Happy First Birthday Iowa DNA Project

The Iowa DNA Project was formed at the end of November 2014 and for its first birthday has now reached 361 members.  The project is ‘geographical’ in nature, and designed for those who have direct ancestors who lived in Iowa, or those researching collateral lines that lived in Iowa. Our focus is on autosomal, aka Family Finder results, but we also have members who have had or are in the process of having their mtDNA and YDNA tested.  Those new to DNA testing are especially welcome and their research aims are supported within the project.

The previous (August 2015) Quarterly Report can be viewed here.

Key Figures

  • Total Iowa DNA Project Members: 361
  • Family Finder Tests Completed: 305
  • Total Donations: $105  Current Balance $6
  • Highest number of database wide matches per member: 3000
  • Lowest number of database wide matches per member: 1
  • Average number of database wide matches per member: 858
  • Inter-Project Matches: 233
  • Highest number of Inter-Project matches per member: 11
  • Weekly Match Updates
  • 96 of 99 Iowa Counties Represented

iowa mapNuts and Bolts

The Iowa DNA Project Surname Index can be found here.  Surnames associated with specific counties can be found in our FAQ here.

  • Total Iowa Surnames: 667
  • Members with Family Trees:276
  • Members with listed Surnames: 317
  • Members with listed Most Distant Ancestors: 292

Iowan Family Groups

The Iowa DNA Project has many pioneers who were the first to test within their immediate family.  However, the backbone of the project is the inclusion of multiple generations and extended family members who have also tested.  These family groups assist in helping inter-project matches determine how they may be connected and which branch of their family trees to examine further.  In October, we teamed up with Göran Runfeldt of dnagen.net  to trial his ICW Tool to map out the interconnectedness of the entire Iowa DNA project.   Below is a depiction of the connections between our current members.

atlas

Using the ICW Tool gives Iowa DNA Project members easy access to a variety of additional information and charts including a tabulation of our members’ Suggested Relationships.  As you can see, our members are actively recruiting close family members to test.

match totals

Suggested Relationships

  • Parent/Child: 64
  • Full Siblings: 38
  • Grandparent/Grandchild/Half Siblings: 22
  • Aunt/Uncle/Niece/Nephew: 22
  • 1st Cousin: 22
  • 2nd Cousin: 30
  • 3rd Cousin: 62
  • 4th Cousin: 114

More can be learned about the process and results here*.

*Additional detailed information is available to Iowa DNA Project members

Haplogroups

Project YDNAAs expected, the most common Y haplogroup is R and its subclades, with I and its subclades the second most common.  23 project members have completed the Big Y test.

Conf Y

Predicted Y

  • R-M269: 48
  • R (excluding R-M269): 44
  • I: 28
  • G: 3
  • E: 3
  • J: 2
  • N: 3

More information on the project’s patriarchs and YDNA results can be found here.

Project mtDNA:  The most common mtDNA continues to be H and its subclades with a variety of other haplogroups also represented. 105 project members have completed Full Mitochondrial Sequencing.

Member Haplogroups:

  • H: 68
  • K: 17
  • T: 16
  • U: 14
  • J: 12
  • I: 5
  • W: 3
  • V: 2
  • B: 2
  • C:1
  • X: 1

Complete information on our project’s mtDNA matriarchs, statistics and mutations can be found here.

conf mtdna

Declared Countries of YDNA and mtDNA Origin

Y COA

mt COA

MyOrigins Leaderboard

Based on percentage points per member, the Iowa DNA Project populations are listed below in order of frequency.  Descriptions of each population cluster can be found here.  Additional admixture tools can be found at Gedmatch.

  • British Isles 12,333
  • Scandinavia 6931
  • Western and Central Europe 6558
  • Southern Europe 1710
  • Eastern Europe 1403
  • Finland and Northern Siberia 345
  • Asia Minor 296
  • West Africa 158
  • Ashkenazi Diaspora 120
  • Eastern Middle East 106
  • (Blended Population Cluster) Eastern, Western and Central European 100
  • Native American 87
  • Northeast Asia 78
  • Central Asia 75
  • North Africa 35
  • East Central Africa 5
  • South-Central Africa 4
  • (Blended Population Cluster) British Isles and Western and Central Europe 1

As a matter of interest:

  • 100% British Isles 3 members
  • 100% Western and Central Europe 2 members
  • 100% Scandinavian 1 member
  • 100% Eastern, Western and Central European 1 member

Coming Results:

Currently, we are waiting for 3 kits to be returned to the lab for testing: 1 Factoid, 1 YDNA 67 Marker and 1 mtFull Sequence.  We have 13 members who have kits that have been transferred but not yet unlocked. Current members, please keep in mind you cannot be checked for inter-project matches without a completed and unlocked Family Finder test.

From the FTDNA lab, we are waiting for:

  • 2 mtFull Sequence (1 delayed)
  • 1 YDNA 37 marker
  • 7 Factoids (same project member)
  • 1 Y Haplogroup Backbone (delayed)
  • 1 R1b-CTS4466 SNP Pack
  • 1 R1b-L21 SNP Pack
  • 1 Big Y
  • 5 individual SNPS (same project member, 4 delayed)

Do You have Iowan Roots?

I would like to thank the project members for their patience and many efforts over the last year.  In October, I attended the Irish Genetic Genealogy Conference in Dublin, Ireland and had the pleasure of attending lectures, meeting cousins, members of ISOGG and other project administrators.  Lots of great information came out of the conference as well as ideas to make the project better. I look forward to making and sharing our discoveries in the months to come.

You can read more about the benefits of joining a project at FTDNA here.  If you would like to join the Iowa DNA Project, please visit our homepage here.  The project has converted to MyGroups and has activated its Activity Feed to encourage collaboration. The Feed may be accessed after joining and of course our links section is available to all.

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MDLP K13 Ultimate

For you ancestral-origin addicts, if your kit isn’t already uploaded to Gedmatch, you might want to consider adding it to their free database.  Not only can you get additional matches from the Big Three DNA companies, but there are numerous admixture tools to explore, including Vadim Verenich’s new MDLP K13 Ultimate.

Vadim discusses and explains his methodology in creating MDLP K13 Ultimate here.  According to Vadim, the tool is especially useful for showing the deep ancestry of Western Europeans.

MDLP K13 Ultimate’s Components

  • Amerindian – the modal component of the Native American
  • ANE – the modal component of the Northern Eurasians, which has been isolated from the common cluster with WHG – the highest values ​​in the samples of MA1, AG2, as well as the ancient genomes from Sintashta, Andronov, Afanasievo, Yamnaya, Corded Ware etc. Among the modern populations the highest percentage of ANE has been detected in Kalash population. Almost the same with the ANE component in Lazaridis et al. 2014
  • Arctic – modal component with peak populations Koryak, Chukchi, Eskimos and Itelmens
  • ASI – еру modal component of South Indian populations (i assume that this component is identical to ASI in (Reich et al. 2009).
  • Caucasus-Gedrosia – identical to Pontikos’s Caucasus-Gedrosia cluster
  • EastAsian – the modal component of East Asia
  • ENF – the component of the ancient European Neolithic farmers with the peak in the ancient samples of LBK culture (Lazaridis et al. 2014, Haak et al. 2015). Among the modern populations – the highest values ​​have been detected in Sardinians, Corsicans and Basques.
  • NearEast – the modal component of Middle Easterners
  • Oceanian – the modal component of the aboriginal inhabitants of Oceania, Austronesian, Melanesia and Micronesia (the peak in modern Papuans and Australian Aborigines)
  • Paleo-African – the modal component of African Pygmies and Bushmen
  • Siberian – the modal component of south eastern Siberia
  • Subsaharian – the second African component (Mandinka, Yoruba and Esan)
  • WHG-UHG – the native component of the ancient European Mesolithic hunter-gatherers (Lazaridis et al. 2014, Haak et al. 2015). Among the modern populations – the highest percentage in the population of Estonians, Lithuanians, Finns and others.

A Test Run

The testers in this experiment include 3 family groups that are predominantly Western European.

Group 1

Group 1 consists of 4 generations of my immediate family as shown in Figure 1.

chart

Group 1 MDLP K13 Ultimate Admixture Results:

group 1

Oracles

Below are each tester’s Oracle 4 results. Oracles are designed to find the population(s) you are most similar to.   As an example of what you might see in your own results, for Gladys I have shown the full Oracle 4 output.  For the rest of the testers, I’ve only included the top estimate in each of the 4 population approximations.  When using the Oracles, ideally you want your results to be a distance close to 1 or less.  The further the distance, the less your sample matches the reference population.  Please note, the MDLP K13 Ultimate Calculator has no Irish samples and thus no Irish population is included in the Oracle estimates.

