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Why can two individually fair allele transmissions fail to be independent? Genetic loci on the same chromosome can travel together through meiosis. This tendency is genetic linkage; in an ordinary two-locus model, detectable genetic linkage means a recombination fraction below $1/2$. A genetic marker can therefore help track a neighboring genetic locus through a pedigree. See Clark, Douglas and Choi, Biology 2e: chromosomal theory and genetic linkage.

Take a diploid individual heterozygous at two genetic loci, with phase-known genotype $AB/ab$: one homologous chromosome carries alleles $A,B$, the other $a,b$. The parental haplotypes are $AB,ab$; the recombinant haplotypes are $Ab,aB$. Assume fair Mendelian segregation, equal probabilities within each reciprocal pair of gametes, and no allele-dependent differences in gamete recovery. If the recombination fraction is $r=0.2$, the gamete probabilities are $0.4,0.4,0.1,0.1$ in that order. Each genetic locus still transmits its two alleles equally, but $P(AB)=0.4$ exceeds $P(A)P(B)=0.25$: fairness at each genetic locus does not imply independence between genetic loci.

At $r=0$, only the parental haplotypes occur, each with probability $0.5$: this is complete genetic linkage. At $r=0.5$, all four haplotypes have probability $0.25$, giving independent assortment. A recombination fraction near $0.5$ alone cannot establish whether the genetic loci lie on different chromosomes or far apart on one chromosome.

Genetic linkage concerns joint transmission through meiosis. Linkage disequilibrium concerns haplotype frequencies in a population, while genetic association compares allele or genotype frequencies with a phenotype. These questions require different observations; pedigrees directly reveal co-transmission.

A locus is a position or region of a genome. An allele specifies an inherited state there; a haplotype specifies states at several loci on one chromosome.

A genetic marker is an observed inherited genomic state used to track chromosomes, test genetic association, or perform genetic linkage analysis. Genetic marker alleles need not themselves cause the phenotype. Several genetic markers on one nonrecombining region form a single observed haplotype.

Genetics studies how genes are inherited, how their alleles vary, and how that variation contributes to phenotypes. Its central distinction is between an inherited description, the genotype, and what an organism actually expresses, the phenotype. Following alleles through a pedigree answers a family-level question; following allele frequencies through generations leads to population genetics and evolution.

The beauty of Mendelian segregation is that discrete inherited copies can generate predictable statistical patterns without making every offspring identical. A single genetic locus already connects meiosis, probability and observable variation. The Hardy-Weinberg principle then supplies a population baseline: it separates reshuffling inherited copies from processes such as natural selection that change their frequencies. These simple models are useful precisely because their assumptions can be stated and checked.

A genotype records the inherited alleles carried by an individual at one or more genetic loci. At a two-copy diploid genetic locus with alleles $A,a$, the possible unordered genotypes are $AA,Aa,aa$. The first and last are homozygotes; the middle is a heterozygote. The notation records inherited states, without yet asserting their phenotypes. See NHGRI: genotype.

An unordered $Aa$ combines the two parental orders $Aa$ and $aA$. This small bookkeeping point explains the factor two in Hardy-Weinberg proportions and in an $Aa\times Aa$ cross. If parental origin or arrangement across several genetic loci matters, a phase-known genotype retains extra information about the two haplotypes.

Genotyping determines the alleles carried at chosen genetic loci. A diploid genotype call need not identify its two haplotypes; that additionally requires a phase-known genotype or haplotype estimation.

A genotyping error is an incorrect observed allele or genotype call. A probabilistic observation model replaces the deterministic genetic marker factor by $P(X=x\mid G=g)$, allowing nonzero probability of an observed call inconsistent with the latent genotype. Such error can bias family transmission tests and linkage likelihoods.

This model couples deterministic underdominant allele-frequency dynamics in equal-sized habitats by global migration. Starting with $m$ pure habitats of one allele and $N-m$ of the other preserves two within-class frequencies $x,y$, reducing the system to $\dot{x}=f(x)-\sigma (1-q)(x-y)$, $\dot{y}=f(y)+\sigma q(x-y)$ with $q=m/N$. Migration is conservative in the mean, but selection need not conserve global allele frequency.

Consider an explicitly idealized haploid population with two inherited types $A,B$, one allele per individual at the tracked genetic locus, and discrete, nonoverlapping generations. Assume faithful type transmission, no mutation or gene flow, a constant environment, and reproductive contributions independent of type frequency. Let the expected values of offspring surviving to the next census be $m_A,m_B>0$, with relative genotype fitness $w=m_A/m_B$.

