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Understanding recombination scoring in family pedigrees

Understanding recombination scoring in family pedigrees


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I am having some problems understanding recombination, and I am not sure what element I am missing here. This figure is an example from my text book. The pedigree belongs to a family with an autosomal dominant disease, typed for a marker with alleles A1-A6.

So, all meioses are phase-known, meaning we know which combination of alleles was inherited from each parent. Apparently this leads us to be able to unambiguously score III1-III5 as non-recombinant (N) and III6 as recombinant (R).

This is were I get lost, I don't understand how it is possible to unambiguously score each individual as N or R. Also, if the disease is dominant, how is it possible that III6 is affected, as both alleles originate from the both unaffected I1 and II2?

EDIT: Blue colour in the pedigree means the person is affected by disease, white symbolizes unaffected individuals. I believe the red stars symbolizes that the person carries the disease gene. That would mean blue corresponds to not carrying the disease gene. If this is the case The red colour of III2 is a fault in the figure.


Ns are not non-recombinant at the genomic level. What you know is that they do not show recombination events at the risk allele locus as subjects carrying the marker A1 show signs of the disease and similarly healthy subject do not carry the marker A1. Hence they are called non-recombinant. On top of that 3rd generation subjects (excepted III6) show combinations of alleles reflecting inheritance of one allele from each parent.

What is very important is that A1 is not the allele, it is the marker for the risk allele. This means that A1 is not directly the allele but a region of DNA close to the allele provoking the disease, sufficiently close to be co-inherited most of the time with the disease allele.

For III6 you actually know that a recombination must have happened as this subject obviously carry the risk allele (shows the disease) yet not the marker associated with it, therefore a recombination event happened between the marker and the disease allele.


Analysis of Genetic Linkage

Abstract

Linkage analysis is a well-established statistical method for mapping the genes for heritable traits to their chromosome locations. Genome-wide markers are tested in pedigrees segregating a trait. The statistical method of linkage analysis combines these data to identify chromosome regions likely to harbor genes for the trait. Parametric linkage analysis is used for traits with a Mendelian form of inheritance. LOD scores and recombination fractions are used to test the gene locations. Model-free linkage analysis is used for complex traits when the model of inheritance is not known. Linkage analysis can be used to map genes for both binary and quantitative traits.


Immunology of Infection

Aurelie Cobat , . Erwin Schurr , in Methods in Microbiology , 2010

1 Principle

Model-based linkage analysis by the classical lod score method ( Morton, 1955 ) requires to define the model specifying the relationship between the phenotype and factors that may influence its expression, mainly a putative gene with two alleles (d, D) and other relevant risk factors, often referred to as the genetic (or phenotype/genotype) model. In the context of a binary phenotype (e.g. affected/unaffected, seronegative/seropositive), this genetic model should specify, in addition to the frequency of the susceptibility allele denoted as D, the penetrance vector, i.e. the probability for an individual to be affected given a genotype (dd, Dd or DD) and a subject-specific set of relevant covariates such as age or intensity of exposure to an infectious agent. In the context of a quantitative phenotype (e.g. infection levels), the complete specification of the genetic model includes, in addition to the frequency of the allele predisposing to high values of the trait denoted as D, the three genotype-specific means and variances which may also be influenced by some individual covariates. Given the genotype, the distribution of the phenotype is assumed to be normal so that the overall distribution is a mixture of three normal distributions.

The genetic model is generally provided and estimated by segregation analysis which is the first step to determine from family data the mode of inheritance of a given phenotype. The aim of segregation analysis is to discriminate between the different factors causing familial resemblance, with the main goal to test for the existence of a single gene, called a major gene. The major gene term does not mean that it is the only gene involved in the expression of the phenotype, but that, among the set of involved genes, there is at least one gene with an effect important enough to be distinguished from the others. For a binary phenotype, this effect can be expressed in terms of relative risks, e.g. the ratio of the probability for a subject to be affected given a ‘DD’ genotype to the same probability given a ‘dd’ genotype. For a quantitative phenotype, this effect is measured by the proportion of the phenotypic variance explained by the major gene (also denoted as the heritability). An elegant way to express this phenotype/genotype model is to use a regressive approach for binary ( Bonney, 1986 ) as well as for quantitative ( Bonney, 1984 ) traits. A detailed review of the pros and cons of segregation analysis can be found in Jarvik (1998) . Note that in linkage studies the expression ‘major gene’ is used for any gene that underlies a significant linkage peak.

When there is evidence for a major gene by segregation analysis, model-based linkage analysis allows to confirm and to locate this gene, denoted below as the phenotype locus. Model-based linkage analysis tests in families whether the phenotype locus co-segregates with genetic markers of known chromosomal location and provides an estimate of the recombination rate between these two loci ( Ott, 1999 ) (see Box 2 ). Linkage with the phenotype locus can be tested marker by marker (two-point analysis) or considering a set of linked markers (multipoint analysis). In this analysis, as in segregation analysis, all the inferences for individual genotypes at the phenotype locus are made from the individual phenotypes and the specified phenotype/genotype model. For quantitative phenotypes, the probability that an individual carries genotype dd, dD or DD at the phenotype locus will be computed from the mixture of the three normal distributions described above for which means and variances have been estimated through segregation analysis.

Model-based linkage analysis

Principle:

Model-based linkage analysis tests in families whether the trait locus co-segregates with genetic markers of known chromosomal location. The method is based on the estimation of a single parameter, the recombination fraction (denoted θ) between the trait locus and a given genetic marker. The linkage test is a likelihood ratio test comparing the likelihood under the null hypothesis of no linkage (θ0 = 0.5), LH0, to the likelihood under the alternative hypothesis of linkage (θ1 &lt 0.5), LH1. The linkage statistic is classically expressed as a lod score, Z(θ) = log10(LH1/LH0). The classical critical values to declare significant linkage and to exclude linkage are lod score ≥3 (which corresponds to a p-value of 10 –4 ) and lod score &lt–2, respectively ( Morton, 1955 ).

Example:

The pedigree above shows the segregation of a rare autosomal dominant trait locus (D/d, where D is the causal allele) with complete penetrance and absence of phenocopies (i.e. individuals carrying the genotypes Dd or DD are affected and individuals carrying the genotype dd are unaffected) and an informative marker (1/2). The mother is not informative for linkage. As the phase is known, we can count the number of recombinants k (which is 1 and denoted R) out of n = 10 meioses. The working likelihood of the pedigree is L(θ) = θ k (1 – θ) nk = θ(1 – θ) 9 . The likelihood of the pedigree under the null hypothesis is L(0.5) = (0.5) 10 . The maximum likelihood estimate of θ is easy to compute as k/n = 1/10 and the lod score is lod = log10(0.1(1–0.1) 9 /0.5 10 ) = 1.6. Results of model-based linkage analysis are presented in lod score tables where the lod scores are tabulated for a series of recombination fractions from 0 to 0.5.

Weakness:

Model-based linkage analysis requires to define the genetic model that describes the relationship between the phenotype and the genotype, i.e. the disease allele frequency, the mode of inheritance (dominant, recessive or additive) and the pattern of penetrances (i.e. the probability of being affected given the genotype status) need to be specified to infer the disease locus genotype of all individuals from their phenotype. Those parameters are generally estimated by complex segregation analyses.

Strength:

The lod score approach is the most powerful linkage method when the assumed genetic model is the true model. This approach gives a maximum likelihood estimate of the genetic distance (θ) between the genetic marker and the disease trait locus.

Popular Software:

LINKAGE ( Lathrop et al., 1984 ), FASTLINK ( Cottingham et al., 1993 ), MERLIN ( Abecasis et al., 2002 )

A list of genetic analysis software is available at http://linkage.rockefeller.edu/soft

The lod score approach is certainly the most powerful linkage method when the assumed genetic model is (close enough to) the true model. This is the case in a situation of monogenic inheritance where a simple genetic model can be assumed. However, a misspecification of the genetic model can lead to both severe loss of power to detect linkage (and therefore to false exclusion of the region containing the phenotype locus) and bias in the estimation of the recombination fraction (i.e. the genetic distance) between the phenotype locus and the marker locus ( Clerget-Darpoux et al., 1986 ). Nevertheless, such a misspecification does not affect the robustness of the method, i.e. it does not lead to false conclusions in favour of linkage, as long as only one phenotype/genotype model is tested. When there is some knowledge about the prevalence of the disease under study and the level of familial aggregation, a common procedure to reduce the risk of misspecification is to generate a limited number of realistic genetic models to use in lod score analysis. However, when performing the analysis under a number of different genetic models, one needs to introduce a correction for multiple testing and adjust the significance level of the lod score ( MacLean et al., 1993 ). The same issue occurs when several markers are tested, and guidelines have been proposed to adapt lod score thresholds to the context of a genome-wide search. Widely accepted thresholds are the ones proposed by Lander and Kruglyak (1995) . Based on complex analytic calculations these authors defined the p-values that should be used to claim suggestive or significant linkage as 1.7 × 10 –3 and 4.9 × 10 –5 (corresponding to a lod score of 1.9 and 3.3, respectively) ( Lander and Kruglyak, 1995 ). Another problem arises when marker data are missing for some family members. In this case, linkage analysis also depends on marker allele frequencies and misspecification of these frequencies can affect both the power and the robustness of the method. This is an important issue because it means that one should be very cautious when reporting suggestive or significant linkage in the context of a sample with many missing parents. Note that the two latter problems (multiple marker testing and misspecification of marker allele frequencies) are also common to model-free methods.


Complex diseases occur as a result of many genomic variants, paired with environmental influences (such a diet, sleep, stress and smoking). They are also called “polygenic” diseases - with “poly” meaning many and “genic” involving genes.

