Pedigree - Dealing With Unknown Parent Genotypes

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I'm dealing with a pedigree problem, and I'm having some trouble dealing with problems of unknown parental genotypes (where there are multiple possibilities). This is not a homework question; it's an exam practice problem.

The question asks: Suppose brother and sister 6 and 7 are mated. What is the probability that their first pup will be albino?

The answer choices are 1/4, 1/8, 1/16, 1/32, or Not Enough Info.

Here's my attempt at the problem:

Since #2 is black, and some of its offsprings ended up sepia, #2 must carry the sepia gene (since the male mate is cream, and sepia is dominant over cream). So, we know that #2 is $$Cc^k$$.

Now, the male mate can be either $$c^d c^d$$, or $$c^d c^a$$.

This means that in order for the offspring of #6 and #7 to be albino, we need:

1) Male parent needs to carry the albino gene (i.e. male needs to be $$c^d c^a$$).

2) Male parent needs to pass on the albino gene to both #6 and #7.

3) #6 and #7 both need to pass on the albino gene.

I think the probability that the male is $$c^d c^a$$ is $$frac{1}{2}$$, since the male parent can be either $$c^d c^a$$ or $$c^d c^d$$.

If we draw out a punnett square, the probability that the albino gene gets passed onto 1 individual is $$frac{1}{2}$$ (since we already know the offsprings are sepia, we only need to consider the genotypes that contain $$c^d$$).

The probability that #6 and #7 pass on the albino gene is $$frac{1}{4}$$.

This led me to think that the final probability is then P(male parent has albino gene) * P(#6 inherits albino gene) * P(#7 inherits albino gene) * P(#6 and #7 pass on the albino gene), which is $$frac{1}{2}$$ * $$frac{1}{2}$$ * $$frac{1}{2}$$ * $$frac{1}{4}$$, which gives us $$frac{1}{32}$$, but I do not feel confident in my answer. Specifically, I do not know how to account for the fact that the male parent can be either $$c^d c^d$$ or $$c^d c^a$$.

Is my thought process correct? If not, where in my logic did I go wrong? Thank you for your help.

Based on the information given in the pedigree, you can actually be certain that #2's mate has a genotype of CdCa because of the following: Two of 5's siblings are albinos. Since albino is a recessive trait (lowest in hierarchy of dominance), both parents (4 and 4's mate) must have the Ca allele. Let's call 4's mate "8". Since 8 has the Ca allele, at least one of 8's parents must also have the Ca allele. Since you have already (correctly) determined that #2 is CCk, #2's mate must have the Ca allele, and must therefore have a genotype of CdCa.

Thus you can eliminate the extra (1/2) from your calculation, making the answer (1/2) * (1/2) * (1/4), or (1/16).

Pedigree - Dealing With Unknown Parent Genotypes - Biology

The trick to taking the fear out of science is helping students find the science in their daily lives.

Science is the reason why we add salt to the roads when snow is in the forecast. It’s the reason why the arrival of cold temperatures coincides with low tire pressure in our cars. Science explains why salt dissolves in room temperature water while sugar doesn’t and is even the reason why some people sneeze when going outdoors from a dark room on a sunshiny day.

Anytime you can help students relate science to things they understand and care about, you remove one more barrier to learning.

Take learning about DNA and genetics, for instance. What good is it if a student understands how DNA base pairing works and can recite all of the steps of mitosis if they never understand how DNA relates to their daily lives?

The solution? Help them understand how DNA has shaped their family by constructing a family pedigree.

Mendelian Inheritance in Humans

Mendelian inheritance in humans is difficult to study because humans produce relatively few offspring (as compared to many other species) and they have a generation time of about 20 years. These two factors, not to mention ethical issues, make it impossible to design breeding experiments using humans. There are many characters in humans that show a Mendelian pattern of inheritance. This web page, the OMIM (Online Mendelian Inheritance in Man - http://www.omim.org/ ) is a catalog of these characters. You can search for a particular character (for example, dimples) to read what is know about its inheritance.