Gladys mtDNA J1c3e Irish, small amount Alsace

Using 1 population approximation:

1 Germany_South @ 4.725954

2 Welsh @ 4.760169
3 Hungary @ 5.075449
4 Slovenian2 @ 5.144983
5 Slovak @ 5.262948
6 Austria @ 5.263268
7 North_European @ 5.285005
8 Czech2 @ 5.398787
9 Belgian @ 5.461259
10 German @ 5.465462
11 Slovenian @ 5.676105
12 South-German @ 5.796731
13 Austrian @ 5.843022
14 Hungarian @ 5.923850
15 Germany_North @ 6.175276
16 Inkeri @ 6.573199
17 Croat_BH @ 6.697131
18 North_German @ 6.715919
19 English_GBR @ 6.953663
20 Moldavian @ 7.103191

Using 2 populations approximation:

1 50% France +50% Vepsa @ 3.396806

Using 3 populations approximation:

1 50% Icelandic +25% Lak +25% Spanish_Pais_Vasco_IBS @ 2.251328

Using 4 populations approximation:

1 Avar + Basque_French + Orcadian + Swedish @ 1.562552

2 Avar + Basque_Spanish + Norwegian + Swedish @ 1.587619
3 Avar + Basque_French + Norwegian + Swedish @ 1.589907
4 Basque_Spanish + Lak + Norwegian + Swedish @ 1.604312
5 Avar + Basque_French + Icelandic + Swedish @ 1.609077
6 Avar + Basque_Spanish + Orcadian + Swedish @ 1.617271
7 Avar + Basque_Spanish + Scottish_Argyll_Bute_GBR + Swedish @ 1.627082
8 Basque_Spanish + Icelandic + Lak + Swedish @ 1.632314
9 Basque_French + Icelandic + Lak + Swedish @ 1.636813
10 Avar + French_South + Orcadian + Swedish @ 1.652004
11 Basque_Spanish + Norwegian + Swedish + Tabasaran @ 1.676389
12 Basque_French + Lak + Norwegian + Swedish @ 1.702851
13 Avar + Basque_Spanish + Icelandic + Swedish @ 1.707930
14 Basque_Spanish + Orcadian + Swedish + Tabasaran @ 1.720994
15 Avar + Basque_French + Scottish_Argyll_Bute_GBR + Swedish @ 1.737037
16 Avar + Basque_French + Orcadian + Russia @ 1.739913
17 Avar + French_South + Icelandic + Sweden @ 1.740096
18 Avar + Basque_Spanish + Russia + Scottish_Argyll_Bute_GBR @ 1.742547
19 Basque_Spanish + Icelandic + Swedish + Tabasaran @ 1.748318
20 Avar + Basque_Spanish + Orcadian + Russia @ 1.759932

To give a visual representation, I have plotted her 4 population approximation with the Geographic Midpoint Calculator.  The thumbtack with the ‘M’ is the midpoint between the 4 locations. At FTDNA, Gladys is 99% British Isles and 1% Asia Minor.

gladys midpoint

Steve mtDNA J1c3e yDNA R-Z251 ½ Irish, ½ East Flanders

Using 1 population approximation:

1 Germany_South @ 2.569169

Using 2 populations approximation:

1 50% Germany_South +50% Germany_South @ 2.569169

Using 3 populations approximation:

1 50% English_GBR +25% Greek_Comas +25% Vepsa @ 2.216009

Using 4 populations approximation:

1 Basque_Spanish + English_Kent_GBR + Kumyk_Stalskoe + Polish @ 1.199786

Steve’s midpoint plots in the Czech Republic.  Although he is half Irish and half East Flemish, he also has small amounts of Asia Minor and Southern and Eastern European at FTDNA:

steve midpoint

Lori mtDNA W3

Using 1 population approximation:

1 North_European @ 3.871830

Using 2 populations approximation:

1 50% French +50% Vepsa @ 3.446190

Using 3 populations approximation:

1 50% English_Kent_GBR +25% French_South +25% Tajik_Yagnobi @ 2.150152

Using 4  populations approximation:

1 Basque_Spanish + English_Kent_GBR + Orcadian + Tajik_Yagnobi @ 1.698324

lori midpoint

Jeremy mtDNA W3 ¼ English (Newcastle), ¼ Colonial

Using 1 population approximation:

1 Welsh @ 1.196703

Using 2 populations approximation:

1 50% Welsh +50% Welsh @ 1.196703

Using 3 populations approximation:

1 50% English_Cornwall_GBR +25% English_GBR +25% Romanians @ 0.840597

Using 4 populations approximation:

1 English_Cornwall_GBR + English_Cornwall_GBR + English_GBR + Romanians @ 0.840597

J midpoint

Gavan mtDNA W3 half Irish and Scots

Using 1 population approximation:

1 English_GBR @ 3.039722

Using 2 populations approximation:

1 50% France +50% Scottish_Argyll_Bute_GBR @ 2.266037

Using 3 populations approximation:

1 50% English_GBR +25% Spanish_Aragon_IBS +25% Vepsa @ 1.150668

Using 4 populations approximation:

1 Belgian + English_Cornwall_GBR + Spanish_Aragon_IBS + Vepsa @ 1.059918

Ga midpoint

Leona mtDNA W3 ½ Pomeranian, ¼ Norwegian, ¼ Alsatian

Using 1 population approximation:

1 Welsh @ 1.911880

Using 2 populations approximation:

1 50% North European +50% Slovenian2 @ 1.408170

Using 3 populations approximation:

1 50% North_European +25% Slovak +25% Welsh @ 1.243836

Using 4 populations approximation:

1 French_South + Georgian + Latvian + Norwegian @ 1.187358

leona midpoint

Jackie mtDNA W3

Using 1 population approximation:

1 Belgian @ 1.387411

Using 2 populations approximation:

1 50% Belgian +50% Welsh @ 1.096892

Using 3 populations approximation:

1 50% Belgian +25% Welsh +25% Welsh @ 1.096892

Using 4 populations approximation:

1 French_South + Georgian + Icelandic + Sorbs @ 1.075442

jack midpoint

Diane

Using 1 population approximation:

1 South-German @ 1.934086

Using 2 populations approximation:

1 50% South-German +50% South-German @ 1.934086

Using 3 populations approximation:

1 50% North_German +25% Romanian +25% United-Kingdom @ 1.519436

Using 4 populations approximation:

1 English_Cornwall_GBR + Romanians + Slovak + United-Kingdom @ 1.441976

Group 2

MDLP K13 Ultimate Admixture Results

Untitled - 1

As seen in Figure 1, Group 2 includes 2 unrelated mothers, Nancy and Lori, and their sons, AJ and Jeremy who are ½ uncle and nephew.

Oracles

Nancy

Using 1 population approximation:

1 English_GBR @ 1.889876

Using 2 populations approximation:

1 50% English_GBR +50% English_GBR @ 1.889876

Using 3 populations approximation:

1 50% Russian_Orjol +25% Spanish_Pais_Vasco_IBS +25% Welsh @ 1.239205

Using 4 populations approximation:

1 Basque_French + Czech2 + Inkeri + West-Belarusian @ 1.045225

nancy midpoint

AJ

Using 1 population approximation:

1 North_European @ 2.072961

Using 2 populations approximation:

1 50% European_Utah +50% Welsh @ 1.128120

Using 3 populations approximation:

1 50% Czech2 +25% Spanish_Cantabria_IBS +25% Ukranian @ 0.942059

Using 4 populations approximation:

1 Czech2 + Slovak + Sorbs + Spanish_Cataluna_IBS @ 0.914081

aj midpoint

Lori mtDNA W3

Using 1 population approximation:

1 North_European @ 3.871830

Using 2 populations approximation:

1 50% French +50% Vepsa @ 3.446190

Using 3 populations approximation:

1 50% English_Kent_GBR +25% French_South +25% Tajik_Yagnobi @ 2.150152

Using 4  populations approximation:

1 Basque_Spanish + English_Kent_GBR + Orcadian + Tajik_Yagnobi @ 1.698324

Jeremy mtDNA W3 ¼ English (Newcastle), ¼ Colonial

Using 1 population approximation:

1 Welsh @ 1.196703

Using 2 populations approximation:

1 50% Welsh +50% Welsh @ 1.196703

Using 3 populations approximation:

1 50% English_Cornwall_GBR +25% English_GBR +25% Romanians @ 0.840597

Using 4 populations approximation:

1 English_Cornwall_GBR + English_Cornwall_GBR + English_GBR + Romanians @ 0.840597

Group 3

MDLP K13 Ultimate Admixture Results

group 3

Group 3 consists of two full sisters, Jean and Sally, and Sally’s daughter Julie.  Jean and Sally’s father was born in Glasgow from a family that originated primarily in the north of Scotland.  Maternally, they derive mainly from early Irish and German immigrants.  Paternally, Julie’s family has lived in Lippe, Germany for centuries.

Oracles

Jean 1/2 Northern Scots, ½ Colonial

Using 1 population approximation:

1 North_German @ 4.487704

Using 2 populations approximation:

1 50% English_Cornwall_GBR +50% Vepsa @ 3.162740

Using 3 populations approximation:

1 50% English_Cornwall_GBR +25% Scottish_Argyll_Bute_GBR +25% Vepsa @ 2.750026

Using 4 populations approximation:

1 English_Cornwall_GBR + English_Cornwall_GBR + Scottish_Argyll_Bute_GBR + Vepsa @ 2.750026

jean

Sally ½ Northern Scots, half Colonial

Using 1 population approximation:

1 North_European @ 1.952304

Using 2 populations approximation:

1 50% English_GBR +50% North_European @ 1.668977

Using 3 populations approximation:

1 50% Slovak +25% Spanish_Pais_Vasco_IBS +25% Vepsa @ 1.005733

Using 4 populations approximation:

1 Basque_French + Hungary + Russian_Smolensk + Vepsa @ 0.987646

Sally has a small amount of Central Asia in her FTDNA ancestral origins that her sister Jean does not have.

sally midpoint

Julie ¼ Northern Scots, ¼ Colonial, half German (Lippe)

Using 1 population approximation:

1 English_GBR @ 3.404993

Using 2 populations approximation:

1 50% French +50% Vepsa @ 3.007144

Using 3 populations approximation:

1 50% North_German +25% Spanish_Valencia_IBS +25% Vepsa @ 2.082997

Using 4 populations approximation:

1 Basque_French + Slovak + Vepsa + Vepsa @ 1.434730

julie midpoint

Conclusion:

For my testers, the admixture calculators (ANR, ENF, WHG etc) appear reasonable and tick all the boxes I look for when considering the value of admixture tools.  The Oracle estimates are another consideration. As the calculator is exposing deep ancestry, and as a whole very little of the group’s paper trail reaches the 1500’s, let alone precedes it, we have no way of determining its precision.  We can, however, look at the population approximations  passed down and across the group’s generations, as well as consistency between estimated regions and estimated regions/populations from other calculators.