Coronary artery disease is a complex disease. Researchers have found about 60 genomic variants that are present more frequently in people with coronary artery disease. Most of these variants are dispersed across the genome and do not cluster on one specific chromosome.


Discussion

Recombination and reassortment of parental genomes lead to an increase of genetic variation. Understanding the mechanisms regulating recombination rate during meiosis would allow their manipulation to increase or decrease recombination rates according to specific requirements described in [39]. Although many genes controlling the basic process of CO formation have been identified, little is known about factors influencing CO numbers and distribution, and GWRR. To our knowledge the present work is the most comprehensive study comparing meiotic recombination rates and CO interference within one species and between gene pools of the same species. We used two panels of maize half-sib families comprising 23 full-sib populations with a total of 2,233 DH lines to analyze intraspecific variation of recombination rates and recombination landscapes. The parents of the two panels for Dent and Flint maize were chosen to represent the diversity present in European maize germplasm. We analyzed DH lines produced by in vivo haploid induction, which reflect a single female meiosis. As expected, the average number of crossovers per DH line in our study (15.1) was about half the number observed in maize RILs (28.9) [32]. This drawback of DH lines is counterbalanced by the faster development of the DH populations compared to RILs and by the complete homozygosity of DH lines, which are an immortal resource. Moreover, working on DH populations offers the unique possibility to analyze CO interference, while this is impossible in RILs because the successive independent meioses superpose the COs arising during each meiosis. The design of our connected half-sib panels comprising a large number of populations allowed for comparisons of GWRR, recombination landscape, and interference (1) across parents within each panel and (2) across panels via crosses of the central lines with line B73.

As a prerequisite for our approach, we constructed high-density genetic maps for a large number of populations. Genetic map lengths of the 23 populations varied from 1,180 cM to 1,893 cM with a mean of 1,508 cM, which is in the range observed in other high-density genetic maps of maize [32, 33]. Due to IBD regions in some of our populations, gaps were observed in the genetic maps. However, since most of these gaps were in pericentromeric regions, where recombination rates are low, most of them were not larger than 15 cM, the most extreme gap (53.7 cM) being on chromosome 7 in CFF13. For 76 markers, we found chromosome assignments different from their annotation in the B73 AGPv2. We provided genetic coordinates for 118 markers where no annotation was available. These results may help to improve future B73 genome assemblies. In addition, genetic maps for several chromosomal regions were found non-colinear with the B73 sequence, as was previously detected from two other maize populations [33]. These discrepancies may be the consequence of mis-assemblies in the B73 genome, or due to lack of locus specificity for some SNP markers that would fall into duplicated genomic regions. The hypothesis of structural rearrangements between some of the parental lines and B73 cannot be excluded. However, given the design of the experiment, the possibility that both parents of a cross within one of the half-sib panels share a structural variation absent from both parents of another cross in the same panel is very limited.

Intraspecific variation of recombination rate

The average GWRR in our populations was 0.73 cM/Mbp, which is in the same range as can be calculated from other maize maps [30]. This value is about 5-fold lower than in A. thaliana (3.6 cM/Mb) [40] which has a 20-fold smaller genome than maize, reflecting the well-known negative correlation of recombination rate with genome size among species [41]. We found clear differences between chromsome-wide recombination rates, with chromosome 4 having the lowest average value (0.60 cM/Mbp) and chromosome 9 the highest (0.88 cM/Mb). We also observed a negative correlation between recombination rate and the physical length of the chromosomes (r = 0.66, P value 0.003), similar to what arises in human, mouse and rat [19]. Such correlations suggest that the mechanisms regulating CO formation in these organisms tend to enforce some level of homeostasis in the number of COs per meiosis and per chromosome. For very short chromosomes, the obligatory CO ensures that there is at least one CO for each bivalent. We observed clear intraspecific variation for GWRR in this study. Similar levels of variation for GWRR were observed in both pools once the effect of the central line was removed.

Calculating GWRR assumes constant genome size in maize. Genome size differences were reported in maize, with temperate maize lines having up to 10% smaller genomes relative to B73 and these differences were correlated with the number of knob repeats [42]. Genome sizes for the lines in our study are unknown however, the up to 60% difference in map lengths (1,180 to 1,893 cM) in our study is much larger than the genome size differences recently reported [42]. Therefore, it can be assumed that beyond possible genome size variation genetic factors influencing GWRR play a strong role in our plant material. Differences in recombination frequencies between maize inbred lines have been described based on genetic linkage maps and by using cytological methods to detect recombination nodules and to calculate CO rates [3, 43]. Trans-acting QTL affecting GWRR were identified in A. thaliana, maize, mouse, and wheat [44]. In cattle, several QTL were mapped for male GWRR [45]. For two QTL, putative causal variants were detected in the genes REC8 (a member of the kleisin family of 'structural maintenance of chromosome' proteins), and RNF212, a putative homolog of the yeast ZIP3 gene, which is involved in meiotic recombination. RNF212 is also known to be associated with genome-wide recombination in humans [46]. Our findings on different GWRR between individual maize lines pave the way for development of specific crosses to identify genetic factors influencing GWRR by QTL mapping [47]. Given the advances in high-throughput genotyping and genome sequencing, such QTL can be a starting point for fine-mapping and subsequent cloning of genes determining GWRR in plants. Characterizing the structural and functional variation of such genes would promote our understanding of the molecular mechanisms regulating meiotic recombination in plants and would be a highly valuable tool for plant breeders and geneticists.

Recombination landscapes in maize

Not only GWRR but also the landscapes of recombination along chromosomes are highly variable in our populations. We observed characteristic shapes of Marey map curves for each of the 10 maize chromosomes. The overall recombination profiles for each chromosome tend to follow gene density [34] but this does not explain local differences in recombination rate between populations. Structural variation between parental lines may be one mechanism influencing local recombination rates, as shown for a 26 kb retrotransposon cluster that reduced local recombination rate around the bz1 locus by a factor two [48]. For the same genomic region it was shown that haplotype structure, as defined by the presence of helitrons and retrotransposons, strongly affected the occurrence of recombination events in heterozygous plants [49]. The extensive structural variation in the maize genome can be seen already at the karyotype level, where large-scale variation was reported [50], but even more at the sequence level, where large variation was observed for repetitive element content, presence-absence or copy number variants [42, 51, 52]. Such structural variations may influence the pairing of homologs and recombination [53], although inverted regions may also pair normally in pachytene [54]. Apart from the low recombination rates in the heterochromatic pericentromeric and NOR regions and a general increase of recombination rates towards the telomeres, we observed kinks in our Marey map curves in regions where heterochromatic knobs have been mapped in maize. This is the case, for example, on chromosome 4L in all populations, but only in some populations on chromosome 1L, suggesting that variation in knob regions may exist in our lines. Although 34 distinct knob regions were described in maize and its wild progenitor teosinte, most maize lines contain fewer than 12 such knobs, for which in addition polymorphisms are observed between lines [38, 55]. Due to a lack of data on knob positions in our parental lines, we could not examine the influence of knob polymorphism on the shape of the recombination landscapes for individual chromosomes more closely. Since knobs are often located in gene-dense regions and suppress local recombination [38] it is likely that some differences in the shapes of the recombination landscape are caused by knob polymorphism. Also outside putative knob regions, we observed many significant local differences in the pairwise comparison of recombination profiles between populations and for chromosomes 2, 4, 5, and 6 between the pooled Dent and Flint populations. We found no significant correlation between parental genetic similarity as determined by SNP markers and recombination rate, a result corroborated by a recent study in A. thaliana[40]. Thus, factors influencing local recombination rates other than gene density and similarity at the DNA level must exist. It must be stated though, that for characterizing the influence of genomic features such as nucleotide diversity on local recombination rate, the 10 Mbp scale may be too coarse, so much higher resolution at the kilobase scale might be required, as recently shown in the model plant Medicago truncatula[56]. In addition, the parental diversity as assessed by the mainly genic SNPs of the MaizeSNP50 array may not well reflect the structural differences that can have a major impact on local recombination rates [49]. Our study has identified genomic regions with large differences in local recombination rates between inbred lines. This is an important basis for future studies to identify recombination hotspots and to study the influence of structural variation and genome diversity in defined crosses and genomic regions in maize.

Crossover interference

Interference was previously shown to occur in maize [17], based on numbers and positions of late recombination nodules. That work found two pathways to be operating in maize, one interfering (P1) and the other (P2) contributing a proportion p of non-interfering crossovers. In the present study, we also found in most cases values of p significantly different from zero, with values averaging 0.1. This conclusion is compatible with the previous estimations that reported an average value of 0.15 [17]. The populations where we found p = 0 (CFF04, CFF06, CFF13) may mostly reflect a low power due to limited population sizes: here, individual populations have between 50 and 134 DH lines, whereas in [17], the data set had more than 200 pachytene synaptonemal complexes (SCs), each of them giving about four times more power to the analysis than one DH line. It should be noticed, however, that our statistical tests were very conservative due to Bonferroni correction. Still, we found that CFF02 had a proportion p of non-interfering (P2) crossovers significantly higher than almost all other populations when considering all chromosomes pooled. To our knowledge, differences in interference features between different genotypes of the same species have not been shown so far. Values of p between 0 and 0.2 for different chromosomes were reported in A. thaliana[11], and between 0 and 0.21 in humans [57]. Based on comparisons between MLH1 foci and late recombination nodules, p values around 0.3 were found in tomato [14]. Considering the intensity nu of interference in the interfering pathway P1, our results indicated values ranging between 2.5 and 8 for all chromosomes pooled, which is similar to the range 4 to 10 found previously in maize [17]. In Arabidopsis, nu was in the 10 to 21 range [11], whereas nu was estimated to be in the 6.9 to 7.9 range in tomato [14]. Finally, based on the 23 full-sib populations, we found a significant negative correlation between the chromosome-pooled nu and the genome-wide recombination rate. This result is consistent with the hypothesis that interference may be one of the mechanisms at work to regulate the level of meiotic recombination, biasing the repair of DSBs towards non-COs rather than COs. Compared with the highly significant differences found for GWRR in our study, the variation for CO interference is detected here with much less statistical power. In future works with higher population sizes, providing smaller confidence intervals, but using the same half-sib design it should be possible to estimate the effects of the founder parents alone on interference parameters by removing the effects of the central lines, as we did for GWRR. Similarly as for GWRR, this should also enable the identification and localization of genetic factors influencing interference parameters.

