Genome sequencing can help us better understand, diagnose and treat disease. For example, healthcare providers are increasingly using genome sequencing to diagnose rare genetic diseases, such as elevated risk for breast cancer<\/a> or pulmonary arterial hypertension<\/a>, which are estimated to affect roughly 8% of the population<\/a>.<\/p>\n At Google Health, we\u2019re applying our technology and expertise to the field of genomics. Here are recent research and industry developments we\u2019ve made to help quickly identify genetic disease and foster the equity of genomic tests across ancestries. This includes an exciting new partnership with Pacific Biosciences<\/a> to further advance genomic technologies in research and the clinic.<\/p>\n<\/div>\n<\/div>\n Genetic diseases can cause critical illness, and in many cases, a timely identification of the underlying issue can allow for life-saving intervention. This is especially true in the case of newborns. Genetic or congenital conditions affect nearly 6% of births<\/a>, but clinical sequencing tests to identify these conditions typically take days or weeks to complete.<\/p>\n We recently worked with the University of California Santa Cruz Genomics Institute<\/a> to build a method \u2013 called PEPPER-Margin-DeepVariant<\/a> \u2013 that can analyze data for Oxford Nanopore sequencers, one of the fastest commercial sequencing technologies used today. This week, the New England Journal of Medicine published a study<\/a> led by the Stanford University School of Medicine detailing the use of this method to identify suspected disease-causing variants in five critical newborn intensive care unit (NICU) cases.<\/p>\n In the fastest cases, a likely disease-causing variant was identified less than 8 hours after sequencing began, compared to the prior fastest time<\/a> of 13.5 hours. In five cases, the method influenced patient care. For example, the team quickly turned around a diagnosis of Poirier\u2013Bienvenu neurodevelopmental disorder for one infant, allowing for timely, disease-specific treatment.<\/p>\n<\/div>\n<\/div>\n Looking forward, new sequencing instruments can lead to dramatic breakthroughs in the field. We believe machine learning (ML) can further unlock the potential of these instruments. Our new research partnership with Pacific Biosciences (PacBio)<\/a>, a developer of genomic sequence platforms, is a great example of how Google\u2019s machine learning and algorithm development tools can help researchers unlock more information from sequencing data.<\/p>\n PacBio\u2019s long-read HiFi<\/a> sequencing provides the most comprehensive view of genomes, transcriptomes and epigenomes. Using PacBio\u2019s technology in combination with DeepVariant<\/a>, our award-winning<\/a> variant detection method, researchers have been able to accurately identify diseases that are otherwise difficult to diagnose with alternative methods<\/a>.<\/p>\n Additionally, we developed a new open source method called DeepConsensus that, in combination with PacBio\u2019s sequencing platforms, creates more<\/a> accurate reads of sequencing data. This boost in accuracy will help researchers apply PacBio\u2019s technology to more challenges, such as the final completion of the Human Genome<\/a> and assembling the genomes of all vertebrate species<\/a>.<\/p>\n<\/p>\n Like other areas of health and medicine, the genomics field grapples with health equity issues that, if not addressed, could exclude certain populations. For example, the overwhelming majority of participants in genomic studies<\/a> have historically been of European ancestry. As a result, the genomics resources that scientists and clinicians use to identify and filter genetic variants and to interpret the significance of these variants are not equally powerful across individuals of all ancestries.<\/p>\n In the past year, we\u2019ve supported two initiatives aimed at improving methods and genomics resources for under-represented populations. We collaborated with 23andMe to develop an improved resource for individuals of African ancestry<\/a>, and we worked with the UCSC Genomics Institute<\/a> to develop pangenome methods<\/a> with this work recently published in Science<\/i><\/a>.<\/p>\n In addition, we recently published two open-source methods<\/a> that improve genetic discovery by more accurately identifying disease labels<\/a> and improving the use of health measurements<\/a> in genetic association studies.<\/p>\n We hope that our work developing and sharing these methods with those in the field of genomics will improve overall health and the understanding of biology for everyone. Working together with our collaborators, we can apply this work to real-world applications.<\/p>\n<\/div>\n<\/div>\n<\/div>\n Go to Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" Author: Genome sequencing can help us better understand, diagnose and treat disease. For example, healthcare providers are increasingly using genome sequencing to diagnose rare genetic […] Read More<\/a><\/span><\/p>\n","protected":false},"author":1,"featured_media":5354,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/5353"}],"collection":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/comments?post=5353"}],"version-history":[{"count":0,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/posts\/5353\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media\/5354"}],"wp:attachment":[{"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/media?parent=5353"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/categories?post=5353"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aiproblog.com\/index.php\/wp-json\/wp\/v2\/tags?post=5353"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Helping identify life-threatening disease when minutes matter<\/b><\/h3>\n
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<\/div>\n<\/div>\n<\/div>\nApplying machine learning to maximize the potential in sequencing data<\/b><\/h3>\n
Supporting more equitable genomics resources and methods<\/b><\/h3>\n