For $A$ frequency $p$, expected offspring contributions are proportional to $p{m}_A$ and $(1-p)m_B$. Normalizing gives the deterministic large-population mathematical modelThe denominator is mean relative genotype fitness. For $w=1.2$ and $p_0=0.2$, $p_1=0.24/1.04=3/13\approx 0.23077$. Equivalently, 200 $A$ and 800 $B$ parents with $m_A=1.2,m_B=1$ contribute expected counts 240 and 800. Fractional per-parent expected values are averages, not literal fractional offspring. The ratio of these expectations is a deterministic approximation, not generally the expectation of a random ratio.

The odds multiply particularly simply:Thus $p_{10}\approx 0.60753$: repeated modest advantage can substantially change composition. For $w>1$ and $0<p<1$, $p^{\prime }-p=(w-1)p(1-p)/(1+(w-1)p)>0$. The deterministic boundary $p=0$ remains unchanged: natural selection needs existing variation.

A distinct stochastic extension fixes $N$ offspring and samples their types independently using a binomial distribution: $K\sim \mathrm{Binomial}(N,q)$, where $q=wp/(wp+1-p)$, then $p^{\prime }=K/N$. Its conditional expected value is $q$ and conditional variance is $q(1-q)/N$. With $w=1$, the conditional expected value remains $p$, yet realized frequencies wander through genetic drift. With $w>1$, genetic drift can still lower $p$ or lose $A$ entirely. Over several generations, the deterministic curve need not equal the mean stochastic trajectory, because the recurrence is nonlinear.

A haplotype is the set of alleles on one chromosome across specified genetic loci. Closely linked markers without intervening genetic recombination are transmitted as one haplotype; they are not independent observations of inheritance.

Haplotype estimation reconstructs which alleles share a chromosome from observed genotypes and other information, such as pedigrees. A doubly heterozygous genotype can be $AB/ab$ or $Ab/aB$; a phase-known genotype distinguishes them. Uncertainty in that reconstruction must propagate to estimated haplotype frequencies and linkage disequilibrium.

A haplotype frequency is the proportion of chromosome copies carrying specified alleles together across chosen genetic loci. Its marginal distributions give allele frequencies; its departures from products of those frequencies are linkage disequilibrium.

The haplotype-relative-risk design compares transmitted alleles to the corresponding untransmitted parental copies, used as internal controls for an affected offspring. If allele-1/allele-2 transmitted counts are $w,x$ and untransmitted counts are $y,z$, the allele-1 odds ratio estimate is $wz/(xy)$. An unmatched Pearson chi-squared test of independence treats this as a two-by-two table. The transmission disequilibrium test instead keeps parent-specific pairing and uses only discordant transmissions. The sampling assumptions of the unmatched calculation and any missing-parent reconstruction must be justified separately.

The Hardy-Weinberg principle describes the genotype proportions produced by independently combining two parental gametes from the same allele pool. For alleles $A,a$ with allele frequencies $p,q$, where $p+q=1$, the Hardy-Weinberg proportions areThe two ordered heterozygote outcomes contribute $pq+qp$. These proportions sum to $(p+q{)}^2=1$, and their implied $A$ allele frequency is $p^2+(2pq)/2=p$. Reshuffling copies therefore preserves their frequencies.

For stable Hardy-Weinberg equilibrium across generations, use a large population with random mating, equal allele frequencies in the parental pools, ordinary Mendelian segregation, and no natural selection, mutation, gene flow or genetic drift. Negligible genetic drift is an idealization of large population size. The one-generation random-mating equilibrium needs independent union of gametes; persistence additionally requires processes that change allele frequencies to be absent.

At $p=0.70,q=0.30$, the predicted genotype proportions are $0.49,0.42,0.09$. For 100 sampled individuals the expected values of the counts are $49,42,9$, rather than the $25,50,25$ of a specified $Aa\times Aa$ cross. The latter fixes both parental genotypes; Hardy-Weinberg proportions average over population mating.

An observed population need not have these proportions. For example, $70AA$, no $Aa$, and $30aa$ among 100 individuals still gives $p=0.70$, but has no heterozygotes. Random mating predicts $0.42$ heterozygosity in the next generation under the stated model. A finite sample can also depart by chance, so a discrepancy is a starting point for investigating inbreeding, population stratification or genotyping error, rather than proof of one cause.

Hardy's 1908 note made the conceptual payoff explicit: a dominant phenotype does not by itself make its allele spread. Complete dominance describes expression; changes in frequency require a separate mechanism such as natural selection. See Hardy, Mendelian Proportions in a Mixed Population, Science 28 (1908), 49–50.