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Pedigree Analysis

This page contains free web service and stand-alone calculation for pedigree analysis of crops, data files of several crops, method, programs in Prolog, example and literatures about pedigree analysis by YOSHIDA, Tomohiko.

"Pedigree Analysis Web" service Entrance. If this service is not available, use stand alone calculation shown below.

Using this service, you can draw pedigree tree, calulate coefficient of parentage/inbreeding, number of ancestors in pedigree, and maximum generation traced. You can use your data and apply to your own work.

Suited for plant breeders or anyone interested in cultivar's performance, kinship or characteristics of complicated modern cultivars, molecular genetists who want to relate your data to the pedigree, and others. Pedigree trees of modern cultivars are too complicated to characterize only by watching them (see here). Use this web and you can characterize them with figures, which can be used for further analysis with agronomic traits. It must be interesting. See example and literatures listed below.

Prepare crossing records of your interest as shown in the following. Or use the example file for the practice. That's all. No knowledge about Prolog is necessary. Publishing the results is strongly expected. When publishing, citing "Pedigree Analysis Web Service" with this address in the reference or somewhere is enough for me.

Data files

Almost all data of modern Japanese cultivars are included. Within /* */ is comment and not functional.
outcross crops; sweet potato (13KB), potato (11KB), strawberry (7KB), sample data (2KB)
self-fertilization crops; rice (33KB), wheat (13KB), barley (11KB), sample data (2KB)

Indonesian rice data (with many IRRI lines) (9KB)


See illustration.

1. Upload your data, which contains crossing records of your crop. For practice, use data files listed above and draw pedigree tree.
2. Select self-fertilization crop or outcross crop. Even if mistaken, values are obtained, which you must not use.
== Go to the Pedigree Analysis Web service from here and practice by trial and error. ==
If you have any problem or question, come back to the following sections.

3. "kin" calculates coefficient of parentage between two cultivars of self-fertilization crops or coefficient of inbreeding of offspring crossed between two outcross parents. Input these names in "cultivar names".
4. "inbreeding coefficient" calculates coefficient of inbreeding for a cultivar of outcross crop shown in "cultivar name"
5. For a cultivar shown in "cultivar name" of self-fertilization or outcross crops,
   "pedigree"     ; pedigree tree (full)    "pedigree2"    ; same (duplicates are not shown)
   "ancestors"   ; number of ancestors in pedigree (total)    "ancestors2"  ; same (unique one)
   "generations" ; maximum generation traced in pedigree    is drawn or calculated.
6. "kin3" calculates values among "namae1" and "namae2" listed in your data file. This can be used for a large number of, repeated, calculations. Copy the result shown in the bottom and paste into Excel.

[New database]
7. When you constructed your own data or added a new cultivar into data, make sure that the pedigree tree of the cultivar is completely drawn to the last ancestors. If missing, add the parents data, and repeat this process.
8. All 'f1's are retrieved as a common one, so distinguish them like 'f1_cv1/cv2'.
9. Use common name for entries having another names. I used the newest name for experimental lines and the original name for mutants. Do not duplicate same entries. If duplicated, the result is not "error" but wrong. Check the data by sorting in "EXCEL".
10. Don't worry so much for the origin of old cultivars, because discrepancy of old ancestors had little effect on the coefficients of parentage/inbreeding values of modern cultivars (see literatures).

[Especially for outcross crop]
11. For further calculations of inbreeding coefficients, put the following data after you computed the new cultivar as;
kiti('sinhinshu', 0.1).
when the new cultivar, sinhinshu, had the value of 0.1.
12. In case of a big new database, start from the possible oldest ancestors with inbreeding, watching the pedigree tree. It seems a tedious job for the first trial but not later, because common ancestors retrieved are not so many, many candidate cultivars have same ancestors and necessary 'kiti' data usually decrease rapidly for later computations of inbreeding coefficients.
13. In pedigree tree of outcross crops, cultivars with 'kiti' are attached with '*'. The data '0' is OK. No inbreeding is assumed for cultivars when they have no 'kiti' data.
14. For data of sweet potato, potato and strawberry, 'kiti' data of old ancestors are, I believe, completed. So, don't change them. If changed, you must start from the beginning.
15. Selfing is permissive for outcross crops. Put same names for female and male parent.

