{'fraction_non_alphanumeric': 0.0239309533150255, 'fraction_numerical': 0.005623120177847522, 'mean_word_length': 5.133119486768244, 'pattern_counts': {'":': 1, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Interpreting a genetic case-control ﬁ nding: What can be said, what cannot be said and implications in Indian populations

Identiﬁ cation of genetic variants responsible for complex disorders using association mapping is an active area of research. There are two broad classes of association methodologies: population-based case-control studies and family-based transmission analyses. While case-control analyses are more popular and in general, more powerful than family-based analyses, they suffer from some inherent limitations. Thus, it is of importance, to understand the implications of an association ﬁ nding obtained from a case-control study design. This article discusses the relative advantages and disadvantages of the two classes of association analyses, particularly in the context of genetic diversity in Indian populations.


Introduction
With the development of dense maps of highly polymorphic microsatellite markers and single nucleotide polymorphisms (SNPs), there have been considerable successes in mapping of susceptible genes for heritable traits. Most of these genes pertain to Mendelian disorders [for example, the CFTR gene for Cystic Fibrosis [1] which are believed to be controlled by a single major locus with a known mode of inheritance. However, there has also been successful mapping of genes involved in complex disorders like the 5q31 ctyokine gene cluster for Crohn's Disease. [2] Unlike simple Mendelian traits, complex traits are believed to be controlled by multiple loci, some with minor gene effects and genetic variation at any one locus does not completely determine the trait. These traits tend to be more common than Mendelian traits and environmental factors often modify the genetic risk to develop the disease.
The two major statistical tools used in the mapping of genes are linkage and association. Linkage analysis [3] is a statistical method to identify chromosomal regions where related individuals exhibit similar patterns of inheritance of a particular trait. Association studies [4] are designed to narrow down the regions identifi ed by linkage analyses to localize the position of a mutant allele responsible for the underlying trait.
Studies have shown that association studies are statistically more powerful than linkage studies in genemapping of complex traits. [5] This is because association, which is measured by a parameter called linkage disequilibrium exists over small distances on the genome, while linkage exists over larger distances. Thus, a positive association fi nding gives a more precise location of a locus responsible for the trait. This article concentrates on the different statistical methods for genetic association analyses and discusses their relative advantages and disadvantages.

Population-based case-control studies
The most popular and intuitively simple statistical method for testing allelic association is case-control analysis. When studying a particular disorder, a random set of individuals who are affected with the disorder  Table 1. Suppose we perform a case-control study at a biallelic locus with alleles A and a. Based on 120 cases and 120 controls (denoted by "pooled population" in Table 1), we can see that the frequency of allele A among cases is 0.6, while that among controls is 0.3. This indicates a statistically signifi cant difference at level 0.05 and would lead to an inference that allele A predisposes to high risk of the disease. However, it may happen in reality that the population considered is a combination of two sub-populations (denoted by "Population 1" and "Population 2" in the Table). In While there is no efficient procedure to test for population stratification, there are some intelligent methods to correct the test statistic for possible population stratifi cation effect. The most popular approach is known as "genetic controls". [6,7] A large set of marker loci dispersed throughout the different chromosomes on the genome, preferably neutral (like Alu insertion/deletion polymorphisms), unlinked to the polymorphism under study and known to have varying allelic distributions across populations are genotyped and case-control tests are performed at all these loci using the same sample. Since these loci are expected to be unrelated to the disease, the value of the case-control Chi-squares trend statistic [8] at the candidate locus should be higher than most of the values at the "genomic control" loci in the absence of population stratifi cation. However, in the presence of population stratifi cation, these Chi-squares values are infl ated and the test statistic at the candidate Ghosh: Case-control fi ndings: Interpretation and implications locus needs to be normalized by a factor depending on the median of the Chi-squares values obtained for the "genomic control" loci. However, one requires a very large number of "genomic control" loci for effi cient estimation of this normalizing factor and hence the strategy may not be very cost-effective, particularly in view of the fact that these loci do not provide any information on genetic association with the disease under study. Pritchard et al [9] proposed a likelihood-based approach to adjust for population stratifi cation, which involves estimation of allele frequencies for different sub-populations within the data and the ancestry of these alleles. The major limitation of this method is that it depends on apriori classifi cation of individuals into sub-populations. Thus, the problem of population stratification in genetic case-control studies has not yet been circumvented satisfactorily and continues to be an active area of research.

The alternative
The alternative to population-based case-control studies is analyses-based on family-based designs. The most popular of them is the transmission disequilibrium test (TDT) proposed by Spielman et al. [10] The design comprises "trios": a nuclear family ascertained by an affected to obtain data on parents. Sham and Curtis [11] showed that even if one heterozygous parent is available and it can be deciphered which allele the parent transmitted to the offspring (for example, if one parent has genotype Aa, the other parent is missing and the offspring has genotype aa), the TDT can be biased if the heterozygosity at the polymorphism is very low. Trios where both parents are homozygous at the polymorphism under study need to be excluded from the statistical analysis after genotyping, making the design less cost-effective. Moreover, some simulation studies [12] have shown that the number of required TDT families is virtually equal to the number of cases required for similar power in case-control studies with an equal number of cases and controls. This implies that one needs to genotype 1.5 times more individuals under a TDT design to achieve similar power as a casecontrol design.

The Indian context
Recent studies [13][14][15] have shown that there is a signifi cant genetic diversity among different ethnic groups of India. This diversity can be observed between different linguistic groups residing in different geographical locations and different caste and tribal populations. Thus, genetic case-control studies in Indian populations need to be designed with great caution. The common belief that there are genetic differences only between "North" and "South" Indian populations but not within them is contrary to the recent empirical fi ndings. Positive case-control fi ndings need to be validated by the use of genomic controls as described earlier or a family-based design like the TDT, failing which there is a high probability of reporting a false positive result