Results

To illustrate kinship calculations for cases of unknown and uncertain parentage and various forms of sexual and asexual reproduction, the pedigree of 12 individuals shown in Figure 1 was analyzed with PMx. The pedigree contains individuals produced from partly unknown parentage (#6), uncertain parentage (#10), cloning (#8), selfing (#11), haploidy (#9), and haplodiploidy (#12). Table 2 shows all pairwise kinships for this pedigree, calculated with assignment of unknown parents as founders (below the diagonal) or with omitting contributions from unknown parents (above the diagonal). The relative value of each individual to retention of gene diversity of the captive population is given by its mean kinship to the nonfounders ( Equation 9), and these values are given in Table 2. The gene diversity of the nonfounders (#6–12) is calculated as one minus the mean of all their pairwise kinships, weighted by the portion of each genome that derives from known founders ( Equation 10). This population gene diversity, as a proportion of the gene diversity of the source population from which the founders were assumed to have been randomly sampled, is G = 0.816 if the unknown parent is treated as a founder and G = 0.791 if unknown ancestry is excluded.

Kinships for the pedigree in Figure 1

 ID 1 2 3 4 5 6 7 8 9 10 11 12 MK 1 0.5 0 0 0 0 0.500 0 0 0 0.071 0 0 0.049 2 0 0.5 0 0 0 0 0.25 0 0 0.071 0 0 0.049 3 0 0 0.5 0 0 0 0.25 0 0 0.071 0 0 0.049 4 0 0 0 0.5 0 0 0 0.5 0 0.286 0.5 0.25 0.235 5 0 0 0 0 0.5 0 0 0 0.5 0 0 0.25 0.118 6 0.25 0 0 0 0 0.5, 1.0 0 0 0 0.143 0 0 0.098 7 0 0.25 0.25 0 0 0 0.5 0 0 0.143 0 0 0.098 8 0 0 0 0.5 0 0 0 0.5 0 0.286 0.5 0.25 0.235 9 0 0 0 0 0.5 0 0 0 1 0 0 0.5 0.235 10 0.062 0.062 0.062 0.25 0 0.125 0.125 0.25 0 0.5, 0.510 0.286 0.143 0.216 11 0 0 0 0.5 0 0 0 0.5 0 0.250 0.75 0.25 0.274 12 0 0 0 0.25 0.25 0 0 0.25 0.5 0.125 0.25 0.5 0.255 MK 0.045 0.045 0.045 0.214 0.107 0.089 0.089 0.214 0.214 0.196 0.25 0.232 0.184, 0.209
 ID 1 2 3 4 5 6 7 8 9 10 11 12 MK 1 0.5 0 0 0 0 0.500 0 0 0 0.071 0 0 0.049 2 0 0.5 0 0 0 0 0.25 0 0 0.071 0 0 0.049 3 0 0 0.5 0 0 0 0.25 0 0 0.071 0 0 0.049 4 0 0 0 0.5 0 0 0 0.5 0 0.286 0.5 0.25 0.235 5 0 0 0 0 0.5 0 0 0 0.5 0 0 0.25 0.118 6 0.25 0 0 0 0 0.5, 1.0 0 0 0 0.143 0 0 0.098 7 0 0.25 0.25 0 0 0 0.5 0 0 0.143 0 0 0.098 8 0 0 0 0.5 0 0 0 0.5 0 0.286 0.5 0.25 0.235 9 0 0 0 0 0.5 0 0 0 1 0 0 0.5 0.235 10 0.062 0.062 0.062 0.25 0 0.125 0.125 0.25 0 0.5, 0.510 0.286 0.143 0.216 11 0 0 0 0.5 0 0 0 0.5 0 0.250 0.75 0.25 0.274 12 0 0 0 0.25 0.25 0 0 0.25 0.5 0.125 0.25 0.5 0.255 MK 0.045 0.045 0.045 0.214 0.107 0.089 0.089 0.214 0.214 0.196 0.25 0.232 0.184, 0.209

Below the diagonal are kinships if unknown parents are assumed to be founders above the diagonal are kinships if unknown ancestries are omitted. Kinships to self of 2 animals with partly unknown ancestries are different if unknown ancestry is omitted (second value) or not (first value). Marginal values are mean kinships to the nonfounders (6–12) and the mean of all 49 such kinships. MK values in the last row are the mean kinships to the 7 nonfounders when unknown parents are assumed to be founders (values on or below the diagonal). MK values in the last column are the mean kinships to the nonfounders when unknown ancestries are omitted (values on or above the diagonal). The MK values in the last column are weighted by the proportion of the genome of each individual that is known (k6 = 0.500 and k10 = 0.875, whereas k = 1 for all other individuals).