16. Difference of algorithm for self-fertilization and outcross crops; inbreeding coefficient value of ancestors = 1 for the former and = 'kiti' value for the latter.
17. For self-fertilization crops, 'kiti' data are not necessary.
18. For crops of self-fertilization and outcross, see Taguchi et al.
19. For self-fertilization crops and F1, F2 ... are very frequent/recent common ancestors, assuming of coefficient of inbreeding = 1 for all ancestors may cause significant error. In that case, select 'outcrossing' and prepare 'kiti' data for F1, F2 ..., and value=1 for all inbred common ancestors.
20. In pedigree tree, ancestors within 6th generation and top 10 characters are drawn.
21. Too many for "kin3" may be rejected (depend on the data). In case of complicated cultivars shown in example, for upper 10 cultivars (average total ancestors was 320), 10 x 10 was rejected but 10 x 5 was OK. When rejected, divide data.
22. All ancestors are retrieved without ending at the halfway generation in these programs.

For more details, see Japanese page. For brief manual in English, see here.
This online service using CGI script was prepared in cooperation with SOFNEC Co., LTD.

Program files in AZ-Prolog for stand alone calculation

This section is for expert of Prolog. Run the following programs on AZ-Prolog in Windows.

Start interpreter "PROLOG.EXE"
Upload your file by inputing ['filename']. After ?-
Do not forget period "."
?-kakei(name).Or kakei3(name).;Pedigree tree
?-kin. > name1?: name1. , name2?: name2. ;Coefficient of parentage between name1 and name2
?-kin2.;Coefficient of parentage/inbreeding for cultivars listed in kin2
?-inb. > name?: name.;Coefficient of inbreeding
Coefficient of parentage for self-fertilization crops (3KB)
Coefficient of inbreeding for outcross crops (3KB)
Number of ancestors and maximum generation traced (3KB)
Prolog short course lesson (3KB)
Az-Prolog is based on DEC-10 Prolog. Programs here run in SOFNEC's trial service. For other Prolog, even in DEC-10, modifications of programs are necessary.
In web version, 'out.txt' dumping is not included. Others are same.

AZ-Prolog interpreter used in this study is stored here. Also, free AZ-Prolog Ver. 6 in Windows is available from Sofnec website (In Japanese).


Some results and applications of pedigree analysis for self-fertilization and outcross crops.

Self-fertilization crops

Pedigree characterestics of modern rice cultivars in Japan.
No.NameReleaseMain=Ancestors=Max.====== Coefficient of parentage to ; =====
1 Koshihikari 1956 Niigata 12 11 3 1.000 0.250 0.125 0.125 0.531 0.532
2 Hitomebore 1992 Miyagi 162 51 12 0.796 0.215 0.116 0.116 0.417 0.473
3 Hinohikari 1989 Kumamoto 266 70 12 0.608 0.184 0.130 0.130 0.292 0.455
4 Akitakomachi 1984 Akita 136 64 11 0.615 0.286 0.237 0.237 0.308 0.454
5 Haenuki 1992 Yamagata 360 84 12 0.535 0.245 0.237 0.237 0.307 0.411
6 Kirara 397 1987 Hokkaido 220 71 11 0.244 0.173 0.035 0.035 0.263 0.107
7 Kinuhikari 1988 Hyogo 130 53 12 0.534 0.152 0.099 0.099 0.348 0.309
8 Hoshinoyume 2000 Hokkaido 560 117 13 0.300 0.193 0.077 0.077 0.249 0.176
9 Tsugaruroman 1997 Aomori 472 96 14 0.412 0.201 0.217 0.217 0.243 0.347
10 Nanatsuboshi 2001 Hokkaido 908 114 15 0.350 0.239 0.043 0.043 0.291 0.189
11 Yumeakari 1999 Aomori 518 111 13 0.555 0.232 0.205 0.205 0.316 0.399
12 Asahinoyume 1999 Tochigi 944 96 15 0.393 0.192 0.231 0.231 0.171 0.395
13 Aichinokaori 1987 Aichi 156 54 12 0.390 0.215 0.304 0.304 0.183 0.363
14 Yumetsukushi 1994 Fukuoka 144 54 13 0.767 0.201 0.112 0.112 0.440 0.420
15 Sasanishiki 1963 Miyagi 20 14 4 0.375 0.250 0.313 0.187 0.328 0.422
  Ave of 'East' 275 70 11 0.548 0.238 0.221 0.169 0.320 0.418
   Ave of 'West' 328 65 13 0.538 0.189 0.175 0.175 0.287 0.388
   Ave of Hokkaido 563 101 13 0.298 0.202 0.052 0.052 0.268 0.157
   Grand average 332 71 11 0.525 0.215 0.165 0.145 0.307 0.335
Top 15 cultivars most produced in 2005 are shown. For Koshihikari vs. Nanatsubosi, 225 paths were retrieved for common ancestors. In case of Nanatsuboshi vs. Asahinoyume, 5784 paths were retrieved. 'East' contains No. 2, 4, 5, 9, 11 and 15. 'West' contains No. 3, 7, 12, 13 and 14, with no specific trend. Relation to Koshihikari increases gradually even for cultivars in Hokkaido.