Admittedly, I needed to google some of the reference populations to find where to plot them on the map.  Despite the seemingly exotic populations some of our testers received in their estimates, once each 4 population approximation was plotted (and the non-tested parent’s known ancestry taken into account) for the most part each closely related family member plotted within the same general region.

Of course, as always, we must remember admixture calculators are estimates of ancestry, and a work in progress being improved upon as the science and reference samples grow.  They are interesting, and as long as you don’t let unexpected admixture results derail your genetic and genealogical efforts, they can be entertaining.

Missing and Small Matches

One of the pleasures of having multiple generations tested is the ability to make comparisons.  Like many DNA testers, I’d love to track the source of each match’s connection and map as much of my ancestors’ DNA as possible. When a new match arrives the first task is to determine whether it is a maternal or paternal match, which grandparent it might belong to, and if possible, to assign a more distant ancestral couple.

The more matches you have to work with, the more likely you are to connect with those that will help determine the source of your DNA.  Due to the FTDNA match requirement of at least 20 cM total shared, each kit I manage has many more FTDNA matches at Gedmatch than it does at FTDNA. In some cases, these FTDNA kits match myself and my paternal grandmother Gladys, but not my father Steve.  To determine whether these matches are false or victim of the FTDNA 20 cM total requirement, I took the advice of the larger community.  It was a simple matter to search for FTDNA kits at Gedmatch that matched grandmother Gladys and myself but not my dad.

Regardless of where you have tested, at Gedmatch it is possible to lower the minimum threshold and compare kits on a 1 to 1 basis to tease out or force matches, depending on your perspective.  Is it wise to do so?  According to an ISOGG article on phasing, “It may be reasonable to map some segments in the 3-5 cM range if both the parent and the child share that same segment with the relative but caution is warranted when mapping segments that don’t contain at least 700 or more SNPs because some matching segments could be IBS and not IBD.

Normally, I do not lower the minimum defaults at Gedmatch nor do I recommend doing so.  If you are reducing the threshold at Gedmatch to compare with a suspected relative, to ‘prove’ a match below Gedmatch’s default levels, you are taking a big risk and not necessarily proving what you intended.  However, in this special case of using 4 generations to illustrate missing matches, small matches, and small matches that could appear to some as valid and belonging to a specific ancestral couple, for all comparisons I dropped the minimum levels to 350 SNPS and 3 cM.

Cousin Malcolm

Malcolm fit the pattern of matching my grandmother Gladys, myself and one of my children at FTDNA, but he didn’t match my dad Steve. It was a different story at Gedmatch.

Glad and Malcolm

Figure 1

Steve and Malcolm

Figure 2

As seen by comparing Figures 1-3, Malcolm, Gladys and Steve all match on the highlighted portions of chromosome 1.  I also have an overlapping segment on chromosome 1 as does my son Gavan.  Are the small, non-highlighted segments valid?  Generally, I consider them to be IBS. and whatever their origin, not large enough to pursue.

malcolm

Figure 3

My son Jeremy also matches Malcolm at segments up to 4.4 cM/784 SNPs, none of which were inherited from his grandmother Gladys or our common ancestor shared with Malcolm.   

Jeremy and Malcolm

Cousin Kay

Kay, too, fits the pattern of matching grandmother Gladys, myself and one of my children at FTDNA, and while she matches him at Gedmatch, she doesn’t match Steve at FTDNA.

Gladys and Kay

Figure 1

Steve and Kay

Figure 2

Lori and Kay

Figure 3

When comparing Figure 1 and Figure 2, Steve’s match on chromosome 1 occupies an area within the same start and stop points as the segment he shares with Gladys.  In Figure 3, you can see I also match Kay on chromosome 1, but my start point is 69, 937,274.  The DNA I match with my dad and grandmother has a start point for my dad of 70, 268, 967.  The differences in those start points makes it appear as though I share 8.2 cM of DNA as opposed to the 8.0 cM of DNA my dad shares with Kay.  Although a trivial amount, it could indicate my dad having no calls in that portion, or it could be a case of recombination.

Meanwhile, Kay matches my son Gavan on the same 8 cM segment he shares with me/Steve/Gladys.  On the other hand, my son Jeremy shares 5.6 cM/691 SNPs on chromosome 9 with Kay that he also shares with me but not my father or grandmother.  Jeremy and Kay may have a common ancestor through my grandmother on paper, but it is not reflected in their phased DNA results.

Jeremy and Kay

Cousin Bonnie

Bonnie also matches Gladys at FTDNA but does not match Steve.  Again, Steve has another new match at Gedmatch.

bonnie

On chromosome 1, Bonnie, my dad and I share 6.1 cM/362 SNPs that we don’t share with my paternal grandmother Gladys.  362 SNPs falls well short of the ISOGG recommended minimum of 7 cM/700 SNPs. Chromosome 18 shows a match between Bonnie and myself at 3.2 cM/514 SNPs.  My dad also shares a match with Bonnie on chromosome 18 at 3.4 cM/492 SNPs.  My start point on chromosome 18 precedes the start point for my dad, and is another example of possible no calls or IBS.  Either way, neither small segment was inherited through Gladys and her ancestor in common with Bonnie.

Bonnie and Lori

At reduced threshold levels of 3cM/300 SNPs, Bonnie also matches Gavan on segments that she only otherwise shares with me and not Steve or Gladys.  Bonnie and Jeremy share segments that are shared with no one else.

Gavan and Bonnie Jeremy and Bonnie

Cousin Sean

In this example, at FTDNA Sean matches only my grandmother Gladys.  However, at Gedmatch he is also on my dad’s match list.

Glad and Sean

Figure 1

As seen when comparing Figure 1 and Figure 2, Steve matches an 8.6 cM segment on chromosome 1 with his mother Gladys.  On chromosome 17 we see another small overlap:  3.1 cM/465 SNPs.  The start points vary.

Steve and Sean

Figure 2

Neither I nor my children match Sean at FTDNA or Gedmatch, but our results do represent what type of results we could expect if we were lowering thresholds and comparing 1 to 1 with out the safety net of additional tested generations and their data.  In Figure 3, my results are added and compared against my grandmother and father.  While Sean and I have numerous ‘matches’, up to 5.1 cM,  I didn’t inherit the vast majority of them from my grandmother Gladys or the ancestor we share with Sean, and none of them fall within the phased extreme minimum recommended range of 3-5cM/700 SNPs.

Sean and Lori

Figure 3

In Figure 4, my Irish born son Gavan is compared to Northern Irish Sean.  He also has matches, with segments up to 4.4cM/683 cM with Sean, although none of them were inherited from Gladys or the ancestor we share with Sean.

Gavan and Sean

Similarly, my son with deep American Colonial ancestry Jeremy also matches with Northern Irish Sean at up to 5.4 cM/838 SNPs, but again, none of the matches were provided by Gladys and our common ancestor.

Jeremy and Sean

Closer Cousins

chart

Previously, I’ve been concentrating on distant cousin matches that could easily be missed by FTDNA’s minimum requirements but could appear at Gedmatch with single blocks of shared DNA above 7cM/700 SNPs.  In the following examples I am concentrating on another multi-generationally tested family.  Their patriarch, CK is my grandmother Gladys’s 2nd cousin through common ancestors Henry Kane and Katherine Forrestal of County Mayo, Ireland.

Gladys and CK, as second cousins, share abundant DNA in excess of 7 cM/700 SNPs from Henry and Katherine.  They also share 2 segments above 5cM/700 SNPs on chromosome 15.

Gladys and CK

Two of CK’s children, HaK and HeK have also tested. In Figures 1 and 2, we see the DNA they share with both Gladys (G) and CK (C) highlighted, as well as segments that are partial overlaps with Gladys and CK.

Gladys HaK

Figure 1

Glad and HeK

Figure 2

In Figure 3, we see the DNA shared between CK and Steve, Gladys’s son.  All the highlighted portions match CK, Steve and his mother Gladys, including a 3.9 cm/772 SNPs segment on chromosome 15 and  3.9 cM/400 SNPs segment on chromosome 16.  The segments that don’t match Gladys fall below the 700 SNP range.

Steve and CK

Figure 3

Adding a 3rd Generation

HeK and HaK both have children that have tested.  HeK’s son does not appear on any of the match lists of Gladys, Steve, Lori, Jeremy or Gavan.  However, HaK’s children, KaK and KeK do and are shown below, with the highlighted portions illustrating the segments they share with Gladys (G), their grandfather CK (C) and their father HaK (H).

Gladys and KaK

Above, KaK matches Gladys, her grandfather and father  on chromosomes 3, 4 and 13 and partially overlaps on chromosome 6.  The match she shares with her father on chromosome 8 doesn’t match the DNA shared by CK and Gladys. Of the remaining segments KaK  shares with Gladys, on chromosome 1 she shares 3.1 cM/854 SNPs which she does not share with her grandfather CK.

