کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5765490 1626777 2017 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Incorporating non-baseline characters into genetic mixture analyses
ترجمه فارسی عنوان
ترکیب شخصیت های غیر خطی در تجزیه و تحلیل ترکیبات ژنتیکی
کلمات کلیدی
مخلوط جمعیت، تجزیه و تحلیل مخلوط، آمار بیزی، تجزیه و تحلیل ژنتیکی، پایه ژنتیکی،
موضوعات مرتبط
علوم زیستی و بیوفناوری علوم کشاورزی و بیولوژیک علوم آبزیان
چکیده انگلیسی


- Population-specific differences in non-baseline characters can be estimated from a mixture.
- Non-baseline characters only slightly improved estimates of population proportions in a mixture.
- Non-baseline characters are more useful in assigning population identities to individuals.
- Non-baseline characteristics may be useful in other ways; e.g., age and size is related to mortality.
- Individual assignment allows better spatio-temporal resolution than mixture analysis.

In a mixture of individuals from different populations, population proportions and individual identities are estimated by comparing the characteristics of individuals in the mixture to a (usually) genetic baseline of population-specific characteristics. Using simulated data sets, we examined the performance of a genetic mixture analysis that incorporated data on non-baseline character state frequencies. Population-specific state frequencies of non-baseline characters were well-estimated in many scenarios. We found benefits of incorporating non-baseline characters in mixture analysis; both individual assignments and estimates of population proportions were improved. However, both the sample size and the quality of the baseline data were more important. We did not see any improvement in estimating baseline character state frequencies even when highly informative non-baseline data was used. Our results suggest that non-baseline data might improve mixture analyses, and we note that population-specific estimates of non-baseline character state frequencies are often useful in and of themselves.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fisheries Research - Volume 193, September 2017, Pages 217-222
نویسندگان
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