scholarly journals The Use of Racial, Ethnic, and Ancestral Categories in Human Genetics Research

2005 ◽  
Vol 77 (4) ◽  
pp. 519-532 ◽  
Nature ◽  
2012 ◽  
Vol 482 (7385) ◽  
pp. 300-301 ◽  
Author(s):  
Gholson J. Lyon

2004 ◽  
Vol 23 (1) ◽  
pp. 60-66 ◽  
Author(s):  
Paul Achter ◽  
Roxanne Parrott ◽  
Kami Silk

Author(s):  
R. Ndiaye Diallo ◽  
M. Gadji ◽  
B. J. Hennig ◽  
M. V. Guèye ◽  
A. Gaye ◽  
...  

The 9th meeting of the African Society of Human Genetics, in partnership with the Senegalese Cancer Research and Study Group and the Human Heredity and Health in Africa (H3Africa) Consortium, was held in Dakar, Senegal. The theme was Strengthening Human Genetics Research in Africa. The 210 delegates came from 21 African countries and from France, Switzerland, UK, UAE, Canada and the USA. The goal was to highlight genetic and genomic science across the African continent with the ultimate goal of improving the health of Africans and those across the globe, and to promote the careers of young African scientists in the field. A session on the sustainability of genomic research in Africa brought to light innovative and practical approaches to supporting research in resource-limited settings and the importance of promoting genetics in academic, research funding, governmental and private sectors. This meeting led to the formation of the Senegalese Society for Human Genetics.


2017 ◽  
Author(s):  
Olivier Harismendy ◽  
Jihoon Kim ◽  
Xiaojun Xu ◽  
Lucila Ohno-Machado

AbstractGenetic ancestry and admixture are critical co-factors to study phenotype-genotype associations using cohorts of human subjects. Most publically available molecular datasets – genomes, exomes or transcriptomes - are however missing this information or only share self-reported ancestry. This represents a limitation to identify and re-purpose datasets to investigate the contribution of race and ethnicity to diseases and traits. we propose an analytical framework to enrich the meta-data from publically available cohorts with admixture information and a resulting diversity score at continental resolution, calculated directly from the data. We illustrate the utility and versatility of the framework using The Cancer Genome Atlas datasets indexed and searched through the DataMed Data Discovery Index. Data repositories or data contributors can use this framework to provide, as metadata, admixture for controlled access datasets, minimizing the work involved in requesting a dataset that may ultimately prove inadequate for a researcher’s purpose. With the increasingly global scale of human genetics research, research on disease risk and susceptibility would benefit greatly from the adequate estimation and sharing of admixture data following a framework such as the one presented.


2020 ◽  
Vol 84 (4) ◽  
pp. 313-314 ◽  
Author(s):  
David Curtis ◽  
François Balloux

Author(s):  
Florian Privé ◽  
Julyan Arbel ◽  
Bjarni J. Vilhjálmsson

AbstractPolygenic scores have become a central tool in human genetics research. LDpred is a popular method for deriving polygenic scores based on summary statistics and a matrix of correlation between genetic variants. However, LDpred has limitations that may reduce its predictive performance. Here we present LDpred2, a new version of LDpred that addresses these issues. We also provide two new options in LDpred2: a “sparse” option that can learn effects that are exactly 0, and an “auto” option that directly learns the two LDpred parameters from data. We benchmark predictive performance of LDpred2 against the previous version on simulated and real data, demonstrating substantial improvements in robustness and predictive accuracy compared to LDpred1. We then show that LDpred2 also outperforms other polygenic score methods recently developed, with a mean AUC over the 8 real traits analyzed here of 65.1%, compared to 63.8% for lassosum, 62.9% for PRS-CS and 61.5% for SBayesR. Note that, in contrast to what was recommended in the first version of this paper, we now recommend to run LDpred2 genome-wide instead of per chromosome. LDpred2 is implemented in R package bigsnpr.


Author(s):  
John Hartigan Jr.

I open by thanking Terence Keel both for inviting me to participate in this dialogue and for so effectively framing two distinct stances on how to understand the role of race in human genetics research today. This discussion is important to critical assessments of racial thinking—its depth and breadth in contemporary societies—but also to encouraging a correct understanding of genes and risk factors related to disease more broadly. For the sake of debate, I first elaborate on the position I have taken in critiquing social constructivist claims about race, then offer my thoughts on how an attention to Neanderthals provides a broader understanding of racial thinking as it pertains to the question of variation within a species.


2018 ◽  
Vol 20 (3) ◽  
pp. 129-130 ◽  
Author(s):  
Nelson B. Freimer ◽  
David C. Mohr

Sign in / Sign up

Export Citation Format

Share Document