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Y-DNA haplogroups are based solely on specific genetic markers passed from father to son on the Y chromosome. These haplogroups are defined by permanent mutations that occur in the non-recombining regions of the Y chromosome. Two main markers are used in classification: STRs (short tandem repeats) and SNPs (single nucleotide polymorphisms). Researchers have used Y-DNA haplogroups to explore the migratory paths of early human populations and to trace their origins back to Africa. These groupings help map the male line of descent over thousands of years.

TMRCA ESTIMATIONS OF ALL Y-DNA HAPLOGROUPS: https://www.yfull.com/tree

mtDNA haplogroups trace maternal lineage based solely on mutations in mitochondrial DNA passed from mothers to their children. These haplogroups represent the main branches of the evolutionary tree of human mtDNA and are used to study maternal ancestry throughout history. Researchers have used mtDNA haplogroups to explore the migratory paths of early human populations and to trace their origins back to Africa. These groupings help map the female line of descent over thousands of years.

TMRCA ESTIMATIONS OF ALL mt-DNA HAPLOGROUPS: https://www.yfull.com/mtree

Autosomal DNA calculators compare a person’s genetic data (excluding the sex chromosomes) with individuals from reference populations (composed of genetically distinct individuals) in the database and provide probabilistic estimates of ethnic origins. For example, a result such as “50% XX” means that your genetic profile is 50% likely to match with the “XX” ethnic group or with individuals from the “XX” region; it does not mean that 50% of your DNA comes from that group or region. Most calculators apply lower thresholds, such as 3 or 5 centiMorgans (commonly used to measure distance along a chromosome, though not a true physical distance), when detecting autosomal matches. Therefore, the results are generally more reliable for tracing ancestry within the past few centuries (approximately 500–600 years).

A different type of calculation, PCA (Principal Component Analysis) and ADMIXTURE, does not count shared IBD (Identical by Descent) segments (measured in centiMorgans) between individuals. Instead, it statistically processes the frequencies of individual SNPs (Single Nucleotide Polymorphisms) across the entire genetic dataset. PCA measures the similarity in SNP allele frequencies among individuals. However, in these calculations, populations that share common ancestors in the recent past also tend to show similar allele frequencies, meaning they appear closer in PCA. Allele frequency differentiation generally increases with the number of generations (due to “genetic drift” and new gene flow after populations split).

If two groups that share the same origin separated in the distant past, each will have received varying amounts of gene flow from their surrounding populations over thousands of years. These new gene flows can shift their position in PCA away from the original sibling group and closer to new groups geographically adjacent to them. The reasons for this can be summarized as follows:

1-Genetic drift: Groups that have been separated for long periods accumulate small, random changes in allele frequencies.

2-Local gene flow: Each group receives DNA from neighboring populations in its geographical region. Allele frequencies change rapidly, and even within a few hundred years, new gene flow can shift the genetic position of a population.

3-Time factor: Over time, the signal of the common ancestor becomes “diluted” in SNP frequencies and is replaced by the effect of more recent admixture. Since PCA is based on the current distribution of allele frequencies, recent gene flow and admixture become more visible than the ancient shared origin.

In summary, communities from different origins who have lived in the same region for a long time may show similar results due to recent gene exchange. Conversely, communities from the same origin who have lived in different regions for a long time may show different results, as they have not exchanged genes for an extended period. Ultimately, it must be remembered that the percentage values in autosomal tests are probability-based. Two individuals with similar percentages may have different genetic structures, while two individuals with different percentages may share similar genetic structures. When making historical interpretations of PCA or ADMIXTURE results, one should be very cautious, as these results only reflect current allele frequencies and are not an absolute “map” of the past.