II errors in conditional analysis, which is a critical tool for identifying independently associated variants.\n\nTo address this challenge and complement imputation methods, we developed a method to combine summary statistics across participating studies and consistently estimate joint effects, even when the contributed summary statistics contain large amount of missing values. Based on this estimator, we propose a score statistic we call PCBS (partial correlation based score statistic) for conditional analysis of single-variant and gene-level associations. Through extensive analysis of simulated and real data, we showed that the new method produces well-calibrated type-I errors and is substantially more powerful than existing approaches. We applied the proposed approach to analyze the CHRNA5-CHRNB4-CHRNA3 locus in a large-scale meta-analysis for cigarettes-per-day. Using the new method, we identified three novel variants, independent of known association signals, which were otherwise missed by alternative methods. Together, the phenotypic variance explained by these variants is .46%, improving that of previously reported associations by 17%. These findings illustrate the extent of locus allelic heterogeneity and can help pinpoint causal variants.\n\nAUTHOR SUMMARYIt is of great interest to estimate the joint and conditional effects of multiple correlated variants from large scale meta-analysis, in order to fine map causal variants and understand the genetic architecture for complex traits. The contributed summary statistics from participating studies in a meta-analysis often contain missing values, as the imputation methods are not often effective, especially when the underlying genetic variant is rare or the participating studies use targeted genotyping array that is not suitable for imputation. Existing meta-analysis methods do not properly handle missing data, and can incorrectly estimate correlations between score statistics. As a result, they can produce highly biased estimates of joint effects and highly inflated type-I errors for conditional analysis, which will in turn result in overestimated phenotypic variance explained and incorrect identification of causal variants. We systematically evaluated this bias and proposed a novel partial correlation based score statistic. The new statistic has valid type-I errors for conditional analysis and much higher power than the existing methods, even when the contributed summary statistics in the meta-analysis contain a large fraction of missing values. We expect this method to be highly useful in the sequencing age for complex trait genetics.

genetics↗

bioRxiv2017

Association Analysis and Meta-Analysis of Multi-allelic Variants for Large Scale Sequence Data

MotivationThere is great interest to understand the impact of rare variants in human diseases using large sequence datasets. In deep sequences datasets of >10,000 samples, [~]10% of the variant sites are observed to be multi-allelic. Many of the multi-allelic variants have been shown to be functional and disease relevant. Proper analysis of multi-allelic variants is critical to the success of a sequencing study, but existing methods do not properly handle multi-allelic variants and can produce highly misleading association results.\n\nResultsWe propose novel methods to encode multi-allelic sites, conduct single variant and gene-level association analyses, and perform meta-analysis for multi-allelic variants. We evaluated these methods through extensive simulations and the study of a large meta-analysis of [~]18,000 samples on the cigarettes-per-day phenotype. We showed that our joint modeling approach provided an unbiased estimate of genetic effects, greatly improved the power of single variant association tests, and enhanced gene-level tests over existing approaches.\n\nAvailabilitySoftware packages implementing these methods are available at (https://github.com/zhanxw/rvtests http://genome.sph.umich.edu/wiki/RareMETAL).\n\nContactxiaowei.zhan@utsouthwestem.edu; dajiang.liu@psu.edu

bioinformatics↗

bioRxiv2017

Genetics of the Research Domain Criteria (RDoC): genome-wide association study of delay discounting

Delay discounting (DD), which is the tendency to discount the value of delayed versus current rewards, is elevated in a constellation of diseases and behavioral conditions. We performed a genome-wide association study of DD using 23,127 research participants of European ancestry. The most significantly associated SNP was rs6528024 (P = 2.40 x 10-8), which is located in an intron of the gene GPM6B. We also showed that 12% of the variance in DD was accounted for by genotype, and that the genetic signature of DD overlapped with attention-deficit/hyperactivity disorder, schizophrenia, major depression, smoking, personality, cognition, and body weight.

genetics↗