Result of cluster analysis using coefficients of parentage among cultivars (most produced in 2007) is here. Cultivars were classified according to the growing area. Raw data (took about 30 min., by stand-alone Let's Note R3) in Excel is here.

Koshihikari alone is planted in 1/3 of total paddy areas of Japan. Considering the relationship of other cultivars to Koshihikari (Hitomebore's acerage x 0.796 is added to the area, ...), it can be said that Koshihikari's genetic background dominates 2/3 of the paddy (literature in Japanese). Only 5 ancestors (Aikoku, Asahi, Shinriki, Joshu and Ooba) contributed 62.5% of the gene pool (see).

Reported that correlation between coefficient of parentage to Koshihikari and palatability was significant (Fig), not significant (Fig) and depending on circumstances (Fig). If correlated, good eating crossings can be estimated by the value of kinship to Koshihikari(see).

Outcross crops

Relationship between yield and coefficient of inbreeding for sweet potato, strawberry and sugar beet.
Coefficients of inbreeding of candidate crosses for sweet potato and strawberry.
Select the crosses having less than the critical value of coefficient of inbreeding, judging from these figures.
The value must be 0.1 - 0.2 for sweet potate, 0.2 - 0.3 for strawberry and 0.1 for beet.

Published papers (English summary)

Author, title and abstract of literatures of pedigree analysis of several crops, relationship between kinship and DNA markers and programs in Prolog by Yoshida and co-workers are listed.

CP ; coefficient of parentage, CI ; coefficient of inbreeding.
Ab ; abstract. * ; full text in Japanese with English summary. ** ; full text in English.

1. Pedigree analysis of self-fertilization crops

K. Mizuta and T. Yoshida. Agric. Info. Res. 3:65-78 (1994).
Construction and Use of a Datebase from Malting Barley Crossing Records.
Ab; Recent lines were about 10th generation's from Golden melon, the number of original cultivars was a very few, CP between Golden melon and them were large (40%) and the gene pool was extremely narrow. Prolog program on MS-DOS was used. (text) *

K. Mizuta and T. Yoshida. Agric. Info. Res. 5:57-67 (1996).
Coefficient of Parentage and Its Relationship to Quality in Wheat Cultivars.
Ab; 7 ancestors contributed 66.9 %. Cultivars related to 'Asakazekomugi' had low flour whiteness, showing poor combining ability in quality though it was used as cross parents extensively. Cultivars related to 'Kanto 107' had high maximum viscosity values. (text) *

K. F. Oosato and T. Yoshida. Japan. J. Breed. 46:295-301 (1996).
Coefficient of Parentage in Rice Breeding Lines and Its Relationship to Eating Quality.
Ab; 7 ancestors contributed more than 70 %. Significant correlation between CP to Koshihikari and eating quality showed that we could estimate combining ability of eating quality by the CP. CP of candidate crosses showed 40 % had the value more than 0.5, which could produce high eating quality. (text) *

T. Yoshida and K. F. Oosato. Plant Prod. Sci. 1:296-297 (1998).
Difference with Rice Cultivars in the Rate of Root Regeneration from Embryo Callus and Its Relationship with the Genetic Background.
Ab; Genotypes related to Koshihikari tended to have a low root induction rate in embryo callus. Those related to Nishihomare had a high root induction rate. (text) **

T. Yoshida and S. Imabayashi. Jpn. J. Crop Sci. 67:101-103 (1998).
Genetic Background of Highly Palatable Cultivars of Rice.
Ab; The highly palatable cultivars showed high CP with 'Koshihikari'. The high-yield cultivars showed low CP with Koshihikari, while showing high CP with 'Shinrei', 'Toyotama', and 'Hoyoku', which were grown extensively in northern Kyushu with low CP with highly palatable cultivars. (text) *

T. Yoshida. H.Y.Lu ed., Proceeding of 3rd Asian Crop Science Conference, Taichun, Taiwan. 404-412 (1998).
Pedigree Analysis of Rice, Barley and Wheat
Ab; For rice, malting barley and wheat modern Japanese cultivars, pedigree analysis was conducted. Comparison among these crops was conducted. (text) **

