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Acharya, C.B.

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Acharya
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C.B.
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Acharya, C.B.

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    Novel methods to optimize genotypic imputation for low-coverage, next- generation sequence data in crops plants
    (CSSA, 2014) Swarts, K.; Huihui Li; Romero Navarro, J.A.; Dong An; Romay, M.C.; Hearne, S.; Acharya, C.B.; Glaubitz, J.C.; Mitchell, S.; Elshire, R.; Buckler, E.; Bradbury, P.
    Publication
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    The conservation agriculture roadmap for India: policy brief
    (ICAR, 2018) Jat, M.L.; Biswas, A.K.; Pathak, H.; Mcdonald, A.; Patra, A.K.; Acharya, C.B.; Sharma, P.C.; Chaudhari, S.K.; Singh, R.; Bhaskar, S.; Sharma, R.; Jat, H.S.; Agarwal, T.; Gathala, M.K.; Pal, S.; Sidhu, H.S.; Yadvinder-Singh; Chhokar, R.S.; Keil, A.; Saharawat, Y.S.; Jat, R.K.; Singh, B.; Malik, R.; Sharma, A.R.; Parihar, C.M.; Das, T.K.; Singh, V.K.; Jat, S.L.; Jha, B.K.; Pratibha, M.; Singh, P.; Singh, R.C.; Choudhary, O.P.; Sharma, S.; Satyanarayana, T.; Sidhu, B.S.; Gehlawat, S.K.; Sen, S.K.; Singh, A.K.; Sikka, A.K.
    Agriculture remains central to the Indian economy, providing livelihood to the majority of its population. Though Indian agriculture have made spectacular progress for food self-sufficiency, yet growing challenges of large management yield gaps, low water and nutrient efficiency, imbalance and inadequate use of external production inputs, diminishing farm profits, deterioration of soil health and environmental quality coupled with climate risks are major concerns. Feeding a growing population with increasing dietary preferences for resource-intensive food products is a major challenge. Moreover, with no scope for horizontal expansion of farming to produce needed food; improving agronomic productivity and achieving high and stable yields under changing and uncertain climate are important for feeding the growing population. Increasing climatic variability affects most of the biological, physical and chemical processes that drive productivity of agricultural systems. The productivity and stability of agricultural systems depends upon measurable factors and processes controlled by climate and non-climate drivers of production paradigm. It is therefore vitally important to develop strategies and practices to sustainably increase food production while increasing farm income, protecting natural resources and minimizing environmental footprints.
    Publication
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    Dissecting genome-wide association signals for loss-of-function phenotypes in sorghum flavonoid pigmentation traits
    (Genetics Society of America, 2013) Morris, G.P.; Rhodes, D.H.; Brenton, Z.; Punna, R.; Vinayan, M.T.; Deshpande, S.; Hash, C.T.; Acharya, C.B.; Mitchell, S.; Buckler, E.; Jianming Yu; Kresovich, S.
    Genome-wide association studies (GWAS) are a powerful method to dissect the genetic basis of traits, though in practice the effects of complex genetic architecture and population structure remain poorly understood. To compare mapping strategies we dissect the genetic control of flavonoid pigmentation traits in the cereal grass sorghum using high-resolution genotyping-by-sequencing (GBS) SNP markers. Studying the grain tannin trait, we find that General Linear Models (GLM) are not able to precisely map tan1-a, a known loss-of-function allele of the Tannin1 gene, with either a small panel (n = 142) or large association panel (n = 336), and that indirect associations limit the mapping of the Tannin1 locus to Mb-resolution. A GLM that accounts for population structure (Q) or standard Mixed Linear Model (MLM) that accounts for kinship (K) can identify tan1-a, while compressed MLMs performs worse than the naive GLM. Interestingly, a simple loss-of-function genome scan, for genotype-phenotype covariation only in the putative loss-of-function allele, is able to precisely identify the Tannin1 gene without considering relatedness. We also find that the tan1-a allele can be mapped with gene resolution in a biparental recombinant inbred line (RIL) family (n = 263) using GBS markers, but lower precision in the mapping of vegetative pigmentation traits suggest that consistent gene-level resolution will likely require larger families or multiple RILs. These findings highlight that complex association signals can emerge from even the simplest traits given epistasis and structured alleles, but that gene-resolution mapping of these traits is possible with high marker density and appropriate models.
    Publication