{"id":354,"date":"2012-08-13T14:31:57","date_gmt":"2012-08-13T14:31:57","guid":{"rendered":"https:\/\/blogs.bmj.com\/jmg\/?p=354"},"modified":"2026-02-25T20:33:47","modified_gmt":"2026-02-25T20:33:47","slug":"quantitative-trait-locus-analysis-for-next-generation-sequencing-with-the-functional-linear-models","status":"publish","type":"post","link":"https:\/\/blogs.bmj.com\/jmg\/2012\/08\/13\/quantitative-trait-locus-analysis-for-next-generation-sequencing-with-the-functional-linear-models\/","title":{"rendered":"Quantitative trait locus analysis for next-generation sequencing with the functional linear models (Contributed by Dr. Momiao Xiong)"},"content":{"rendered":"

It is now well documented that next-generation sequencing (NGS) can generate several millions or even dozens of millions of genetic variation data. As a consequence, these genetic variation data are so densely distributed across the genome that the genetic variation can be modeled as a function of genomic location. But, standard multivariate statistical analysis often fails with functional data. The emergence of NGS demands a paradigm shift in the analytic methods for QTL(eQTL) analysis from standard single-variate or multivariate data analysis to functional data analysis. We propose a functional linear model (FLM) as a general analytic platform for testing the association of the entire allelic spectrum of genetic variation with a quantitative trait. Simulations and real data analysis show that the FLM can substantially outperform many exiting statistics for QTL analysis with NGS data. The FLM is expected to open a new route for QTL analysis. (http:\/\/jmg.bmj.com\/content\/49\/8\/513.abstract?etoc<\/a> )<\/p>\n","protected":false},"excerpt":{"rendered":"

It is now well documented that next-generation sequencing (NGS) can generate several millions or even dozens of millions of genetic variation data. As a consequence, these genetic variation data are so densely distributed across the genome that the genetic variation can be modeled as a function of genomic location. But, standard multivariate statistical analysis often […]<\/p>\n

Read More…<\/a><\/p>\n","protected":false},"author":123,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-354","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\nQuantitative trait locus analysis for next-generation sequencing with the functional linear models (Contributed by Dr. Momiao Xiong) - JMG Contact blog<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blogs.bmj.com\/jmg\/2012\/08\/13\/quantitative-trait-locus-analysis-for-next-generation-sequencing-with-the-functional-linear-models\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Quantitative trait locus analysis for next-generation sequencing with the functional linear models (Contributed by Dr. Momiao Xiong) - JMG Contact blog\" \/>\n<meta property=\"og:description\" content=\"It is now well documented that next-generation sequencing (NGS) can generate several millions or even dozens of millions of genetic variation data. As a consequence, these genetic variation data are so densely distributed across the genome that the genetic variation can be modeled as a function of genomic location. 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