Below are highlighted the shared segments of KeK, his father HaK (H), grandfather CK (C), and Gladys(G). KeK matches his father, grandfather and Gladys on chromosomes 6 and 13 match and partially matches them on chromosomes 3, 15 and 19 . On chromosome 1 KeK share the same 3.1 cM/852 SNPs as his full sibling KaK.

Gladys and KeK

Gladys(G) has a grandchild and Steve (S) a child who has tested (me), so I am also able to compare my segments against the group to ferret out DNA I’ve inherited from Katherine and Henry via my grandmother Gladys.

Lori and CK

In the above example all highlighted matches except one on chromosome 15 match Gladys, Steve and CK.  The smaller segments match neither Gladys nor Steve.

Adding a 4th Generation

My sons make up the 4th generation.  Below, Jeremy has one match at 50.3 cM/13,569 SNPs shared by CK, Gladys, Steve and myself as well as one that is 3.4 cM/492 SNPs. Two of the non-highlighted matches he shares with CK but not his mother or the rest of the group are in excess of 700 SNPs.

Jeremy and CK

Gavan has 4 matches shared between CK, his mother, grandfather Steve and great-grandmother Gladys. None of the non-highlighted matches Gavan shares with CK but not the rest of the group are in excess of 700 SNPs.

Gavan and CK

Putting it Together

Determining whether a match is valid is clearly extremely important when working with your results.  Making sure you have added your kit to Gedmatch, whether you have tested at Ancestry, 23andMe or FTDNA is an essential part of the process.  I haven’t yet gone through all the FTDNA matches missing from my dad’s list that Gladys and I share, but I have yet to find a FTDNA match that has also uploaded to Gedmatch that he doesn’t also match above 7cM/700 SNPs.

Recombination, crossover, IBS, IBD and phasing are important concepts all budding genetic genealogists must grapple with.  Minimum acceptable segment length will doubtlessly continue to be debated and redefined.  Certainly, if you are using reduced thresholds/small segments and haven’t tested a parent(s) you are taking a very big gamble.

For further reading you might be interested in: Small Segments and Triangulation by Jim Bartlett,  Hotspots and Crossover,  and Anatomy of an IBS Segment and What a Difference a Phase Makes by Ann Turner.  You might also wish to add your voice to the subject at the FTDNA forums.

Surnames and Associated Counties

iowa map

With 441 members and 98 of 99 counties represented, the Iowa DNA Project is continuing to grow.  Each new member increases the likelihood of finding matches and learning more about our ancestors and the settlement of Iowa.  Is your surname represented?  If not, consider joining!  If you don’t already have a Family Finder test at FTDNA but have tested with another company, you may wish to consider transferring your raw data.

Surnames and Associated Counties

  •  Adair: Aspinwall, Bates,Hoisington, Lounsbury, Scott, Sias, Nichols, Stillians/Stillions, Newcomb
  • Adams: Shiffer, Riggle, Henry, Jones, Newcomb, Ankeny, Rogers, Fleharty, Knee, Runge
  • Allamakee: Whalen, Regan, Devine, Laughlin, Danaher, Ryan, Fitzgerald, Born, Dee, O’Conner/Conner,Kruger, Winke, Flage, Henning, Ludeking, Baxter, Butler, Buckley, Ralston,Archibald, Sires, Duff, Speigler, Healy, Brady, Werhan
  • Appanoose: Milburn, Awalt, Morlan, Murphy, Brown, Robinson, Phares, Flowers, Crawford, Martin, Jackson, Gates, Wilcox,Watson, Zimmerman, Richards, Bowman, Richards, Van der Heyden
  • Audubon: Drake, Finch, Burns, Chase, Follmer, Liles, McGuire
  • Benton: Gallup, Dilley, Stewart, Cue, Calhoon, Younglove, Hinkle
  • Black Hawk: Belt, Whaylen, Corrigan, O’Neill, Stewart, McNaughton, House, Purdie,Mallett, Richmond, Bates, Robinson, Kerns,Beirschmitt, Duffy, Kane, Forrestal, Burns, Flaherty, Kennedy, Harned, Singer, Robertshaw, Olsen, Jensen,Hansen, Morgensen, Baer, Bender, Buehner, Call,Carpenter, Fuller, Hare, Haun, Meisch,Neisen, DuBois, Kelly
  • Boone: Lyman, Benjamin, Fenn,Harmon, Smith, Miller, Peachey, McGregor,Ballentine
  • Bremer: Harned, Singer, Baer, Bender, Buehner, Call, Carpenter, Fuller, Hare,Haun
  • Buchanan: Leach, Chicken, Grim,Duffy, Kane, Forrestal, McCloskey, Kinney, Clark, Harned, Singer
  • Buena Vista: Jessip, Carney, Marshall, Howard, Dale, Ginn, Taylor,Larson, Johnson,  Lydell
  • Butler:  Bigsby
  • Calhoun: Osborn, Godwin
  • Carroll: Wilkens, Piper, Conner, Wenck, Best, Rabe, Brunen, Wilberding, Grever/Grefer, Willenborg
  • Cass: Scovel, Baker, Gillpatrick, Randles,
  • Cedar: Orcutt, Dutton, Baker,Gaines, Gillpatrick, Randles, Wagner, Knipfer, Mottschall, Follmer, Liles
  • Cerro Gordo:  Hacker
  • Cherokee: Beyer, Schubert, Sorensen,Smith, Larson, Johnson, Lydell, Gengler, Niehus, Foerster, Heinis, Niggeling, Nothem, Engeldinger, Wanderscheid, McCulla
  • Chickasaw: Robinson, Colligan, Pierson/Pearson, Hawkins, Glass, Pangborn
  • Clarke: Sowers, Lee
  • Clay: Ewing, Knee
  • Clayton: Scovel, Cagley, Hulverson/Halverson, Sass, Roth, Kamin, Wilke, Meye, Clark, Weideman/Wedeman, Stevens, Greene, Beckmann, Stutheit, Hempeler, Ewing, Richards
  • Clinton: Berg, Wink/Wienke,Johnson, Halversen, Halverson, Halvorsdatter, Maklebust, Hansen, Johannesdatter,Dossland, Ask, Olson, Carter, Edwards, Hazlett, Whitaker, Coffman, Cunningham, Van Cruijningen, Alcorn, Chase, Hartson, Clark
  • Crawford: Endrulat, Reese, Jahn, Krause, Kutschinski, Wiese, Klaus, Eyer,Neddermeyer
  • Dallas: Hanlon, Brady, Shiffer, Cone, Ballentine, Andersdotter, Jonsson,Curfman, Nichols
  • Davis: Lohrengel, McGachey
  • Decatur: Davis, Newcomer, Lushbaugh, Webster, Roselle, Dale,Marksbury, Higgs, Weable, Anderson,Sly
  • Delaware: Klaus, Clark, Webb, Arnold, Duncan, Field, Alloway, Fuller, Anderson, Rexford, Paddleford, Walker, Cline, Willenborg, Braun
