Reducing ascertainment bias in Pharmacogenomic research

Author(s):  
Laura Scheinfeldt
Keyword(s):  
2017 ◽  
Vol 10 (4) ◽  
pp. 201-210 ◽  
Author(s):  
Meg C. Gravley ◽  
George K. Sage ◽  
Joel A. Schmutz ◽  
Sandra L. Talbot

The Alaskan population of Emperor Geese ( Chen canagica) nests on the Yukon–Kuskokwim Delta in western Alaska. Numbers of Emperor Geese in Alaska declined from the 1960s to the mid-1980s and since then, their numbers have slowly increased. Low statistical power of microsatellite loci developed in other waterfowl species and used in previous studies of Emperor Geese are unable to confidently assign individual identity. Microsatellite loci for Emperor Goose were therefore developed using shotgun amplification and next-generation sequencing technology. Forty-one microsatellite loci were screened and 14 were found to be polymorphic in Emperor Geese. Only six markers – a combination of four novel loci and two loci developed in other waterfowl species – are needed to identify an individual from among the Alaskan Emperor Goose population. Genetic markers for identifying sex in Emperor Geese were also developed. The 14 novel variable loci and 15 monomorphic loci were screened for polymorphism in four other Arctic-nesting goose species, Black Brant ( Branta bernicla nigricans), Greater White-fronted ( Anser albifrons), Canada ( B. canadensis) and Cackling ( B. hutchinsii) Goose. Emperor Goose exhibited the smallest average number of alleles (3.3) and the lowest expected heterozygosity (0.467). Greater White-fronted Geese exhibited the highest average number of alleles (4.7) and Cackling Geese the highest expected heterozygosity (0.599). Six of the monomorphic loci were variable and able to be characterised in the other goose species assayed, a predicted outcome of reverse ascertainment bias. These findings fail to support the hypothesis of ascertainment bias due to selection of microsatellite markers.


2017 ◽  
Vol 25 (04) ◽  
pp. 587-603 ◽  
Author(s):  
YUSUKE ASAI ◽  
HIROSHI NISHIURA

The effective reproduction number [Formula: see text], the average number of secondary cases that are generated by a single primary case at calendar time [Formula: see text], plays a critical role in interpreting the temporal transmission dynamics of an infectious disease epidemic, while the case fatality risk (CFR) is an indispensable measure of the severity of disease. In many instances, [Formula: see text] is estimated using the reported number of cases (i.e., the incidence data), but such report often does not arrive on time, and moreover, the rate of diagnosis could change as a function of time, especially if we handle diseases that involve substantial number of asymptomatic and mild infections and large outbreaks that go beyond the local capacity of reporting. In addition, CFR is well known to be prone to ascertainment bias, often erroneously overestimated. In this paper, we propose a joint estimation method of [Formula: see text] and CFR of Ebola virus disease (EVD), analyzing the early epidemic data of EVD from March to October 2014 and addressing the ascertainment bias in real time. To assess the reliability of the proposed method, coverage probabilities were computed. When ascertainment effort plays a role in interpreting the epidemiological dynamics, it is useful to analyze not only reported (confirmed or suspected) cases, but also the temporal distribution of deceased individuals to avoid any strong impact of time dependent changes in diagnosis and reporting.


2008 ◽  
Vol 111 (2) ◽  
pp. 271-281 ◽  
Author(s):  
A. Jonathan Shaw ◽  
Tong Cao ◽  
Li-song Wang ◽  
Kjell Ivar Flatberg ◽  
Bergfrid Flatberg ◽  
...  

2020 ◽  
Author(s):  
Erich J. Greene ◽  
Peter Peduzzi ◽  
James Dziura ◽  
Can Meng ◽  
Michael E. Miller ◽  
...  

The Lancet ◽  
1995 ◽  
Vol 346 (8984) ◽  
pp. 1223-1224 ◽  
Author(s):  
JuriI. Averkin ◽  
Theodor Abelin ◽  
JürgP. Bleuer

Cancer ◽  
2016 ◽  
Vol 122 (12) ◽  
pp. 1913-1920 ◽  
Author(s):  
Rodrigo Santa Cruz Guindalini ◽  
Andrew Song ◽  
James D. Fackenthal ◽  
Olufunmilayo I. Olopade ◽  
Dezheng Huo

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