No back migration of Eurasian with R1b-V88? - Daniel Shriner

[Tukuler al~Takruri]

Again, thanks for everything you posted.


I don't know why you won't answer my question by doing my suggested search.


"... Ibn Furtu, the grand Imam of the Bornu Empire during the reign of Mai Idris Alooma ..."

is definitely in the article's text.



I found it via my suggested search keys.
GOOGLE: shriner bornu

GOOGLE's 5th hit returned:

Genetic history of Chad - Shriner - - American Journal of Physical ...
Wiley › onlinelibrary › doi › full › ajpa
Sep 27, 2018 · Daniel Shriner ..... Ibn Furtu, the grand Imam of the Bornu Empire during the reign of Mai Idris Alooma ...



You didn't search Shriner's text for Bornu
because you didn't come back with at least that much text.

Cheap as I am I guess I'll have to buy the damn article myself.
If I'm lucky some science or university library might access for me for free.

[djoser-xyyman]

Let me figure out a way to upload the files to some share site. ElMaestro has done it in the past


from a genetic point of view the study/data is useless. He is inferring via autosomal frequency.

If you are in his historical comments...there may be something there but I searched using the key words

[abdulrahman]

Oct 1, 2018 12:36:49 GMT -5 djoser-xyyman said:

No back migration of Eurasians with R1b-V88? - Daniel Shriner2018

Genetic History of Chad-Daniel Shriner making name for himself. Nice!!

Abstract
Objectives: The Sahel is a semi-arid zone stretching from the Atlantic Ocean in the west to the
Red Sea in the east and from the Sahara in the north to the Sudanian Savanna in the south.
Here, we investigated the genetic history of the spread of Northern African ancestry common
among Berbers, the Y DNA haplogroup R1b-V88, and Chadic languages throughout the Sahel,
with a focus on Chad.

Materials and methods: We integrated and analyzed genotype data from 751 individuals from
Chad, Burkina Faso, Mali, South Sudan, and Sudan in the context of a global reference panel of
5,966 individuals.

Results: We found that genetic diversity in Chad was broadly divided by a north–south axis.
The core ancestry of Southern Chadians was Central African, most closely related to Pygmies.
Southern Chadians then experienced four waves of gene flow over the last 3,000 years from
West-Central Africans, Eastern Africans, West-Central Africans again, and then Arabians?. In contrast,
Northern Chadians did not share Central African ancestry and were not influenced by the
first wave of West-Central Africans but were influenced by Northern African ancestry.

Discussion: We found that Y DNA haplogroup R1b entered the Chadian gene pool during Baggarization.
Baggara Arabs spoke Arabic, not Chadic, implying that people carrying R1b-V88 were
not responsible for the spread of Chadic languages, which may have spread approximately
3,700 years ago.

We found ***no evidence*** for migration of ***Near Eastern farmers*** or any ancient
Episodes**** involving Eurasian backflow****.

Xyyman comment: Question aren't the Arabs near Easterners? Isn't the statement contradictory? But let us read the paper!!!

[abdulrahman]

Oct 1, 2018 12:46:54 GMT -5 djoser-xyyman said:

As I stated many time. Unsupervised analysis is better. Here Shrine is stating "supervised' is more "efficient....not accurate.

Quote:
2.2 | Projection analysis
There are three ways to perform clustering analysis to learn about
population structure in a sample. In unsupervised clustering, there are
no reference data and population structure in the sample is learned de
novo. The number of ancestries, ancestry-specific allele frequencies,
and mixing proportions are all unknown. In semi-supervised clustering,
the number of ancestries is fixed by the reference panel. Initial estimates
of the ancestry-specific allele frequencies are also provided by
the reference panel and then are updated, jointly considering the sample
and reference data, while mixing proportions in the sample are
learned. In supervised clustering, both the number of ancestries and
the ancestry-specific allele frequencies are fixed by the reference
panel. Only the mixing proportions in the sample are learned. With a
small amount of sample data and a large amount of reference data,
supervised clustering is the most efficient of the three approaches.