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Evan Koch

Ph.D.

Postdoctoral Research Fellow

Department of Biomedical Informatics, Harvard Medical School

Dr. Koch is a postdoctoral researcher in the Department of Biomedical Informatics at Harvard Medical School. He works on statistical and theoretical problems in population and evolutionary genetics. His current research projects include using precise mutation rate estimates in the human genome to measure natural selection on a fine scale, and the investigation of selection on human traits using the results of genome-wide association studies. He has also worked on arabidopsis and wolf genetics, and currently has added the SARS-CoV-2 virus to this mix. 

Dr. Koch obtained my PhD in Ecology and Evolution from the University of Chicago, where he was an NSF GRFP fellow. Before that, he graduated from the University of Texas with a BS in Biology (honors), and a certificate in computational science. 

Evan Koch

Ph.D.

Postdoctoral Research Fellow

Department of Biomedical Informatics, Harvard Medical School

Dr. Koch is a postdoctoral researcher in the Department of Biomedical Informatics at Harvard Medical School. He works on statistical and theoretical problems in population and evolutionary genetics. His current research projects include using precise mutation rate estimates in the human genome to measure natural selection on a fine scale, and the investigation of selection on human traits using the results of genome-wide association studies. He has also worked on arabidopsis and wolf genetics, and currently has added the SARS-CoV-2 virus to this mix. 

Dr. Koch obtained my PhD in Ecology and Evolution from the University of Chicago, where he was an NSF GRFP fellow. Before that, he graduated from the University of Texas with a BS in Biology (honors), and a certificate in computational science. 

Recent Publications

Reliability of brain metrics derived from a Time-Domain Functional Near-Infrared Spectroscopy System

Published On 2024 Jul 30

Journal article

With the growing interest in establishing brain-based biomarkers for precision medicine, there is a need for noninvasive, scalable neuroimaging devices that yield valid and reliable metrics. Kernel's second-generation Flow2 Time-Domain Functional Near-Infrared Spectroscopy (TD-fNIRS) system meets the requirements of noninvasive and scalable neuroimaging, and uses a validated modality to measure brain function. In this work, we investigate the test-retest reliability (TRR) of a set of metrics...


Pervasive correlations between causal disease effects of proximal SNPs vary with functional annotations and implicate stabilizing selection

Published On 2024 Jan 03

Journal article

The genetic architecture of human diseases and complex traits has been extensively studied, but little is known about the relationship of causal disease effect sizes between proximal SNPs, which have largely been assumed to be independent. We introduce a new method, LD SNP-pair effect correlation regression (LDSPEC), to estimate the correlation of causal disease effect sizes of derived alleles between proximal SNPs, depending on their allele frequencies, LD, and functional annotations; LDSPEC...