A. Shigemune, K. Miura, H. Sasahara, A. Goto and T. Yoshida. Jpn. J. Crop Sci. 75(2):153-158(2006).
Pedigree Analysis of Rice Bred in Hokuriku Research Center.
Ab; For 143 varieties in Hokuriku Natl Agr. Exp. Stn, the total ancestors increased from 1980s and it reached 1122. The average CP between Koshihikari and 16 Hokuriku Lines was 0.463, having no significant correlation with eating quality. Kinuhikari contributed to improvement of eating quality. (text) *

H. Ohta, I. Ando and T. Yoshida. Jpn. J. Crop Sci. 75(2):159-164 (2006).
Grouping of Rice Breeding Lines According to Coefficient of Parentage and the Relationship between Severity of Leaf Blast, Eating Quality and Coefficient of Parentage.
Ab; CP between 'Kanto' lines formed clusters relating to 'Koshihikari', 'Kotikaze' and 'Nipponbare'. There was a cluster having low CP with 'Koshihikari'. Significant correlation between CP and eating quality was not found at breeding lines with good eating quality and high resistance of leaf blast. Good eating and high disease resistance with wide genetic background could be combined. (text) *

H. Sato and T. Yoshida. Jpn. J. Crop Sci. 76(2):238-244 (2007).
Pedigree Analysis of Rice Breeding Lines in Fukushima Prefecture.
Ab; For rice in Fukushima, breeding lines showed high CP to Asahi and Norin 22. A negative correlations of Cp to Norin 1 and yieid was found in comparative varieties, but not in breeding lines, showing yield improvement of breeding lines. Positive correlation of CP to Koshihikari or Norin 22 and eating quality was found for comparative varieties but not for breeeding lines. (text) *

Takako IIda and Kazuhiko Oya. Rep. Kanto Br. Crop Sci. Soc. Japan 23:54-55 (2008).
Pedigree Analysis of Rice Cultivars Developed in Tochigi Prefecture:
Ab: Six ancestors contributed 79.4%. CP to Koshihikari was 0.5 - 0.9, averaging 0.627, with no correlation to palatability. (text)

T. Ushiyama, K. Nakamura, Anas and T. Yoshida.Plant Prod. Sci. 12:80-87(2009).
Pedigree Analysis of Early Maturing Wheat Cultivars in Japan.
Ab; For Tozan line, Chunaga contributed 24.0 % of the genetic background. KS831957 showed positive effect for crude protein content of flour. (text) **

T. Sotome, M. Oozeki, S. Kobayashi and T. Yoshida. Jpn. J. Crop Sci. 78(3): 344-355 (2009).
Pedigree Analysis of Two-Lowed Brewing Barley Breeding Lines in Tochigi Prefecture.
Ab; For brewing barley in Tochigi, ancestors most contributed were Harunanijo (CP ave. 0.457), Misatogolden (0.442) and Goldenmelon (0.396). CP with disease resistance parents were very row as 0.125〜0.008. (text) *

T. Yoshida, Anas, S. Rosniawaty and R. Setiamihardja. Jpn. J. Crop Sci. 78(3): 335-342 (2009).
Genetic Background of Indonesia Rice Germplasm and its Relationship to Agronomic Characteristics and Eating Quality
Ab; IRRI cultivars accounted for the largest part of the genetic background. Ciapus could be used as cross parents for higher yield. (text) * (cross data)

S.Kobayashi, T.Sotome and M.Oozeki. Jpn. J. Crop Sci. 79(1): 37-43 (2010).
Pedigree analysis of two-rowed malting barleys developed from multiple breeding programs in Japan
Ab; Cluster nalysis based on CP could distinguish the breeding programs by old breeding lines but not recent materials. (text) *

2. Pedigree analysis of outcross/vegitatively propagated crops

T. Yoshida. Japan. J. Breed. 36:409-415 (1986).
Inbreeding coefficient and yield in sweet potato (Ipomoea batatas (L.) LAM.).
Ab; The yield decreased when CI was higher than 0.2 and not decreased when lower than 0.1. The crosses with CI higher than 0.1 - 0.2 should be avoided. PISP for animal breeding in MAFF was modefied and used through TSS. (text) *

Y. Inaba and T. Yoshida. Hort. Res.(Japan) 5(3):219-225 (2006).
The Inbreeding Coefficients of Recently Developed Cultivars and the Relationship between Inbreeding Coefficiets and Yield in Strawberry.
Ab; Correlation of CI and yield was significant -0.37. CI less than 0.3 showed no inbreeding depression. CI of recent June-bearing type was more than 0.2. It was less than 0.1 of ever-bearing cultivars except 'Summer Princess' and 'Kiminohitomi'. Crossings among 15 June-bearing cultivars resulted CI from 0.067 to 0.440 and average 0.210. Prolog on Windows was used. (text) *