Kinships for the pedigree in Figure 1

 ID 1 2 3 4 5 6 7 8 9 10 11 12 MK 1 0.5 0 0 0 0 0.500 0 0 0 0.071 0 0 0.049 2 0 0.5 0 0 0 0 0.25 0 0 0.071 0 0 0.049 3 0 0 0.5 0 0 0 0.25 0 0 0.071 0 0 0.049 4 0 0 0 0.5 0 0 0 0.5 0 0.286 0.5 0.25 0.235 5 0 0 0 0 0.5 0 0 0 0.5 0 0 0.25 0.118 6 0.25 0 0 0 0 0.5, 1.0 0 0 0 0.143 0 0 0.098 7 0 0.25 0.25 0 0 0 0.5 0 0 0.143 0 0 0.098 8 0 0 0 0.5 0 0 0 0.5 0 0.286 0.5 0.25 0.235 9 0 0 0 0 0.5 0 0 0 1 0 0 0.5 0.235 10 0.062 0.062 0.062 0.25 0 0.125 0.125 0.25 0 0.5, 0.510 0.286 0.143 0.216 11 0 0 0 0.5 0 0 0 0.5 0 0.250 0.75 0.25 0.274 12 0 0 0 0.25 0.25 0 0 0.25 0.5 0.125 0.25 0.5 0.255 MK 0.045 0.045 0.045 0.214 0.107 0.089 0.089 0.214 0.214 0.196 0.25 0.232 0.184, 0.209
 ID 1 2 3 4 5 6 7 8 9 10 11 12 MK 1 0.5 0 0 0 0 0.500 0 0 0 0.071 0 0 0.049 2 0 0.5 0 0 0 0 0.25 0 0 0.071 0 0 0.049 3 0 0 0.5 0 0 0 0.25 0 0 0.071 0 0 0.049 4 0 0 0 0.5 0 0 0 0.5 0 0.286 0.5 0.25 0.235 5 0 0 0 0 0.5 0 0 0 0.5 0 0 0.25 0.118 6 0.25 0 0 0 0 0.5, 1.0 0 0 0 0.143 0 0 0.098 7 0 0.25 0.25 0 0 0 0.5 0 0 0.143 0 0 0.098 8 0 0 0 0.5 0 0 0 0.5 0 0.286 0.5 0.25 0.235 9 0 0 0 0 0.5 0 0 0 1 0 0 0.5 0.235 10 0.062 0.062 0.062 0.25 0 0.125 0.125 0.25 0 0.5, 0.510 0.286 0.143 0.216 11 0 0 0 0.5 0 0 0 0.5 0 0.250 0.75 0.25 0.274 12 0 0 0 0.25 0.25 0 0 0.25 0.5 0.125 0.25 0.5 0.255 MK 0.045 0.045 0.045 0.214 0.107 0.089 0.089 0.214 0.214 0.196 0.25 0.232 0.184, 0.209

Below the diagonal are kinships if unknown parents are assumed to be founders above the diagonal are kinships if unknown ancestries are omitted. Kinships to self of 2 animals with partly unknown ancestries are different if unknown ancestry is omitted (second value) or not (first value). Marginal values are mean kinships to the nonfounders (6–12) and the mean of all 49 such kinships. MK values in the last row are the mean kinships to the 7 nonfounders when unknown parents are assumed to be founders (values on or below the diagonal). MK values in the last column are the mean kinships to the nonfounders when unknown ancestries are omitted (values on or above the diagonal). The MK values in the last column are weighted by the proportion of the genome of each individual that is known (k6 = 0.500 and k10 = 0.875, whereas k = 1 for all other individuals).

Sample pedigree for testing kinship calculations. “U” indicates an unknown sire. Individuals 1–5 are founders, assumed to be noninbred diploids that are unrelated to each other. Individual 4 was cloned to produce 8. Individual 9 is a haploid progeny of 5. The sire of Individual 10 was either 6 or 7, with an assumption of equal probability. Individual 11 was produced by a hermaphroditic selfing of 8.

