A Correlation Technique to Reduce the Number of Predictors to Estimate the Survival Time of HIV/ AIDS Patients on ART

Till now, many research papers have been published which aims to estimate the survivle time of the HIV/AIDS patients taking into consideration all the predictors viz, Age, Sex, CD4, MOT, Smoking, Weight, HB, Coinfection, Time, BMI, Location Status, Marital Status, Drug etc, although all the predictors need not to be included in the model. Since some of the predictors may be correlated/ associated and may have some influence on the outcome variable, therefore, instead of taking both the significantly correlated/ associated predictors, we may take only one of the two. In this way, we may be able to reduce the number of predictors without affecting the estimated survival time. In this paper we have tried to reduce the number of predictors by determining the highly positively correlated predictors and then evaluating the effect of correlation/ association on the survival time of HIV/AIDS patients. These predictors that we have considered in the starting are Age, Sex, State, Smoking, Alcohol, Drugs, Opportunistic Infections (OI), Living Status (LS), Occupation (OC), Marital Status (MS) and Spouse for the data collected from 2004 to 2014 of AIDS patients in an ART center of Delhi, India. We have performed one – way ANOVA to test the association between a quantitative and a categorical variable and Chi-square test to test between two categorical variables. To select one of the two highly correlated/ associated predictors, a suitable model is fitted keeping one predictor independent at a time and other dependent and the model having the smaller AIC is considered and the independent variable in the model is included in the modified model. The fitted models are logistic, linear and multinomial logistic depending on the type of the independent variable to be fitted. Then the true model (having all the predictors) and the modified model (with reduced number of predictors) are compared on the basis of their AICs and the model having minimum AIC is chosen. In this way we could reduce the number of predictors by almost 50% without affecting the estimated survival time with a reduced standard error

Toward the sequence-based breeding in legumes in the post-genome sequencing era

Efficiency of breeding programs of legume crops such as chickpea, pigeonpea and groundnut has been considerably improved over the past decade through deployment of modern genomic tools and technologies. For instance, next-generation sequencing technologies have facilitated availability of genome sequence assemblies, re-sequencing of several hundred lines, development of HapMaps, high-density genetic maps, a range of marker genotyping platforms and identification of markers associated with a number of agronomic traits in these legume crops. Although marker-assisted backcrossing and marker-assisted selection approaches have been used to develop superior lines in several cases, it is the need of the hour for continuous population improvement after every breeding cycle to accelerate genetic gain in the breeding programs. In this context, we propose a sequence-based breeding approach which includes use of independent or combination of parental selection, enhancing genetic diversity of breeding programs, forward breeding for early generation selection, and genomic selection using sequencing/genotyping technologies. Also, adoption of speed breeding technology by generating 4–6 generations per year will be contributing to accelerate genetic gain. While we see a huge potential of the sequence-based breeding to revolutionize crop improvement programs in these legumes, we anticipate several challenges especially associated with high-quality and precise phenotyping at affordable costs, data analysis and management related to improving breeding operation efficiency. Finally, integration of improved seed systems and better agronomic packages with the development of improved varieties by using sequence-based breeding will ensure higher genetic gains in farmers’ fields

Molecular markers and genomic resources for disease resistance in peanut-A review

Recent polyploidation of peanut genome and geographical isolation has rendered peanut to be a highly monomorphic species. Due to its narrow genetic base, cultivated peanut has been susceptible to various diseases, causing economic loss to farmers. Availability of only a few disease resistance sources in cultivated peanut has resulted in limited success using the conventional breeding practices. Also, scarcity of markers has been the major limiting factor to precisely identify the disease resistance genomic regions. Recent identification of large number of molecular markers using advanced genomic resources and high throughput sequencing technologies has and will continue to assist in improvement of peanut diversity and breeding. This review gives an update on recent discovery of molecular markers associated with major diseases and the available genomic resources in peanut

Does improved oleic acid content due to marker-assisted introgression of ahFAD2 mutant alleles in peanuts alter its mineral and vitamin composition?

Peanuts (Arachis hypogaea L.) with high oleic acid content have extended shelf life and several health benefits. Oleic, linoleic, and palmitic acid contents in peanuts are regulated by ahFAD2A and ahFAD2B mutant alleles. In the present study, ahFAD2A and ahFAD2B mutant alleles from SunOleic 95R were introgressed into two popular peanut cultivars, GG-7 and TKG19A, followed by markers-assisted selection (MAS) and backcrossing (MABC). A total of 22 MAS and three MABC derived lines were developed with increased oleic acid (78–80%) compared to those of GG 7 (40%) and TKG 19A (50%). Peanut kernel mineral and vitamin composition remained unchanged, while potassium content was altered in high oleic ingression lines. Two introgression lines, HOMS Nos. 37 and 113 had over 10% higher pooled pod yield than respective best check varieties. More than 70% recurrent parent genome recovery was observed in HOMS-37 and HOMS-113 through recombination breeding. However, the absence of recombination in the vicinity of the target locus resulted in its precise introgression along with ample background genome recovery. Selected introgression lines could be released for commercial cultivation based on potential pod yield and oleic acid content