  • Des Moines: Peterson, Childs
  • Dickinson: Guthrie, Lambertus, Franker, McCulla, Nicolas
  • Dubuque: Wentz, Consor, Krueger, Metcalf, Noesen, Nattrass, Robson, Daykin, Hoffmann,Heiter, Pauly, Gloesener, Kayser, Miller/Mueller, Jordan, Singer, Boock, Wilberding, Johanning, Feldmann, Schaupmann, Jasper, Siemes, Tauke, Braun, Kleespies, Albert, Blitsch, Conzett, Jecklin, Mathis, Moser, Osterberger, Schauer, Strauch
  • Emmet: Hansen, McCulla, Allen, Crim, Doyle, Wilson
  • Fayette: Glass, Pangborn, Kappes, Bodensteiner,Vanginderhuyser, Wise, De Temmerman, Georgi, Kern, Amundsen, Kerns,Beirschmitt, McCloskey, Gifford, Johnston, Tope, Mittelstedt, Wroe, Burns/Burnes, Clark, McCann, Houlsworth, Perry, Wait/Waite, Finch, Kuhens/Kuhnes, Ewing, Johnston
  • Floyd: Miller, Klaus, Reed, Stickney
  • Fremont:  Garcia, Enos, Davina
  • Guthrie:  Dilley
  • Grundy: Campbell, Whitehead, Miller
  • Hamilton: Teget, Toedt, Pahl, Wing,Johnson, Dale
  • Hancock:  Nix
  • Hardin: Wing, Johnson, Vinje, Kelsey
  • Harrison:  Kirley, McBride, Davis, Jordan, Lewis, Anderson, Bolte
  • Henry: House, Sample, Shelton, Allen, Billingsley, Malone, Houston
  • Howard: Osborn, Gifford, Cushing,Roberts, McCulla, Johnston
  • Humboldt: Hilbert, Ewing
  • Ida: Beyer, Endrulat, Grell, Haase, Helkenn, Reese, Schubert, Bauer, Meyer,Paustian, Ruhser/Ruser, Schroeder, Sorensen, Wink/Wienke
  • Iowa: Duffy, Burns, Masteller,Gallagher, Burns, Kinney, Murphy, Duggan, Welch
  • Jackson: Berg, Meyer, Wink/Wienke,Johnson, Halversen, Halverson, Halvorsdatter, Maklebust, Hoffmann, Miller/Mueller, Johannesdatter, Naegle, Nagel, Mueller, Carter, Edwards, Zeimet,Winkel, Conzett
  • Jasper: Belt, Hyde, Pahl, Toedt, Holliday, Hickey, Debolt/De Bolt, Ross, Nichols, Weigel, McKlveen
  • Jefferson:  Wygle
  • Johnson: Minnich/Minnick, Lyle,Crossen, Fitzgerald, McCarthy, James,Bigsby, Coffman, Kile
  • Jones: Hanlon, Brady
  • Keokuk: Wilson, Willson, King, Belveal
  • Kossuth: Young, Hilbert, Becker, Richter, Sires
  • Lee: Childs
  • Linn: Stewart, Cue, Calhoon, Gaines, Rickert, Richard, Wagner, Poorman,Gates, Wilcox, Watson, Zimmerman, Richards, Bowman, Stevens, Webb, Osterberger
  • Louisa: Johnston, Herron, Ramsey, Smith, Hand, Vanloon
  • Lucas: Stevenson, Coffelt, Vickroy,Welch, Truman, Hickman, Hasting, Davis, Mumford, Cain, McGlothlen, Rodgers,
  • Lyon: Follmer, Liles
  • Madison: Shutt, Black, Cashman, Benedict, Marchel, Wolfe, Peed, Ross, Debolt/DeBolt, Gates, Wilcox, Watson, Zimmerman, Richards, Bowman
  • Mahaska: Heberer, Howard, Burke, Conklin, Holliday, Adair, Ives, Lowry,Ferrell, Addis, Adkisson, Zeppernick, Williams, Parr, Hoskinson, Myers, Wymore,James, McMains, Hollingsworth
  • Marion: McCombs, Howard, Godwin, Barr, Wilson, Cashman, Williams, Newman, Childs
  • Marshall: Wantz, Bryant, Brown
  • Mills: Gowdy, Chamberlain, Hambsch, Oestreicher
  • Mitchell: Baker, Gaines, Mackin,Kinney, Gerbig, Decker, Galt, Tretton, Gemaehlich, McCulla
  • Monona: Nepper,Ordway, Ziems, Freerking
  • Montgomery: Lee, Pittman
  • Muscatine: Orcutt, Allen, Kuiper, Heuer, Pasdach, Yeater, Huff, O’Brien,Cain/Kain/Kane, Cashman, Alcorn, Ipock, Yates, Freers,Schreurs, Washburn, Bigsby, Ager, Everett, Follmer, Liles, Fulmer/Fullmer, Kingsbury, Stiles
  • O’Brien: Stewart, Runge
  • Osceola: Hamann, Schubert
  • Page: Nicolson, Teget, Cox, Krey
  • Palo Alto: Williamson
  • Plymouth: Storer, Gengler, Niehus, Foerster, Heinis, Niggeling, Nothem, Engeldinger, Wanderscheid, Wilberding
  • Polk:  Smith, Hanlon, Brady, O’Connell, Wilson,Beatty, Stevenson, Coffelt, Burnett, Scovel, Foutch,Halterman, Boatwright, Davis, Freel, Stewart,Johnson, Warren, Flesher, Deaton, Powell, Freel, Butler, Shiffer, Brooks,Holliday, Lawrence, Cooper, Shutt, Cone, Mason, Baber, Nalley, Higgs, Kirsher,Huggins, Jones, Debolt/De Bolt, Nichols, Klaus, Poorman, Gates, Wilcox, Watson,Zimmerman, Richards, Bowman, Hendricks, Compton, Giese, Childs, Ewing, Mills, Bowers, Town
  • Pottawattamie:Shanahan, Gallup, Stuart, Dolan, Hale, Davis, Wires, Fitzgerald, McCarthy, Randles, James, Slingerland, Kirley, McBride, Johannsen
  • Poweshiek:Watson, Sebring, Krouskop, Carpenter, Krise
  • Ringgold: Hazen, McCurdy, Carpenter,Humphreys, Cone, Arnold, Thorla, Newman
  • Sac: Schroeder, Masteller,Staton, Ragsdale, Masteller
  • Scott: Conrad, DahlDall/Doll,Frauen, Grell, Haase, Hamann, Helkenn, Reese, Rusch, Steffen, Bauer, Schween,Heckt, Paustian, Ruhser/Ruser, Sorensen, Kivlin, Feeney, Baugh, Collins,Jacobs, Crouch, Wulfe, Brus, Aufdenspring,Murphy, Foley, Ginn, Mills, Jones, Reed, Elshorst, Boock, Eggers, Fendt, Meier, Runge, Tiedje, Parker, Snider, Miller, Herr, Villain,  Moravek, Byers, Finkenhoefer, Traeger, Busch
  • Shelby: Gallup
  • Sioux: Meyn
  • Story: McDowell, Allen, Wing,Page, Hansens, Guddal
  • Taylor: White, Pace, Stephens
  • Tama: Howard, Krise, Boock
  • Union: Krise
  • Van Buren: Downard, Payne, Freel, Miller, Marriott, Shipley, Watt/Watts, Childs, Billingsley
  • Wapello: Ward,Hartshorn, Lowery, Robertson
  • Warren: Stewart,Black, Halterman, Flesher, Turnipseed, Deaton, Freel, Shutt, Cashman, Braucht,Mason, Vickroy, Wiley, Douglas, Williams, Martindale, Pierce, Hasting, Michael,Daugherty, Grant, Davis, Stewart, Cue, Calhoon, Flowers, Crawford, Fulmer/Fullmer
  • Washington: Longwell, Jury, Phillips,Deen, Downing, Bradford, Lambert, Story, Carr
  • Wayne:  Hanlon, Jones
  • Webster: Carpenter, Porter, Feeney, Carter, McQuiston, Mabe, Berry, Cackler, Doherty, Coles, Kellum, Stillions,Shiffer, Burrell, Meyn
  • Winnebago:  Forrestal, Dahl, Bolstad, Loberg, Paulson, Moe, Horvei
  • Winneshiek: Dörr/Doerr, Untereiner, Kruger, Winke, Flage, Henning, Ludeking, Buddenberg, Carolan
  • Woodbury: Berg, Wink/Wienke, Storer,Jessip
  • Worth:  Halgrimson, Vold, Turvold, Moe, Horvei

