{"id":441591,"date":"2017-10-13T00:00:32","date_gmt":"2017-10-13T07:00:32","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=441591"},"modified":"2022-01-03T11:19:21","modified_gmt":"2022-01-03T19:19:21","slug":"using-large-scale-genomic-databases-to-improve-disease-variant-interpretation","status":"publish","type":"msr-video","link":"https:\/\/www.microsoft.com\/en-us\/research\/video\/using-large-scale-genomic-databases-to-improve-disease-variant-interpretation\/","title":{"rendered":"Using Large Scale Genomic Databases to Improve Disease Variant Interpretation"},"content":{"rendered":"<p>Rapid advances in sequencing technology have led to the generation of genome-scale DNA sequencing data for more than 2 million individuals worldwide. These data represent incredibly powerful information about the distribution and impact of genetic variation, but major challenges remain to aggregating and harmonizing them. In this presentation, I will describe the development of the Exome Aggregation Consortium (ExAC) and Genome Aggregation Database (gnomAD) databases, which combined represent exome and genome sequencing data for over 135,000 individuals. I will discuss approaches to analyzing genome data at massive scale and the applications of these data to understanding human variation and gene function.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Rapid advances in sequencing technology have led to the generation of genome-scale DNA sequencing data for more than 2 million individuals worldwide. These data represent incredibly powerful information about the distribution and impact of genetic variation, but major challenges remain to aggregating and harmonizing them. In this presentation, I will describe the development of the [&hellip;]<\/p>\n","protected":false},"featured_media":441597,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"research-area":[13553],"msr-video-type":[],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-441591","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-medical-health-genomics","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/7Igd4I4m_Ng","msr_secondary_video_url":"","msr_video_file":"","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/441591"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":1,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/441591\/revisions"}],"predecessor-version":[{"id":441594,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/441591\/revisions\/441594"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/441597"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=441591"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=441591"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=441591"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=441591"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=441591"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=441591"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}