Livestock Research for Rural Development 30 (7) 2018 Guide for preparation of papers LRRD Newsletter

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Within-population genetic structure and trend in genetic diversity for Sahiwal cattle breed in Kenya

S Mwangi, T K Muasya and A K Kahi

Animal Breeding and Genomics Group, Department of Animal Sciences, Egerton University, P O Box 536, 20115 Egerton, Kenya


Pedigree data of 18315 animals born from 1961 to 2008 from the National Sahiwal Stud, Kenya was used to assess and predict genetic diversity for the Kenyan Sahiwal population. Current (Nec) and predicted effective population sizes (Nef) were calculated from rates of inbreeding (∆F) and coancestry (∆f) per generation, respectively. Population structure was inferred by computing Nei’s minimum distance and Wright’s F statistics. Genetic conservation index (GCI) was computed from the genetic contributions of all identified founders. In the most recent population group, the rate of inbreeding per generation was 0.79% with a rate of coancestry per generation of 0.85%. Predicted rate of inbreeding per generation for individuals with at least 3 and 6 complete generations were 0.82 and 1.19% and Ne of 54 and 35, respectively. The respective coancestries were 0.89 and 1.36%. Values of predicted rate of inbreeding for individuals with more complete pedigrees were >1%, implying that the population is losing genetic diversity and may not be viable in the longterm. GCI for the whole population was 5.93% whereas individuals with at least three complete generations and the reference population had values of 7.87% and 9.87%, respectively. Parameters of genetic differentiation for the whole population under study (1961-2008) were FIS=- 0.0071, FST= 0.0036 and FIT =- 0.0034. Loss in genetic diversity for the whole population was -0.023, an indication of random genetic drift and unequal contribution of founders. Improved pedigree completeness led to higher estimates of parameters describing the structure of the population (GCI, Nec, Ne f,, ∆F and ∆f). Mating should be carried out between mates with low coancestries and also by allowing more animals that are not related into breeding herd. Pedigree recording should be improved for accurate estimation of parameters.

Key words: breeding structure, coancestry, conservation index, genetic differentiation


The Sahiwal cattle is a dual purpose breed that was introduced into Kenya in 1930s and 1940s from India and Pakistan (Ilatsia et al 2007). The National Sahiwal Stud (NSS) at Naivasha, Kenya is the main breeding station where the population is bred under a closed nucleus. The mandate of the NSS is to breed Sahiwal cattle that are suitable to low-input production systems due to its adaptive characteristics (Ilatsia et al 2011). The breed is also utilized for crossbreeding for dairy production in a low input-output production system (Roessler et al 2010). Being a closed nucleus, intense pedigree and performance recording and selection is carried out in the nucleus (Muhuyi et al 1999; Ilatsia et al 2007). The resultant genetic gain is disseminated to the commercial population through breeding males, semen and surplus heifers.

The Sahiwal cattle breed in Kenya had been reported to have a decreasing effective population size and increasing level and rate of inbreeding (Muasya et al 2011; Kamiti, 2014; Mwangi et al 2016), which imply declining genetic variability. The rate of inbreeding and effective population in the current population have surpassed critical levels beyond which a population begins to lose it viability and fitness in the longterm (FAO, 1998; Franklin and Frankham, 1998). This has raised concern over the long-term sustainability of the current breeding strategies. The reduced genetic variability could be attributed to increase in average relatedness and ineffective procedures for genetic evaluation for the population (Muasya et al 2011). Exploration of the genetic structure of a breed such as the Sahiwal, which has genetic heritage, ecological and socio-economic values in order to assess its genetic status and risk of extinction is important (Krupa et al 2015).