Impact of variation in recombination rates on genetic studies and applied breeding programs

Covering the whole genome using dense genetic maps increases the chance to detect marker-trait associations, both in linkage analyses and association mapping. The construction of high-density genetic maps is now feasible for many crop species in a very cost-efficient way, either by SNP genotyping arrays or through genotyping-by-sequencing approaches [33, 58]. For precise estimation of QTL effects in genome-wide association studies and high accuracy genome-wide prediction of breeding values, it is a prerequisite that markers tag either the causal alleles or they must be in high linkage disequilibrium with the QTL of interest [24, 59]. The linkage phase between marker and favorable QTL alleles is crucial when predicting breeding values across breeds or gene pools [60]. Recombination events may invert linkage phases of marker and QTL alleles between unrelated pools, and thus lead to reduced accuracies in prediction of breeding values and marker effects. In the context of genomic prediction or genome-wide association studies, understanding the landscape of recombination within a species is of particular interest, since regions with high recombination rates require higher marker densities. In addition, a detailed genome-wide and local picture of recombination rates permits adequate dimensioning of map-based cloning projects, marker-assisted selection strategies for specific traits, and crossing programs in cases where unfavorable linkage between traits needs to be broken. Recombination is a key factor determining the success with introgression of new variability from distantly related plant genetic resources or poorly adapted material, since introgression of new alleles or traits such as resistance genes in recurrent selection is often accompanied by undesired linkage drag. These effects of linkage drag may be drastic if the regions of interest are located in (peri)centromeric, recombination-poor regions. Choosing elite lines with high GWRR as recurrent backcross parents may help to speed up the introgression process. Finally, identifying genotypes carrying alleles for higher recombination rate may guide the choice of adequate parents for optimizing the number of generations required in breeding schemes. Variability in interference strength may also be of interest in breeding programs because interference is believed to mechanistically affect recombination rates. Just as for the selection of lines with higher or lower recombination rates [61], it should be feasible to develop maize lines with different levels of CO interference or proportion of P2 COs.


RootsTech Connect 2021: Comprehensive DNA Session List

I wondered exactly how many DNA sessions were at RootsTech this year and which ones are the most popular.

Unfortunately, we couldn’t easily view a list of all the sessions, so I made my own. I wanted to be sure to include every session, including Tips and Tricks and vendor sessions that might only be available in their booths. I sifted through every menu and group and just kept finding more and more buried DNA treasures in different places.

I’m sharing this treasure chest with you below. And by the way, this took an entire day, because I’ve listed the YouTube direct link AND how many views each session had amassed today.

Sales Extended

The Family Tree DNA RootsTech Sales prices including upgrades are still available – here.

  • The FamilyTreeDNA autosomal Family Finder testis now only $49.Click here to purchase using coupon code RTCTFF. is offering the advanced tool unlock for only $9 after a free transfer through March 7 th . Click here to sign on, upload your DNA file if you’ve tested elsewhere, and then unlock using code RTCAU10.

MyHeritage has extended their RootsTech deals too.

  • MyHeritage has waived the unlock fee of $29 if you transfer your DNA kit from another vendor between now and March 7 th . You can upload, free, here. You’ll get all of the advanced tools for free.
  • The MyHeritage DNA kit is on sale for $79, here.

Neither Ancestry nor 23andMe had show sales, but you can purchase at their regular prices.

All serious genealogists will want to test at or transfer to all 4 major vendors and test their Y DNA and mitochondrial DNA at FamilyTreeDNA.

RootsTech Sessions

As you know, RootsTech was shooting for TED talk format this year. Roughly 20-minute sessions. When everything was said and done, there were five categories of sessions:

  • Curated sessions are approximately 20-minute style presentations curated by RootsTech meaning that speakers had to submit. People whose sessions were accepted were encouraged to break longer sessions into a series of two or three 20-minute sessions.
  • Vendor booth videos could be loaded to their virtual boots without being curated by RootsTech, but curated videos by their employees could also be loaded in the vendor booths.
  • DNA Learning Center sessions were by invitation and provided by volunteers. They last generally between 10-20 minutes.
  • Tips and Tricks are also produced by volunteers and last from 1 to 15 minutes. They can be sponsored by a company and in some cases, smaller vendors and service providers utilized these to draw attention to their products and services.
  • 1-hour sessions tend to be advanced and not topics could be easily broken apart into a series.

Look at this amazing list of 129 DNA or DNA-related sessions that you can watch for free for the next year. Be sure to bookmark this article so you can refer back easily.

Please note that I started compiling this list for myself and I’ve shortened some of the session names. Then I realized that if I needed this, so do you.

Top 10 Most-Viewed Sessions

I didn’t know whether I should list these sessions by speaker name, or by the most views, so I’m doing a bit of both.

The top 10 most viewed sessions as of today are:

Speaker/Vendor Session Title Type Link Views
Libby Copeland How Home DNA Testing Has Redefined Family History Curated Session https://youtu.be/LsOEuvEcI4A 13,554
Nicole Dyer Organize Your DNA Matches in a Diagram Tips and Tricks https://youtu.be/UugdM8ATTVo 6175
Roberta Estes DNA Triangulation: What, Why, and How 1 hour https://youtu.be/nIb1zpNQspY 6106
Tim Janzen Tracing Ancestral Lines in the 1700s Using DNA Part 1 Curated Session https://youtu.be/bB7VJeCR6Bs 5866
Amy Williams Ancestor Reconstruction: Why, How, Tools Curated Session https://youtu.be/0D6lAIyY_Nk 5637
Drew Smith Before You Test Basics Part 1 Curated Session https://youtu.be/wKhMRLpefDI 5079
Nicole Dyer How to Interpret a DNA Cluster Chart Tips and Tricks https://youtu.be/FI4DaWGX8bQ 4982
Nicole Dyer How to Evaluate a ThruLines Hypothesis Tips and Tricks https://youtu.be/ao2K6wBip7w 4823
Kimberly Brown Why Don’t I Match my Match’s Matches DNA Learning Center https://youtu.be/A8k31nRzKpc 4593
Rhett Dabling, Diahan Southard Understanding DNA Ethnicity Results Curated Session https://youtu.be/oEt7iQBPfyM 4287

Libby Copeland must be absolutely thrilled. I noticed that her session was featured over the weekend in a highly prominent location on the RootsTech website.

Sessions by Speaker

The list below includes the English language sessions by speaker. I apologize for not being able to discern which non-English sessions are about DNA.

Don’t let a smaller number of views discourage you. I’ve watched a few of these already and they are great. I suspect that sessions by more widely-known speakers or ones whose sessions were listed in the prime-real estate areas have more views, but what you need might be waiting just for you in another session. You don’t have to pick and choose and they are all here for you in one place.

