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Hans Ersson, 1655, illustrates the impact an individual can have on the genetic make up of a region. 25% of those born 1890-1930 in Sorsele had a strait paternal line to him according to the database Kråken. Quite extrem! To see if there were more extrem cases I made a histogram of the number of “founding fathers” and their number of descendents in the database with a strait paternal line to them.

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The maps illustrates the impact of the “Örträsk Finns” on the interior of Västerbotten by three maps showing how their descendants spread. It is based on the database Kråken (http://www.xn--krken-nra.se/kr%C3%A5ken/) which is not compleatly finished for all of Västerbotten but it can anyway give a rough picture of how they spread over the land. For illustration three of the founding fathers are used namely: Johan Philipsson Hilduinen ca 1620-1697 Mårten Hindersson ca 1625-1697 Erik ca 1640 only known from his two sons Erik and Håkan The first map shows the proportion of the persons born 1890-1930 who descend from them.

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Visited again the old settlements of some of my forefathers. The house on the picture is standing on the place where Erik Ersson (b. ca. 1669) helped his brother Håkan (b. ca. 1670) to start a farm in Örträsk in 1706. It has not been properly dated but might be from around 1750. (Green dot on the map) The memorial stone over Helena Larsdotter (b. 1635) and Mats Hindersson (b.

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I made a map of the YFull kits with province listed for the Scandinavian countries and Finland with the basic idear that one kit should be one point. Mainly as an experimental alternative to other maps for showing the geographical distributions of the haplogroups. To avoid over plotting each point searches for a free space to be plotted and can also adjust size to squese in. This causes the ball shapes in especially Finland as most points are located to province landmass centroides.

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This is the geographical locations of the YFull kits. Non YFull kits gets a highly transparent dot in the predicted branch colour that can show the dencity of Q-L804.

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This is the current status of the family tree of Q-L804 based on the data at YFull. Branch length in years I have estimated with the Ape package in R using maximum likelihood and the “strikt model”. The rightmost codes refer to actuall test persons (YFxxxxx). YF02661 is a decendant of Erik Ersson in Knaften. YFulls version of the tree can be found at: https://www.yfull.com/tree/Q-L804/. The idea of maximum likelihood here is to choose the branch lengh that gives the most likely “pedigree” given the observed number of SNPs at every branch.

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Here is a more detailed view of the North Swedish subbranch Q-Y45428 of Q-L804. All nine boxes at the bottom are kits tested at ftDNA and verified by “paper research”. Of the nine kits, eight can be followed to Erik, the father of Erik Ersson (6 kits) and Håkan Ersson (2 kits). Erik has made a big contribution to the population of Västerbotten and probably most people in the region can find him on their pedigree.

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(English summary: Link to a paper showing that the Rh-negative blood type stod for a big part of stillbirths and infant mortality clustering within families in Skellefteå.) När jag har släktforskat på Skellefteå släkter har jag märkt att det verkar som att det vilar en förbannelse över vissa familjer genom att en stor del av deras barn var dödfödda eller dog strax efter födseln. Skellefteå har även en mycket stor andel som har blodgrupp Rh-negativ.

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This is the current status of the family tree of Q-L527 based on the data at YFull (Updated 2019-01-04). Branch length in years I have estimated with the Ape package in R using maximum likelihood and the “strikt model”. The idea is to calculate the most likely branch length given the number of SNPs on each segment. The rightmost codes refer to actuall test persons (YFxxxxx). Notice that this way of dating puts less weight on the Belarus sample compared to Yfulls estimate, pushing the origin of Q-L527 back in time.

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The GINI (inequality measure) developement in the municipalities Sweden 1986-2013. GINI on the Y-axis, mean income in 100 SEK on the X-axis and bubbles corresponding to populaition size. First graph shows the biggest 50% of the municipalities and the second the smallest 50% by population size 2013.

Big:

Small:

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