Inbreeding level for the Sahiwal breed are increasing (Dahlin et al 1995; Muasya et al 2009) and have already surpassed the 1% increase per generation (Mwangi et al 2016) for a population to maintain its long-term genetic variability (FAO, 1998). Adverse effects on performance have been reported for the same population (Musingi et al 2018). Therefore there is need to describe the within-population differentiation for the Sahiwal cattle population in Kenya and also, to determine genetic diversity trend for the breed. This will assist in developing a strategy to for sustainable management genetic diversity and genetic improvement in the population. The objective of this study was to assess the within-population genetic structure and genetic diversity loss or gain necessary when implementing selection programs.

Materials and methods

Data for this study were obtained from the National Sahiwal Stud, Kenya. A total of 18315 animals born between 1961 and 2008 were included in the study. Information on each animal included animal identification, date of birth and sex. The genealogy of each animal was traced as far back as possible in the birth record book database. This way all possible ancestors and relatives of each individual were included in the analyses.

Effective population size

Effective population size (Ne) was calculated as the regression coefficient b of the individual increase in inbreeding on discrete and complete generations (t). If b is the change in inbreeding over two generations then

where Ft and Ft-1 are the average inbreeding of the i th generation, respectively. Therefore.

Estimates of Ne calculated using maximum and equivalent generations provide guides for upper and real limits of Ne for a population. Individual increase in inbreeding ∆Fi was calculated as (Gutierrez et al 2009)

Where Fi is the individual coefficient of inbreeding and CGE is the complete generation equivalent (Maignel et al 1996). Complete generation equivalent GCE, defined as the farthest generation for which all ancestors of an individual are known was computed as:

where nj is the number of ancestors for animal j and gij is the number of generations between an individual j and its ancestor i (Sölkner et al 1998).

Genetic conservation index for the Kenyan Sahiwal population

The Genetic Conservation Index (GCI) was calculated from the genetic contributions of all established founders in the reference population. The following formula was used according to Alderson (1992):

where is the proportion of founder i’s genes in an animal’s pedigree. Estimated this way, GCI is based on the assumption that the objective of a conservation program is to retain the full range of alleles possessed by the base population. In this respect, the ideal individual would receive equal contributions from all the founder ancestors in the population and consequently, the higher the GCI value the higher the values of an animal for conservation (Alderson 1992).

Contribution of sub-populations to total diversity

The average coancestry (Malècot 1948) f, over an entire metapopulation of NT individuals consisting ofn sub-populations, population i with Ni breeding individuals, is:

with Fij being the average pairwise coancestry between individuals of sub-populations i and j, including all Ni × N j pairs; fii is the average pairwise coancestry within sub-population i and where Dij is the Nei’s minimum genetic distance (Nei 1987) between sub-populations i and j computed as Dij= [(fii + fj j )/2] − fij. The sub-populations in this study were defined based on year of birth cohorts as follows; 1960-1969, 1970-1979, 1980-1989, 1990-1999 and 2000-2008.

Genetic structure

Genetic structure of the Kenyan Sahiwal population was assessed using F-statistics (FIT, FST and FIS; Wright 1978) where FIT is the inbreeding coefficient of the whole population; FST is the expected average inbreeding estimated from a hypothetical population under random mating and FIS is the deviation produced from actual mating (Falconer and Mackay 1996).

Change in genetic diversity for the Sahiwal population in Kenya

The amount of genetic diversity (GD) in the reference population was calculated according to Lacy (1995) as;

where fge isnumber of equally contributing founders with no loss of founder alleles that would give the same amount of genetic diversity as is presented in the reference population (Caballero and Toro 2000). To measure the genetic diversity (GD) lost in the reference population of the Sahiwal cattle since the founder generation due to both bottlenecks and genetic drift was computed as , where

where fe is a measure of founder contributions to the population and is defined as the number of founders with equal contribution, which would give the same amount of genetic diversity that is present in the current population (Lacy 1989). The formula was used to estimate the loss of genetic diversity that occurred in the population due to the unequal contributions of founders before their contributions converged (Caballero and Toro 2000; Honda et al 2004). For this study, the reference population included animals born between 2000 and 2008.