Speaker/Vendor Session Title Type Link Views
Alison Wilde SCREEN Method: A DNA Match Note System that Really Helps DNA Learning Center https://youtu.be/WaNnh_v1rwE 791
Amber Brown Genealogist-on-Demand: The Help You Need on a Budget You Can Afford Curated Session https://youtu.be/9KjlD6GxiYs 256
Ammon Knaupp Pattern of Genetic Inheritance DNA Learning Center https://youtu.be/Opr7-uUad3o 824
Amy Williams Ancestor Reconstruction: Why, How, Tools Curated Session https://youtu.be/0D6lAIyY_Nk 5637
Amy Williams Reconstructing Parent DNA and Analyzing Relatives at HAPI-DNA, Part 1 Curated Session https://youtu.be/MZ9L6uPkKbo 1021
Amy Williams Reconstructing Parent DNA and Analyzing Relatives at HAPI-DNA, Part 2 Curated Session https://youtu.be/jZBVVvJmnaU 536
Ancestry DNA Matches Curated Session https://youtu.be/uk8EKXLQYzs 743
Ancestry ThruLines Curated Session https://youtu.be/RAwimOgNgUE 1240
Ancestry Ancestry DNA Communities: Bringing New Discoveries to Your Family History Research Curated Session https://youtu.be/depeGW7QUzU 422
Andre Kearns Helping African Americans Trace Slaveholding Ancestors Using DNA Curated Session https://youtu.be/mlnSU5UM-nQ 2211
Barb Groth I Found You: Methods for Finding Hidden Family Members Curated Session https://youtu.be/J93hxOe_KC8 1285
Beth Taylor DNA and Genealogy Basics DNA Learning Center https://youtu.be/-LKgkIqFhL4 967
Beth Taylor What Do I Do With Cousin Matches? DNA Learning Center https://youtu.be/LyGT9B6Mh00 1349
Beth Taylor Using DNA to Find Unknown Relatives DNA Learning Center https://youtu.be/WGJ8IfuTETY 2166
David Ouimette I Am Adopted – How Do I Use DNA to Find My Parents? Curated Session https://youtu.be/-jpKgKMLg_M 365
Debbie Kennett Secrets and Surprises: Uncovering Family History Mysteries through DNA Curated Session https://youtu.be/nDnrIWKmIuA 2899
Debbie Kennett Genetic Genealogy Meets CSI Curated Session https://youtu.be/sc-Y-RtpEAw 589
Diahan Southard What is a Centimorgan Tips and Tricks https://youtu.be/uQcfhPU5QhI 2923
Diahan Southard Using the Shared cM Project DNA Learning Center https://youtu.be/b66zfgnzL0U 3172
Diahan Southard Understanding Ethnicity Results DNA Learning Center https://youtu.be/8nCMrf-yJq0 1587
Diahan Southard Problems with Shared Centimorgans DNA Learning Center https://youtu.be/k7j-1yWwGcY 2494
Diahan Southard 4 Next Steps for Your DNA Curated Session https://youtu.be/poRyCaTXvNg 3378
Diahan Southard Your DNA Questions Answered Curated Session https://youtu.be/uUlZh_VYt7k 3454
Diahan Southard You Can Do the DNA – We Can Help Tips and Tricks https://youtu.be/V5VwNzcVGNM 763
Diahan Southard What is a DNA Match? Tips and Tricks https://youtu.be/Yt_GeffWhC0 314
Diahan Southard Diahan’s Tips for DNA Matches Tips and Tricks https://youtu.be/WokgGVRjwvk 3348
Diahan Southard Diahan’s Tips for Y DNA Tips and Tricks https://youtu.be/QyH69tk-Yiw 620
Diahan Southard Diahan’s Tips about mtDNA testing Tips and Tricks https://youtu.be/6d-FNY1gcmw 2142
Diahan Southard Diahan’s Tips about Ethnicity Results Tips and Tricks https://youtu.be/nZFj3zCucXA 1597
Diahan Southard Diahan’s Tips about Which DNA Test to Take Tips and Tricks https://youtu.be/t𔃂R8H8q0U 2043
Diahan Southard Diahan’s Tips about When Your Matches Don’s Respond Tips and Tricks https://youtu.be/LgHtM3nS60o 3009
Diahan Southard Three Next Steps: Using Known Matches Tips and Tricks https://youtu.be/z1SVq8ME42A 118
Diahan Southard Three Next Steps: MRCA/DNA and the Paper Trail Tips and Tricks https://youtu.be/JB0cVyk-Y4Q 80
Diahan Southard Three Next Steps: Start With Known Matches Tips and Tricks https://youtu.be/BSNhaQCNtAo 68
Diahan Southard Three Next Steps: Additional Tools Tips and Tricks https://youtu.be/PqNPBLQSBGY 140
Diahan Southard Three Next Steps: Ancestry ThruLines Tips and Tricks https://youtu.be/KWayyAO8p_c 335
Diahan Southard Three Next Steps: MyHeritage Theory of Relativity Tips and Tricks https://youtu.be/Et2TVholbAE 80
Diahan Southard Three Next Steps: Who to Test Tips and Tricks https://youtu.be/GyWOO1XDh6M 111
Diahan Southard Three Next Steps: Genetics vs Genealogy Tips and Tricks https://youtu.be/Vf0DC5eW_vA 294
Diahan Southard Three Next Steps: Centimorgan Definition Tips and Tricks https://youtu.be/nQF935V08AQ 201
Diahan Southard Three Next Steps: Shared Matches Tips and Tricks https://youtu.be/AYcR_pB6xgA 233
Diahan Southard Three Next Steps: Case Study – Finding an MRCA Tips and Tricks https://youtu.be/YnlA9goeF7w 256
Diahan Southard Three Next Steps: Why Use DNA Tips and Tricks https://youtu.be/v-o4nhPn8ww 266
Diahan Southard Three Next Steps: Finding Known Matches Tips and Tricks https://youtu.be/n3N9CnAPr18 688
Diana Elder Using DNA Ethnicity Estimates in Your Research Tips and Tricks https://youtu.be/aJgUK3TJqtA 1659
Diane Elder Using DNA in a Client Research Project to Solve a Family Mystery 1 hour https://youtu.be/ysGYV6SXxR8 1261
Donna Rutherford DNA and the Settlers of Taranaki, New Zealand Curated Session https://youtu.be/HQxFwie4774 214
Drew Smith Before You Test Basics Part 1 Curated Session https://youtu.be/wKhMRLpefDI 5079
Drew Smith Before You Test Basics Part 2 Curated Session https://youtu.be/Dopx04UHDpo 2769
Drew Smith Before You Test Basics Part 3 Curated Session https://youtu.be/XRd2IdtA-Ng 2360
Elena Fowler Whakawhanaungatanga Using DNA – It’s Complicated (Māori heritage) Curated Session https://youtu.be/6XTPMzVnUd8 470
Elena Fowler Whakawhanaungatanga Using DNA – FamilyTreeDNA (Māori heritage) Curated Session https://youtu.be/fM85tt5ad3A 269
Elena Fowler Whakawhanaungatanga Using DNA – Ancestry (Māori heritage) Curated Session https://youtu.be/-byO6FOfaH0 191
Esmee Mortimer-Taylor Living DNA: Anathea Ring – Her Story Tips and Tricks https://youtu.be/MTE4UFKyLRs 189
Esmee Mortimer-Taylor Living DNA: Coretta Scott King Academy – DNA Results Reveal Tips and Tricks https://youtu.be/CK1EYcuhqmc 82
Fonte Felipe Ethnic Filters and DNA Matches: The Way Forward to Finding Your Lineage Curated Session https://youtu.be/mt2Rv2lpj7o 553
FTDNA – Janine Cloud Big Y: What is it? Why Do I Need It? Curated Session https://youtu.be/jiDcjWk4cVI 2013
FTDNA – Sherman McRae Using DNA to Find Ancestors Lost in Slavery Curated Session https://youtu.be/i3VKwpmttBI 738
Jerome Spears Elusive Distant African Cousins: Using DNA, They Can Be Found Curated Session https://youtu.be/fAr-Z78f_SM 335
Karen Stanbary Ruling Out Instead of Ruling In: DNA and the GPS in Action 1 hour https://youtu.be/-WLhIHlSyLE 548
Katherine Borges DNA and Lineage Societies Tips and Tricks https://youtu.be/TBYGyLHHAOI 451
Kimberly Brown Why Don’t I Match my Match’s Matches DNA Learning Center https://youtu.be/A8k31nRzKpc 4593
Kitty Munson Cooper Basics of Unknown Parentage Research Using DNA Part 1 Curated Session https://youtu.be/2f3c7fJ74Ig 2931
Kitty Munson Cooper Basics of Unknown Parentage Research Using DNA Part 2 Curated Session https://youtu.be/G7h-LJPCywA 1222
Lauren Vasylyev Finding Cousins through DNA Curated Session https://youtu.be/UN7WocQzq78 1979
Lauren Vasylyev, Camille Andrus Finding Ancestors Through DNA Curated Session https://youtu.be/4rbYrRICzrQ 3919
Leah Larkin Untangling Endogamy Part 1 Curated Session https://youtu.be/0jtVghokdbg 2291
Leah Larkin Untangling Endogamy Part 2 Curated Session https://youtu.be/-rXLIZ0Ol-A 1441
Liba Casson-Budell Shining a Light on Jewish Genealogy Curated Session https://youtu.be/pHyVz94024Y 162
Libby Copeland How Home DNA Testing Has Redefined Family History Curated Session https://youtu.be/LsOEuvEcI4A 13,554
Linda Farrell Jumpstart your South African research Curated Session https://youtu.be/So7y9_PBRKc 339
Living DNA How to do a Living DNA Swab Tips and Tricks https://youtu.be/QkbxhqCw7Mo 50
Lynn Broderick Ethical Considerations Using DNA Results Curated Session https://youtu.be/WMcRiDxPy2k 249
Mags Gaulden Importance and Benefits of Y DNA Testing DNA Learning Center https://youtu.be/MVIiv0H7imI 1032
Maurice Gleeson Using Y -DNA to Research Your Surname Curated Session https://youtu.be/Ir4NeFH_aJs 1140
Melanie McComb Georgetown Memory Project: Preserving the Stories of the GU272 Curated Session https://youtu.be/Fv0gHzTHwPk 320
Michael Kennedy What Can You Do with Your DNA Test? DNA Learning Center https://youtu.be/rKOjvkqYBAM 616
Michelle Leonard Understanding X-Chromosome DNA Matching Curated Session https://youtu.be/n784kt-Xnqg 775
MyHeritage How to Analyze DNA Matches on MH Curated Session https://youtu.be/gHRvyQYrNds 1192
MyHeritage DNA – an Overview Curated Session https://youtu.be/AIRGjEOg_xo 389
MyHeritage Advanced DNA Tools Curated Session https://youtu.be/xfZUAjI5G_I 762
MyHeritage How to Get Started with Your DNA Matches Tips and Tricks https://youtu.be/rU_dq1vt6z4 1901
MyHeritage How to Filter and Sort Your DNA Matches Tips and Tricks https://youtu.be/aJ7dRwMTt90 1008
Nicole Dyer How to Interpret a DNA Cluster Chart Tips and Tricks https://youtu.be/FI4DaWGX8bQ 4982
Nicole Dyer How to Evaluate a ThruLines Hypothesis Tips and Tricks https://youtu.be/ao2K6wBip7w 4823
Nicole Dyer Organize Your DNA Matches in a Diagram Tips and Tricks https://youtu.be/UugdM8ATTVo 6175
Nicole Dyer Research in the Southern States Curated Session https://youtu.be/Pouw_yPrVSg 871
Olivia Fordiani Understanding Basic Genetic Genealogy DNA Learning Center https://youtu.be/-kbGOFiwH2s 810
Pamela Bailey Information Wanted: Reuniting an American Family Separated by Slavery Tips and Tricks https://youtu.be/DPCJ4K8_PZw 105
Patricia Coleman Getting Started with DNA Painter DNA Learning Center https://youtu.be/Yh_Bzj6Atck 1775
Patricia Coleman Adding MyHeritage Data to DNA Painter DNA Learning Center https://youtu.be/rP9yoCGjkLc 458
Patricia Coleman Adding 23andMe Data to DNA Painter DNA Learning Center https://youtu.be/pJBAwe6s0z0 365
Penny Walters Mixing DNA with Paper Trail DNA Learning Center https://youtu.be/PP4SjdKuiLQ 2693
Penny Walters Collaborating with DNA Matches When You’re Adopted DNA Learning Center https://youtu.be/9ioeCS22HlQ 1222
Penny Walters Differences in Ethnicity Between My 6 Children DNA Learning Center https://youtu.be/RsrXLcXRNfs 400
Penny Walters Differences in DNA Results Between My 6 Children DNA Learning Center https://youtu.be/drnzW3FXScI 815
Penny Walters Ethical Dilemmas in DNA Testing DNA Learning Center https://youtu.be/PRPoc0nB4Cs 437
Penny Walters Adoption – Background Context Curated Session https://youtu.be/qC1_Ln8WCNg 1054
Penny Walters Adoption – Utilizing DNA Testing to Construct a Bio Family Tree Curated Session https://youtu.be/zwJ5QofaGTE 941
Penny Walters Adoption – Ethical Dilemmas and Varied Consequences of Looking for Bio Family Curated Session https://youtu.be/ZLcHHTSfCIE 576
Penny Walters I Want My Mummy: Ancient and Modern Egypt Curated Session https://youtu.be/_HRO50RtzFk 311
Rebecca Whitman Koford BCG: Brief Step-by-Step Tour of the BCG Website Tips and Tricks https://youtu.be/YpV9bKG6sXk 317
Renate Yarborough Sanders DNA Understanding the Basics DNA Learning Center https://youtu.be/bX_flUQkBEA 2713
Renate Yarborough Sanders To Test or Not to Test DNA Learning Center https://youtu.be/58-qzvN4InU 1048
Rhett Dabling Finding Ancestral Homelands Through DNA Curated Session https://youtu.be/k9zixg4uL1I 505
Rhett Dabling, Diahan Southard Understanding DNA Ethnicity Results Curated Session https://youtu.be/oEt7iQBPfyM 4287
Richard Price Finding Biological Family Tips and Tricks https://youtu.be/L9C-SGVRZLM 101
Robert Kehrer Will They Share My DNA (Consent, policies, etc.) DNA Learning Center https://youtu.be/SUo-jpTaR1M 480
Robert Kehrer What is a Centimorgan? DNA Learning Center https://youtu.be/dopniLw8Fho 1194
Roberta Estes DNA Triangulation: What, Why and How 1 hour https://youtu.be/nIb1zpNQspY 6106
Roberta Estes Mother’s Ancestors DNA Learning Center https://youtu.be/uUh6WrVjUdQ 3074
Robin Olsen Wirthlin How Can DNA Help Me Find My Ancestors? Curated Session https://youtu.be/ZINiyKsw0io 1331
Robin Olsen Wirthlin DNA Tools Bell Curve Tips and Tricks https://youtu.be/SYorGgzY8VQ 1207
Robin Olsen Wirthlin DNA Process Trees Guide You in Using DNA in Family History Research Tips and Tricks https://youtu.be/vMOQA3dAm4k 1708
Shannon Combs-Bennett DNA Basics Made Easy DNA Learning Center https://youtu.be/4JcLJ66b0l4 1560
Shannon Combs-Bennett DNA Brick Walls DNA Learning Center https://youtu.be/vtFkT_PSHV0 450
Shannon Combs-Bennett Basics of Genetic Genealogy Part 1 Curated Session https://youtu.be/xEMbirtlBZo 2263
Shannon Combs-Bennett Basics of Genetic Genealogy Part 2 Curated Session https://youtu.be/zWMPja1haHg 1424
Steven Micheleti, Joanna Mountain Genetic Consequences of the Transatlantic Slave Trade Part 1 Curated Session https://youtu.be/xP90WuJpD9Q 2284
Steven Micheleti, Joanna Mountain Genetic Consequences of the Transatlantic Slave Trade Part 2 Curated Session https://youtu.be/McMNDs5sDaY 742
Thom Reed How Can Connecting with Ancestors Complete Us? Curated Session https://youtu.be/gCxr6W-tkoY 392
Tim Janzen Tracing Ancestral Lines in the 1700s Using DNA Part 1 Curated Session https://youtu.be/bB7VJeCR6Bs 5866
Tim Janzen Tracing Ancestral Lines in the 1700s Using DNA Part 2 Curated Session https://youtu.be/scOtMyFULGI 3008
Ugo Perego Strengths and Limitations of Genetic Testing for Family History DNA Learning Center https://youtu.be/XkBK1y-LVaE 480
Ugo Perego A Personal Genetic Journey DNA Learning Center https://youtu.be/Lv9CSU50xCc 844
Ugo Perego Discovering Native American Ancestry through DNA Curated Session https://youtu.be/L1cs748ctx0 884
Ugo Perego Mitochondrial DNA: Our Maternally-Inherited Family History Curated Session https://youtu.be/Z5bPTUzewKU 599
Vivs Laliberte Introduction to Y DNA DNA Learning Center https://youtu.be/rURyECV5j6U 752
Yetunde Moronke Abiola 6% Nigerian: Tracing my Missing Nigerian Ancestor Curated Session https://youtu.be/YNQt60xKgyg 494