Sample pedigree for testing kinship calculations. “U” indicates an unknown sire. Individuals 1–5 are founders, assumed to be noninbred diploids that are unrelated to each other. Individual 4 was cloned to produce 8. Individual 9 is a haploid progeny of 5. The sire of Individual 10 was either 6 or 7, with an assumption of equal probability. Individual 11 was produced by a hermaphroditic selfing of 8.

To confirm the accuracy of the methods, a “gene drop” simulation ( MacCluer et al. 1986) of the transmission of founder alleles through the pedigree (provided within PMx) was repeated for 1 000 000 iterations. The gene diversity calculated from resultant allele frequencies in the 7 nonfounder individuals (G = 1 − ∑pi 2 , for founder allele frequencies pi) was confirmed to be 0.816 when alleles from the unknown parent was included and was 0.789 when alleles from the unknown parent were excluded. The small imprecision of the estimated gene diversity (0.789 vs. 0.791) when unknown parents are excluded was expected. A small bias when using the equations in Table 1 occurs because the iterative kinship calculations and resultant gene diversity estimates assume homogeneity of genetic processes across loci. However, for individuals that have partly missing ancestry, some loci may be diploid and others haploid (rather than, e.g., an individual with 1 unknown grandparent somehow being 1.5-ploid at each locus), and there may be different distributions of ancestral alleles among the loci having different ploidy. For example, an individual, i, with ki = 0.5 may have had 1 unknown parent (e.g., sire ks = 0) and 1 fully known parent (e.g., dam kd = 1) or may have had 2 parents each with ks = kd = 0.5. For offspring of i, these 2 possibilities have different consequences for kinships and inbreeding. In the first case, individual i is considered haploid at all loci, and 2 offspring that receive from i a known allele at a locus will necessarily have received the same allele from the grandmother d. In the second case, individual i will be diploid at 25% of its loci, haploid at 50% of its loci, and null-ploid at 25% of its loci, and 2 offspring that receive a known allele at a given locus from i will have a 50% chance of receiving alleles derived independently from the paternal grandparents, d and s. This dependency on grandparent k is not accounted for in Equations 7 and 8. In small pedigrees in which unknown parents are at most a few generations deep, it would be possible to calculate unbiased kinships from probabilities of shared alleles based on the specific pedigree structure. However, for large pedigrees with many generations, calculating the effects of nonhomogeneity of loci (dependent on k in earlier generations) becomes prohibitively complex. The one-generation method of Equations 7 and 8, as given by Ballou and Lacy (1995), can be used to obtain approximate results that will likely be sufficiently accurate except when many individuals have only partly known ancestries.

Pedigree Analysis Pedigree Analysis is a tabular representation of a family history by taking a particular disease or character into consideration.

Proband or Propositusis an individual from which a pedigree is initiated.

Female are represented in circles.

Male are represented in squares.

Individuals carrying the character to be studied are shaded. or

It helps us to find out whether the gene is dominant or recessive and autosomal or sex-linked. And the chances of expressing itself in the coming generations.

In the case of sex-linked genes:
• Affects the males as they are hemizygous.
• The gene shows criss-cross inheritance i.e., the gene from the father is transferred to the grandson through the daughters.
• In the case of a sex-linked dominant gene, more females are affected than males.
• Never transferred from father to son.
In the case of dominant genes:
• One or both the parents have the disorder.
• It expresses itself in every generation.
• The disorder is common in the pedigree.
• The genotype is either homozygous (BB) or heterozygous (Bb).
• It affects one-half of the children.
In the case of recessive genes:
• Neither of the parents may have the disorder.
• The disorder is rare in the pedigree.
• Both parents are either heterozygous or homozygous recessive.
• The disorder skips generations.
• The genotype is always homozygous (bb).
• Affected offspring are born to unaffected parents.
In the case of Holandric ( Y-linked) genes:
• Affects the males only.
• Father transfers it to son.
• It never skips generations.
In the case of Cytoplasmicgenes:
• Gene is inherited from mother.
• Affected mother transfers the gene to all its offspring.

Pedigree 1:

A sex-linked recessive character.

• It mainly affects the males.
• The gene skips generation.
• Criss-cross inheritance is seen.

Pedigree 2:

It is an autosomal dominant character.