Realized and predicted effective population size

The realized effective population size was calculated from the rate of inbreeding per generation (∆F) as:

Rate inbreeding per generation was estimated according to the method of VanRaden (1992). The rate of increase in coancestry, ∆f is alternatively interpreted as future rate of inbreeding per year (Falconer and Mackay, 1996). Therefore the expected effective population size was calculated by replacing ∆Fy with ∆f. The coancestry or coefficient of kinship (f) between any two individuals is the probability that any two gametes taken at random, one from each, carry alleles that are identical by descent (Gutiérrez et al 2009; Cervantes et al 2011). The coefficient of kinship provides a measure of the relationship by descent between any two mates. The coancestry of two individuals A and B, whose parents are respectively P and Q; and M and N was estimated as fAB= ¼fPM+¼fPN+¼fQM+¼fQN (Gutiérrez and Goyache 2005) and the inbreeding coefficient of an offspring between A and B is the coancestry between A and B. Coancestry estimates between mate pairs were compared against predetermined inbreeding levels. All the parameters were computed using the ENDOG V4.8 (Gutierrez and Goyache 2005).


Table 1 shows the distribution of Sahiwal cows within different levels of inbreeding. Among all animals in the pedigree, a total of 4493 inbred animals had levels of inbreeding ranging from 0.01 to 26.6 %. The number of inbred animals constituted 24.5 % of the whole population. The highest inbreeding level (26.6%) for an individual was recorded in 2006.

Table 1. Percentage of inbred animals for the Sahiwal cattle in Kenya within different inbreeding levels

class, %

Number of




















The parents’ mean age when the offspring were born was 7.1 years for the whole population and 10.2 years for the reference population (Table 2). The age increased as the years progressed while the number of animals in each year cohort reduced over the same time period. The lowest mean age of 4.23 years was recorded for 1960-1969 cohort while the highest (10.23 years) was recorded for 2000-2008 cohort. Effective population size estimated from regression of individual increase in inbreeding on discrete and complete generation equivalents increased over the years following the trend of inbreeding (Table 2).

Table 2. Mean age of parents when offsprings were born and effective population size for various year groups for the Sahiwal cattle population in Kenya


No. of

Parents mean age when
offspring were born

Effective population




















9.70 ±0.07








Entire population


7.10 ±0.02



*Ne1 and Ne2 represent effective population size computed through regression of individual increase in inbreeding on discrete and complete equivalent generations, respectively.

In Table 3, the current rates of inbreeding and coancestry per generation were used as basis for calculation of the current and future effective population size respectively. Rate of inbreeding and rate of coancestry per generation increased as more complete generations were considered. Effective population size also decreased with increase in complete generations. The rates of coancestry were higher than the reported rates of inbreeding levels for the reference population (Table 3).

Table 3. Generation interval, rate of inbreeding (%) and rate of increase in coancestry (%) per generation and current and future effective population size for Kenyan Sahiwal cattle breed.


Generation Interval

Rate of inbreeding per generation (%)

Rate of coancestry per generation (%)



2000-2008 birth year cohort






Individuals with 3 complete generations






Individuals with 6 complete generations






Entire population






Nec and Nef are current and future effective population sizes, respectively

Table 4 shows the number of actual and effective number of founder males, which successfully contributed breeding sons to the population up to the sixth generation. Characterization of the number of sires was carried out through the recovery of founders. The number of males that had at least one progeny in the population were 453. When additive genetic relationships were accounted for, this number decreased to 157. The effective number of sires of sire, great grandsires, great grand-grand-sires, founders, effective number of founders decreased over the generations, an indication of low concentration of origin of reproductive animals.

