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                <title xml:lang="en">A Bioinformatics Workflow for Genetic Association Studies of Traits in Indonesian Rice</title>
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                  <title>2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia)</title>
                  <date type="start">2014-04-14</date>
                  <date type="end">2014-04-17</date>
                  <settlement>Bali</settlement>
                  <country key="ID">Indonesia</country>
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                <editor>David Hutchison</editor>
                <editor>Takeo Kanade</editor>
                <editor>Bernhard Steffen</editor>
                <editor>Demetri Terzopoulos</editor>
                <editor>Doug Tygar</editor>
                <editor>Gerhard Weikum</editor>
                <editor>Linawati</editor>
                <editor>Made Sudiana Mahendra</editor>
                <editor>Erich J. Neuhold</editor>
                <editor>A Min Tjoa</editor>
                <editor>Ilsun You</editor>
                <editor>Josef Kittler</editor>
                <editor>Jon M. Kleinberg</editor>
                <editor>Alfred Kobsa</editor>
                <editor>Friedemann Mattern</editor>
                <editor>John C. Mitchell</editor>
                <editor>Moni Naor</editor>
                <editor>Oscar Nierstrasz</editor>
                <editor>C. Pandu Rangan</editor>
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                  <biblScope unit="serie">Information and Communication Technology</biblScope>
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                <term xml:lang="en">statistical genetics</term>
                <term xml:lang="en">bioinformatics</term>
                <term xml:lang="en">genome-wide association study</term>
                <term xml:lang="en">agriculture genetics</term>
                <term xml:lang="en">workflow</term>
                <term xml:lang="en">data analysis</term>
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              <p>Asian rice is a staple food in Indonesia and worldwide, and its production is essential to food security. Cataloging and linking genetic variation in Asian rice to important traits, such as quality and yield, is needed in developing superior varieties of rice. We develop a bioinformatics workflow for quality control and data analysis of genetic and trait data for a diversity panel of 467 rice varieties found in Indonesia. The bioinformatics workflow operates using a back-end relational database for data storage and retrieval. Quality control and data analysis procedures are implemented and automated using the whole genome data analysis toolset, PLINK, and the [R] statistical computing language. The 467 rice varieties were genotyped using a custom array (717,312 genotypes total) and phenotyped for 12 traits in four locations in Indonesia across multiple seasons. We applied our bioinformatics workflow to these data and present prototype genome-wide association results for a continuous trait - days to flowering. Two genetic variants, located on chromosome 4 and 12 of the rice genome, showed evidence for association in these data. We conclude by outlining extensions to the workflow and plans for more sophisticated statistical analyses.</p>
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