What is he best known for?
The fields of study he is best known for:
Antti Sajantila mostly deals with Genetics, Haplotype, Y chromosome, Allele frequency and Genetic variation.
His research in Microsatellite, Allele, Locus, Haplogroup and Genetic marker are components of Genetics.
His study in Haplotype is interdisciplinary in nature, drawing from both Natural selection, Melanocortin 1 receptor, Function and SLC24A5.
His Y chromosome research integrates issues from Germline mutation, Commission and Germline.
The concepts of his Allele frequency study are interwoven with issues in Population structure and Population genetics.
His Genetic variation study combines topics from a wide range of disciplines, such as mtDNA control region, Genetic variability, Genetic diversity and Ecology.
His most cited work include:
- A promoter-level mammalian expression atlas (1307 citations)
- Ancient human genomes suggest three ancestral populations for present-day Europeans (811 citations)
- The Simons Genome Diversity Project: 300 genomes from 142 diverse populations (684 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Genetics, Haplotype, Microsatellite, Allele and Evolutionary biology.
His works in Allele frequency, Genetic variation, Locus, Y chromosome and Mitochondrial DNA are all subjects of inquiry into Genetics.
His Mitochondrial DNA research also works with subjects such as
- Haplogroup that connect with fields like Population genetics,
- Genome that intertwine with fields like Computational biology.
Antti Sajantila works in the field of Haplotype, namely Linkage disequilibrium.
He interconnects Identification, Mutation rate and DNA profiling in the investigation of issues within Microsatellite.
His work in Allele addresses issues such as Molecular biology, which are connected to fields such as DNA.
He most often published in these fields:
- Genetics (48.26%)
- Haplotype (20.14%)
- Microsatellite (15.62%)
What were the highlights of his more recent work (between 2017-2021)?
- Pharmacogenetics (5.90%)
- Ancient DNA (7.64%)
- CYP2D6 (4.17%)
In recent papers he was focusing on the following fields of study:
Pharmacogenetics, Ancient DNA, CYP2D6, Internal medicine and Genetics are his primary areas of study.
The study incorporates disciplines such as Bronze Age, Genetic diversity, Genome, Mitochondrial DNA and Iron Age in addition to Ancient DNA.
His Mitochondrial DNA study incorporates themes from Evolutionary biology, Typing, Computational biology and Haplogroup.
The Odds ratio research Antti Sajantila does as part of his general Internal medicine study is frequently linked to other disciplines of science, such as Citalopram, therefore creating a link between diverse domains of science.
His research investigates the connection between Genetics and topics such as ADME that intersect with problems in Genome-wide association study.
Antti Sajantila combines subjects such as Polymorphism, MEDLINE, Genotyping, Massive parallel sequencing and Haplotype with his study of 1000 Genomes Project.
Between 2017 and 2021, his most popular works were:
- The population history of northeastern Siberia since the Pleistocene. (83 citations)
- Ancient Fennoscandian genomes reveal origin and spread of Siberian ancestry in Europe (44 citations)
- Neuropathologic features of four autopsied COVID-19 patients. (42 citations)
In his most recent research, the most cited papers focused on:
- Gene
- DNA
- Internal medicine
The scientist’s investigation covers issues in Internal medicine, Pharmacogenetics, Odds ratio, Ancient DNA and Ethnology.
Within one scientific family, Antti Sajantila focuses on topics pertaining to Population study under Internal medicine, and may sometimes address concerns connected to CYP2D6 and CYP2C19.
Pharmacogenetics is a subfield of Genetics that Antti Sajantila studies.
Genetics is closely attributed to Drug development in his work.
His Odds ratio research is multidisciplinary, incorporating elements of Disease progression and Disease.
His Ancient DNA research is multidisciplinary, relying on both Genome and Mitochondrial DNA.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