Table 4. Parameters describing genetically important and effective number of sires for the Sahiwal population in Kenya

of sires

number of sires




Sires of sire



Great grandsires



Great grand-grand-sires



Actual number of founder sires



Effective number of founder sires



*A founder sire was defined as a sire for whom both or either parent were not known

The genetic conservation index was assessed from the genetic impact of the identified founders for the population (Figure 1). The mean index for the whole population was 5.9% whereas for the individuals with at least three complete generations and the reference population (2000 to 2008 birth year cohort) the value was 10.4% and 12.9%, respectively. The indices for these two groups increased over the years. Genetic conservation index for animals with at least 3 complete generations was on average 40% compared to that of the entire population, implying that poor pedigree quality can lead to underestimation of this parameter.

Figure 1. Mean genetic conservation index of the Kenyan Sahiwal population for animals
the entire population and for those born in the period 2000-2008

F-statistics were used to assess within-population genetic differentiation from the pedigree (Nei, 1987). The analysis was carried out based on the reference population , which was defined as the most recent population (2000-2008 cohort). The reference population had 5 sub-populations with mean coancestry within sub-populations of 0.012 whereas mean coancestry in the metapopulation was 0.0086. Selfcoancestry, inbreeding and Nei distance were 0.50, 0.0052, and 0.0036, respectively. Table 5 gives the contributions of each cohort from 1960 to 2008, while Table 6 shows loss or gain in genetic diversity for the reference population for the various sub-populations.

Table 5. Loss or gain of genetic diversity for the various year groups of the Sahiwal cattle population in Kenya


Genetic diversity

Internal diversity

Mean distance

Loss or gain


























*Grouping of the breed in ten years cohorts

The overall trend in the genetic diversity of the Sahiwal cattle breed (Table 5) indicates that the breed has been losing diversity over the last five decades. It was found that there was loss in genetic diversity for the whole population as well as the reference population (-0.023).

Table 6. New genetic diversity, and loss (-)/gain (+) of diversity (in %) for the reference population (2000-2008) of the Kenyan Sahiwal cattle


Genetic diversity

Internal diversity

Mean distance

Loss or gain


























*The subpopulations were derived from reference group based on the genetic diversity


All animals that had inbreeding levels above 20% were born in the most recent year group (2000-2008) which explains the increased inbreeding levels in the recent years (Kamiti 2014; Mwangi et al 2016). Among the inbred animals, majority (62.2%) had levels of inbreeding above 1%. High inbreeding levels reported in the current study may have been because of few bulls being selected every year for breeding (Muhuyi et al 1999). The average mean age of 7.1 years for the whole population was lower than 8 years for Nelore, Gir and Guzerat zebu cattle registered in Brazil (Faria et al 2009). Gutiérrez et al (2003) reported lower estimates for eight Spanish beef cattle breeds with generation intervals ranging from 3.7 to 5.5 years, similar to those for Chianina, Marchigiana and Romagnola breeds of 5.35, 4.93 and 5.15 years, respectively (Bozzi et al 2006). The value of 7.1 years for the whole population was however lower compared to Brazilian Gyr dairy cattle which was 8.41 years (Filho et al 2010). The long generation intervals for the Kenyan Sahiwal population could be due to the breakdown of progeny testing scheme, leading to use of old bulls (Mwangi et al 2016).

The effective population size for the whole population was 192 and it was much lower for the recent year group (66), which was as a result of increased average inbreeding levels (Muasya et al 2009; Mwangi et al 2016). The value of 66 was much lower than 138, 122 and 124 for Chianina, Marchigiana and Romagnola breeds, respectively (Bozzi et al 2006) but higher than Danish dairy breeds that ranged from 47 to 53 (Sørensen et al 2005). The future effective population for individuals with at high pedigree completeness was below the range of 50 to 500 recommended for sustainable management of genetic diversity (FAO 1998; Franklin and Frankham 1998). Kamiti, (2014) and Muasya et al. (2011) reported low pedigree completeness, which may have contributed to overestimation of the parameter for the entire population. The decline in future effective population size was due to increase in levels of average coancestry in the Sahiwal cattle breed. Similar results were reported by Muasya et al (2013) for the Holstein-Friesian population in Kenya. The effective population size of the most recent cohort was lower than values of 263 Holstein-Friesian cattle populations in Kenya (Muasya et al 2013) and 76 for Canadian Brown Swiss Milking Shorthorn dairy breeds (Melka et al 2013). The depth and quality of the pedigree has been shown to have correlation to effective population size and inbreeding levels (Gutierrez et al 2003).