Identification of two major quantitative trait locus for fresh seed dormancy using the diversity arrays technology and diversity arrays technology-seq based genetic map in Spanish-type peanuts

Seed quality for both germination in the next generation and for human consumption is adversely affected due to preharvest sprouting in peanut. It also makes seeds more vulnerable to infection by a number of pathogens. Therefore, it is desirable to have 2–3 weeks of fresh seed dormancy (FSD) in the peanut varieties. In this context, one F2 population was developed from a cross between non-dormant (ICGV 00350) and dormant (ICGV 97045) genotypes. Phenotyping of this population showed control of the trait by two recessive genes. In parallel, genotyping of the population with Diversity Arrays Technology (DArT) and DArT-seq markers provided a genetic map with 1152 loci covering a map distance of 2423.12 cM and map density of 2.96 cM/loci. Quantitative trait locus (QTL) analysis identified two major QTLs, namely qfsd-1 and qfsd-2 explaining 22.14% and 71.21% of phenotypic variation, respectively. These QTLs, after validation in different genetic backgrounds, may be useful for molecular breeding for FSD in peanut

Marker-assisted introgression of a QTL region to improve rust resistance in three elite and popular varieties of peanut (Arachis hypogaea L.)

Leaf rust, caused by Puccinia arachidis Speg, is one of the major devastating diseases in peanut (Arachis hypogaea L.). One QTL region on linkage group AhXV explaining upto 82.62 % phenotypic variation for rust resistance was validated and introgressed from cultivar ‘GPBD 4’ into three rust susceptible varieties (‘ICGV 91114’, ‘JL 24’ and ‘TAG 24’) through marker-assisted backcrossing (MABC). The MABC approach employed a total of four markers including one dominant (IPAHM103) and three co-dominant (GM2079, GM1536, GM2301) markers present in the QTL region. After 2–3 backcrosses and selfing, 200 introgression lines (ILs) were developed from all the three crosses. Field evaluation identified 81 ILs with improved rust resistance. Those ILs had significantly increased pod yields (56–96 %) in infested environments compared to the susceptible parents. Screening of selected 43 promising ILs with 13 markers present on linkage group AhXV showed introgression of the target QTL region from the resistant parent in 11 ILs. Multi-location field evaluation of these ILs should lead to the release of improved varieties. The linked markers may be used in improving rust resistance in peanut breeding programmes

Genomic tools in groundnut breeding program: Status and perspectives

Groundnut, a nutrient-rich food legume, is cultivated world over. It is valued for its good quality cooking oil, energy and protein rich food, and nutrient-rich fodder. Globally, groundnut improvement programs have developed varieties to meet the preferences of farmers, traders, processors, and consumers. Enhanced yield, tolerance to biotic and abiotic stresses and quality parameters have been the target traits. Spurt in genetic information of groundnut was facilitated by development of molecular markers, genetic, and physical maps, generation of expressed sequence tags (EST), discovery of genes, and identification of quantitative trait loci (QTL) for some important biotic and abiotic stresses and quality traits. The first groundnut variety developed using marker assisted breeding (MAB) was registered in 2003. Since then, USA, China, Japan, and India have begun to use genomic tools in routine groundnut improvement programs. Introgression lines that combine foliar fungal disease resistance and early maturity were developed using MAB. Establishment of marker-trait associations (MTA) paved way to integrate genomic tools in groundnut breeding for accelerated genetic gain. Genomic Selection (GS) tools are employed to improve drought tolerance and pod yield, governed by several minor effect QTLs. Draft genome sequence and low cost genotyping tools such as genotyping by sequencing (GBS) are expected to accelerate use of genomic tools to enhance genetic gains for target traits in groundnut

Comparative transcriptome analysis identified candidate genes for late leaf spot resistance and cause of defoliation in groundnut