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If clear, he had normal blood clotting.

Pedigree analysis worksheet answers part a shows.

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It will show how to make an accurate pedigree chart.

.4 interpreting pedigree diagrams work in a small group or alone to.

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Results

ReLERNN: An Accurate Method for Estimating the Genome-Wide Recombination Landscape

We developed ReLERNN, a new deep learning method for accurately predicting genome-wide per-base recombination rates from as few as four chromosomes. Briefly, ReLERNN provides an end-to-end inferential pipeline for estimating a recombination map from a population sample: it takes as input either a variant call format (VCF) file or, in the case of ReLERNN for Pool-seq data, a vector of allele frequencies and genomic coordinates. ReLERNN then uses the coalescent simulation program, msprime ( Kelleher et al. 2016), to simulate training, validation, and test data sets under either constant population size or an inferred population size history. Importantly, these simulations are parameterized to match the distribution of Watterson’s estimator, θW, calculated from the empirical samples. ReLERNN trains a specific type of RNN, known as a gated recurrent unit (GRU Cho et al. 2014), to predict the per-base recombination rate for these simulations, using only the raw genotype matrix and a vector of genomic coordinates for each simulation example ( fig. 1 and supplementary figs. S1 and S2 , Supplementary Material online). It then uses this trained network to estimate genome-wide per-base recombination rates for empirical samples using a sliding-window approach. ReLERNN can optionally estimate 95% CI around each prediction using a parametric bootstrapping approach, and it uses the predictions generated while bootstrapping to correct for inherent biases in the training process (see Materials and Methods supplementary fig. S3 , Supplementary Material online).

A cartoon depicting a typical workflow using ReLERNN’s four modules (shaded boxes) for (A) individually sequenced genomes or (B) pooled sequences. ReLERNN can optionally (dotted lines) utilize output from stairwayplot, SMC++, and MSMC to simulate under a demographic history with msprime. Training inlays show the network architectures used, with the GRU inlay in (B) depicting the gated connections within each hidden unit. Here, r, z, ht, and h t ˜ are the reset gate, update gate, activation, and candidate activation, respectively ( Cho et al. 2014). The genotype matrix encodes alleles as reference (−1), alternative (1), or padded/missing data (0 not shown). Variant positions are encoded along the real number line (0–1).

A cartoon depicting a typical workflow using ReLERNN’s four modules (shaded boxes) for (A) individually sequenced genomes or (B) pooled sequences. ReLERNN can optionally (dotted lines) utilize output from stairwayplot, SMC++, and MSMC to simulate under a demographic history with msprime. Training inlays show the network architectures used, with the GRU inlay in (B) depicting the gated connections within each hidden unit. Here, r, z, ht, and h t ˜ are the reset gate, update gate, activation, and candidate activation, respectively ( Cho et al. 2014). The genotype matrix encodes alleles as reference (−1), alternative (1), or padded/missing data (0 not shown). Variant positions are encoded along the real number line (0–1).

A key feature of ReLERNN’s network architecture is the bidirectional GRU layer ( fig. 1 and supplementary fig. S1 , Supplementary Material online), which allows us to model genomic sequence alignments as a time series. Although feed-forward networks use as input a full block of data for each example, recurrent layers break each genotype alignment into time steps corresponding to discrete genomic coordinates, and iterate over the time steps sequentially. At each time step, the GRUs modulate the flow of information, using reset and update gates that control how the activation is updated ( Cho et al. 2014 Chung et al. 2014). This process allows the gradient descent algorithm, known as backpropagation through time, to share parameters across time steps, as well as make inferences based on the ordering of SNPs—that is, to have a spatial memory of allelic associations along the chromosome. The bidirectional attribute of the GRU layer simply means that each example is duplicated and reversed, so the sequence data are analyzed from both directions and then merged by concatenation. We present a generalized GRU for analyzing genomic sequence data, along with a more detailed look at the network architecture parameters used by ReLERNN in supplementary figure S1 , Supplementary Material online.

