Greetings,
I am posting this here because I think it is relevant to discussion on this topic. My goal isn't to prove or disprove anything related to true origins of Japanese, and I will identify several flaws in the fundamentals that would rule this info out as hard evidence of one origin vs. another. However, I think the results are interesting in the context of discussion of Japanese origins on this thread.
I am of Japanese and mixed European maternal ancestry, born in the USA. My maternal grandmother is from Osaka. My maternal grandfather, from what I can tell through traditional genealogy, is primarily of north European ancestry, with most ancestors arriving from Germany, Prussia, Norway, and England in the 1800's. My mtDNA is Haplogroup D, which is common among multiple east Asian nationalities. I do not have a D sub-group identified at this time.
I am of mixed European paternal ancestry, with a few traditional genealogical references to what may have been full or mixed Cherokee lineage. I have no hard evidence (DNA, pictures, etc.) for Native American ancestry. My Y-DNA is Haplogroup G and arrived in North America in the 1600's from England. It is in minority there among R1a,R1b,etc. and exactly how it arrived in England is unknown. G is more common among populations in the Mediterranian and Caucasus regions, and also exists in some fairly large numbers in Central Asia among the Magyars (likely ancestors of Huns) and other groups like Uighurs. It also exists in Afghanistan, Pakistan, and to a lesser degree India. All of this is background on my paternal lineage, not relevant to origins of Japanese, but relevant to the autosomal DNA analysis results below, which should be based on both paternal and maternal genetic matches.
The provider for the autosomal DNA results below is a private company DNA Tribes. I have some skepticism about the accuracy of their methods. Their autosomal matching algorithms and database are proprietary. The science of autosomal matching for deep ancestry is criticized as innaccurate. However, I can say that I have a completely anglo-American name, and answered no questionaire to indicate I had any Asian heritage, so all Asian matches they have provided were not biased by outside information.
The aspect of these results that surprised me the most were the number and strength of Indian and Australian Aboriginal matches that I had. I fully expected to have many European matches, as well as a number of east and central Asian matches. The other thing that surprised me was that despite Japanese being my single most identifiable ethnicity at 25%, there were very few hits on Japanese groups, and those hits were weak, none in the top 100. This may be due to a simple lack of comprehensive Japanese reference samples in the DNA Tribes database.
See the list below. The label is obviously the nationality or ethnicity being compared to, the (0.nn) is the percentage of match compared to the entire reference population for that group, and the nnn.nn is a multiplier representing the number of times more likely I am to be that nationality, compared to a reference population for the entire world population. I have included only my top 100 matches.
1 Salar (Qinghai, China) (0.62) 370.84
2 Kirgiz (Xinjiang, Chinese Turkestan) (0.3) 208.96
3 Oman (0.39) 206.58
4 Indian (Singapore) (0.51) 197.29
5 Turkey (0.28) 190.69
6 Evenki (Inner Mongolia, China) (0.34) 183.25