Iowa DNA Project April Update

With 161 members the Iowa DNA Project is gaining momentum.  139 members have completed the Family Finder, or autosomal testing, and 90 of those members already have matches within the project. Once a week new matches are notified of their connections, and assisted with making contact. Each new member increases the likelihood of finding matches and learning more about our ancestors and the settlement of Iowa. Featured image Polk, Black Hawk, Fayette, Buchanan and Pottawattamie Counties are currently our most common counties of origin, but as indicated by the black dots on the map, most counties are being researched by members looking to expand their understanding of their Iowa family history.  The project presently represents 386 Iowan surnames. Is your surname and county represented?  If not, consider joining! If you don’t already have an account at FTDNA but have tested with another company, consider transferring your raw data. Surnames and Associated Counties

  •  Thompson, Tinnes, Schantz, Starr:  Keokuk, Henry
  • Zeiler, Tinnes, Starr, Leffler, Evans, Eicher, Miller: Jefferson, Lucas, Washington
  • Vickroy, White, Norem, Oleson, Good:  Warren, Des Moines, Humboldt
  • Albert, Mustard, Hennick, Henderson, Masters, Stainbrook, Munden:  Buchanan, Emmet, Black Hawk
  • Shanahan, Gallup, Hafner:  Pottawattamie
  • McCombs, Heberer, Marshall, Flaherty, Howard,  Kennedy, Carney, Ferguson, Huston, Freerking, Young:  Marion, Mahaska, Wapello, Black Hawk, Buena Vista, Wright
  • Nepper, Kutschinkski, Schultz, Ziems, Neddermeyer, Ordway, Wiese, Stahl, Ewoldt, Jahn, Krause, Klause: Polk, Dallas, Crawford, Monona, Woodbury, Clinton, Carroll
  • Stephens:  Taylor
  • Burns:  Polk
  • Hartshorn:  Wapello
  • Park, Sweet, Carl, Kell, Hayden:  Washington, Cedar, Louisa
  • Gifford, King, Davison:  Howard, Dallas, Fayette
  • Ortgies, Antons, Oltmanns, Carpenter, Schollars, Ralston:  Jones, Pottawattamie, Linn, Jackson
  • King, Schulz, Wagner, Kartes:  Mitchell, Worth
  • Dolson, White, Peed:  Madison
  • Baker, Gaines, Randles:  Cedar, Mitchell
  • LaRue, Rasmussen,  Evans, Carpenter,  Christopher:  Franklin, Cerro Gordo,  Hamilton
  • Hanno, Seel, Albrecht:   Cerro Gordo, Audobon, Pottawattamie, Woodbury
  • Dee, Born:  Allamakee
  • Hinshaw, Williams, Lundy:  Hardin, Appanoose
  • McGachey:  Davis
  • Bruce, Petersen, Conley:  Marshall, Tama, Humboldt
  • Guthrie, Lambertus, Franker:  Woodbury, Dickinson
  • Sullivan, Bork:  Howard, Black Hawk
  • Sargent, Parsons, Alisey,Turner:  Clayton, Clay, Jefferson
  • Anderson
  • Stratton, Cole, Frederick, Carney, Grey, Gillin, Ferguson, Roland, Palmer, Love, Huss:  Des Moines, Linn, Iowa, Johnson, Marion, Polk, Kossuth, Louisa, Guthrie
  • Debolt: Polk
  • Rovang, Ranum, Sivesind, Linnevold, Olson, Askelson, Larson, Holm, Renslebraaten:   Winneshiek, Fayette, Clayton
  • Worrall, Simmers, Howard, Bishop, Mitchell, Stout, Davis, Farr, Johnson, Bloodsworth, Jobusch, Lee, Blommers,  Poling:  Mahaska, Wapello, Monroe, Marion, Polk
  • Humphreys, McCurdy:  Wright, Ringgold
  • Vickroy, White, Warren, Douglas, Stewart, Williams, Cue, Pierce, Martindale, Welch:  Lucas, Warren, Polk, Clarke
  • Rousch, McCormick
  • Volbrecht, Winters:  Floyd
  • Miller: Van Buren
  • Wise, De Temmerman, Vanginderhuyser, Georgi, Kern, Miller, Kerns, Linden, Amundsen, Ross, Debolt, Duffy, Kane, Forrestal, Burns, Mackin, McCloskey, Bierschmitt: Buchanan, Fayette, Madison, Polk, Guthrie, Black Hawk, Bremer
  • Nicolson, Teget, Toedt, Pahl:  Page, Hamilton, Jasper
  • Marchel:  Madison
  • Hickey:  Jasper
  • Heberer: Mahaska, Marion
  • Kane, Cashman:  Muscatine, Scott
  • Johnston, Herron, Ramsey, Smith: Louisa
  • Stuart, Dolan:  Pottawattamie
  • Murphy, Foley: Scott
  • Kruger, Winke, Flage, Henning, Ludeking, Stock, Goebel, Dalbkmeier: Allamakee, Winneshiek, Wright
  • Bruning, Lowry, Robertson:  Wapello
  • Naglestad, Farstead:  Lyon, Winneshiek
  • Oberton, Forrestal:  Franklin, Winnebago
  • Sly: Decatur
  • Jahn, Neumann, Ostermann, Mueller, Brandenburg:  Keokuk, Sioux, Osceola, Plymouth
  • Cagley:  Chickisaw
  • Lyman, Fenn, Benjamin, Harmon, Page:  Boone
  • Smith, Belt, Leach:  Polk, Jasper, Buchanan, Black Hawk
  • Hanlon, Brady, O’Connell, Milburn, Awalt, Morlan, Jones:  Wayne, Jones, Dallas, Apanoose, Polk
  • Colligan, Robinson:  Chickasaw, Howard
  • Beyer, Conrad, DahlDall/Doll, Endrulat, Frauen, Grell, Haase, Hamann, Helkenn, Reese, Rusch, Schubert, Steffen:  Ida, Cherokee, Scott, Crawford, Osceola
  • Bauer, Berg, Heckt, Meyer, Paustian, Ruhser/Ruser, Schroeder, Schween, Sorensen:  Scott, Ida, Clinton, Jackson, Woodbury, Sac, Cherokee, Woodbury
  • Ward:  Wapello
  • Feeney, Jordan, Collins, Baugh, Jacobs , Couch, Betella, Drake, Finch:  Audubon, Scott, Dubuque, Johnson
  • Cagley, Nalley, Higgs, Weable:  Chickasaw, Polk
  • Kirsher, Huggins, Jones:  Polk
  • Sebring, Watson:  Poweshiek, Boone
  • Baber, Mason, Arnold, Cone, Braucht, Cashman, Cooper, Shutt, Davis, Scovel, Boatwright:  Ringgold, Madison, Marion, Polk, Clayton, Cass, Warren, O’Brien, Van Buren, Dallas, Webster, Adams, Jasper, Mahaska
  • Longwell, Jury, Phillips, Deen, Downing: Washington
  • Davis, Dale, Marksbury, Webster, Newcomer, Roselle, Lushbaugh, Young: Decauter, Mills
  • Orcutt, Hyde, Dutton: Cedar, Muscatine, Jasper
  • Kuiper, Heuer, Pasdach & Yeate, Huff, O’Brien
  • Carpenter, Porter, Feeney, Carter, McQuiston, Mabe, Berry, Cackler, Doherty, Coles, Kellum, Stillions: Webster
  • Kivlin, Van Hoeck, McCarthy, Foley, Lehnerer, Hussey: Scott
  • Naegle, Nagel, Mueller:  Jackson, Dubuque, Clinton
  • Beaty, Hull, Crabtree, Young, Martin, Salyer
  • Bryant, Huff, Wantz, Spence , Plummer:   Marshall, Story, O’Brien
  • Downard, Payne: Van Buren
  • Jessip, Smith: Buena Vista, Cherokee, Howard
  • Untereiner, Dorr, Doerr, Kappes, Bodensteiner: Winneshiek, Fayette
  • Wentz, Consor, Krueger, Lohrengel: Dubuque, Davis
  • Hansen, Olson, Dossland, Guddal: Clinton, Storey
  • Masteller, Newland, Staton, Ragsdale: Iowa, Sac
  • Osborn, Godwin, Barr, Smith: Calhoun, Marion, Howard
  • Mallet, Richmond, Bates, Robinson:  Black Hawk
  • Chicken, Grim, House, Minnich, Minnick, Sample, Shelton:  Buchanan, Black Hawk, Henry, Johnson

Tick Tock…The FTDNA Lab Backlog

Naturally, we’re thrilled when any of the ‘Big 3’  DNA companies offers a sale. If the sale is big enough, like the FTDNA December 2014 sale, the additional strain is going to add to the burden of already busy labs.  Although new matches are wonderful, the flip side of the coin is that frequently, we will also have to wait for those results because of lab delays.

To add to FTDNA’s delay woes, the company that supplied the reagent needed to perform all YDNA 12 marker tests stopped production and caused a serious backlog in processing YDNA.  Although the reagent problem was sorted, and FTDNA hired additional staff to catch up, questions such as, “Batch 599 Delayed by Holidays?” and “For those awaiting Y results” are regular features at the FTDNA Forum.

Although the Iowa DNA Project is focused on Family Finder testing our microcosm of members are, across the board,  active testers.

Pending Lab Results

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As you can see from the Pending Results tables above, several of the Iowa DNA Project members took advantage of the sale and placed orders during December.  Many of them already had samples on file which normally reduces batching time.  As is pointed out in, “Poll/Survey about time frame/delayed results“, people whose tests are coming in on time don’t complain and a number of project member tests have returned on time in January and February and have since been removed from the list.  We hope the lab catches up and keeps churning out or results.  In the meantime, our project has learned:

  • SNP and YDNA upgrades are the most delayed
  • SNP tests from batches 587 and 600 have received no notification or explanation for delay
  • The most overdue test is from batch 587
  • 2 of 4 Family Finder tests have been delayed but have not required new samples
  • Autosomal Tests have been transferring within 5 business days
  • No recently ordered test has completed processing early but several have completed on time
  • Tests have received delay notifications but have had results posted within the week

Iowan DNA : Quarterly Roundup

The Iowa DNA Project was formed at the end of November 2014 and is already beginning to paint a picture of  Iowan DNA.  While the project is for folks who have ancestors who lived in Iowa, or who have collateral lines who lived in Iowa, and our focus is on autosomal, aka Family Finder results, we also have members who already have or are in the process of having their mtDNA and YDNA tested.

Some of our project members have roots in Iowa going back almost 200 years. As of February 2015, descendents of settlers from most counties in Iowa are now represented, and this month will see additional results being produced, including those of a descendent of the bulk migration from Lippe, Germany to Allamakee County, Iowa.

New members are always welcome and are of course needed to move the project forward.

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A few facts have already begun to present themselves:

  • The lowest number of matches any member has is 280
  • The highest number of matches any member has is 1350
  • Success is often achieved with 2nd-4th cousins or closer.  Our members have on average 20 2nd-4th cousins each
  • As expected, the most common Y haplogroup is R and subclades, with I and its subclades the second most common
  • As expected,the most common mtDNA is H and its subcaldes, but there are a variety of other haplogroups also appearing

According to MyOrigins the bulk of project member ancestry is from :

  1. British Isles
  2. Western and Central Europe
  3. Scandinavian

There is also a moderate representation of ancestry from Southern Europe and Eastern Europe, in that order.

Ancestry also represented but less common, in order, is:

  • Northeast Asia
  • Finland and Siberia
  • Central Asia
  • Asia Minor
  • Eastern Middle East
  • Ashkenazi
  • West African

All members are encouraged to upload their raw DNA to Gedmatch to expand their match lists and to take advantage of additional ancestral admixture tools.

To finish, I would like to express my admiration and gratitude that the vast majority of project members have done so much to help themselves make the most of their results by uploading their trees, surnames, and most distant ancestors.  Many have also recruited family to test and are joining other projects which will also help them with their research. I wish you all continued success and look forward to working with you in the months ahead.

An Irish Sampler

Born a Duffy, and extremely proud of her Irish heritage, it was no surprise when FTDNA’s MyOrigins returned a 99% British Isles result for my grandmother Gladys.  After seeing she also returned a 1% Asia Minor result, I decided to compare her ancestry results to her group of Irish cousins to see what other ancestry they might or -might not- have in common.

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FTDNA is the go-to company for international testers, and Gladys has many matches living in Ireland, the UK, New Zealand and Australia.  Some have also uploaded their raw data to Gedmatch, which provides the opportunity to use advanced admixture tools to compare cousins who are either completely or predominately Irish.  Two of the most popular admixture tools for Europeans, Eurogenes K13 and Dodecad V3 have also published the average results of their Irish samples.  Those results are entered in the last column of each table*.