Pedigree 3:

It is an autosomal recessive character.

Pedigree 4:

It is a holandric gene.

Pedigree 5:

It is a cytoplasmic gene.

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Methods

Plant material

A set of 1425 diploid genotypes was used in this study (Additional file 1: Table S1), and each was given a unique genotype code (MUNQ, for Malus UNiQue genotype code) as a development from the FBUNQ code (for FruitBreedomics UNiQue code) described by Urrestarazu et al. [32] based on SSR data. The set largely replicated the panel built for association studies by Urrestarazu et al. [68], but with an addition of almost 160 accessions from the germplasm collections of the National Fruit Collection (NFC, UK), the Research Center of Laimburg (Italy) and the Biological Resources Center RosePom of INRA (France). Two small segregating populations, each containing 46 progenies and their parents (‘Golden Delicious’ (MUNQ 65) x ‘Renetta Grigia di Torriana’ (MUNQ 435) and ‘Fuji’ (MUNQ 318) x ‘Pinova’ (MUNQ 651)) were also included. In addition, eight triploid genotypes were included (Additional file 1: Table S1).

For each genotype in Additional file 1: Table S1, a “preferred” name was given on the basis of documented synonymy, collection listings, websites and reference pomological books, in addition to information about matching accessions (“duplicates”) as described by Urrestarazu et al. [32]. Historical data on the date of origin, first description, introduction, recording or inclusion in collections of cultivars, and documentation on any presupposed parents were collected from sources mentioned in Additional file 4 and indicated in Additional file 1: Table S1. For the sake of simplicity, such information was further referred to as “documented date” or “documented parentage”, respectively. The SSR data obtained by Fernandez-Fernandez [69], Lassois et al. [39] and Urrestarazu et al. [32] were used to allocate the MUNQ of the accessions and the name initially indicated in these papers was sometimes consequently replaced by the “preferred” name in this study. In our interpretations, we mostly refer to genotypes using their preferred name and these preferred names correspond directly to the MUNQ according to Additional file 1: Table S1.

SNP genotyping

All genotypes were analyzed with the Axiom®Apple480K array containing 487,249 SNPs evenly distributed over the 17 apple chromosomes [55]. The sub-set of 275,223 robust SNPs previously selected by Bianco et al. [55] was initially used for analysis. After the first step of parent-offspring analysis (described below), 22,128 SNPs showing a Mendelian error in two or more accepted relationships were further removed from the genotyping data, leaving a total of 253,095 SNPs for further analyses (Additional file 2: Table S2). A random set of 25,310 (i.e., 10%) of these SNPs was selected for a grandparent search (again, as described below). SNP positions were based on the latest version (v1.1) of the apple genome based on the doubled haploid GDDH13 ( [70] see also https://iris.angers.inra.fr/gddh13/ for the genome browser).

Parent-offspring relationships

All possible pairings of diploid individuals were analyzed using PLINK (https://www.cog-genomics.org/plink/1.9/ [56]) for computing Identity By Descent (IBD) sharing probabilities, using the ‘PI_HAT’ parameter. The expected value for first degree relatedness is 0.5. A total of 3655 pairings with a PI_HAT value greater than 0.4 were selected before estimating the number of Mendelian errors (ME) based on a hypothesis that the two individuals were parent and offspring: for example, if an individual had an AA SNP score at a given locus and the other individual of the pairing had a BB SNP score at the same locus, this was considered as a Mendelian inheritance error for a parent-offspring relationship. In an attempt to ensure the inclusion of all first degree relationships in the tested set, the PI_HAT threshold of 0.4 was selected to be lower, and therefore more inclusive, than the value of 0.466 indicated by Myles et al. [21] for a similar study performed on grape cultivars. Any pairings showing fewer than 1000 ME (0.36%) when using the initial set of 275,223 SNPs were considered as potential parent-offspring relations (duos). Using the final set of 253,095 SNPs, this error threshold was reduced to 600 ME (0.24%) such that the corresponding parent-offspring duos could be considered with increased confidence.