Performance on Simulated Chromosomes

To assess our method, we performed coalescent simulations using msprime ( Kelleher et al. 2016), generating whole chromosome samples using a fine-scale genetic map estimated from crosses of D. melanogaster ( Comeron et al. 2012). We then used ReLERNN to estimate the landscape of recombination for these simulated examples. ReLERNN is able to predict the landscape of per-base recombination rates to a high degree of accuracy across a wide range of realistic parameter values, assumptions, and sample sizes ( ⁠ R 2 ≥ 0.82 ⁠ mean absolute error [MAE] ≤ 1.28 × 10 − 8 ⁠ ). Importantly, the accuracy of ReLERNN is only modestly diminished when comparing predictions based on 20 samples ( ⁠ R 2 = 0.93 ⁠ MAE = 3.72 × 10 − 9 ⁠ fig. 2A) to those based on four samples ( ⁠ R 2 = 0.82 ⁠ MAE = 6.66 × 10 − 9 ⁠ supplementary fig. S4 , Supplementary Material online). We also show that ReLERNN performs equally well on phased and unphased genotypes (W = 68.5 P = 0.17 Mann–Whitney U test supplementary fig. S5 , Supplementary Material online), suggesting that any effect of computational phasing error might be mitigated by treating the inputs as unphased variants.

(A) Recombination rate predictions for a simulated Drosophila chromosome (black line) using ReLERNN for individually sequenced genomes (red line). The recombination landscape was simulated for n = 20 chromosomes under constant population size using msprime ( Kelleher et al. 2016), with per-base crossover rates taken from D. melanogaster chromosome 2L ( Comeron et al. 2012). Gray ribbons represent 95% CI. R 2 is reported for the general linear model of predicted rates on true rates and mean absolute error was calculated across all 100-kb windows. (B) Distribution of raw error ( ⁠ r predicted − r true ⁠ ) using ReLERNN for Pool-seq data. Pools simulated from the same recombination landscape as above, with n = 20 and (C) n = 50 chromosomes across a range of simulated read depths ( ⁠ 0.5 × to 5× Inf represents infinite simulated sequencing depth). Both the bootstrap-corrected predictions (red) and the nonbootstrap-corrected (NBSC white) predictions are shown.

(A) Recombination rate predictions for a simulated Drosophila chromosome (black line) using ReLERNN for individually sequenced genomes (red line). The recombination landscape was simulated for n = 20 chromosomes under constant population size using msprime ( Kelleher et al. 2016), with per-base crossover rates taken from D. melanogaster chromosome 2L ( Comeron et al. 2012). Gray ribbons represent 95% CI. R 2 is reported for the general linear model of predicted rates on true rates and mean absolute error was calculated across all 100-kb windows. (B) Distribution of raw error ( ⁠ r predicted − r true ⁠ ) using ReLERNN for Pool-seq data. Pools simulated from the same recombination landscape as above, with n = 20 and (C) n = 50 chromosomes across a range of simulated read depths ( ⁠ 0.5 × to 5× Inf represents infinite simulated sequencing depth). Both the bootstrap-corrected predictions (red) and the nonbootstrap-corrected (NBSC white) predictions are shown.

Because ReLERNN performed exceedingly well on unphased genotypes, we speculated that it might be able to glean crucial information about recombination rates from a vector of allele frequencies alone. Therefore, we set out to extend ReLERNN to work with Pool-seq data, where the only inputs are a vector of allele frequencies and their corresponding genomic coordinates. Surprisingly, ReLERNN exhibits modest accuracy on simulated Pool-seq data, despite simulated sample and read depths as low as n = 50 and coverage = 50 × ( ⁠ R 2 = 0.54 ⁠ MAE = 1.59 × 10 − 8 ⁠ supplementary fig. S6 , Supplementary Material online). Increasing the read depth to a nominal 5×, the sample depth (e.g., n = 50 and coverage = 250 × ⁠ ) produced substantially greater accuracy ( ⁠ R 2 = 0.69 ⁠ MAE = 1.20 × 10 − 8 ⁠ supplementary fig. S7 , Supplementary Material online). As a general trend, we show that prediction error is reduced by increasing the number of chromosomes sampled in the pool (i.e., increasing allele frequency resolution) and by increasing the depth of sequencing (i.e., reducing sampling error) ( fig. 2B). Although there currently exists software for estimating LD in Pool-seq data ( Feder et al. 2012), to our knowledge, ReLERNN is the first software to directly estimate rates of recombination using these data.

Although ReLERNN retains accuracy at small sample sizes, it exhibits somewhat greater sensitivity to both the assumed genome-wide average mutation rate, μ ¯ ⁠ , and the assumed maximum value for recombination, ρmax. To assess the degree of sensitivity to these assumptions, we ran ReLERNN on simulated chromosomes assuming μ ¯ was both 50% greater and 50% less than the simulated mutation rate, μtrue. In both scenarios, ReLERNN predicts crossover rates that are highly correlated with the true rates ( ⁠ R 2 > 0.91 ⁠ ). However, in both scenarios, MAE is inflated but still modest, and the absolute rates of recombination are underpredicted ( ⁠ R 2 = 0.91 ⁠ MAE = 1.23 × 10 − 8 ⁠ supplementary fig. S8 , Supplementary Material online) and slightly overpredicted ( ⁠ R 2 = 0.94 ⁠ MAE = 1.28 × 10 − 8 ⁠ supplementary fig. S9 , Supplementary Material online) when assuming μ ¯ is less than or greater than μtrue, respectively. Moreover, underestimating ρmax causes ReLERNN to underpredict rates of recombination roughly proportional to the magnitude of the underestimate ( supplementary figs. S10 and S11 , Supplementary Material online), whereas overestimating ρmax causes only a minor loss in accuracy ( ⁠ R 2 = 0.90 ⁠ MAE = 4.07 × 10 − 9 ⁠ supplementary fig. S12 , Supplementary Material online). Together, these results suggest that ReLERNN is in fact learning information about the ratio of crossovers to mutations, and although ReLERNN is highly robust to errant assumptions when predicting relative recombination rates within a genome, caution must be taken when comparing absolute rates between organisms with large differences in per-base mutation rate estimates or for species. One additional limitation to ReLERNN is its inability to fully resolve narrow recombination rate hotspots (herein defined as ≤ 10 -kb genomic regions with r ≥ 50 × the genome-wide average). We simulated hotspots of different lengths [ length ∈ < 2 kb , 4 kb , 6 kb , 8 kb , 10 kb >, r background = 2.5 e − 9 , r hotspot = 1.25 e − 7 ] and found that errors at hotspots were negatively correlated with hotspot length ( supplementary fig. S13 , Supplementary Material online), suggesting that signal for crossovers at hotspots is being swamped by the background rate within the focal window, especially for very narrow hotspots relative to the focal window. This limitation could be of particular importance when attempting to resolve hotspots in human data, where lengths are often between 1 and 2 kb ( Jeffreys et al. 2001 Jeffreys and May 2004).

ReLERNN Compares Favorably to Competing Methods, Especially for Small Sample Sizes and under Model Misspecification

To assess the accuracy of ReLERNN relative to existing methods, we took a comparative approach, whereby we made predictions on the same set of simulated test chromosomes using methods that differ broadly in their approaches. Specifically, we chose to compare ReLERNN against two types of machine learning methods—a boosted regression method, FastEPRR ( Gao et al. 2016), and a CNN recently described in Flagel et al. (2019)—and both LDhat ( McVean et al. 2002) and LDhelmet ( Chan et al. 2012), two widely cited approximate-likelihood methods. We independently simulated 10 5 chromosomes using msprime ( Kelleher et al. 2016) [parameters: sample _ size ∈ < 4 , 8 , 16 , 32 , 64 >, recombination _ rate = U ( 0.0 , 6.25 e − 8 ) , mutation _ rate = U ( 1.875 e − 8 , 3.125 e − 8 ) , length = 3 e 5 ]. Half of these were simulated under demographic equilibrium and half were simulated under a realistic demographic model (based on the out-of-Africa expansion of European humans see Materials and Methods). We show that ReLERNN outperforms all other methods, exhibiting significantly reduced absolute error ( ⁠ | r predicted − r true | ⁠ ) under both the demographic model and under equilibrium assumptions ( ⁠ T ≤ − 31 ⁠ P < 10 − 16 ⁠ post hoc Welch’s two-sample t-tests for all comparisons supplementary figs. S14 and S15 , Supplementary Material online). ReLERNN also exhibited less bias than likelihood-based methods across a range of sample sizes ( fig. 3), although all methods generally performed well at the largest sample size tested (n = 64).

(A) Distribution of raw error ( ⁠ r predicted − r true ⁠ ) for each method across 5,000 simulated chromosomes (1,000 for FastEPRR). Independent simulations were run under a model of population size expansion or (B) demographic equilibrium. Sampled chromosomes indicate the number of independent sequences that were sampled from each msprime ( Kelleher et al. 2016) coalescent simulation. LDhelmet was not able be used with n = 64 chromosomes and FastEPRR was not able to be used with n = 4.

(A) Distribution of raw error ( ⁠ r predicted − r true ⁠ ) for each method across 5,000 simulated chromosomes (1,000 for FastEPRR). Independent simulations were run under a model of population size expansion or (B) demographic equilibrium. Sampled chromosomes indicate the number of independent sequences that were sampled from each msprime ( Kelleher et al. 2016) coalescent simulation. LDhelmet was not able be used with n = 64 chromosomes and FastEPRR was not able to be used with n = 4.

We also sought to assess the robustness of ReLERNN to demographic model misspecification, where different generative models are used for simulating the training and test sets—for example, training on assumptions of demographic equilibrium when the test data were generated by a population bottleneck. Methods robust to this type of misspecification are crucial, as the true demographic history of a sample is often unknown and methods used to infer population size histories can disagree or be unreliable (see supplementary fig. S21 , Supplementary Material online). Moreover, population size changes alter the landscape of LD across the genome ( Slatkin 1994 Rogers 2014), and thus have the potential to reduce accuracy or produce biased recombination rate estimates.