Our Cousins

  • Gladys and CK are known second cousins through the Kane family from Castlebar, County Mayo. Gedmatch predicts: 2.9 generations to their common ancestor
  • Bridget is a cousin with an ancestor upstream of those shared by Gladys and CK, through the Kane/Forrestal families from Castlebar, Co Mayo. Gedmatch predicts a shared ancestor at 4.8.
  • Sean is a Mackin cousin from Northern Ireland. Gedmatch predicts a shared ancestor at 4.3 generations
  • Dennis belongs to a group of Northern Irish cousins. Their common ancestor is estimated at 4.8 generations
  • SK is a cousin with a shared ancestor upstream of the Mayo Kane ancestors shared by Gladys and CK. Gedmatch predicts a shared ancestor at 4.8 generations.
  • Maire is a Northern Irish X match cousin, and is probably shares an upstream ancestor of our Mackin or McCloskey ancestors. A common ancestor is estimated at 4.6 generations

Eurogenes K13

Detailed information about the Eurogenes K13 tool can be found here.  Information about all the Eurogenes tools can be found here.  A spreadsheet detailing the average results by population, including Irish samples, can be found here.

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Figure 1

In addition to the broad breakdowns illustrated in Figure 1, Eurogenes K13 also provides an ‘oracle’.  This utility breaks down ancestry even further.  The ‘oracle’ attempts to match the tester’s DNA with the DNA of testers from various populations.  The closer the genetic distance (example @3.699) the better the DNA matches the population.

For each of the cousins, the Oracle 4 utility was run and the top 5 populations are listed below:

Gladys

Using 1 population approximation:
1 Irish @ 3.699319
2 West_Scottish @ 4.227885
3 Orcadian @ 5.009747
4 Southwest_English @ 5.606747
5 North_Dutch @ 6.093086

CK

Using 1 population approximation:
1 South_Dutch @ 3.740911
2 Southeast_English @ 4.757324
3 West_German @ 4.948592
4 Southwest_English @ 6.509983
5 Orcadian @ 6.739860

Sean

Using 1 population approximation:
1 Irish @ 4.181266
2 Southwest_English @ 4.201335
3 West_Scottish @ 4.536220
4 Orcadian @ 4.843127
5 Southeast_English @ 5.027599

Maire

Using 1 population approximation:
1 Southeast_English @ 4.672073
2 West_Scottish @ 4.773833
3 Southwest_English @ 4.905573
4 Orcadian @ 5.280790
5 Irish @ 5.639902

Dennis

Using 1 population approximation:
1 Southeast_English @ 3.840825
2 Orcadian @ 4.607929
3 West_Scottish @ 4.867234
4 Southwest_English @ 5.397524
5 Irish @ 5.514109

Bridget

Using 1 population approximation:
1 Irish @ 4.226087
2 West_Scottish @ 4.787307
3 Southwest_English @ 5.084749
4 Orcadian @ 5.592003
5 Southeast_English @ 7.030124

SK

Using 1 population approximation:
1 Irish @ 2.728588
2 West_Scottish @ 3.401917
3 Orcadian @ 4.520518
4 Southwest_English @ 5.304957
5 North_Dutch @ 5.565923

The cousins with Kane ancestry from the port towns of Castlebar and Westport return nearly identical North Atlantic values that are lower than the Irish norm.  Their West Asian score is also almost identical and higher than the Irish average. Only two cousins, both from Castlebar return Sub Saharan results and they are slightly higher than the Irish average.  3 of the 4 Kane cousins return South Asian ancestry and it is also higher than the Irish norm.  Only one of the Northern Irish cousins returns Northeast African ancestry and it is at less than half of the Irish average. When looking at the oracle results, the Kane cousin who differs has a German ancestor, and the oracle matches him most closely to a South Dutch population, while the rest of the cousins most closely resemble to Irish population samples.

The Northern Irish cousin group shows more variety in general.  As seen in Figure 2, their Baltic scores are lower than the Irish average, and their West Asian scores are higher.

Dodecad V3

Detailed information about the Dodecad project can be found here.  A spreadsheet detailing the average results by population, including Irish samples, can be found here.

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Figure 2

Gladys

Using 1 population approximation:
1 Argyll @ 2.802603
2 N._European @ 3.152682
3 CEU @ 3.180986
4 Orcadian @ 3.840527
5 Orkney @ 4.039654

Bridget

Using 1 population approximation:
1 Argyll @ 1.383295
2 Orcadian @ 1.830747
3 Orkney @ 2.134289
4 CEU @ 2.153667
5 N._European @ 2.927236

Sean

Using 1 population approximation:
1 Argyll @ 5.047738
2 Orcadian @ 5.080379
3 N._European @ 5.158332
4 Orkney @ 5.187712
5 CEU @ 5.332207

CK

Using 1 population approximation:
1 CEU @ 6.219530
2 N._European @ 7.462104
3 Argyll @ 7.730925
4 Orcadian @ 8.337189
5 German @ 8.603835

Dennis

Using 1 population approximation:
1 Orcadian @ 0.850356
2 Orkney @ 1.155132
3 Argyll @ 2.134491
4 CEU @ 2.274293
5 N._European @ 3.791664

Maire

Using 1 population approximation:
1 Orkney @ 1.008123
2 Argyll @ 1.034154
3 Orcadian @ 1.225209
4 CEU @ 3.116245
5 N._European @ 3.219170

SK

Using 1 population approximation:
1 Orkney @ 2.687200
2 Orcadian @ 2.962321
3 Argyll @ 3.461854
4 N._European @ 4.474788
5 CEU @ 4.554245

Much like the Eurogenes K13 tool, all of the cousins were most closely matched to British Isles populations, except for the one cousin with a German ancestor.  Oddly, all of our Irish cousins have much more Eastern European and much less Western European than the Irish samples reported by Dodecad.

Mixed Modes and Calculator Effect

To produce ‘mixed mode’ results, the tools compare your DNA to sample populations. The algorithm then calculates the differences or similarities, or ‘genetic distance’ between your DNA and the population in question. Mixed mode results are the estimate of the 1-4 most similar populations to your DNA.   The algorithms also attempt to estimate which combined populations, or ‘admixtures’ most closely resemble your DNA.  This is often referred to as a ‘best fit’, or best estimate.

The Eurogenes K13 oracle 4 populations gives a 1st choice guess of Gladys having 4 Irish grandparents.  Its next best guess is an Irish parent and a parent 1/2 Irish and 1/2 Scottish.  Both guesses are very good considering her known ancestry. Dodecad’s algorithm didn’t do quite so well!

Gladys Eurogenes K13

Using 4 populations approximation:
1 Irish + Irish + Irish + Irish @ 3.699319
2 Irish + Irish + Irish + West_Scottish @ 3.780499
3 Irish + Irish + West_Scottish + West_Scottish @ 3.893456
4 Irish + Irish + Irish + Southwest_English @ 3.898322
5 Irish + Irish + Irish + Orcadian @ 3.937483

Gladys Dodecad V3

Using 4 populations approximation:
1 British_Isles + French + Hungarians + Argyll @ 0.643529
2 British + French + Hungarians + Argyll @ 0.673814
3 British_Isles + French + Hungarians + N._European @ 0.727025
4 British_Isles + French + Hungarians + Argyll @ 0.735014
5 French + Hungarians + Kent + Argyll @ 0.758637

This is a young, growing science, and it is important to realize results will improve as more samples are taken and compared. 

No tool is 100% accurate and results must be taken as part of a work in progress. Also, different tools work better with different populations.  None the less, they are another tool at your disposal to use as a general guideline in your research.  Eurogene’s Polako points out, “users from the UK often come out much more continental European than they should. Some of them actually believe that this is because they’re genetically more Norman or Saxon than the average Brit. Nope, the real reason is what I call the ‘calculator effect’.”  Dodecad’s  explains, “This is when the algorithm produces different results for people who are part of the original ADMIXTURE runs that set up the allele frequencies used by the calculators, than those who aren’t, even though both sets of users are of exactly the same origin, and should expect basically identical results.”   More information about the calculator effect can be found here.

Do we match? Yes! No. Maybe!

My cousin Pete is often the inspiration behind my blog posts.  Last October, he saw my family tree at Ancestry and wrote to ask if his grandfather, Carl Toemmes was related to my Toemmes ancestors.  At the time I didn’t know, but between consulting our mutual cousin Bernhard in Germany and a bit of sleuthing through Illinois records we soon learned that indeed we were related.

Pete is my 3rd cousin 2x removed.  We both descend from 2 of the 8 children born to Johann Temmes and Maria Schmitt who were married in 1815 in Trier, Germany.  His grandfather Carl was the son of the only sibling who didn’t immigrate to Illinois along with the rest of the family. I’d had no idea Johann existed until Bernhard went through the German church books and confirmed our relationship by providing his baptismal, marriage and death records, as well as the German baptismal for Pete’s grandfather Carl.

Featured imageOur mutual ancestors are my 6th great grandparents, and as luck would have it the line had already been successfully triangulated, or proven by DNA.  Two descendents of a 3rd sibling, Katherine, had already tested and been identified.  GB and Steve are my 3rd cousin 2x removed and my 4th cousin, respectively.  According to ISOGG, statistically such cousin-ships share 13.28 cm.  GB, Steve and I all share the same 29.2 cm segment on chromosome 17.  To each other,  GB and Steve are 1st cousins 1x removed.

Once Pete’s connection had been proven on paper, he dove head first into DNA testing.  3rd cousins 2x removed is the DNA equivalent of 4th cousins and Pete and I had a 50-50% chance of matching just as I had had with GB and Steve.  When his results came in, unfortunately we fell into the 50% of those who don’t match.  However, he did match GB for a total of 88.1 cm. As Pete and GB are 3rd cousins, they had a 90% chance of matching.