K. Taguchi, K. Nakatsuka, H. Takahashi, K. Okazaki and T. Yoshida. Breeding Research 8:151-159 (2006).
Relationship between the Coefficient of Parentage and Sugar Yield in Sugar Beet F1 Hybrid.
Ab; The correlation between the CP and sugar yield was R2=0.92. The CP increased by 0.1 and the sugar yield decreased by 4%. In the majority of the hybrids with a high yield, the CP was below 0.1. In this study, program for outcross crop was used assuming that common ancestor had CI of 1 for self-fertilized parent and 0.5 for male sterile parent. Changing the latter value 0.3 - 0.6 resulted no large discrepancy. (text)*

3. Relationship between kinship and DNA markers

Y. Uchimura, M. Furusho and T. Yoshida. Jpn. J. Crop Sci. 73:410-415 (2004).
Relationships between Coefficient of Parentage and Genetic Distance Based on DNA Polymorphism in Barley Cultivars.
Ab; A significant correlation (-0.526 to -0.650) between CP and Nei's genetic distances was found among 22 cultivars. Thus, the validity of the CP between two cultivars, which are calculated assuming that cultivars derived from the crossing have half of the genetic materials of each parent, was supported by the genetic distance estimated from DNA polymorphism. (text) *

S. Kobayashi and T. Yoshida. Jpn. J. Crop Sci. 75(2):175-181 (2006).
Relationships between Coefficient of Parentage Estimated from Pedigree Record and Genetic Distance Estimated from DNA Polymorphism in Wheat and Barley Cultivars
Ab; Correlation between CP and number of the same DNA markers was 0.581 to 0.904 in wheat and 0.731 to 0.805 in barley. The correlation between CP and Nei's genetic distance was −0.511 to −0.892 in wheat and −0.659 to −0.770 in barley. The number of the same DNA markers was highly correlated with Nei's genetic distance. (text) *

O. Ideta, I. Kono, Y. Takeuchi, H. Hirabayashi, M. Hirayama, H. Ohta, H. Sato, I. Ando, H. Kato, H. Nemoto, M. Yano, T. Imbe, M. Yamasaki and T. Yoshida. Breeding Research 14:106-113 (2012).
Genetic Diversity and Relationships between Coefficient of Parentage and Genetic Distance Estimated by SSR Markers in Japanese Rice Cultivars
Ab; A total of 226 SSR markers were used among 124 rice cultivars. The coefficient of correlation between CP and genetic distance ranged from −0.20 to −0.88.

4. Programs in Prolog & Web service

K. Mizuta, A. Sasaki and T. Yoshida. Agric. Info. Res. 5:19-28 (1996).
Prolog Computer Program for Evaluation of Coefficients of Parentage and Its Application to the Analysis of Pedigree of Malting Barley Cultivars.
Ab; A program for CP for self-pollinating crops was written in Prolog on MS-DOS, in which any pair could be computed. Only three ancestors contributed 70 %. 'Harunanijo' showed CP as high as 0.561, having a significant correlation (0.421) with malting quality. (text) *

T. Yoshida. Agric. Info. Res. 7:97-104 (1998).
Modification of a Coefficient of Parentage Assuming the Existence of Mutual Relationship among Original Introductions - In case of modern cultivars of rice and barely -.
Ab; Original introductions for first crossings are assumed not related to each other. Simulation showed CP for modern cultivars were calculated with little bias even if original introductions were closely related. (text) *

T. Yoshida. Jpn. J. Crop Sci. 72:309-313 (2003).
Inbreeding in Several Recently Bred Cultivars of Vegetatively Propagated Crops.
Ab; Program for computing CI was written in Prolog. CI of potato was smaller than sweet potato and strawberry. CI of strawberry was very high beyond the reported critical value. Discrepancy of old ancestors had little effect on CI. (text) *

T. Yoshida. Rep. Kanto Br. Crop Sci. Soc. Japan 19:54-55 (2004).
Windows Prolog Computer Program for Pedigree Analysis of Crop Cultivars.
Ab: Prolog programs for CP, CI and pedigree tree in MS-DOS (Prolog-KABA & WING) were rewritten into Windows (AZ-Prolog for Win32). (text)

T. Yoshida, Anas and T. Inaba. Jpn. J. Crop Sci. 78:92-94 (2009).
Construction and Use of Pedigree Analysis Web.
Ab; Online web service system for a pedigree analysis was constructed. (text) *

Files (zip) for this Web except Prolog-CGI interpreter.
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