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        <datestamp>2026-09-18T06:05:08Z</datestamp>
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          <dc:title>Data Sheet 1_Considering microRNAs as measures of extrinsic domains of risk in the context of underlying genetic characteristics: type 2 diabetes as an exemplar.docx</dc:title>
          <dc:creator>Benjamin M. Stroebel (20548382)</dc:creator>
          <dc:creator>Alexis Jimenez (25070593)</dc:creator>
          <dc:creator>Karla J. Lindquist (4594804)</dc:creator>
          <dc:creator>Dara Torgerson (345397)</dc:creator>
          <dc:creator>Kayla D. Longoria (19118367)</dc:creator>
          <dc:creator>J. Cesar Ignacio-Espinoza (25070596)</dc:creator>
          <dc:creator>Elena Flowers (3498278)</dc:creator>
          <dc:subject>Genetics</dc:subject>
          <dc:subject>disease etiology</dc:subject>
          <dc:subject>genetic admixture</dc:subject>
          <dc:subject>microRNAs</dc:subject>
          <dc:subject>risk prediction</dc:subject>
          <dc:subject>social determinants of health</dc:subject>
          <dc:description>&lt;p&gt;The etiology of most health conditions is complex, arising from a combination of risk factors that include both intrinsic (i.e., genetic characteristics) and extrinsic (e.g., behaviors, the environment, social factors) domains. Until relatively recently, health science research typically conflated the biological construct of genetic ancestry with the social constructs of race and ethnicity. The advent of ancestry informative markers (AIMs) and related statistical methods marked an important advance, enabling the quantification of genetic ancestry independent of the constructs of race and ethnicity. Another type of molecular marker (microRNAs) has shown potential utility for quantifying extrinsic domains of risk, including behavioral, environmental, and social factors, within the context of underlying genetic characteristics. Given these markers offer distinct yet complementary information, combining them may provide a more rigorous and comprehensive approach to evaluating the combined domains of intrinsic genetic risk with extrinsic risk factors to improve accuracy in prediction of risk for complex diseases like type 2 diabetes (T2D).&lt;/p&gt;</dc:description>
          <dc:date>2026-09-18T06:05:08Z</dc:date>
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          <dc:identifier>10.3389/fgene.2026.1934355.s001</dc:identifier>
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