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Among the most fundamental of these summary statistics are\n                    <jats:italic>\u03c0<\/jats:italic>\n                    and\n                    <jats:italic>\n                      d\n                      <jats:sub>XY<\/jats:sub>\n                    <\/jats:italic>\n                    , which are used to describe genetic diversity within and between populations, respectively. Here, we address a widespread issue in\n                    <jats:italic>\u03c0<\/jats:italic>\n                    and\n                    <jats:italic>\n                      d\n                      <jats:sub>XY<\/jats:sub>\n                    <\/jats:italic>\n                    calculation: systematic bias generated by missing data of various types. Many popular methods for calculating\n                    <jats:italic>\u03c0<\/jats:italic>\n                    and\n                    <jats:italic>\n                      d\n                      <jats:sub>XY<\/jats:sub>\n                    <\/jats:italic>\n                    operate on data encoded in the variant call format (VCF), which condenses genetic data by omitting invariant sites. When calculating\n                    <jats:italic>\u03c0<\/jats:italic>\n                    and\n                    <jats:italic>\n                      d\n                      <jats:sub>XY<\/jats:sub>\n                    <\/jats:italic>\n                    using a VCF, it is often implicitly assumed that missing genotypes (including those at sites not represented in the VCF) are homozygous for the reference allele. Here, we show how this assumption can result in substantial downward bias in estimates of\n                    <jats:italic>\u03c0<\/jats:italic>\n                    and\n                    <jats:italic>\n                      d\n                      <jats:sub>XY<\/jats:sub>\n                    <\/jats:italic>\n                    that is directly proportional to the amount of missing data. We discuss the pervasive nature and importance of this problem in population genetics, and introduce a user\u2010friendly UNIX command line utility, pixy, that solves this problem via an algorithm that generates unbiased estimates of\n                    <jats:italic>\u03c0<\/jats:italic>\n                    and\n                    <jats:italic>\n                      d\n                      <jats:sub>XY<\/jats:sub>\n                    <\/jats:italic>\n                    in the face of missing data. We compare pixy to existing methods using both simulated and empirical data, and show that pixy alone produces unbiased estimates of\n                    <jats:italic>\u03c0<\/jats:italic>\n                    and\n                    <jats:italic>\n                      d\n                      <jats:sub>XY<\/jats:sub>\n                    <\/jats:italic>\n                    regardless of the form or amount of missing data. In summary, our software solves a long\u2010standing problem in applied population genetics and highlights the importance of properly accounting for missing data in population genetic analyses.\n                  <\/jats:p>","DOI":"10.1111\/1755-0998.13326","type":"journal-article","created":{"date-parts":[[2021,1,16]],"date-time":"2021-01-16T16:42:01Z","timestamp":1610815321000},"page":"1359-1368","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":751,"title":["<scp>pixy<\/scp>\n                    : Unbiased estimation of nucleotide diversity and divergence in the presence of missing data"],"prefix":"10.1111","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2648-4707","authenticated-orcid":false,"given":"Katharine L.","family":"Korunes","sequence":"first","affiliation":[{"name":"Department of Evolutionary Anthropology Duke University Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0396-465X","authenticated-orcid":false,"given":"Kieran","family":"Samuk","sequence":"additional","affiliation":[{"name":"Department of Biology Duke University Durham NC USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,2,5]]},"reference":[{"key":"e_1_2_8_2_1","volume-title":"Picard toolkit. 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