To this end, we trained ReLERNN on examples generated under equilibrium and made predictions on 5,000 chromosomes generated by the human demographic model specified above (and also carried out the reciprocal experiment fig. 4). We compared ReLERNN with the CNN, LDhat, and LDhelmet, with all methods similarly misspecified (see Materials and Methods). We found that ReLERNN outperforms these methods under nearly all conditions, exhibiting significantly lower absolute error under both directions of demographic model misspecification ( ⁠ T ≤ − 26 ⁠ P WTT < 10 − 16 for all comparisons, with the exception of the comparison to LDhelmet using 16 chromosomes supplementary figs. S16 and S17 , Supplementary Material online). We show that the error directly attributed to model misspecification (which we term marginal error see Materials and Methods) is occasionally higher in ReLERNN relative to other methods, even though ReLERNN exhibited the lowest absolute error among methods. As a prime example of this, we found predictions from LDhelmet were not affected by our misspecification regime at all, but these predictions were still, on an average, less accurate than those made by a misspecified ReLERNN. Interestingly, marginal error is significantly greater when ReLERNN was trained on equilibrium simulations and tested on demographic simulations than under the reciprocal misspecification (T = 26.3 P WTT < 10 − 16 ⁠ supplementary fig. S18 , Supplementary Material online). Although this is true, it is important to note that mean marginal error for ReLERNN, in both directions of misspecification and across all sample sizes, never exceeded 3.90 × 10 − 9 ⁠ , suggesting that the additional information gleaned from an informative demographic model is limited.

(A) Distribution of raw error ( ⁠ r predicted − r true ⁠ ) for each method across 5,000 simulated chromosomes after model misspecification. For the CNN and ReLERNN, predictions were made by training on equilibrium simulations while testing on sequences simulated under a model of population size expansion or (B) training on demographic simulations while testing on sequences simulated under equilibrium. For LDhat and LDhelmet, the lookup tables were generated using parameters values that were estimated from simulations where the model was misspecified in the same way as described for the CNN and ReLERNN above. Sampled chromosomes indicate the number of independent sequences that were sampled from each msprime ( Kelleher et al. 2016) coalescent simulation. LDhelmet was not able be used with n = 64 chromosomes and the demographic model could not be intentionally misspecified using FastEPRR.

(A) Distribution of raw error ( ⁠ r predicted − r true ⁠ ) for each method across 5,000 simulated chromosomes after model misspecification. For the CNN and ReLERNN, predictions were made by training on equilibrium simulations while testing on sequences simulated under a model of population size expansion or (B) training on demographic simulations while testing on sequences simulated under equilibrium. For LDhat and LDhelmet, the lookup tables were generated using parameters values that were estimated from simulations where the model was misspecified in the same way as described for the CNN and ReLERNN above. Sampled chromosomes indicate the number of independent sequences that were sampled from each msprime ( Kelleher et al. 2016) coalescent simulation. LDhelmet was not able be used with n = 64 chromosomes and the demographic model could not be intentionally misspecified using FastEPRR.

In addition to model misspecification, differences in the ratio of homologous gene conversion events to crossovers can also bias the inference of recombination rates, as conversion tracts break down LD within the prediction window ( Przeworski and Wall 2001 Gay et al. 2007). We treated the effect of gene conversion as another form of model misspecification, by training on examples that lacked gene conversion and testing on examples that included gene conversion. As ReLERNN uses msprime for all training simulations, and msprime cannot currently simulate gene conversion, we generated all test set simulations with ms ( Hudson 2002). We found that including gene conversion in our simulations biased our predictions, resulting in an overestimate of the true recombination rate ( supplementary fig. S19 , Supplementary Material online). Moreover, the magnitude of this bias increased with the ratio of gene conversion events to crossovers, r GC r CO ⁠ . As expected, we also observed a similar pattern of bias for LDhelmet, although the magnitude of bias for LDhelmet was less than that exhibited by ReLERNN for r GC r CO > 2 (T > 4.37 P WTT < 1.32 × 10 − 5 ⁠ supplementary fig. S19 , Supplementary Material online). As errors in genotype calls can mimic gene conversion—for example, a heterozygous sample being called as a homozygote—filtering low-quality SNP calls, either by removing the individual genotype or by masking sites, has the potential to mitigate gene conversion-induced bias. However, missing genotypes and inaccessible sites have the potential to introduce their own biases, highlighting an area where deep learning methods may have a unique advantage over traditional tools.

ReLERNN Retains High Accuracy on Simulated Low-Quality Genomic Data Sets

Deep learning tools have the potential to perform exceptionally well on poor-quality genomic data sets, such as those with low-quality or low-complexity reference genomes, under sampling regimes where individual samples are at a premium, or where base- and map-quality scores are suspect. This is in part because such attributes of genomic quality can be readily incorporated during training, and deep learning methods can generalize despite these limitations. To address the potential for ReLERNN to serve as an asset for researchers working with low-quality data—for example, those studying nonmodel organisms—we simulated 1-Mb chromosomes under a randomized fine-scale recombination landscape, and then masked increasing fractions of both genotypes and sites. We then trained ReLERNN with both missing genotypes and genome inaccessibility, and generated predictions on the simulated chromosomes.

We show that ReLERNN exhibits high accuracy and low bias on data sets with missing genotypes, even as the fraction of missing data increases to half of all genotypes ( fig. 5). Moreover, we found that ReLERNN had reduced bias and significantly lower absolute error than LDhelmet at 50% missing genotypes for both n = 4 and n = 20 ( ⁠ T ≤ − 2.8 ⁠ PWTT < 0.007 for both comparisons). Here, we define missing genotypes as any genotype call set to a “.” in the VCF, although in theory, a simple quality threshold to identify missing genotypes could also be implemented. Additionally, we tested ReLERNN across increasing levels of genome inaccessibility (up to 75% of all sites inaccessible), simulating a scenario where the vast majority of sites cannot be accurately mapped—for example, in low-complexity genomic regions or for taxa without reference assemblies. Here, genome inaccessibility refers to any site overlapping a window in the accessibility mask, where the entire genotype array at this site is discarded. Again, ReLERNN exhibited reduced bias in error across all levels of genome accessibility relative to LDhelmet ( supplementary fig. S20 , Supplementary Material online). However, levels of absolute error were not significantly different between the methods after correcting for multiple tests ( ⁠ T ≤ − 2.1 ⁠ P WTT ≥ 0.043 for all comparisons). Together, these results suggest that ReLERNN may be of particular interest to researchers studying nonmodel organisms or for those without access to high-quality reference assemblies.

(A) Distribution of raw error ( ⁠ r predicted − r true ⁠ ) for LDhelmet and ReLERNN when presented with varying levels of missing genotypes for simulations with n = 4 and (B) n = 20 chromosomes. (C) Fine-scale rate predictions generated by ReLERNN for a 1-Mb recombination landscape (gray line) simulated with varying levels of missing genotypes, for n = 4 and (D) n = 20 chromosomes.

(A) Distribution of raw error ( ⁠ r predicted − r true ⁠ ) for LDhelmet and ReLERNN when presented with varying levels of missing genotypes for simulations with n = 4 and (B) n = 20 chromosomes. (C) Fine-scale rate predictions generated by ReLERNN for a 1-Mb recombination landscape (gray line) simulated with varying levels of missing genotypes, for n = 4 and (D) n = 20 chromosomes.

Recombination Landscapes Are Largely Concordant among Populations of African D. melanogaster

Using our method, we characterized the genome-wide recombination landscapes of three populations of African D. melanogaster (sampled from Cameroon, Rwanda, and Zambia). Each population was derived from the sequencing of ten haploid embryos (detailed in Pool et al. 2012 Lack et al. 2015), hence these data represent an excellent opportunity to exploit ReLERNN’s high accuracy on small sample sizes. The lengths of genomic windows selected by ReLERNN were roughly consistent among populations, and ranged from 38 kb for chromosomes 2R, 3L, and 3R in Zambia, to 51 kb for the X chromosome in Cameroon. We show that fine-scale recombination landscapes are highly correlated among all three populations of D. melanogaster (genome-wide mean pairwise Spearman’s ρ = 0.76 ⁠ P < 10 − 16 ⁠ 100-kb windows fig. 6). The genome-wide mean pairwise coefficient of determination between populations was somewhat lower, R 2 = 0.63 ( ⁠ P < 10 − 16 ⁠ 100-kb windows), suggesting there may be important population-specific differences in the fine-scale drivers of allelic association. These differences may also contribute to within-chromosome differences in recombination rate between populations. Indeed, we estimate that mean recombination rates are significantly different among populations for all chromosomes with the exception of chromosome 3L ( ⁠ P ≤ 3.78 × 10 − 4 ⁠ one-way analysis of variance). Post hoc pairwise comparisons suggest that this difference is largely driven by an elevated rate of recombination in Zambia, identified on all chromosomes ( ⁠ P ≤ 8.21 × 10 − 4 ⁠ Tukey’s HSD tests) except for 3L ( ⁠ P HSD ≥ 0.15 ⁠ ). ReLERNN predicts the recombination rate in simulated test sets to a high degree of accuracy for all three populations ( ⁠ R 2 ≥ 0.93 ⁠ P < 10 − 16 ⁠ supplementary fig. S23 , Supplementary Material online), suggesting that we have sufficient power to discern fine-scale differences in per-base recombination rates across the genome.

(A) Genome-wide recombination landscapes for Drosophila melanogaster populations from Cameroon (teal lines), Rwanda (purple lines), and Zambia (orange lines). Gray boxes denote the inversion boundaries predicted to be segregating in these samples ( Corbett-Detig and Hartl 2012 Pool et al. 2012). Red triangles mark the top 1% of global outlier windows for recombination rate. Blue, purple, and orange triangles mark the top 1% of population-specific outlier windows for recombination rate, with triangle color indicating the outlier population (see Materials and Methods). (B) Per-chromosome recombination rates for each population. Spearman’s ρ and R 2 are reported as the mean of pairwise estimates between populations for each chromosome. **P < 0.01 and ***P < 0.001 are based on Tukey’s HSD tests for all pairwise comparisons.

