Importance of human demographic history knowledge in genetic studies involving multi-ethnic cohorts

Kulohoma, Benard W.

doi:10.12688/wellcomeopenres.14692.3

Cited by 4 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternatively, Kulohoma 52 found structure within the LWK samples corresponding to outlier individuals, who may be from different tribes, which could also explain differentiation within the African samples.…”

Section: Population Identi Cationmentioning

confidence: 99%

Determining population structure from k-mer frequencies.

Hrytsenko,

Daniels,

Schwartz

2023

Preprint

View full text Add to dashboard Cite

Background: Understanding population structure within species provides information on connections among different populations and how they evolve over time. This knowledge is important for studies ranging from evolutionary biology to large-scale variant-trait association studies. Current approaches to determining population structure include model-based approaches, statistical approaches, and distance-based ancestry inference approaches. In this work, we identify population structure from DNA sequence data using an alignment-free approach. We use the frequencies of short DNA substrings from across the genome (k-mers) with principal component analysis (PCA). K-mer frequencies can be viewed as a summary statistic of a genome and have the advantage of being easily derived from a genome by counting the number of times a k-mer occurred in a sequence. In contrast, most population structure work employing PCA uses multilocus genotype data (SNPs, microsatellites, or haplotypes). No genetic assumptions must be met to generate k-mers, whereas current population structure approaches often depend on several genetic assumptions and can require careful selection of ancestry informative markers to identify populations. Results: In this work, we show that PCA is able to determine population structure just from the frequency of k-mers found in the genome. The application of PCA and a clustering algorithm to k-mer profiles of genomes provides an easy approach to detecting the number and composition of populations (clusters) present in the dataset. The results are comparable to those found by a model-based approach using genetic markers. We validate our method using 48 human genomes from populations identified by the 1000 Human Genomes Project, as well as simulations. Conclusions: This study shows that PCA, together with the clustering algorithm, is able to detect population structure from k-mer frequencies and can separate samples of admixed and non-admixed origin. Using k-mer frequencies to determine population structure has the potential to avoid some challenges of existing methods.

show abstract

Section: Population Identi Cationmentioning

confidence: 99%

Determining population structure from k-mer frequencies.

Hrytsenko,

Daniels,

Schwartz

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Scientists still remain puzzled by why the pandemic seems to have "spared" Africa, which has fragile healthcare systems [6][7][8] . Several hypotheses have been advanced to explain this occurrence, that include: warmer climate that does not favour the viral pathogen viability, fewer COVID-19 associated deaths because of a comparatively younger population, lower case numbers due to inadequate testing, population-wide immune priming due to previous exposure to other infectious diseases, and genetic factors that protect Africans from severe disease 6,7,9 . The relative contribution of these factors still remains unknown.…”

Section: Disclaimermentioning

confidence: 99%

Kenya’s response to the COVID-19 pandemic: a balance between minimising morbidity and adverse economic impact

Wangari¹,

Gichuki²,

Abuor³

et al. 2021

AAS Open Res

Self Cite

View full text Add to dashboard Cite

Coronavirus disease 2019 (COVID-19) has ravaged the world’s socioeconomic systems forcing many governments across the globe to implement unprecedented stringent mitigation measures to restrain its rapid spread and adverse effects. A disproportionate number of COVID-19 related morbidities and mortalities were predicted to occur in Africa. However, Africa still has a lower than predicted number of cases, 4% of the global pandemic burden. In this open letter, we highlight some of the early stringent countermeasures implemented in Kenya, a sub-Saharan African country, to avert the severe effects of the COVID-19 pandemic. These mitigation measures strike a balance between minimising COVID-19 associated morbidity and fatalities and its adverse economic impact, and taken together have significantly dampened the pandemic’s impact on Kenya’s populace.

show abstract

“…Scientists still remain puzzled by why the pandemic seems to have “spared” Africa, which has fragile healthcare systems 6 – 8 . Several hypotheses have been advanced to explain this occurrence, that include: warmer climate that does not favour the viral pathogen viability, fewer COVID-19 associated deaths because of a comparatively younger population, lower case numbers due to inadequate testing, population-wide immune priming due to previous exposure to other infectious diseases, and genetic factors that protect Africans from severe disease 6 , 7 , 9 . The relative contribution of these factors still remains unknown.…”

Section: Disclaimermentioning

confidence: 99%