7 Bonan (Gansu, China) (0.4) 175.48
8 Indian (United Arab Emirates) (0.45) 174.53
9 Lazio, Italy (0.2) 162.10
10 Kamma Chaudhary (Andhra Pradesh, India) (0.43) 153.21
11 Uzbek (Xinjiang, Chinese Turkestan) (0.37) 143.12
12 Israel (0.22) 142.61
13 Tomsk, Russia (0.26) 135.26
14 South Asian (United Kingdom) (0.33) 132.52
15 European-Aboriginal (mixed) (South Australia) (0.31) 125.98
16 Costa Rica (0.23) 122.53
17 Spain (0.14) 121.39
18 Italy (0.2) 112.89
19 East Indian (Canada) (0.25) 111.88
20 Han (Xian, Shaanxi, China) (0.16) 110.41
21 Han (Henan, China) (0.15) 107.49
22 Han (Qinghai, China) (0.19) 104.64
23 Indian (Dubai, UAE) (0.4) 102.81
24 Mestizo (Argentina) (0.16) 100.55
25 European-Aboriginal (mixed) (Riverine Region, Australia) (0.23) 99.30
26 Southeast Asian (New Zealand) (0.36) 97.12
27 Kuwait (0.11) 94.29
28 Puerto Rican (Springfield, Massachusetts, U.S.A.) (0.17) 92.06
29 Greece (0.14) 91.84
30 Turkey (0.15) 90.14
31 European-Aboriginal (mixed) (Western Australia) (0.25) 89.98
32 Sergipe, Brazil (0.14) 88.96
33 Calabria, Italy (0.16) 87.31
34 European-Aboriginal (mixed) (Queensland, Australia) (0.32) 86.45
35 Italy (0.11) 83.20
36 Kurdish (Northern Iraq) (0.15) 82.77
37 Tu (Qinghai China) (0.4) 81.87
38 European-Aboriginal (mixed) (Northeast Australia) (0.55) 80.73
39 Istanbul, Turkey (0.16) 80.43
40 Oman (0.28) 79.74
41 Caucasian (Tasmania, Australia) (0.12) 78.48
42 Schleswig-Holstein, Germany (0.08) 78.30
43 Beijing, China (0.13) 78.10
44 Pakistan (0.29) 74.03
45 Spain (0.08) 71.68
46 Caucasian (New South Wales, Australia) (0.12) 70.32
47 Turkey (0.16) 70.07
48 European-Aboriginal (mixed) (New South Wales, Australia) (0.16) 69.92
49 Hungary (0.11) 69.78
50 Han (Shaanxi, China) (0.15) 69.04
51 Flemish (Belgium) (0.09) 68.93
52 Arab (Israel) (0.12) 66.91
53 Nordrhein-Westfalen, Germany (0.09) 65.91
54 Afghanistan (0.25) 65.74
55 Basque (Basque Country, Spain) (0.08) 65.44
56 Turkey (0.15) 64.96
57 European-Aboriginal (mixed) (Northern Territory, Australia) (0.37) 64.70
58 Caucasian (Capital Territory, Australia) (0.11) 64.66
59 Northwest Spain (0.09) 64.56
60 Bedouin (Negev, Israel) (0.18) 64.23
61 Northern Greece (0.11) 63.90
62 Flemish (0.1) 63.87
63 Hungary (0.11) 63.75
64 Genoa, Italy (0.19) 62.66
65 Dongxiang (Qinghai, China) (0.26) 62.61
66 Central and Southern Iraq (0.14) 62.29
67 Aboriginal (Tiwi Islands, Australia) (0.21) 61.84
68 Xibe (Xinjiang, Chinese Turkestan) (0.15) 60.99
69 Tu (Northwest China) (0.24) 60.24
70 Nepal (0.25) 60.09
71 Buddhist (Ladakh, India) (0.34) 57.56
72 Austria (0.08) 57.18
73 Brac, Croatia (0.1) 57.07
74 Csango (Romania) (0.06) 57.04
75 Turkey (0.13) 56.67
76 Gujarat, India (0.31) 56.49
77 Bogota, Colombia (0.17) 56.28
78 Greece (0.1) 56.24
79 Abov-Gemer, Eastern Slovakia (0.06) 55.70
80 Northern Portugal (0.06) 55.67
81 Han (North China) (0.1) 55.14
82 United Kingdom (0.08) 55.11
83 Greece (0.1) 54.83
84 Caucasian (U.S.A.) (0.09) 54.80
85 Toulouse, France (0.07) 54.73
86 Indian (Malaysia) (0.26) 53.97
87 Santa Fe, Argentina (0.15) 53.69
88 United Kingdom (0.1) 52.80
89 Han (Beijing, China) (0.08) 51.45
90 Buenos Aires, Argentina (0.13) 50.94
91 Central Portugal (0.07) 50.50
92 Northern Portugal (0.07) 50.36
93 London, England (0.09) 50.32
94 Greece (0.14) 49.41
95 Mainland Croatia (0.09) 49.41
96 Serbia (0.08) 49.41
97 Northern Pakistan (0.22) 49.32
98 Iban (Sarawak, Malaysia) (0.07) 48.66
99 Belem, Brazil (0.14) 48.62
100 Mendoza, Argentina (0.14) 47.97