(A) Genome-wide recombination landscapes for Drosophila melanogaster populations from Cameroon (teal lines), Rwanda (purple lines), and Zambia (orange lines). Gray boxes denote the inversion boundaries predicted to be segregating in these samples ( Corbett-Detig and Hartl 2012 Pool et al. 2012). Red triangles mark the top 1% of global outlier windows for recombination rate. Blue, purple, and orange triangles mark the top 1% of population-specific outlier windows for recombination rate, with triangle color indicating the outlier population (see Materials and Methods). (B) Per-chromosome recombination rates for each population. Spearman’s ρ and R 2 are reported as the mean of pairwise estimates between populations for each chromosome. **P < 0.01 and ***P < 0.001 are based on Tukey’s HSD tests for all pairwise comparisons.

When comparing our recombination rate estimates to those derived from experimental crosses of North American D. melanogaster (reported in Comeron et al. 2012), we find that the coefficients of determination averaged over all three populations were R 2 = 0.46 , 0.70 , 0.47 , 0.08 , 0.73 for chromosomes 2L, 2R, 3L, 3R, and X, respectively ( supplementary fig. S24 , Supplementary Material online 1-Mb windows). These results differ from those observed by Chan et al. (2012), who compared 22 D. melanogaster sampled from the same Rwandan population with the FlyBase map and found R 2 = 0.55 , 0.63 , 0.45 , 0.42 , 0.41 for the same chromosomes. The minor differences we observed between methods for chromosomes 2L, 2R, 3L, and the X chromosome can likely be attributed to the fact that we are comparing estimates from two different methods, using different African flies, to a different experimentally derived map. However, the larger differences found between methods for chromosome 3R seem less likely attributable to methodological differences. Importantly, African D. melanogaster is known to harbor large polymorphic inversions often at appreciable frequencies ( Lemeunier and Aulard 1992 Aulard et al. 2002). For example, the inversion In(3R)K segregates in our Cameroon population at p = 0.9. These differences in inversion frequencies potentially contribute to the exceptionally weak correlation observed using our method for chromosome 3R.

An important cause of population-specific differences in recombination landscapes might be population-specific differences in the frequencies of chromosomal inversions, as recombination is expected to be strongly suppressed between standard and inversion arrangements. To test for an effect of inversion frequency inferences made by ReLERNN, we resampled haploid genomes from Zambia to create artificial population samples with the cosmopolitan inversion In(2L)t segregating at varying frequencies, p ∈ < 0.0 , 0.2 , 0.6 , 1.0 >⁠ . In Zambia, In(2L)t arose recently ( Corbett-Detig and Hartl 2012) and segregates at p = 0.22 ( Lack et al. 2015), suggesting that recombination within the inversion breakpoints may be strongly suppressed in individuals with the inverted arrangement relative to those with the standard arrangement. For these reasons, we predict that the inferred recombination rate should decrease as the low-frequency inverted arrangement is increasingly overrepresented in the set of sampled chromosomes (i.e., as more of the samples contain the high-LD inverted arrangements). As predicted, we found a strong effect of the sample frequency of In(2L)t on estimated rates of recombination for chromosome 2L in Zambia ( supplementary fig. S27 , Supplementary Material online), demonstrating that ReLERNN is sensitive to the frequency of recent inversions.

To further explore population-specific differences in recombination landscapes, we took a statistical outlier approach, whereby we define two types of recombination rate outliers—global outliers and population-specific outliers (see Materials and Methods). Global outliers are characterized by windows with exceptionally high variance in rates of recombination between all three populations ( fig. 6 red triangles), whereas population-specific outliers are those windows where the rate of recombination in one population is strongly differentiated from the rates in the other two populations ( fig. 6 population-colored triangles). We find that population-specific outliers, but not global outliers, are significantly enriched within inversions (P = 0.005 randomization test fig. 6 gray boxes). Moreover, this enrichment remains significant when extending the inversion boundaries by up to 250 kb ( ⁠ P rand ≤ 0.004 ⁠ ). However, extending the inversion boundaries beyond 250 kb, or restricting the overlap to windows surrounding only the breakpoints (250 kb, 500 kb, 1 Mb, 2 Mb), erodes this pattern ( ⁠ P rand ≥ 0.055 for all comparisons), suggesting that the role for inversions in generating population-specific differences in recombination rates is complex, at least for these populations.

Selection is another important factor that may confound the inference of recombination rates. For instance selective sweeps generate localized patterns of high LD on either side of the sweep site ( Kim and Nielsen 2004 Schrider et al. 2015) thus, regions flanking selective sweeps may mimic regions of reduced recombination. Inasmuch population-specific selective sweeps are expected to contribute to population-specific differences in recombination rate estimates. We used diploS/HIC ( Kern and Schrider 2018) to identify hard and soft selective sweeps in our African D. melanogaster populations, and we tested for an excess of recombination rate outliers overlapping with windows classified as sweeps. In total, diploS/HIC classified 27.4%, 28.1%, and 26.8%, of all genomic widows as selective sweeps (either “hard” or “soft”) for Cameroon, Rwanda, and Zambia, respectively, when looking at 5-kb nonoverlapping windows. The associated false discovery rates (FDR) for calling sweeps in these populations were appreciable: 33.9%, 33.1%, and 34.7%, respectively ( supplementary fig. S26 , Supplementary Material online). As expected, windows classified as sweeps had significantly lower rates of recombination relative to neutral windows in all three populations ( ⁠ P WTT ≤ 10 − 16 for all comparisons supplementary fig. S25 , Supplementary Material online). However, we found that neither global- nor population-specific outliers were enriched for selective sweeps ( ⁠ P rand ≥ 0.246 for both comparisons), suggesting that, when treated as a class, recombination rate outliers are not likely driven by sweeps in these populations. When treated separately (i.e., independent permutation tests for each recombination rate outlier window), we identified seven outliers enriched for sweeps at the P ≤ 0.05 threshold, corresponding to an expected FDR of 77%. However, given our FDR for calling sweeps in these populations, our measure of the enrichment in overlap with recombination rate outliers is likely to be conservative. Two of these outlier windows may represent potential true positives an outlier in Cameroon contains five out of six nonoverlapping 5-kb windows classified as “hard” sweeps, the second from Rwanda has 10 out of 12 windows classified as “hard” sweeps (Prand = 0.0 for both comparisons). These two recombination rate outlier windows are potentially ripe for future studies on selective sweeps in these populations, and suggest that in at least some instances, selection contributes to observed differences in estimates of recombination rates between Drosophila populations.


Results

To simulate realistic pedigree data, SNPs were selected from HapMap that span 100 mb on both sides of a loosely-linked pair of sites. There are 40 SNPs total, with 20 tightly linked SNPs on each side of a strong recombination breakpoint having θ = 0.25. The haplotypes for these SNPs were selected randomly from HapMap. Pedigree haplotype and genotype data were simulated for each child by uniformly selecting one of the parental alleles for the first locus, and subsequent loci were selected on the same parental haplotype with probability θ j for each locus j. Inheritance was simulated for 500 simulation replicates.

The simulation yielded completely typed pedigrees. For each pedigree, we removed the genotype and haplotype information for increasing numbers of untyped individuals. For each instance of a specific number of untyped individuals, two values were computed on the estimated number of recombinations between the central pair of loci: the haplotype and genotype accuracies. Accuracy was computed as a function of the l1 distance between the deterministic number of recombinations and the calculated distribution. Specifically, accuracy was 2 - Σi≥0|x i- a i |, where x i was the estimated probability for i recombinations and a i was the deterministic indicator of whether there were i recombinations in the data simulated on the pedigree.

In all the instances we observed a trend where the best accuracy was obtained with haplotype data where everyone in the pedigree was haplotyped. For example, a five-individual pedigree with two half-siblings is shown in Figure 3. With the three founders untyped, the haplotype data yielded similar accuracy as the genotype data. Consider a three-generation pedigree having two parents, their two children, an in-law, and a grandchild for a total of six individuals, three of them founders. This pedigree has a similar trend in accuracy as the number of untyped founders increases, Figure 4. As the number of untyped individuals increases, the accuracies of genotype and haplotype estimates appear to converge.

Predicting Recombinations for Half-Siblings. This is the average accuracy for predictions from a pedigree with two half-siblings and three parents. Five hundred simulation replicates were performed, and the average accuracy of estimates from the haplotype data is superior to those from genotype data. However, as the number of untyped founders increases, in both cases, the accuracy of estimates from haplotype data drop relative to the accuracy from genotype data. The accuracies of genotype and haplotype estimates appear to converge.

Predicting Recombinations for Three Generations. This figure shows accuracy results from a six-individual, three-generation pedigree. Again, five hundred simulation replicates were performed, and the average accuracy of estimates from the haplotype data is superior to those from genotype data. Once again, as the number of untyped founders increases, the accuracy of estimates from haplotype data drop relative to the accuracy from genotype data. The accuracies of genotype and haplotype estimates appear to converge.


Electronic supplementary material

13062_2011_277_MOESM1_ESM.PDF

Additional file 1: Gene loci identified as being under intragenic homologous recombination using the 4 methods of Substitution analysis of recombination (p-value < 0.05). (PDF 129 KB)

Additional file 2: Gene loci under positive selection inferred based on the overall test (Test 1). (PDF 104 KB)

Additional file 3: Gene loci under positive selection based on the branch-site specific test (Test 2). (PDF 112 KB)

13062_2011_277_MOESM4_ESM.PDF

Additional file 4: Inter-clade events inferred by ClonalFrame grouped according to the affected clade and the clade of origin. (PDF 98 KB)


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