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While statistics suggest Pete and I could have shared about 13 cm of DNA, and we don’t match at FTDNA, once he uploads his results to Gedmatch we will be able to investigate our exact shared segments more thoroughly.  Meanwhile, statistically Pete and GB were only expected to share 53.13 cm but exceeded that at an impressive 88.1 cm spread over 3 chromosomes.

When I shot off an excited email to let Pete know his results were up, he sent me an equally excited email asking, “So, are we related?”

The simple answer is yes, no and maybe.

  • Yes, Pete and I both descend from Johann and Maria according to traditional genealogy, as do GB and Steve.
  • Yes, Steve, GB and I are related by DNA. Yes, Pete and GB are related by DNA. Pete could also be related to Steve, but Steve has withdrawn the ability to be seen by close matches.
  • No, Pete and I are not genetic matches according to FTDNA.  FTDNA will only show a match with a segment of at least 7 cm and a minimum of 20 cm total shared.
  • Maybe we share a valid segment, but if Pete and I are under the 20 cm total mark, the match will not be shown.  Gedmatch will answer that question. edited to add 1/25/2014 Pete and I share a segment on chromosome 8 of 5.6 cm/1288 snps.
  • Maybe my Aunt Jackie, who is a 3rd cousin 1x removed from Pete, and should statistically share 26.56 cm will match him.  Her test is en route to the lab for processing.

The Moral of the Story: Test Everyone You Can

To confirm your paper trail and prove your genetic relationships, you are going to want to have as many people sharing segments as possible, which can be kept track of with Genome Mate.  This will increase the odds of identifying a common ancestor as each new match will be able to (hopefully) contribute useful information about their family’s background.  All it takes is one familiar name or location to unravel how you are related to an entire group of matches.  Once that has happened, and at least two of them (not descended from each other) point to a specific ancestor, your ancestry for that line is proven.

In the case of Cousin Pete, no one else in the FTDNA or Gedmatch databases share the segment I overlap with GB and Steve.  No one else in those databases shares the same segment on chromosomes 5 and 18 that Pete shares with GB, and he only has 3 matches between 10-13 cm on their shared chromosome 2 segment.  He is the first in his line to test DNA, so he doesn’t yet know if his 3 matches on chromosome 2 are paternal (Toemmes) or maternal.  Pete will need to contact them and compare trees, and thanks to another Toemmes cousin, Bernhard, if they are paternal, he will probably be able to provide them with a great deal of information.

So, Are We Related?

Of course Pete and I are related. Our family tree is documented well enough to know that, but I must admit I have become a bit of a DNA snob.  Whenever I’m contacted by traditional genealogists, I can’t help but think in the back of my mind that the paper trail is great and all but…have they tested yet?  My descent from Johann and Maria was already triangulated so GB, Steve, and I can be confident of our places within the Toemmes tree. Thankfully, three different lines from the same ancestral couple have tested, and that has expanded the odds of proving our mutual ancestry back to Johann and Maria. With an 88 cm match between Pete and GB their 3rd cousin relationship is also confirmed, as is the importance of  testing as many different cousins as possible. I am hopeful that Pete and Jackie will also match under FTDNA’s criteria, which will be icing on the cake.

Looking back at our earliest emails it is funny to see how formal they were originally.  Over the months we have become friends as well as cousins.  Every time I write an article prompted by one of his questions, his ears burn.   Although genealogy can be a worst case scenario of cutting, pasting, and copying names, dates and facts, occasionally you get a best case scenario like cousin Pete, and manage to graft an entire lost branch of the family back onto the tree.  To me, that is the spirit of genealogy, and genealogy at its best.

Must-Have Tools for FTDNA Users: Genome Mate

When tracing your family tree, whether through traditional genealogy or by making use of DNA testing, ongoing success will rely on a few simple factors: patience, luck, and good organizational skills.  You may not be able to control your luck, and you may struggle with patience, but you absolutely can take charge of your records and results.  Whether your FTDNA Family Finder results yield 10 matches or 10,000, one free third party tool you want to take advantage of to help keep track of and understand the significance of your results is Genome Mate.

Genome Mate is a desktop tool used to organize in one place the data collected while researching DNA comparisons. Besides data storage it has many features to aid in identifying common ancestors.

Features

  • Multiple Profiles for multiple kits
  • Import of 23andMe, FTDNA and GedMatch data
  • Chromosome Mapping of Common Ancestor
  • In Common With (ICW) Groups
  • Import of Gedcom data for each Profile
  • Surname Matching and Searching
  • Display of Overlapping Segments
  • X-List of X Chromosome Donors

Getting Those Matches Organized: Download and Install Genome Mate

To download and install Genome Mate, visit the tool’s main webpage here.  For the tool to run properly, you must have Silverlight installed on your computer, which may be freely downloaded here.  Once Genome Mate and Silverlight are installed on your computer, it is time to set up your ‘Profile’.  You may set up as many profiles as you like, so each kit you administer is represented and the results are available in one place for easy and instant comparisons.

Getting Those Matches Organized: Your Profile

Once you have Genome Mate installed, setting up your profile is straightforward.  Open the program and from the top toolbar select ‘Profiles’.  In the pop up box enter the profile’s name and click ‘Add’. At this point, you have the option to enter your Gedmatch number, which you should do if you are registered, and to add a gedcom, or generic family tree to your new profile.

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Get those Results Organized!

Voila!  You have now installed a fantastic tool and set up your profile.  All that is left to do is import your data from FTDNA. From your ‘Matches’ page, scroll to the bottom and download the CSV version of your data.

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Next, go to your ‘Chromosome Browser’ and repeat the process from the top of the page.

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With both files downloaded to your computer, open Genome Mate and choose ‘Import Data’ from the top tool bar.  In the pop up box select ‘FTDNA’.  Next, click ‘Load Family Finder File’ and choose the Family Finder Matches csv file you downloaded. Once you have done that, click ‘Load Chromosome Browser File’  and choose  the Chromosome Browser CSV file you downloaded.  You will need to repeat this process periodically as new matches come in at FTDNA.

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Choose Your Battles and Put those Matches to Work!

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Now that your data is entered, you can begin to use it to your benefit.  In your profile, under the ‘Relative’ heading, you will see your list of matches.  The bar beside the names indicates where their DNA overlaps with each chromosome you inherited from either your mother or father.  You can examine all your matches and overlapping segments, chromosome by chromosome, by scrolling through the Chromosome drop down list.   Generally speaking, the larger the segment the easier it is to identify a common ancestor.  However, if matches have reasonably complete family trees, the group is large enough, and your cousins are willing to work together, success is achievable even with smaller segments.

The image below is from my son Jeremy’s profile.  The example will familiarize you with some basic Genome Mate terms and  utilities:

  • ICW or In Common With: an individual or group that matches a segment of your DNA.  You may or may not know the common ancestor of the group
  • Chr or Chromosomes
  • Start End: the precise starting and stopping points where the match’s DNA overlaps your DNA
  • cMs or Centimorgans: a unit by which DNA segments are measured
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Figure 1

With the goal of determining the common ancestor shared between the profile and matches, the above image provides a surprising amount of information.  Since I have been tested, and also have a profile Jeremy’s results can be compared against mine. Anyone who also matches me can be added to the ICW group ‘M’ for maternal match, and instantly Jeremy’s search through his family tree for a common ancestor is halved.  Maternal matches can be determined quickly and easily by clicking on the relative’s name and bringing up the extended information:

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Figure 2

Figure 2 shows ‘Profile Overlaps’ shared between Jeremy and his match.  Also on that overlap list are Lori (mother), Gavan (brother) and Kern (maternal great grandmother).  With that additional information, the ICW group can be refined further and the information is then available at a glance.   As his maternal great grandmother is on the overlap list, we know she, the match and Jeremy share a common ancestor and the search through his family tree is narrowed further.  Once you have a theory as to how you are related, the ICW labels can be customized to anything you like to help you organize your results.

Genome Mate conveniently imports the email addresses and surnames of your matches.  It will also look for surnames in common.  However, the ‘Surnames in Common’ utility is literal:  if you have a similar surname or a variant, it will not pick it up.  Make sure to scroll through the surname list manually if the match looks promising.

Had Jeremy been the only member of the family to test, we would have to tackle his matches in another way.

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Figure 3

In Figure 3 a group of 6 matches share segments ranging between 8-13 cm.  While those are small segments, and indicate distant ancestry, the group proved worth investigating as all members had contact info, surnames listed and fairly well fleshed out family trees uploaded to FTDNA.  One after another, each tree showed a common couple as direct ancestors: Jacob Fouts and Anna Maria Kuntz.  When a person shares a segment and common ancestors with at least 2 matches the segment is considered triangulated, and the relationship is considered to be genetically proven.

Take Notes!

Once a common ancestor is suspected or identified, in Genome Mate each match can be clicked on and all pertinent details can be entered into the notes section. It is also a good place to keep track of communications between yourself and your matches.  If you have added a gedcom to your profile, you can click on the match’s name and choose the identified ancestors from the drop down menu.  As more and more ancestors are identified, various segments of each chromosome will become associated with those ancestors.   The more identified segments, the more colorful your chart will become.

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Genome Mate is a powerful tool, which is frequently updated to keep up with the dramatic advances taking place in genetic genealogy.  The creator of Genome Mate, Becky Walker, has provided a detailed tutorial for FTDNA users here and the Stone Family Tree has provided an easy to follow ‘getting started’ overview here.  There is also a Genome Mate Group on Facebook, where you can get additional tips and insights into how to make best use of the program.  If you are struggling to make sense of your FTDNA match list, you will find this tool indispensable.