Identification of complete parent-offspring trios

For all diploid individuals that were accepted to potentially be involved in two or more parent-offspring relationships, we counted the number of ME for all possible trios that could associate the individual with two potential parents. In addition to errors due to mutually exclusive homozygous SNP scores, ME in complete parent-offspring trios can also be identified when a potential offspring is scored as heterozygous (AB) and both potential parents are scored as homozygous for only one allele, i.e. both AA or both BB. Based on the distribution of ME over all possible trios, groups that showed fewer than 600 ME in the set of 253,095 SNPs were considered as likely complete parent-offspring sets.

Identification of parent-offspring duos and complete parent-offspring trios involving triploids

The eight triploids were genotyped and analyzed as if they were diploid, i.e. both genotypes AAB and ABB were treated as AB, while AAA and BBB genotypes were treated as AA and BB, respectively. Consequently, we counted ME as for diploids. To identify potential 2n-gamete parents, we counted the SNPs that were homozygous in the triploid offspring and heterozygous in the diploid parent, here called “tri-hom/di-het” SNPs, as ME for a parent-offspring relationship. This number would be expected to be close to zero for the parent that contributed a 2n-gamete since both alleles should have been passed to the triploid offspring, with the exception of reassortment through crossovers in 2n-gametes formed through first division restitution or second division restitution. The potential n-gamete parent was inferred as above for diploid genotypes. Since several triploid cultivars were previously considered to be the parents of various diploid cultivars ([27], e.g., “Ribston Pippin” as the parent of “Cox’s Orange Pippin”), we developed the following procedure to challenge such situations. When a potential 2n-gamete parent was identified, we examined the dependency of the other individuals suggested to be offspring of the triploid, on the genotype of the potential 2n-gamete (grand)parent. In cases where the triploid was heterozygous and its 2n-gamete parent was homozygous we counted: i) the number of SNPs in the potential offspring that were homozygous for the same allele as the 2n-gamete (grand)parent or heterozygous, and ii) the number of SNPs in the potential offspring that were homozygous for the alternative allele to that of the potential 2n-gamete (grand)parent. Absence (or almost absence) of SNPs in the second category indicated that the supposed triploid intermediary did not pass any alleles received from the other (n-gamete) parent to the potential offspring, and thus the triploid could be excluded as a potential parent of this individual.

Orientation of parent-offspring duos and integration of historical data

For all pairings of diploid individuals inferred to be in a parent-offspring duo that could not be identified as part of a trio, we attempted to determine which individual was the parent and which individual was the offspring. We considered first, that any individual identified as an offspring in a trio would have to be the parent in any other duos that it was involved in the second individual of the duo was thus considered an offspring. Subsequently, any offspring identified in this way could only be considered a parent in further duos, since the other individual would otherwise have been expected to be identified as its other parent in a trio. The pedigrees were thus progressively constructed according to this iterative process.

We then used historical data to orient additional duos: if one individual in a duo had already been documented as the offspring of the other individual, we assumed that this was probably the case. Where documented dates could be found for both individuals in a duo, we considered that the one with the most recent date was most probably the offspring. The same iterative process was then applied to orient further additional duos for which neither previously reported parentage, nor date of origination enabled orientation.

Identification of grandparent couples for parent-offspring duos

For each parent-offspring duo that was not identified as part of a trio, the two potential parents of the missing parent, i.e. the grandparent couple were identified, where possible. To do this, we considered as ME those SNPs where both potential grandparents scored as homozygous for a given allele and: (i) the offspring was scored as homozygous for the alternate allele, or (ii) the offspring was scored as heterozygous and the accepted parent was scored as homozygous for the same allele as the potential grandparents. We used a random set of 25,310 SNPs in order to reduce computation time and retained potential grandparent pairings that reported fewer than 100 ME (0.40%) only. Subsequently, we further checked the groups potentially consisting of a grandparent couple, parent and offspring by counting the number of ME in the set of 253,095 SNPs. Finally, groups with a grandparent couple, parent and offspring that reported fewer than 100 ME in the set of 253,095 SNPs (0.04%) were considered likely grandparents-parent-offspring sets. The segregating populations and triploids were excluded from this process.

Pedigree deduced from all results

All of the inferred trios, oriented duos and groups of grandparents-parent-offspring were used to produce a large pedigree file which could be browsed using the software Pedimap [57]. Again, the two segregating populations were not included in this pedigree.

