Article in Genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
2 · The registry
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what money
Authors and funding
8 authors.
Eun Yong KangDepartment of Computer Science, University of California, Los Angeles, California 90095.
Cue Hyunkyu LeeDepartment of Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea.ORCID 0000-0002-4482-5640
Nicholas A FurlotteDepartment of Computer Science, University of California, Los Angeles, California 90095.
Jong Wha J JooDepartment of Computer Science and Engineering, Dongguk University-Seoul, Seoul 04620, Republic of Korea.
Emrah KostemDepartment of Computer Science, University of California, Los Angeles, California 90095.
Noah ZaitlenDepartment of Medicine, University of California, San Francisco, California 94143.
Eleazar EskinDepartment of Computer Science, University of California, Los Angeles, California 90095 eeskin@cs.ucla.edu buhm.han@snu.ac.kr.
Buhm HanDepartment of Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea eeskin@cs.ucla.edu buhm.han@snu.ac.kr.ORCID 0000-0002-2266-5164
Funding
ULTRASTRUCTURE OF THE INTIMA IN EARLY LESION FORMATIONP01HL030568 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FOGELMAN, ALAN M · 1985 to 2019
$55.2M
Systems genomics of metabolic syndrome traitsP01HL028481 · NHLBI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PAJUKANTA, PAIVI · 1985 to 2019
$50.2M
Substance Abuse & Behavioral Disinhibition: Integrating Genes & EnvironmentU01DA024417 · NIDA · UNIVERSITY OF MINNESOTA · PI IACONO, WILLIAM G. · 2007 to 2010
$10.8M
INTEGRATIVE GENETIC APPROACHES TO GENE-AIR POLLUTION INTERACTIONS IN ASTHMAR01ES021801 · NIEHS · UNIVERSITY OF SOUTHERN CALIFORNIA · PI AKBARI, OMID, ALLAYEE, HOOMAN · 2012 to 2016
$3.9M
An integrated molecular approach to understand variation in iron metabolismR01GM083198 · NIGMS · UNIVERSITY OF FLORIDA · PI VULPE, CHRISTOPHER D · 2012 to 2015
$2.7M
Genetic Regulation of Gene Expression and its Impact on Phenotypes - SupplementR01MH101782 · NIMH · STANFORD UNIVERSITY · PI SABATTI, CHIARA · 2013 to 2016
$1.3M
Correcting for Population Structure in Gene-by-Environment Interaction StudiesR01ES022282 · NIEHS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ESKIN, ELEAZAR · 2013 to 2015
$1.2M
Richer Models of Asthma Risk: Bridging the Environment-Genetics DivideK25HL121295 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ZAITLEN, NOAH A · 2014 to 2018
$784k
Discovering the Genetic Basis of HypertensionK25HL080079 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ESKIN, ELEAZAR · 2006 to 2010
Over the past few years, genome-wide association studies have identified many trait-associated loci that have different effects on females and males, which increased attention to the genetic architecture differences between the sexes. The between-sex differences in genetic architectures can cause a variety of phenomena such as differences in the effect sizes at trait-associated loci, differences in the magnitudes of polygenic background effects, and differences in the phenotypic variances. However, current association testing approaches for dealing with sex, such as including sex as a covariate, cannot fully account for these phenomena and can be suboptimal in statistical power. We present a novel association mapping framework, MetaSex, that can comprehensively account for the genetic architecture differences between the sexes. Through simulations and applications to real data, we show that our framework has superior performance than previous approaches in association mapping.
Indexed as
Sex CharacteristicsAlgorithmsChromosome MappingComputational BiologyFemaleGenome-Wide Association StudyHumansMaleMultifactorial InheritanceQuantitative Trait LociAssociation MappingGenetics of SexGenome-Wide Association StudyLinear Mixed ModelMeta-Analysis
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.
An Association Mapping Framework To Account for Potential Sex Difference in Genetic Architectures. · full record | OpenQuestion