Values for rate of inbreeding and coancestry for the most recent year cohort were higher when assessed for the animals with three and six complete generations (0.82 versus 1.19 and 0.89 versus 1.36, respectively). These values were above the limit of 1% recommended for a population to maintain it viability (FAO 1998). Pedigree completeness for same population have been reported to decrease over time from 100% in 1956 to 76.3% in 2008 (Kamiti 2014). Estimation of these parameters based on better pedigree quality revealed higher rates of inbreeding and rates of coancestry than reported for the entire population. Similar results for other cattle populations were reported by Gutiérrez and Goyache (2005) and Muasya et al (2013). Consequently the future effective population size of 35 was below the range of 50 to 500 recommended for a population to maintain its viability and long-term fitness (FAO 1998; Franklin and Frankham 1998). A similar trend was been reported for the Kenyan Holstein-Friesian (Muasya et al 2013). Higher values of inbreeding have been reported for Kenyan Holstein-Friesian, Tunisian, Luxembourg, Danish Holsteins for individuals with at least three complete generations (Sørensen et al 2005; Hammami et al 2007; Muasya et al 2013; Melka et al 2013).This may be attributed to pedigrees that are deeper and have more historical information as compared to that of the Kenyan Sahiwal population. This means that the population may not be able to maintain its longterm genetic viability and fitness.

Superior animals that are used for breeding ought to get equivalent genetic contribution from the founders in the population if genetic diversity is to be conserved. Therefore, the higher the GCI value the higher the genetic contribution from the founder for an individual (Gutiérrez and Goyache 2005). Genetic conservation index increased as number of maximum generation increased meaning increase in genetic contribution for individual animals from the founder generation (Gutiérrez and Goyache 2005; Hazuchová et al 2012). Similarly, better pedigree quality improved the estimation of this index. This in conformity to previous studies, which reported that estimation of parameters related to genetic diversity of livestock populations were affected by pedigree quality (Gutierrez et al 2003; Muasya et al 2013).

Wright’s F parameters for the 2000-2008 cohort were below 1% for the population, which is an indication of reduced differentiation (FAO 1998; Gutiérrez and Goyache 2005). The wide use of some individual animals within the population (Mwangi et al 2016; Muasya et al 2011) may have led to reduced differentiation in the population. The values for the loss of genetic diversity remained constant over the years and they were low compared to that of Slovak Spotted bulls of +0.063 (Hazuchová et al 2012). Lower values have been reported for Canadian Holsteins, Milking Shorthorn, Brown Swiss, Guernsey and Ayrshire in Canada (Melka et al 2008). The major reason for reduced genetic diversity in most cattle breeds has been due to genetic drift because of small effective population size (Melka et al 2008; Stachowicz et al 2011). Honda et al (2004) also reported a similar decrease in genetic diversity in Japanese Black Cattle as a result of genetic drift. Loss in genetic diversity for the Sahiwal cattle breed in Kenya can be attributed to genetic drift as well as and unequal contribution by founders (Muasya et al 2011; Mwangi et al 2016).



The authors are grateful to the Kenya Agriculture and Livestock Research Organisation (KALRO) for availing data, Innovation for the Indigenous Livestock Industry (ILINOVA) project and National Research Fund (NRF) for partially funding this research and Egerton University, Kenya for provision of computing facilities.


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Received 14 April 2018; Accepted 11 June 2018; Published 3 July 2018

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