What is she best known for?
The fields of study she is best known for:
- Gene
- Genetics
- Internal medicine
Lene Christiansen mostly deals with Genetics, Single-nucleotide polymorphism, Longevity, Genome-wide association study and Allele.
Her Genetics research is multidisciplinary, relying on both Young adult and Physiology.
Her studies in Single-nucleotide polymorphism integrate themes in fields like Odds ratio, Apolipoprotein E, Telomerase RNA component and Cohort.
Her work deals with themes such as Meta-analysis, Longitudinal study and Polymorphism, Genotype, which intersect with Longevity.
Her work investigates the relationship between Longitudinal study and topics such as dNaM that intersect with problems in Twin study.
Lene Christiansen combines subjects such as Rotterdam Study and Genetic association with her study of Genome-wide association study.
Her most cited work include:
- Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence (403 citations)
- Telomere Length and Mortality: A Study of Leukocytes in Elderly Danish Twins (234 citations)
- Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited. (224 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of investigation include Genetics, Longevity, Single-nucleotide polymorphism, Gene and Allele.
Her study brings together the fields of Cohort and Genetics.
She has included themes like Longitudinal study, Mini–Mental State Examination, Survival analysis and Cohort study in her Cohort study.
Her research integrates issues of Case-control study, Genetic variation and Candidate gene in her study of Single-nucleotide polymorphism.
Her Allele study integrates concerns from other disciplines, such as Apolipoprotein E and Immunology.
Her biological study spans a wide range of topics, including Meta-analysis and Computational biology.
She most often published in these fields:
- Genetics (75.34%)
- Longevity (29.15%)
- Single-nucleotide polymorphism (29.60%)
What were the highlights of her more recent work (between 2016-2021)?
- Genetics (75.34%)
- DNA methylation (13.90%)
- Epigenetics (15.70%)
In recent papers she was focusing on the following fields of study:
Her primary scientific interests are in Genetics, DNA methylation, Epigenetics, Gene and Genome-wide association study.
In her works, Lene Christiansen undertakes multidisciplinary study on Genetics and Human blood.
Her DNA methylation research integrates issues from Twin study and Cognition.
Her Epigenetics study combines topics in areas such as Human genetics, Mean age, Physiology and Heritability.
Her research in Genome-wide association study intersects with topics in Meta-analysis, Epigenomics and Computational biology.
Lene Christiansen interconnects Phenotype, Odds, Allele and Locus in the investigation of issues within Longevity.
Between 2016 and 2021, her most popular works were:
- Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence (403 citations)
- A meta-analysis of genome-wide association studies identifies multiple longevity genes (73 citations)
- A meta-analysis of genome-wide association studies identifies multiple longevity genes (73 citations)
In her most recent research, the most cited papers focused on:
- Gene
- Genetics
- Internal medicine
Her primary areas of study are Epigenetics, DNA methylation, Genome-wide association study, Genetics and Longevity.
The study incorporates disciplines such as Genetic variation, Mean age and Successful aging in addition to Epigenetics.
Her study looks at the relationship between DNA methylation and topics such as Twin study, which overlap with Whole blood.
Her research in Genetics is mostly focused on Gene.
Her work deals with themes such as Life expectancy, Allele, Odds and Genetic association, which intersect with Longevity.
Her Genetic association research incorporates themes from Quantitative trait locus and Mendelian Randomization Analysis.
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