Late leaf spot (LLS) caused by fungus Nothopassalora personata in groundnut is responsible for up to 50% yield loss. To dissect the complex nature of LLS resistance, comparative transcriptome analysis was performed using resistant (GPBD 4), susceptible (TAG 24) and a resistant introgression line (ICGV 13208) and identified a total of 12,164 and 9954 DEGs (differentially expressed genes) respectively in A- and B-subgenomes of tetraploid groundnut. There were 135 and 136 unique pathways triggered in A- and B-subgenomes, respectively, upon N. personata infection. Highly upregulated putative disease resistance genes, an RPP-13 like (Aradu.P20JR) and a NBS-LRR (Aradu.Z87JB) were identified on chromosome A02 and A03, respectively, for LLS resistance. Mildew resistance Locus (MLOs)-like proteins, heavy metal transport proteins, and ubiquitin protein ligase showed trend of upregulation in susceptible genotypes, while tetratricopeptide repeats (TPR), pentatricopeptide repeat (PPR), chitinases, glutathione S-transferases, purple acid phosphatases showed upregulation in resistant genotypes. However, the highly expressed ethylene responsive factor (ERF) and ethylene responsive nuclear protein (ERF2), and early responsive dehydration gene (ERD) might be related to the possible causes of defoliation in susceptible genotypes. The identified disease resistance genes can be deployed in genomics-assisted breeding for development of LLS resistant cultivars to reduce the yield loss in groundnut

Genome-Wide Identification and Expression of FAR1 Gene Family Provide Insight Into Pod Development in Peanut (Arachis hypogaea)

The far-red-impaired response 1 (FAR1) transcription family were initially identified as important factors for phytochrome A (phyA)-mediated far-red light signaling in Arabidopsis; they play crucial roles in controlling the growth and development of plants. The reported reference genome sequences of Arachis, including A. duranensis, A. ipaensis, A. monticola, and A. hypogaea, and its related species Glycine max provide an opportunity to systematically perform a genome-wide identification of FAR1 homologous genes and investigate expression patterns of these members in peanut species. Here, a total of 650 FAR1 genes were identified from four Aarchis and its closely related species G. max. Of the studied species, A. hypogaea contained the most (246) AhFAR1 genes, which can be classified into three subgroups based on phylogenic relationships. The synonymous (Ks) and non-synonymous (Ka) substitution rates, phylogenetic relationship and synteny analysis of the FAR1 family provided deep insight into polyploidization, evolution and domestication of peanut AhFAR1 genes. The transcriptome data showed that the AhFAR1 genes exhibited distinct tissue- and stage-specific expression patterns in peanut. Three candidate genes including Ahy_A10g049543, Ahy_A06g026579, and Ahy_A10g048401, specifically expressed in peg and pod, might participate in pod development in the peanut. The quantitative real-time PCR (qRT-PCR) analyses confirmed that the three selected genes were highly and specifically expressed in the peg and pod. This study systematically analyzed gene structure, evolutionary characteristics and expression patterns of FAR1 gene family, which will provide a foundation for the study of genetic and biological function in the future

Identification of QTLs associated with oil content and mapping FAD2 genes and their relative contribution to oil quality in peanut (Arachis hypogaeaL.)

Background Peanut is one of the major source for human consumption worldwide and its seed contain approximately 50% oil. Improvement of oil content and quality traits (high oleic and low linoleic acid) in peanut could be accelerated by exploiting linked markers through molecular breeding. The objective of this study was to identify QTLs associated with oil content, and estimate relative contribution of FAD2 genes (ahFAD2A and ahFAD2B) to oil quality traits in two recombinant inbred line (RIL) populations. Results Improved genetic linkage maps were developed for S-population (SunOleic 97R × NC94022) with 206 (1780.6 cM) and T-population (Tifrunner × GT-C20) with 378 (2487.4 cM) marker loci. A total of 6 and 9 QTLs controlling oil content were identified in the S- and T-population, respectively. The contribution of each QTL towards oil content variation ranged from 3.07 to 10.23% in the S-population and from 3.93 to 14.07% in the T-population. The mapping positions for ahFAD2A (A sub-genome) and ahFAD2B (B sub-genome) genes were assigned on a09 and b09 linkage groups. The ahFAD2B gene (26.54%, 25.59% and 41.02% PVE) had higher phenotypic effect on oleic acid (C18:1), linoleic acid (C18:2), and oleic/linoleic acid ratio (O/L ratio) than ahFAD2A gene (8.08%, 6.86% and 3.78% PVE). The FAD2 genes had no effect on oil content. This study identified a total of 78 main-effect QTLs (M-QTLs) with up to 42.33% phenotypic variation (PVE) and 10 epistatic QTLs (E-QTLs) up to 3.31% PVE for oil content and quality traits. Conclusions A total of 78 main-effect QTLs (M-QTLs) and 10 E-QTLs have been detected for oil content and oil quality traits. One major QTL (more than 10% PVE) was identified in both the populations for oil content with source alleles from NC94022 and GT-C20 parental genotypes. FAD2 genes showed high effect for oleic acid (C18:1), linoleic acid (C18:2), and O/L ratio while no effect on total oil content. The information on phenotypic effect of FAD2 genes for oleic acid, linoleic acid and O/L ratio, and oil content will be applied in breeding selection