Options¶

If you choose to simulate an F1, Flapjack will generate an expected F1 line from the two parental lines that you selected in the dialog. To do this, Flapjack compares the alleles of the two parental lines to determine the alleles for the expected F1. If either parent has missing or heterozygous data at a marker, the expected F1 is left as missing data. For other markers, if parent 1 is A/A and parent 2 is A/A, then the expected F1 is A/A. If parent 1 is A/A and parent 2 is T/T, then the expected F1 is A/T.

The following statistics are provided following F1 analysis in the ‘Results View’

• Data count - the number of markers with non-missing values.
• % Data - the number of markers with non-missing values as a percent of the total markers for a sample.
• Het count - the number of markers with heteozygous genotypes (i.e. alleles that differ) for a sample.
• % Het - the number of markers with heteozygous genotypes as a percent of the total markers for a sample.
• % Het Deviation from Expected - the difference in % hets between the expected F1 and the sampled F1.
• % Allele match to Parent 1 / Parent 2 - alleles matching between the sampled F1 line and the sampled parent line, as a percentage of non-missing data in both the F1 line and parent line, and across all markers for a sample. e.g. if F1 = A/A C/C and sampled P1 = A/T N/N then % allele match to P1 is 50% ie 1 allele at a marker in the sampled F1 matches a P1 allele for the same marker, out of 2 non-missing alleles available for comparison.
• % Genotype match to Expected F1 - matching gneotypes (i.e. both alleles need to match) between the sampled F1 line and the simulated F1 as a percentage of non-missing data in both the F1 line and the simulated F1, and across all markers for a sample. e.g. if sampled F1 = A/T C/C and the simulated F1 = A/T C/T , then % genotype match to expected F1 is 50%. Only 1 marker genotype out of 2 matches at both alleles.

Expected F1 alleles can only be simulated if both parents have homozygous alleles. If either parent has a heterozygous call or missing data for a marker, then the expected F1 allele data cannot be simulated and will have a missing value.

PedCheck: A Program for Identification of Genotype Incompatibilities in Linkage Analysis

Prior to performance of linkage analysis, elimination of all Mendelian inconsistencies in the pedigree data is essential. Often, identification of erroneous genotypes by visual inspection can be very difficult and time consuming. In fact, sometimes the errors are not recognized until the stage of running linkage-analysis software. The effort then required to find the erroneous genotypes and to cross-reference pedigree and marker data that may have been recoded and renumbered can be not only tedious but also quite daunting, in the case of very large pedigrees. We have implemented four error-checking algorithms in a new computer program, PedCheck, which will assist researchers in identifying all Mendelian inconsistencies in pedigree data and will provide them with useful and detailed diagnostic information to help resolve the errors. Our program, which uses many of the algorithms implemented in VITESSE, handles large data sets quickly and efficiently, accepts a variety of input formats, and offers various error-checking algorithms that match the subtlety of the pedigree error. These algorithms range from simple parent-offspring–compatibility checks to a single-locus likelihood-based statistic that identifies and ranks the individuals most likely to be in error. We use various real data sets to illustrate the power and effectiveness of our program.

Summary

The phenotype of an organism arises from the complex interactions of many character traits. In a dihybrid cross two character traits can segregate in several ways, with each leading to a different phenotype. How can one predict the likelihood of the different phenotypes from a dihybrid cross? This becomes relatively straightforward, providing each character trait behaves in a Mendelian fashion that is, a simple dominant-recessive expression pattern. If the character traits are located on different chromosomes, they will segregate independently from each other. This means that the separate probabilities of each character trait can be predicted, as with a monohybrid cross. It follows then that the probabilities of these different phenotypes occurring simultaneously in the same plant will be the product of their probabilities occurring alone. In other words, if a purple/round seeded plant that is heterozygous for both characters is crossed with itself ("selfing"), then the probability of observing round/seeded progeny will be 0.75 x 0.75 = 0.56. Be sure that you understand the logic behind this prediction and how it was calculated. Also, if there are more than two ways a given genotype can arise, then the probability is the sum of each way it can arise.

Relatively few character traits are inherited in a simple Mendelian manner. There are a number of other basic patterns of inheritance that have been described, including incomplete dominance, codominance, multiple alleles, pleiotropy, epistasis and polygenic inheritance. These will be discussed in the The Complex Expression Patterns of Multiple Alleles tutorial.